diff --git a/public/globalSolutions/dfda/12-references.md b/public/globalSolutions/dfda/12-references.md new file mode 100644 index 00000000..3fd46521 --- /dev/null +++ b/public/globalSolutions/dfda/12-references.md @@ -0,0 +1,67 @@ +# 📖 References + +* [Google Spreadsheet of FDA Spending vs Life-Expectancy](https://docs.google.com/spreadsheets/d/e/2PACX-1vTBkVrOYLxloOIADLXA7-k5NBIGgQ\_dfFQ7BLUN0oaJPVQ\_NqdFdVUfhuPkVWgFZ9gfLrwPdjuG1sTn/pubhtml) +* [Summary of NDA Approvals & Receipts, 1938 to the present](https://www.fda.gov/about-fda/histories-product-regulation/summary-nda-approvals-receipts-1938-present) +* [Theory, Evidence, and Examples of FDA Harm](https://www.fdareview.org/issues/theory-evidence-and-examples-of-fda-harm/) +* [DATA](https://docs.google.com/spreadsheets/d/1hltgVd8OO\_nfd9m7FUbbsOTXFX4VbDKuFNw4Cy43f7Q/edit#gid=0) +* [GDP](https://ourworldindata.org/economic-growth) +* [Reform, Regulation, and Pharmaceuticals — The Kefauver–Harris Amendments at 50](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101807/) +* [Consumer Price Index](https://www.bls.gov/regions/midwest/data/consumerpriceindexhistorical\_us\_table.pdf) +* [Estimates of World GDP, One Million B.C. – Present](https://delong.typepad.com/print/20061012\_LRWGDP.pdf) +* [Newspaper Generator](https://newspaper.jaguarpaw.co.uk/) +* [Report suggests drug-approval rate now just 1-in-10](https://www.amplion.com/report-suggests-drug-approval-rate-now-just-1-in-10/) +* [How many people die and how many are born each year?](https://ourworldindata.org/births-and-deaths) +* [Gross World Product per capita](http://statisticstimes.com/economy/gross-world-product-capita.php) +* [History of Clinical Trials](https://en.wikipedia.org/wiki/Clinical\_trial#History) +* [How many life-years have new drugs saved?](https://academic.oup.com/inthealth/article/11/5/403/5420236) +* [CATO](https://www.cato.org/publications/commentary/end-fda-drug-monopoly-let-patients-choose-their-medicines) +* [Medical Innovation](http://valueofinnovation.org/) +* [Timeline History of Clinical Research](https://www.timetoast.com/timelines/history-of-clinical-research) +* [FDA and Clinical Drug Trials: A Short History](https://www.fda.gov/media/110437/download) +* [Do Off-Label Drug Practices Argue Against FDA Efficacy Requirements?](https://www.independent.org/publications/article.asp?id=1302) +* [Reform Options](https://www.fdareview.org/issues/reform-options/) +* [Before Occupy: How AIDS Activists Seized Control of the FDA in 1988](https://www.theatlantic.com/health/archive/2011/12/before-occupy-how-aids-activists-seized-control-of-the-fda-in-1988/249302/) +* [A Brief History of the Center for Drug Evaluation and Research](https://www.fda.gov/about-fda/virtual-exhibits-fda-history/brief-history-center-drug-evaluation-and-research#display\_58) +* [Milestones in U.S. Food and Drug Law History](https://www.fda.gov/about-fda/fdas-evolving-regulatory-powers/milestones-us-food-and-drug-law-history) +* https://go.drugbank.com/stats + +Additional Sources + +1. https://www.ahajournals.org/doi/10.1161/strokeaha.111.621904 +2. https://www.fda.gov/media/110437/download +3. https://www.academia.edu/2801726/Is\_the\_FDA\_safe\_and\_effective +4. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence +5. https://www.cato.org/commentary/end-fda-drug-monopoly-let-patients-choose-their-medicines +6. https://www.fda.gov/files/about%20fda/published/The-Sulfanilamide-Disaster.pdf +7. https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/21st-century-cures-act +8. https://www.fda.gov/media/79922/download +9. https://www.fda.gov/media/120060/download +10. https://www.nature.com/articles/549445a +11. https://www.statista.com/statistics/1041467/life-expectancy-switzerland-all-time/ +12. https://www.statista.com/statistics/195950/infant-mortality-rate-in-the-united-states-since-1990/ +13. https://kof.ethz.ch/en/news-and-events/kof-bulletin/kof-bulletin/2021/07/Improvements-in-Swiss-life-expectancy-and-length-of-life-inequality-since-the-1870s.html +14. https://docs.google.com/spreadsheets/d/1hltgVd8OO\_nfd9m7FUbbsOTXFX4VbDKuFNw4Cy43f7Q/edit#gid=802845894 +15. https://www.visualcapitalist.com/which-rare-diseases-are-the-most-common/ +16. http://valueofinnovation.org/ +17. https://www.medicinesaustralia.com.au/wp-content/uploads/2020/11/Prof-Frank-Lichtenberg\_session-3.pdf +18. Anglemyer A., Horvath H.T., and Bero, L. (2014). Healthcare Outcomes Assessed with Observational Study Designs Compared with Those Assessed in Randomized Trials (Review), Cochrane Database of Systematic Reviews, Issue 4, Art No MR000034. doi:10.1002/14651858.MR000034.pub2. +19. Ball, R., Robb, M., Anderson, S.A., and Dal Pan, G. (2016). The FDA’s Sentinel Initiative—A Comprehensive Approach to Medical Product Surveillance, Clinical Pharmacology & Therapeutics, 99(3):265-268. doi:10.1002/cpt.320. Benson, K. and Hartz, A.J. (2000). A Comparison of Observational Studies and Randomized, Controlled Trials, New England Journal of Medicine, 342:1878-1886. doi:10.1056/NEJM200006223422506. +20. Berger, M.J, Sox, H., Willke, R.J., Brixner, D.L., Hans-Georg, E., Goettsch, W., Madigan, D., Makady, A., Schneeweiss, S., Tarricone, R., Wang, S.V., Watkins, J., and Mullins, C.D. (2017). Good Practices for Real-World Data Studies of Treatment and/or Comparative Effectiveness: Recommendations from the Joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making, Pharmacoepidemiology and Drug Safety, 26(9):1033- 1039. doi:10.1002/pds.4297. +21. Clinical Trial Transformation Initiative (CTTI) (2017). CTTI Recommendations: Registry Trials. Retrieved from https://www.ctti-clinicaltrials.org/files/recommendations/registrytrials-recs.pdf. +22. Cooper, C.J., Murphy, T.P., Cutlip, D.E., Jamerson, K., Henrich, W., Reid, D.M., Cohen, D.J., Matsumoto, A.H., Steffes, M., Jaff, M.R., Prince, M.R., Lewis, E.F., Tuttle, K.R., Shapiro, J.I., Rundback, J.H., Massaro, J.M., D’Agostino, R.B., and Dworkin, L.D. (2014). Stenting and Medical Therapy for Atherosclerotic Renal-Artery Stenosis, New England Journal of Medicine, 370(1):13-22. doi:10.1056/NEJMoa1310753. +23. Eapen, Z.J., Lauer, M., and Temple, R.J. (2014). The Imperative of Overcoming Barriers to the Conduct of Large, Simple Trials. Journal of the American Medical Association, 311(14): 1397-1398. doi:10.1001/jama.2014.1030. Eworuke, E. (2017). Integrating Sentinel into Routine Regulatory Drug Review: A Snapshot of the First Year Risk of Seizures Associated with Ranolazine \[Power Point Presentation]. Retrieved from https://www.sentinelinitiative. org/sites/default/files/communications/publications-presentations/Sentinel-ICPE-2017-Symposium-Snapshotof-the-First-Year\_Ranexa-Seizures.pdf. +24. Food and Drug Administration, Center for Medicare Services, and Acumen Team. (2018). Centers for Disease Control and Prevention, Advisory Committee on Immunization Practices Meeting: Relative Effectiveness of Cell-cultured versus Egg-based Influenza Vaccines, 2017-18 \[Power Point Presentation]. Retrieved from https://www.cdc.gov/ vaccines/acip/meetings/downloads/slides-2018-06/flu-03-Lu-508.pdf. Ford, I. and Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375:454-463. doi:10.1056/ NEJMra1510059. +25. Fralick, M., Kesselheim, A.S., Avorn, J., and Schneeweiss, S. (2018). Use of Health Care Databases to Support Supplemental Indications of Approved Medications, JAMA Internal Medicine, 178(1): 55-63. doi:10.1001/ jamainternmed.2017.3919 +26. Franklin, J.M., and Schneeweiss, S. (2017). When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials, Clinical Pharmacology & Therapeutics, 102(6):924-933. doi:10.1002/cpt.857. +27. Fröbert, O., Lagerqvist, B., Olivecrona, G., Omerovic, E., Gudnason, T., Maeng, M., Aasa, M., Angerås, O., Calais, F., Danielewicz, M., Erlinge, D., Hellsten, L., Jensen, U., Johansson, A.C., Kåregren, A., Nilsson, J., Robertson, L., Sandhall, L., Sjögren, I., Östlund, O., Harnek, J., and James, S.K. (2013). Thrombus Aspiration during STSegment Elevation Myocardial Infarction, New England Journal of Medicine, 369:1587-1597. doi:10.1056/ NEJMoa1308789. +28. Guadino, M., Di Franco, A., Rahouma, M., Tam, D.Y., Iannaccone, M., Deb, S., D’Ascenzo, F., Abouarab, A.A., Girardi, L.N., Taggart, D.P., and Fremes, S.E. (2018). Unmeasured Confounders in Observational Studies Comparing Bilateral Versus Single Internal Thoracic Artery for Coronary Artery Bypass Grafting: A Meta-Analysis, Journal of the American Heart Association, 7:e008010. doi.org/10.1161/JAHA.117.008010. +29. Gliklich, R.E., Dreyer, N.A., and Leavy, M.B., editors. (2014). Registries for Evaluating Patient Outcomes: A User’s Guide \[Internet]. 3rd edition. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Apr. 1, Patient Registries. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK208643. +30. Hemkens, L.G., Contopoulos-Ioannidis, D.G., and Ioannidis, J.P. (2016). Agreement of Treatment Effects for Mortality from Routinely Collected Data and Subsequent Randomized Trials: Meta-Epidemiological Survey, BMJ, 352:i493. doi:10.1136/bmj.i493. +31. Hernandez, A.F., Fleurence, R.L., and Rothman, R.L. (2015). The ADAPTABLE Trial and PCORnet: Shining Light on a New Research Paradigm, Annals of Internal Medicine, 163(8):635-636. doi:10.7326/M15-1460. +32. Izurieta, H.S., Thadani, N., Shay, D.K., Lu, Y., Maurer, A., Foppa, I.M., Franks, R., Pratt, D., Forshee, R.A., MaCurdy, T., Worrall, C., Howery, A.E., and Kelman, J. (2015). Comparative Effectiveness of High-dose versus Standarddose Influenza Vaccines in US Residents Aged 65 Years and Older from 2012 to 2012 Using Medicare Data: a Retrospective Cohort, Lancet Infect Dis, 15(3):293-300. doi:10.1016/S1473-3099(14)71087-4. +33. Izurieta, H.S., Wernecke, M., Kelman, J., Wong, S., Forshee, R., Pratt, D., Lu, Y., Sun, Q., Jankosky, C., Krause, P., Worrall, C., MaCurdy, T., Harpaz, R. (2017). Effectiveness and Duration of Protection Provided by the Liveattenuated Herpes Zoster Vaccine in the Medicare Population Ages 65 Years and Older, Clinical Infectious Diseases, 64(6):785-793. doi.org/10.1093/cid/ciw854. +34. Khozin, S., Abernethy, A.P., Nussbaum, N.C., Zhi, J., Curtis, M.D., Tucker, M., Lee, S.E., Light, D.E., Gossai, A., Sorg, R.A., Torres, A.Z., Patel, P., Blumenthal, G.M., and Pazdur, R. (2018). Characteristics of Real-World Metastatic Non-small Cell Lung Cancer Patients Treated with Nivolumab and Pembrolizumab During the Year Following Approval, Oncologist, 23(3): 328-336. doi: 10.1634/theoncologist.2017-0353. +35. Maggiono, A.P., Franzosi, M.G., Fresco, C., Turazza, F., and Tognoni, G. (1990). GISSI Trials in Acute Myocardial Infarction, CHEST Journal, 97(4), Supplement: 146S-150S. doi:10.1378/chest.97.4\_Supplement.146S +36. [Historical Changes in Causes of Death - Sociological Images (thesocietypages.org)](https://thesocietypages.org/socimages/2012/06/25/historical-changes-in-causes-of-death/) +37. [Causes of Death - Our World in Data](https://ourworldindata.org/causes-of-death)] +38. [NVSS - Mortality - Historical Data (cdc.gov)](https://www.cdc.gov/nchs/nvss/mortality\_historical\_data.htm) diff --git a/public/globalSolutions/dfda/2-solution.md b/public/globalSolutions/dfda/2-solution.md new file mode 100644 index 00000000..37f6da3f --- /dev/null +++ b/public/globalSolutions/dfda/2-solution.md @@ -0,0 +1,355 @@ +--- +description: >- + How we reduce suffering using a decentralized autonomous organization as a + vehicle to use the oceans of real-world evidence to discover new cures. +--- + +# 🎯 Goals + +## The Personalized, Preventive, Precision Medicine of the Future + +Out of an existing pool of big health data, an insilico model of human biology can be developed to discover new interventions and their personalized dosages and combinations. + +One way to achieve this is to view the human body as a black box with inputs and outputs. We can apply [predictive machine learning models](broken-reference/) to [stratified groups](https://en.wikipedia.org/wiki/Stratified\_sampling) of similar people based on the following aspects: + +* [Genomic](https://en.wikipedia.org/wiki/Genomics) +* [Transcriptomic](https://en.wikipedia.org/wiki/Transcriptome) +* [Proteomic](https://en.wikipedia.org/wiki/Proteomics) +* [Metabolomic](https://en.wikipedia.org/wiki/Metabolomics) +* [Microbiomic](https://en.wikipedia.org/wiki/Microbiota) +* [Phenotype](https://en.wikipedia.org/wiki/Phenotype) +* [Diseasomic](http://ijream.org/papers/IJREAMV05I0250057.pdf) +* [Pharmacomicrobiomic](https://en.wikipedia.org/wiki/Pharmacomicrobiomics) +* [Pharmacogenomic](https://en.wikipedia.org/wiki/Pharmacogenomics) +* [Foodomic](https://en.wikipedia.org/wiki/Foodomics) +* [Exposome](https://en.wikipedia.org/wiki/Environmental\_factor#Exposome) + +This will enable the discovery of the full personalized range of positive and negative relationships for all factors without a profit incentive for traditional trials. + +![bb](https://static.crowdsourcingcures.org/dfda/assets/black-box-model-animation.gif) + +### The Potential of Real-World Evidence-Based Studies + +* **Diagnostics** - Data mining and analysis to identify causes of illness +* **Preventative medicine** - Predictive analytics and data analysis of genetic, lifestyle, and social circumstances to prevent disease +* **Precision medicine** - Leveraging aggregate data to drive hyper-personalized care +* **Medical research** - Data-driven medical and pharmacological research to cure disease and discover new treatments and medicines +* **Reduction of adverse medication events** - Harnessing of big data to spot medication errors and flag potential adverse reactions +* **Cost reduction** - Identification of value that drives better patient outcomes for long-term savings +* **Population health** - Monitor big data to identify disease trends and health strategies based on demographics, geography, and socioeconomic + +#### Cost Savings in Drug Development + +Failed drug applications are expensive. A global database of treatments and outcomes could provide information that could avoid massive waste on failed trials. + +* A 10% improvement in predicting failure before clinical trials could save [$100 million](https://drugwonks.com/blog/the-dog-days-of-drug-approvals) in development costs. +* Shifting 5% of clinical failures from Phase III to Phase I reduces out-of-pocket costs by [$15 to $20 million](https://drugwonks.com/blog/the-dog-days-of-drug-approvals). +* Shifting failures from Phase II to Phase I would reduce out-of-pocket costs by [$12 to $21 million](https://drugwonks.com/blog/the-dog-days-of-drug-approvals). + +#### Cost Savings Through Decentralization + +* In phase II studies, the typical decentralized clinical trial (DCT) deployment produced a [400%](https://github.com/cure-dao/docs/blob/main/assets/financial-benefits-of-decentralized-trials.pdf) return on investment in terms of trial cost reductions. +* In phase III studies, decentralization produced a [1300%](https://github.com/cure-dao/docs/blob/main/assets/financial-benefits-of-decentralized-trials.pdf) return on investment. + +#### Problems with Historical Observational Research + +When people think of observational research, they typically think of correlational association studies. + +**Why It Seems Like Diet Advice Flip-Flops All the Time** + +In 1977, the USDA and Time Magazine warned Americans against the perils of dietary cholesterol. Yet, in 1999, TIME released a very different cover, suggesting that dietary cholesterol is fine. + +![eggs time covers](https://static.crowdsourcingcures.org/dfda/assets/eggs-time-covers.png) + +#### Correlational is Not The Same as Causation + +There are two primary ways of undertaking studies to find out what affects our health: + +1. observational studies - the easier of the two options. They only require handing out questionnaires to people about their diet and lifestyle habits, and then again a few years later to find out which patterns are associated with different health outcomes. +2. randomized trials - the far more expensive option. Two groups of randomly selected people are each assigned a different intervention. + +The most significant benefit of randomized trials is the "control group". The control group consists of the people who don't receive the intervention or medication in a randomly-controlled trial. It helps to overcome the confounding variable problem that plagues observational studies. + +A common source of confounding variables in correlational association studies is the "healthy person bias". For instance, say an observational study finds "People Who Brush Teeth Less Frequently Are at Higher Risk for Heart Disease". It may just be a coincidence caused by a third confounding variable. People that brush their teeth more are more likely to be generally concerned about their health. So, the third confounding factor could be that people without heart disease could also exercise more or eat better. + +However, the massive amount of automatically collected, high-frequency longitudinal data we have today makes it possible to overcome the flaws with traditional observational research. + +#### Overcoming the "No Control Group" Problem + +The primary flaw with observational research is that they lack the control group. However, a single person can act as their own control group with high-frequency longitudinal data. This is done by using an A/B experiment design. + +![](https://static.crowdsourcingcures.org/dfda/assets/causal-clues-1024x652.png) + +For instance, if one is suffering from arthritis and they want to know if a Turmeric Curcumin supplement helps, the experimental sequence would look like this: + +1. Month 1: Baseline (Control Group) - No Curcumin +2. Month 2: Treatment (Experimental Group) - 2000mg Curcumin/day +3. Month 3: Baseline (Control Group) - No Curcumin +4. Month 4: Treatment (Experimental Group) - 2000mg Curcumin/day + +The more this is done, the stronger the statistical significance of the observed change from the baseline. However, there are also effects from other variables. These can be addressed using a diffusion-regression state-space model that predicts the counter-factual response. This involves the creation of a synthetic control group. This artificial control illustrates what would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to: + +1. infer the temporal evolution of attributable impact +2. incorporate empirical priors on the parameters in a fully Bayesian treatment +3. flexibly accommodate multiple sources of variation, including: + 1. local trends + 2. seasonality + 3. the time-varying influence of contemporaneous covariates + +At this time, we apply coefficients representative of each of [Hill’s criteria for causation](http://www.drabruzzi.com/hills\_criteria\_of\_causation.htm) to quantify the likelihood of a causal relationship between two measures as: + +* _**Strength Coefficient**_: A relationship is more likely to be causal if the correlation coefficient is large and statistically significant. This is determined through the use of a two-tailed t-test for significance. +* _**Consistency Coefficient**_: A relationship is more likely to be causal if it can be replicated. This value is related to the variation of the average change from baseline for other participants with the same treatment outcome variables in conjunction with the variation in average change from multiple experiments in the same individual. +* _**Specificity Coefficient**_: A relationship is more likely to be causal if there is no other plausible explanation. Relationships are calculated based on different potential predictor variables available for the individual over the same period. The value of the Specificity Coefficient starting at one is decreased by the strength of the most robust relationship of all other factors. +* _**Temporality Coefficient**_: A relationship is more likely to be causal if the effect always occurs after the cause. +* _**Gradient Coefficient**_: The relationship is more likely to be causal if more significant exposure to the suspected cause leads to a greater effect. This is represented by the k-means squared difference between the normalized pharmacokinetic time-lagged treatment outcome curves. +* _**Plausibility Coefficient**_: A relationship is more likely to be causal if a plausible mechanism exists between the cause and the effect. This is derived from the sum of the crowd-sourced plausibility votes on the study. +* _**Coherence**_: A relationship is more likely to be causal if compatible with related facts and theories. This is also derived from the sum of the crowd-sourced plausibility votes on the study. +* _**Experiment Coefficient**_: A relationship is more likely to be causal if it can be verified experimentally. This coefficient is proportional to the number of times an A/B experiment is run. +* _**Analogy**_: A relationship is more likely to be causal if there are proven relationships between similar causes and effects. This coefficient is proportional to the consistency of the result for a particular individual with the number of other individuals who also observed a similar effect. + +![Correlation vs Causation](https://static.crowdsourcingcures.org/dfda/assets/correlation-does-not-equal-causation-comic.png) + +#### Meta-Analyses Support of Real-World Evidence + +Observational real-world evidence-based studies have several advantages over randomized, controlled trials, including lower cost, increased speed of research, and a broader range of patients. However, concern about inherent bias in these studies has limited their use in comparing treatments. Observational studies have been primarily used when randomized, controlled trials would be impossible or unethical. + +However, [meta-analyses](https://www.nejm.org/doi/full/10.1056/NEJM200006223422506) found that: + +> when applying modern statistical methodologies to observational studies, the results are generally **not quantitatively or qualitatively different** from those obtained in randomized, controlled trials. + +#### Mortality Observational Studies + +![Mortality Observational Studies](https://static.crowdsourcingcures.org/dfda/assets/observational-vs-randomized-effect-sizes.png) + +#### Observational Studies for Various Outcomes + +![Observational Studies for Various Outcomes](https://static.crowdsourcingcures.org/dfda/assets/observational-vs-randomized-trial-effect-sizes.png) + +#### Historical Evidence in Support of Real-World Evidence + +There is compelling historical evidence suggesting that large scale efficacy-trials based on real-world evidence have ultimately led to better health outcomes than current pharmaceutical industry-driven randomized controlled trials. + +For over 99% of recorded human history, the average human life expectancy has been around 30 years. + +![historical life expectancy](https://static.crowdsourcingcures.org/dfda/assets/life-expectancy-historical.jpg) + +#### 1893 - The Advent of Safety and Efficacy Trials + +In the late nineteenth and early twentieth century, clinical objectivity grew. The independent peer-reviewed Journal of the American Medical Association (JAMA) was founded in 1893. It would gather case reports from the 144,000 physicians members of the AMA on the safety and effectiveness of drugs. The leading experts in the area of a specific medicine would review all of the data and compile them into a study listing side effects and the conditions for which a drug was or was not effective. If a medicine were found to be safe, JAMA would give its seal of approval for the conditions where it was found to be effective. + +The adoption of this system of crowd-sourced, observational, objective, and peer-reviewed clinical research was followed by a sudden shift in the growth of human life expectancy. After over 10,000 years of almost no improvement, we suddenly saw a strangely linear 4-year increase in life expectancy every single year. + +#### 1938 - The FDA Requires Phase 1 Safety Trials + +A drug called Elixir sulfanilamide caused over [100 deaths](https://www.fda.gov/files/about%20fda/published/The-Sulfanilamide-Disaster.pdf) in the United States in 1937. + +Congress [reacted](https://en.wikipedia.org/wiki/Elixir\_sulfanilamide) to the tragedy by requiring all new drugs to include: + +> "adequate tests by all methods reasonably applicable to show whether or not such drug is safe for use under the conditions prescribed, recommended, or suggested in the proposed labeling thereof." + +These requirements evolved to what is now called the [Phase 1 Safety Trial](https://en.wikipedia.org/wiki/Phase\_1\_safety\_trial). + +This consistent four-year/year increase in life expectancy remained unchanged before and after the new safety regulations. + +![Fda safety trials life expectancy](https://static.crowdsourcingcures.org/dfda/assets/fda-safety-trials-life-expectancy.png) + +This suggests that the regulations did not have a large-scale positive or negative impact on the development of life-saving interventions. + +#### 1950's - Thalidomide Causes Thousands of Birth Defects Outside US + +Thalidomide was first marketed in Europe in [1957](https://en.wikipedia.org/wiki/Thalidomide) for morning sickness. While it was initially thought to be safe in pregnancy, it resulted in thousands of horrific congenital disabilities. + +Fortunately, the existing FDA safety regulations prevented any birth defects in the US. Despite the effectiveness of the existing US regulatory framework in protecting Americans, newspaper stories such as the one below created a strong public outcry for increased regulation. + +![Thalidomide](https://static.crowdsourcingcures.org/dfda/assets/thalidomide.jpg) + +#### 1962 - New Efficacy Regulations Reduce the Amount and Quality of Efficacy Data Collected + +As effective **safety** regulations were already in place, the government instead responded to the Thalidomide disaster by regulating **efficacy** testing via the 1962 Kefauver Harris Amendment. Before the 1962 regulations, it cost a drug manufacturer an average of $74 million (2020 inflation-adjusted) to develop and test a new drug for safety before bringing it to market. Once the FDA had approved it as safe, efficacy testing was performed by the third-party American Medical Association. Following the regulation, trials were instead to be conducted in small, highly-controlled trials by the pharmaceutical industry. + +**Reduction in Efficacy Data** + +The 1962 regulations made these large real-world efficacy trials illegal. Ironically, even though the new regulations were primarily focused on ensuring that drugs were effective through controlled FDA efficacy trials, they massively reduced the quantity and quality of the efficacy data that was collected for several reasons: + +* New Trials Were Much Smaller +* Participants Were Less Representative of Actual Patients +* They Were Run by Drug Companies with Conflicts of Interest Instead of the 3rd Party AMA + +**Reduction in New Treatments** + +The new regulatory clampdown on approvals immediately reduced the production of new treatments by 70%. + +![](https://static.crowdsourcingcures.org/dfda/assets/new-treatments-per-year-2.png) + +**Explosion in Costs** + +Since the abandonment of the former efficacy trial model, costs have exploded. Since 1962, the cost of bringing a new treatment to market has gone from [$74 million](https://publications.parliament.uk/pa/cm200405/cmselect/cmhealth/42/4207.htm) to over [$1 billion](https://publications.parliament.uk/pa/cm200405/cmselect/cmhealth/42/4207.htm) US dollars (2020 inflation-adjusted). + +![](https://static.crowdsourcingcures.org/dfda/assets/cost-to-develop-a-new-drug.png) + +**High Cost of Development Favors Monopoly and Punishes Innovation** + +There's another problem with the increasing costs of treatment development. In the past, a genius scientist could come up with a treatment, raise a few million dollars, and do safety testing. Now that it costs a billion dollars to get a drug to market, the scientist has to persuade one of a few giant drug companies that can afford it to buy his patent. + +Then the drug company has two options: + +**Option 1: Risk $1 billion on clinical trials** + +**Possibility A:** Drug turns out to be one of the 90% the FDA rejects. GIVE BANKER A BILLION DOLLARS. DO NOT PASS GO. + +**Possibility B:** Drug turns out to be one of the 10%, the FDA approves. Now it's time to try to recover that billion dollars. However, very few drug companies have enough money to survive this game. So, this company almost certainly already has an existing inferior drug on the market to treat the same condition. Hence, any profit they make from this drug will likely be subtracted from revenue from other drugs they've already spent a billion dollars on. + +**Option 2: Put the patent on the shelf** + +Do not take a 90% chance of wasting a billion dollars on failed trials. Do not risk making your already approved cash-cow drugs obsolete. + +What's the benefit of bringing better treatment to market if you're just going to lose a billion dollars? Either way, the profit incentive is entirely in favor of just buying better treatments and shelving them. + +**Cures Are Far Less Profitable Than Lifetime Treatments** + +If the new treatment is a permanent cure for the disease, replacing a lifetime of refills with a one-time purchase would be economically disastrous for the drug developer. With a lifetime prescription, a company can recover its costs over time. Depending on the number of people with the disease, one-time cures would require a massive upfront payment to recover development costs. + +How is there any financial incentive for medical progress at all? + +Fortunately, there isn't a complete monopoly on treatment development. However, the more expensive it is to get a drug to market, the fewer companies can afford the upfront R\&D investment. So the drug industry inevitably becomes more monopolistic. Thus there are more situations where the cost of trials for a superior treatment exceeds the profits from existing treatments. + +**People With Rare Disease are Severely Punished** + +In the case of rare diseases, increasing the cost of treatment development to over a billion makes it impossible to recover your investment from a small number of patients. So rare disease patients suffer the most severe harm from the added regulatory burden on development. + +How high should the cost of drug development be on our list of human problems? Well, when something costs more, you get less of it. For people dying of cancer, the fact that we couldn't afford enough research to cure them is definitely at the top of their list of human problems. + +**Delayed Life-Saving Treatments** + +One unanticipated consequence of the amendments was that the new burden of proof made the process of drug development both more expensive and much longer, leading to increasing drug prices and a “drug lag”. After that point, whenever they released some new cancer or heart medication that would save 50 thousand lives a year, it meant that over the previous ten years of trials, 500 people died because they didn't have access to the drug earlier. + +**Deaths Due to US Regulatory "Drug Lag"** + +A comparative analysis between countries suggests that delays in new interventions cost anywhere from [21,000 to 120, 000](https://www.fdareview.org/features/references/#gieringer85) US lives per decade. + +Deaths owing to drug lag have been numbered in the [hundreds of thousands](https://www.fdareview.org/features/references/#wardell78a). It's estimated that practolol, a drug in the beta-blocking family, could save ten thousand lives a year if allowed in the United States. Although the FDA allowed a first beta-blocker, propranolol, in 1968, three years after that drug had been available in Europe, it waited until 1978 to allow propranolol to treat hypertension and angina pectoris, its most essential indications. Despite clinical evidence as early as 1974, only in 1981 did the FDA allow a second beta-blocker, timolol, to prevent a second heart attack. The agency’s withholding of beta-blockers was alone responsible for probably [tens of thousands of deaths](https://www.fdareview.org/features/references/#gieringer85). + +[Data](http://csdd.tufts.edu/databases) from the Tufts Center for the Study of Drug Development suggests that thousands of patients have died because of US regulatory delays relative to other countries, for new drugs and devices, including: + +* interleukin-2 +* Taxotere +* vasoseal +* ancrod +* Glucophage +* navelbine +* Lamictal +* ethyol +* photofrin +* rilutek +* citicoline +* panorex +* Femara +* ProStar +* omnicath + +Before US FDA approval, most of these drugs and devices had already been available in other countries for a year or longer. + +Following the 1962 increase in US regulations, one can see a divergence from Switzerland's growth in life expectancy, which did not introduce the same delays to availability. + +![swiss life expectancy](https://static.crowdsourcingcures.org/dfda/assets/us-swiss-life-expectancy-5.png) + +Perhaps it's a coincidence, but you can see an increase in drug approvals in the '80s. At the same time, the gap between Switzerland and the US gets smaller. Then US approvals go back down in the '90s, and the gap expands again. + +![swiss life expectancy](https://static.crowdsourcingcures.org/dfda/assets/us-swiss-life-expectancy-drug-approvals.png) + +**Increase in Patent Monopoly** + +Industry agitation surrounding the “drug lag” finally led to the modification of the drug patenting system in the Drug Price Competition and Patent Term Restoration Act of 1984. This further extended the life of drug patents. Thus Kefauver's amendments ultimately made drugs more expensive by granting longer monopolies. + +**Decreased Ability to Determine Comparative Efficacy** + +The placebo-controlled, randomized controlled trial helped researchers gauge the efficacy of an individual drug. However, it makes the determination of comparative effectiveness much more difficult. + +**Slowed Growth in Life Expectancy** + +From 1890 to 1960, there was a linear 4-year increase in human lifespan every year. This amazingly linear growth rate had followed millennia with a flat human lifespan of around 28 years. Following this new 70% reduction in the pace of medical progress, the growth in human lifespan was immediately cut in half to an increase of 2 years per year. + +![](https://static.crowdsourcingcures.org/dfda/assets/real-world-evidence-in-efficacy-clinical-trials-vs-rcts.png) + +**Diminishing Returns?** + +One might say “It seems more likely — or as likely — to me that drug development provides diminishing returns to life expectancy.” However, diminishing returns produce a slope of exponential decay. It may be partially responsible, but it’s not going to produce a sudden change in the linear slope of a curve a linear as life expectancy was before and after the 1962 regulations. + +![diminishing returns](https://static.crowdsourcingcures.org/dfda/assets/diminishing-returns.png) + +**Correlation is Not Causation** + +You might say "I don't know how much the efficacy regulations contribute to or hampers public health. I do know that correlation does not necessarily imply causation." However, a correlation plus a logical mechanism of action is the least bad method we have for inferring the most likely significant causal factor for an outcome (i.e. life expectancy). Assuming most likely causality based on temporal correlation is the entire basis of a clinical research study and the scientific method generally. + +**Impact of Innovative Medicines on Life Expectancy** + +A [three-way fixed-effects analysis](https://pubmed.ncbi.nlm.nih.gov/30912800) of 66 diseases in 27 countries, suggests that if no new drugs had been launched after 1981, the number of years of life lost would have been 2.16 times higher it actually was. It estimates that pharmaceutical expenditure per life-year saved was [$2837](https://pubmed.ncbi.nlm.nih.gov/30912800). + +![Graph showing the shift in relative mortality among major diseases over 60 years.](http://valueofinnovation.org/assets/images/power-of-innovation/disease-causing-death-shift.gif) + +More people survive as more treatments are developed. There's a [strong correlation](http://valueofinnovation.org/power-of-innovation) between the development of new cancer treatments and cancer survival over 30 years. + +![Graph showing the correlation of developing new cancer treatments and cancer survival over 30 years.](http://valueofinnovation.org/assets/images/power-of-innovation/more-surviving-more-therapies.gif) + +### FDA Mandate is Not to Maximize Lives Saved + +Increasing lifespan is not the congressional mandate of the FDA. Its mandate is to ensure the "safety and efficacy of drugs and medical devices". It has been very successful at fulfilling its mandate. + +But lots of people with AIDS and cancer will die while waiting for treatment. + +#### Cognitive Bias Against Acts of Commission + +Humans have a cognitive bias towards weighting harmful acts of commission to be worse than acts of omission even if the act of omission causes greater harm. It's seen in the trolley problem where people generally aren't willing to push a fat man in front of a train to save a family even though more lives would be saved. + +Medical researcher Dr. Henry I. Miller, MS, MD described his experience working at the FDA, “In the early 1980s,” Miller wrote, “when I headed the team at the FDA that was reviewing the NDA \[application] for recombinant human insulin…my supervisor refused to sign off on the approval,” despite ample evidence of the drug’s ability to safely and effectively treat patients. His supervisor rationally concluded that, if there was a death or complication due to the medication, heads would roll at the FDA—including his own. So the personal risk of approving a drug is magnitudes larger than the risk of rejecting it. + +#### It's Impossible to Report on Deaths That Occurred Due to Unavailable Treatments + +Here's a news story from the Non-Existent Times by No One Ever without a picture of all the people that die from lack of access to life-saving treatments that might have been. + +![](https://static.crowdsourcingcures.org/dfda/assets/non-existent-times.png) + +This means that it's only logical for regulators to reject drug applications by default. The personal risks of approving a drug with any newsworthy side effect far outweigh the personal risk preventing access to life-saving treatment. + +### Current Regulation Expects Drug Developers to Have Psychic Powers + +When running an efficacy trial, the FDA expects that the drug developer has the psychic ability to predict which conditions a treatment will be most effective for in advance of collecting the human trial data. If it was possible to magically determine this without any trials, it would render efficacy trials completely pointless. + +In 2007, manufacturer Dendreon submitted powerful evidence attesting to the safety and efficacy of its immunotherapy drug Provenge, which targets prostate cancer. They were able to show that the drug resulted in a significant decline in deaths among its study population, which even persuaded the FDA advisory committee to weigh in on the application. But ultimately, the FDA rejected its application. + +The FDA was unmoved by the evidence, simply because Dendreon didn’t properly specify beforehand what its study was trying to measure. Efficacy regulations state that finding a decline in deaths is not enough. The mountains of paperwork must be filled out just so and in the correct order. It took three more years and yet another large trial before the FDA finally approved the life-saving medication. + +Due to all the additional costs imposed by the efficacy trial burden, Dendreon ultimately [filed for chapter 11 bankruptcy](https://www.targetedonc.com/view/dendreon-files-for-bankruptcy-provenge-still-available). + +In addition to the direct costs to companies, the extreme costs and financial risks imposed by efficacy trials have a huge chilling effect on investment in new drugs. If you're an investment adviser, trying to avoid losing your client's retirement savings, you're much better off investing in a more stable company like a bomb manufacturer building products to intentionally kill people than a drug developer trying to save lives. So it's impossible to know all of the treatments that never even got to an efficacy trial stage due to the effects of decreased investment due to the regulatory risks. + +## What We Don't Know + +We’re only 2 lifetimes from the use of the modern scientific method in medicine. Thus it's only been applied for 0.0001% of human history. The more clinical research studies we read, the more we realize we don’t know. Nearly every study ends with the phrase "more research is needed". We know basically nothing at this point compared to what will eventually be known about the human body. + +There are over [7,000](https://www.washingtonpost.com/news/fact-checker/wp/2016/11/17/are-there-really-10000-diseases-and-500-cures/) known diseases afflicting humans. + +![]() + +There are as many untested compounds with drug-like properties as there are [atoms in the solar system](https://www.nature.com/articles/549445a) (166 billion). + +![](https://static.crowdsourcingcures.org/dfda/assets/number-of-molecules-with-drug-like-properties.png) + +If you multiply the number of molecules with drug-like properties by the number of diseases, that's 1,162,000,000, 000,000 combinations. So far we've studied [21,000 compounds](https://www.centerwatch.com/articles/12702-new-mit-study-puts-clinical-research-success-rate-at-14-percent). + +That means we only know 0.000000002% of what is left to be known. + +![]() + +The currently highly restrictive overly cautious method of clinical research prevents us from knowing more faster. + +We’re at the very beginning of thousands or millions of years of systematic discovery. So it’s unlikely that this decline in lifespan growth is the result of diminishing returns due to our running out of things to discover. + +However, to validate the theory that large-scale real-world evidence can produce better health outcomes requires further validation of this method of experimentation. That's the purpose of deFDA. + +## + +#### [Next 3. Platform](broken-reference/) 👉 + +This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/). diff --git a/public/globalSolutions/dfda/README.md b/public/globalSolutions/dfda/README.md new file mode 100644 index 00000000..99f7b7ab --- /dev/null +++ b/public/globalSolutions/dfda/README.md @@ -0,0 +1,449 @@ +--- +description: >- + The mandate of the dFDA is to promote human health and safety by determining + the comprehensive positive and negative effects of all foods and drugs. +layout: + title: + visible: true + description: + visible: true + tableOfContents: + visible: true + outline: + visible: true + pagination: + visible: true +--- +# 💊 The Decentralized FDA + +## + +This [monorepo](https://github.com/FDA-AI/FDAi/blob/develop/docs/contributing/repo-structure.md) contains a set of: + +* [FAIR](https://github.com/FDA-AI/FDAi/blob/develop/docs/contributing/fair.md) [libraries](https://github.com/FDA-AI/FDAi/blob/develop/libs) +* [apps](https://github.com/FDA-AI/FDAi/blob/develop/apps) +* autonomous agents to help people and organizations quantify the positive and negative effects of every food, supplement, drug, and treatment on every measurable aspect of human health and happiness. + +[![fdai-framework-diagram.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/images/fdai-framework-diagram.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/images/fdai-framework-diagram.png) + +# 😕 Why are we doing this? + +[](https://github.com/FDA-AI/FDAi#-why-are-we-doing-this) + +The current system of clinical research, diagnosis, and treatment is failing the billions of people are suffering from chronic diseases. + +[👉 Problems we're trying to fix...](https://github.com/FDA-AI/FDAi/blob/develop/docs/stuff-that-sucks.md) + +# 🧪 Our Hypothesis + +[](https://github.com/FDA-AI/FDAi#-our-hypothesis) + +By harnessing global collective intelligence and oceans of real-world data, we hope to emulate Wikipedia's speed of knowledge generation. + +
👉 How to generate discoveries 50X faster and 1000X cheaper than current systems... + +## Global Scale Clinical Research + Collective Intelligence = 🤯 + +[](https://github.com/FDA-AI/FDAi#global-scale-clinical-research--collective-intelligence--) + +So in the 90's, Microsoft spent billions hiring thousands of PhDs to create Encarta, the greatest encyclopedia in history. A decade later, when Wikipedia was created, the general consensus was that it was going to be a dumpster fire of lies. Surprisingly, Wikipedia ended up generating information 50X faster than Encarta and was about 1000X cheaper without any loss in accuracy. This is the magical power of crowdsourcing and open collaboration. + +Our crazy theory is that we can accomplish the same great feat in the realm of clinical research. By crowdsourcing real-world data and observations from patients, clinicians, and researchers, we hope to generate clinical discoveries 50X faster and 1000X cheaper than current systems. + +## The Potential of Real-World Evidence-Based Studies + +[](https://github.com/FDA-AI/FDAi#the-potential-of-real-world-evidence-based-studies) + +* **Diagnostics** - Data mining and analysis to identify causes of illness +* **Preventative medicine** - Predictive analytics and data analysis of genetic, lifestyle, and social circumstances to prevent disease +* **Precision medicine** - Leveraging aggregate data to drive hyper-personalized care +* **Medical research** - Data-driven medical and pharmacological research to cure disease and discover new treatments and medicines +* **Reduction of adverse medication events** - Harnessing of big data to spot medication errors and flag potential adverse reactions +* **Cost reduction** - Identification of value that drives better patient outcomes for long-term savings +* **Population health** - Monitor big data to identify disease trends and health strategies based on demographics, geography, and socioeconomic + +
+ +# 🖥️ FDAi Framework Components + +[](https://github.com/FDA-AI/FDAi#%EF%B8%8F-fdai-framework-components) + +This is a very high-level overview of the architecture. The three primary primitive components of the FDAi framework are: + +1. [Data Silo API Gateway Nodes](https://github.com/FDA-AI/FDAi#1-data-silo-api-gateway-nodes) that facilitate data export from data silos +2. [PersonalFDA Nodes](https://github.com/FDA-AI/FDAi#2-personalfda-nodes) that import, store, and analyze your data to identify how various factors affect your health +3. [Clinipedia](https://github.com/FDA-AI/FDAi#3-clinipediathe-wikipedia-of-clinical-research) that contains the aggregate of all available data on the effects of every food, drug, supplement, and medical intervention on human health. + +The core characteristics that define the FDAi are: + +* **Modularity** - a set of modular libraries and tools that can be reused in any project +* **Protocols** - an abstract framework of core primitive components rather than a specific implementation +* **Interoperability** - a directory of existing open-source projects that can be used to fulfill the requirements of each primitive or component +* **Collective Intelligence** - a collaborative effort, so please feel free to [contribute or edit anything](https://github.com/FDA-AI/FDAi/blob/develop/docs/contributing.md)! + +[![fdai-framework-diagram.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/images/fdai-framework-diagram.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/images/fdai-framework-diagram.png) + +## 1. Data Silo API Gateway Nodes + +[](https://github.com/FDA-AI/FDAi#1-data-silo-api-gateway-nodes) + +[![dfda-gateway-api-node-silo.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/data-silo-gateway-api-nodes/dfda-gateway-api-node-silo.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-silo-gateway-api-nodes/dfda-gateway-api-node-silo.png) + +[FDAi Gateway API Nodes](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-silo-gateway-api-nodes) should make it easy for data silos, such as hospitals and digital health apps, to let people export and save their data locally in their [PersonalFDA Nodes](https://github.com/FDA-AI/FDAi#2-personalfda-nodes). + +**👉 [Learn More About Gateway APIs](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-silo-gateway-api-nodes/data-silo-api-gateways.md)** + +## 2. PersonalFDA Nodes + +[](https://github.com/FDA-AI/FDAi#2-personalfda-nodes) + +[PersonalFDA Nodes](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/personal-fda-nodes/personal-fda-nodes.md) are applications that can run on your phone or computer. They import, store, and analyze your data to identify how various factors affect your health. They can also be used to share anonymous analytical results with the [Clinipedia FDAi Wiki](https://github.com/FDA-AI/FDAi#3-clinipediathe-wikipedia-of-clinical-research) in a secure and privacy-preserving manner. + +[PersonalFDA Nodes](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/personal-fda-nodes/personal-fda-nodes.md) are composed of two components, a [Digital Twin Safe](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/digital-twin-safe/digital-twin-safe.md) and a [personal AI agent](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/optimiton-ai-agent/optomitron-ai-agent.md) applies causal inference algorithms to estimate how various factors affect your health. + +### 2.1. Digital Twin Safes + +[](https://github.com/FDA-AI/FDAi#21-digital-twin-safes) + +[![digital-twin-safe-no-text.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/digital-twin-safe/digital-twin-safe-no-text.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/digital-twin-safe/digital-twin-safe-no-text.png)aider + +A local application for self-sovereign import and storage of personal data. + +**👉[Learn More or Contribute to Digital Twin Safe](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/digital-twin-safe/digital-twin-safe.md)** + +### 2.2. Personal AI Agents + +[](https://github.com/FDA-AI/FDAi#22-personal-ai-agents) + +[Personal AI agents](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/optimiton-ai-agent/optomitron-ai-agent.md) that live in your [PersonalFDA nodes](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/personal-fda-nodes/personal-fda-nodes.md) and use [causal inference](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/optimiton-ai-agent/optomitron-ai-agent.md) to estimate how various factors affect your health. + +[![data-import-and-analysis.gif](https://github.com/FDA-AI/FDAi/raw/develop/docs/images/data-import-and-analysis.gif)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/optimiton-ai-agent/optomitron-ai-agent.md) + +**👉[Learn More About Optimitron](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/optimiton-ai-agent/optomitron-ai-agent.md)** + +## 3. Clinipedia—The Wikipedia of Clinical Research + +[](https://github.com/FDA-AI/FDAi#3-clinipediathe-wikipedia-of-clinical-research) + +[![clinipedia_globe_circle.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/clinipedia/clinipedia_globe_circle.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/clinipedia/clinipedia.md) + +The [Clinipedia wiki](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/clinipedia/clinipedia.md) should be a global knowledge repository containing the aggregate of all available data on the effects of every food, drug, supplement, and medical intervention on human health. + +**[👉 Learn More or Contribute to the Clinipedia](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/clinipedia/clinipedia.md)** + +### 3.1 Outcome Labels + +[](https://github.com/FDA-AI/FDAi#31-outcome-labels) + +A key component of Clinipedia is [**Outcome Labels**](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/outcome-labels/outcome-labels.md) that list the degree to which the product is likely to improve or worsen specific health outcomes or symptoms. + +[![outcome-labels.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/outcome-labels/outcome-labels.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/outcome-labels/outcome-labels.png) + +**👉 [Learn More About Outcome Labels](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/outcome-labels/outcome-labels.md)** + +## Human-AI Collective Intelligence Platform + +[](https://github.com/FDA-AI/FDAi#human-ai-collective-intelligence-platform) + +A collective intelligence coordination platform is needed for facilitating cooperation, communication, and collaborative actions among contributors. + +**[👉 Learn More or Contribute to the FDAi Collaboration Framework](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/human-ai-collective-intelligence-platform/dfda-collaboration-framework.md)** + +# Roadmap + +[](https://github.com/FDA-AI/FDAi#roadmap) + +We'd love your help and input in determining an optimal roadmap for this project. + +**[👉 Click Here for a Detailed Roadmap](https://github.com/FDA-AI/FDAi/blob/develop/docs/roadmap.md)** + + +# v1 Prototype + +[](https://github.com/FDA-AI/FDAi#fdai-v1-prototype) + +We've got a monolithic centralized implementation of the FDAi at [apps/dfda-1](https://github.com/FDA-AI/FDAi/blob/develop/apps/dfda-1) that we're wanting to modularize and decentralize into a set of [FAIR](https://github.com/FDA-AI/FDAi/blob/develop/docs/contributing/fair.md) [libraries](https://github.com/FDA-AI/FDAi/blob/develop/libs) and plugins that can be shared with other apps. + +Currently, the main apps are the [Demo Data Collection, Import, and Analysis App](https://safe.dfda.earth/) and the [Journal of Citizen Science](https://studies.dfda.earth/). + +### Features + +[](https://github.com/FDA-AI/FDAi#features) + +* [Data Collection](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-collection/data-collection.md) +* [Data Import](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-import/data-import.md) +* [Data Analysis](https://github.com/FDA-AI/FDAi#data-analysis) + * [🏷️Outcome Labels](https://github.com/FDA-AI/FDAi#-outcome-labels) + * [🔮Predictor Search Engine](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/predictor-search-engine/predictor-search-engine.md) + * [🥕 Root Cause Analysis Reports](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/root-cause-analysis-reports/root-cause-analysis-reports.md) + * [📜Observational Mega-Studies](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/observational-studies/observational-studies.md) +* [Real-Time Decision Support Notifications](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/decision-support-notifications) +* [No Code Health App Builder](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/no-code-app-builder) +* [Personal AI Agent](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/optimiton-ai-agent/optomitron-ai-agent.md) +* [Browser Extension](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/browser-extension) + +[![FDAi screenshots](https://github.com/FDA-AI/FDAi/raw/develop/apps/dfda-1/public/app/public/img/screenshots/record-inbox-import-connectors-analyze-study.png)](https://github.com/FDA-AI/FDAi/blob/develop/apps/dfda-1/public/app/public/img/screenshots/record-inbox-import-connectors-analyze-study.png) + +[![Reminder Inbox](https://github.com/FDA-AI/FDAi/raw/develop/apps/dfda-1/public/app/public/img/screenshots/reminder-inbox-screenshot-no-text.png)](https://github.com/FDA-AI/FDAi/blob/develop/apps/dfda-1/public/app/public/img/screenshots/reminder-inbox-screenshot-no-text.png) + +Collects and aggregate data on symptoms, diet, sleep, exercise, weather, medication, and anything else from dozens of life-tracking apps and devices. Analyzes data to reveal hidden factors exacerbating or improving symptoms of chronic illness. + +### Web Notifications + +[](https://github.com/FDA-AI/FDAi#web-notifications) + +Web and mobile push notifications with action buttons. + +[![web notification action buttons](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/data-collection/web-notification-action-buttons.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-collection/web-notification-action-buttons.png) + +### Browser Extensions + +[](https://github.com/FDA-AI/FDAi#browser-extensions) + +By using the Browser Extension, you can track your mood, symptoms, or any outcome you want to optimize in a fraction of a second using a unique popup interface. + +[![Chrome Extension](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/browser-extension/browser-extension.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/browser-extension/browser-extension.png) + +### Data Analysis + +[](https://github.com/FDA-AI/FDAi#data-analysis) + +The Analytics Engine performs temporal precedence accounting, longitudinal data aggregation, erroneous data filtering, unit conversions, ingredient tagging, and variable grouping to quantify correlations between symptoms, treatments, and other factors. + +It then pairs every combination of variables and identifies likely causal relationships using correlation mining algorithms in conjunction with a pharmacokinetic model. The algorithms first identify the onset delay and duration of action for each hypothetical factor. It then identifies the optimal daily values for each factor. + +[👉 More info about data analysis](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/data-analysis/data-analysis.md) + +### 🏷 Outcome Labels + +[](https://github.com/FDA-AI/FDAi#-outcome-labels) + +[![outcome-labels-plugin.png](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/outcome-labels/outcome-labels.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/outcome-labels/outcome-labels.png) + +[More info about outcome labels](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/outcome-labels/outcome-labels.md) + +### Real-time Decision Support Notifications + +[](https://github.com/FDA-AI/FDAi#real-time-decision-support-notifications) + +[![](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/decision-support-notifications/notifications-screenshot-slide.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/decision-support-notifications/notifications-screenshot-slide.png) + +[More info about real time decision support](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/outcome-labels/outcome-labels.md) + +### 📈 Predictor Search Engine + +[](https://github.com/FDA-AI/FDAi#-predictor-search-engine) + +[![Predictor Search Engine](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/predictor-search-engine/predictor-search-simple-list-zoom.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/predictor-search-engine/predictor-search-engine.md) + +[👉 More info about the predictor search engine...](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/predictor-search-engine/predictor-search-engine.md) + +### Auto-Generated Observational Studies + +[](https://github.com/FDA-AI/FDAi#auto-generated-observational-studies) + +[![](https://github.com/FDA-AI/FDAi/raw/develop/docs/components/observational-studies/observational-studies.png)](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/observational-studies/observational-studies.png) + +[👉 More info about observational studies...](https://github.com/FDA-AI/FDAi/blob/develop/docs/components/observational-studies/observational-studies.md) + +## 🧞Be Careful What You Wish For + +Imagine you met a magical genie. Imagine you wished for it to fulfill the FDA Congressional Mandate to: + +> Ensure the safety and efficacy of all drugs and medical devices + +**Q: How could the genie PERFECTLY achieve this? 🤔** + +**A: Ensure that no one ever takes a new drug again. ** + +That would 100% guarantee that no one ever takes a medication that may or may not be effective. + +In practice, we've seen a less absolutist interpretation of the mandate. So instead of rejecting all new treatments, we have simply exponentially increased the regulatory barrier. Since 1962, when Congress imposed the current efficacy requirement, the cost of bringing a new therapy to market has increased 15X (1521%). + +## [](https://www.dfda.earth/#the-real-goal-is-human-health-and-safety) + +The Real Goal Is Human Health and Safety + +☠️ [60 million](https://www.theworldcounts.com/populations/world/deaths) people die every year because we don't have adequate treatments for them. + +🤒 [2.5 billion](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214883/) people suffer from chronic diseases for which we have no cures. + +Congress created the FDA to protect and promote human health and safety. Unfortunately, the 1962 efficacy amendment has **become a significant obstacle to the work of scientists who are trying to discover new cures.** + +### [](https://www.dfda.earth/#problems-that-could-be-solved-with-a-decentralized-fda) + +Problems that Could be Solved with a Decentralized FDA + +It takes over [10 years and \$2.6 billion](https://www.semanticscholar.org/paper/Innovation-in-the-pharmaceutical-industry%3A-New-of-DiMasiGrabowski/3275f31c072ac11c6ca7a5260bd535720f07df41) to bring a drug to market (including failed attempts). It costs [\$41k](https://www.clinicalleader.com/doc/getting-a-handle-on-clinical-trial-costs-0001) per subject in Phase III clinical trials. + +The high costs lead to: + +**1. No Data on Unpatentable Molecules** + +[🥫**No Data on Unpatentable Molecules**](https://www.dfda.earth/1-introduction-and-challenges/no-data-on-unpatentable-molecules)We still know next to nothing about the long-term effects of 99.9% of the 4 pounds of over [7,000](https://www.dailymail.co.uk/health/article-8757191/Are-additives-food-making-ill.html) different synthetic or natural compounds. This is because there's only sufficient incentive to research patentable molecules. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-85b02a7606536e4c41115ae363dda1b916c66fd8%252Fchemicals-in-our-diet.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=3912abb6&sv=1) + +Number of Food Additives for Which We Have Long-Term Toxicology Data + +**2. Lack of Incentive to Discover Every Application of Off-Patent Treatments** + +Most of the known diseases (approximately 95%) are classified as rare diseases. Currently, a pharmaceutical company must predict particular conditions to treat before running a clinical trial. Suppose a drug is effective for other diseases after the patent expires. In that case, there isn't a financial incentive to get it approved for the different conditions. + +**3. No Long-Term Outcome Data** + +It's not financially feasible to collect a participant's data for years or decades. Thus, we don't know if the long-term effects of a drug are worse than the initial benefits. + +**4. Negative Results Aren't Published** + +Pharmaceutical companies tend to only report "positive" results. That leads to other companies wasting money repeating research on the same dead ends. + +**5. Trials Exclude a Vast Majority of The Population** + +One investigation found that only [14.5%](https://www.ncbi.nlm.nih.gov/pubmed/14628985) of patients with major depressive disorder fulfilled the eligibility requirements for enrollment in an antidepressant trial. Furthermore, most patient sample sizes are very small and sometimes include only 20 people. + +**6. We Only Know 0.000000002% of What is Left to be Researched** + +The more research studies we read, the more we realize we don't know. Nearly every study ends with the phrase "more research is needed". + +If you multiply the [166 billion](https://www.nature.com/articles/549445a) molecules with drug-like properties by the [10,000](https://www.washingtonpost.com/news/fact-checker/wp/2016/11/17/are-there-really-10000-diseases-and-500-cures/) known diseases, that's 1,162,000,000,000,000 combinations. So far, we've studied [21,000 compounds](https://www.centerwatch.com/articles/12702-new-mit-study-puts-clinical-research-success-rate-at-14-percent). That means we only know 0.000000002% of the effects left to be discovered. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-2445afa966b10f78567b89d51b12a4fee9d8edb4%252Fstudied-molecules-chart-no-background.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=84a999cc&sv=1) + +[](https://www.dfda.earth/#how-a-decentralized-fda-could-overcome-perverse-incentives) + +How a Decentralized FDA Could Overcome Perverse Incentives + +**Overcoming Cognitive Bias Against Acts of Commission** + +Humans have a cognitive bias towards weighting harmful acts of commission to be worse than acts of omission even if the act of omission causes greater harm. It's seen in the trolley problem where people generally aren't willing to push a fat man in front of a train to save a family even though more lives would be saved. + +Medical researcher Dr. Henry I. Miller, MS, MD described his experience working at the FDA, “In the early 1980s,” Miller wrote, “when I headed the team at the FDA that was reviewing the NDA [application] for recombinant human insulin…my supervisor refused to sign off on the approval,” despite ample evidence of the drug’s ability to safely and effectively treat patients. His supervisor rationally concluded that, if there was a death or complication due to the medication, heads would roll at the FDA—including his own. So the personal risk of approving a drug is magnitudes larger than the risk of rejecting it. + +In a DAO comprised of a large number of prominent experts, no individual could be blamed or have their career destroyed for making a correct decision to save the invisible lives of the many at the risk of the lives of the few. + +**It's Impossible to Report on Deaths That Occurred Due to Unavailable Treatments** + +Here's a news story from the Non-Existent Times by No One Ever without a picture of all the people that die from lack of access to life-saving treatments that might have been. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-3cdae51e77f6a2cbb4c8087d9a67fbbe0aeadc33%252Fnon-existent-times.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=c4309f23&sv=1) + +This means that it's only logical for regulators to reject drug applications by default. The personal risks of approving a drug with any newsworthy side effect far outweigh the personal risk of preventing access to life-saving treatment. + +**Types of Error in FDA Approval Decision** + + +| | Drug Is Beneficial | Drug Is Harmful | +| --------------------------- | --------------------------------------------- | --------------------------------------------------- | +| FDA Allows the Drug | Correct Decision | Victims are identifiable and might appear on Oprah. | +| FDA Does Not Allow the Drug | Victims are not identifiable or acknowledged. | Correct Decision | + +#### [](https://www.dfda.earth/#undefined) + +[🔮**Pre-Determining Clinical Endpoints Requires Psychic Powers**](https://www.dfda.earth/1-introduction-and-challenges/pre-determining-clinical-endpoints-requires-psychic-powers)[❓**We Know Next to Nothing**](https://www.dfda.earth/1-introduction-and-challenges/page-1)### [](https://www.dfda.earth/#undefined-1) + +[🌎**Cost Savings from Decentralized Clinical Trials**](https://www.dfda.earth/2-solution/cost-savings-from-decentralized-clinical-trials) + +### [](https://www.dfda.earth/#historical-evidence-suggesting-that-crowdsourcing-clinical-research-works) + +**Historical Evidence Suggesting That Crowdsourcing Clinical Research Works** + +There is compelling historical evidence suggesting that large scale efficacy-trials based on real-world evidence have ultimately led to better health outcomes than current pharmaceutical industry-driven randomized controlled trials. + +For over 99% of recorded human history, the average human life expectancy has been around 30 years. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-9e400e2bde318d3bdefba9cd726a1f75602e9e98%252Flife-expectancy-historical.jpg%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=d3dd5675&sv=1) + +historical life expectancy + +**1893 - The Advent of Safety and Efficacy Trials** + +In the late nineteenth and early twentieth century, clinical objectivity grew. The independent peer-reviewed Journal of the American Medical Association (JAMA) was founded in 1893. It would gather case reports from the 144,000 physicians members of the AMA on the safety and effectiveness of drugs. The leading experts in the area of a specific medicine would review all of the data and compile them into a study listing side effects and the conditions for which a drug was or was not effective. If a medicine were found to be safe, JAMA would give its seal of approval for the conditions where it was found to be effective. + +The adoption of this system of crowd-sourced, observational, objective, and peer-reviewed clinical research was followed by a sudden shift in the growth of human life expectancy. After over 10,000 years of almost no improvement, we suddenly saw a strangely linear 4-year increase in life expectancy every single year. + +**1938 - The FDA Requires Phase 1 Safety Trials** + +A drug called Elixir sulfanilamide caused over [100 deaths](https://www.fda.gov/files/about%20fda/published/The-Sulfanilamide-Disaster.pdf) in the United States in 1937. + +Congress [reacted](https://en.wikipedia.org/wiki/Elixir_sulfanilamide) to the tragedy by requiring all new drugs to include: + +> "adequate tests by all methods reasonably applicable to show whether or not such drug is safe for use under the conditions prescribed, recommended, or suggested in the proposed labeling thereof." + +These requirements evolved to what is now called the [Phase 1 Safety Trial](https://en.wikipedia.org/wiki/Phase_1_safety_trial). + +This consistent four-year/year increase in life expectancy remained unchanged before and after the new safety regulations. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-96451d4b09e89f546d9e66e30c11daf2f5c64010%252Ffda-safety-trials-life-expectancy.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=cbe0c2ff&sv=1) + +Fda safety trials life expectancy + +This suggests that the regulations did not have a large-scale positive or negative impact on the development of life-saving interventions. + +#### [](https://www.dfda.earth/#id-1950s-thalidomide-causes-thousands-of-birth-defects-outside-us) + +**1950's - Thalidomide Causes Thousands of Birth Defects Outside US** + +Thalidomide was first marketed in Europe in [1957](https://en.wikipedia.org/wiki/Thalidomide) for morning sickness. While it was initially thought to be safe in pregnancy, it resulted in thousands of horrific congenital disabilities. + +Fortunately, the existing FDA safety regulations prevented any birth defects in the US. Despite the effectiveness of the existing US regulatory framework in protecting Americans, newspaper stories such as the one below created a strong public outcry for increased regulation. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-3340b6c51d9cf56c83b596136b10b49622230837%252Fthalidomide.jpg%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=18b52b0a&sv=1) + +Thalidomide + +#### [](https://www.dfda.earth/#id-1962-new-efficacy-regulations-reduce-the-amount-and-quality-of-efficacy-data-collected) + +**1962 - New Efficacy Regulations Reduce the Amount and Quality of Efficacy Data Collected** + +As effective **safety** regulations were already in place, the government instead responded to the Thalidomide disaster by regulating **efficacy** testing via the 1962 Kefauver Harris Amendment. Before the 1962 regulations, it cost a drug manufacturer an average of \$74 million (2020 inflation-adjusted) to develop and test a new drug for safety before bringing it to market. Once the FDA had approved it as safe, efficacy testing was performed by the third-party American Medical Association. Following the regulation, trials were instead to be conducted in small, highly-controlled trials by the pharmaceutical industry. + +**Reduction in Efficacy Data** + +The 1962 regulations made these large real-world efficacy trials illegal. Ironically, even though the new regulations were primarily focused on ensuring that drugs were effective through controlled FDA efficacy trials, they massively reduced the quantity and quality of the efficacy data that was collected for several reasons: + +* New Trials Were Much Smaller +* Participants Were Less Representative of Actual Patients +* They Were Run by Drug Companies with Conflicts of Interest Instead of the 3rd Party AMA + +**Reduction in New Treatments** + +The new regulatory clampdown on approvals immediately reduced the production of new treatments by 70%. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-bbda5b9dbea73c35980dbdce1de2dbbf0a9f8045%252Fnew-treatments-per-year-2.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=6871f30c&sv=1) + +**Explosion in Costs** + +Since the abandonment of the former efficacy trial model, costs have exploded. Since 1962, the cost of bringing a new treatment to market has gone from [\$74 million](https://publications.parliament.uk/pa/cm200405/cmselect/cmhealth/42/4207.htm) to over [\$1 billion](https://publications.parliament.uk/pa/cm200405/cmselect/cmhealth/42/4207.htm) US dollars (2020 inflation-adjusted). + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-81591ff46ae70ca01021fc1f22a303c11760906d%252Fcost-to-develop-a-new-drug.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=cfe747bb&sv=1) + +[🧐**Greater Competitive Innovation and Fewer Monopolies**](https://www.dfda.earth/2-solution/greater-competitive-innovation-and-fewer-monopolies)[🎭**More Cures and Less Lifelong Attempts at Masking Symptoms**](https://www.dfda.earth/2-solution/more-cures-and-less-lifelong-attempts-at-masking-symptoms)[🤒**People With Rare Disease are Severely Punished**](https://www.dfda.earth/1-introduction-and-challenges/people-with-rare-disease-are-severely-punished)[⏱️**Deaths Due to US Regulatory "Drug Lag"**](https://www.dfda.earth/1-introduction-and-challenges/deaths-due-to-us-regulatory-drug-lag)**Increase in Patent Monopoly** + +Industry agitation surrounding the “drug lag” finally led to the modification of the drug patenting system in the Drug Price Competition and Patent Term Restoration Act of 1984. This further extended the life of drug patents. Thus Kefauver's amendments ultimately made drugs more expensive by granting longer monopolies. + +**Decreased Ability to Determine Comparative Efficacy** + +The placebo-controlled, randomized controlled trial helped researchers gauge the efficacy of an individual drug. However, it makes the determination of comparative effectiveness much more difficult. + +**Slowed Growth in Life Expectancy** + +From 1890 to 1960, there was a linear 4-year increase in human lifespan every year. This amazingly linear growth rate had followed millennia with a flat human lifespan of around 28 years. Following this new 70% reduction in the pace of medical progress, the growth in human lifespan was immediately cut in half to an increase of 2 years per year. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-a1a1c16f51ea41f3f5d024e3aa409d7028afa99a%252Freal-world-evidence-in-efficacy-clinical-trials-vs-rcts.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=dbdb4d2e&sv=1) + +Average Life Expectancy Over Time + +**Diminishing Returns?** + +One might say “It seems more likely — or as likely — to me that drug development provides diminishing returns to life expectancy.” However, diminishing returns produce a slope of exponential decay. It may be partially responsible, but it’s not going to produce a sudden change in the linear slope of a curve a linear as life expectancy was before and after the 1962 regulations. + +![](https://www.dfda.earth/~gitbook/image?url=https%3A%2F%2F2775799074-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FgAWf5oBWdPgEKHRT7zcm%252Fuploads%252Fgit-blob-b593c6f3ff77604ab13246b840fd9cf878ebca64%252Fdiminishing-returns.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=44e70be0&sv=1) + +What diminishing returns would look like + +**Correlation is Not Causation** + +You might say "I don't know how much the efficacy regulations contribute to or hampers public health. I do know that correlation does not necessarily imply causation." However, a correlation plus a logical mechanism of action is the least bad method we have for inferring the most likely significant causal factor for an outcome (i.e. life expectancy). Assuming most likely causality based on temporal correlation is the entire basis of a clinical research study and the scientific method generally. + +[Next**Historical Evidence Supporting Decentralized Efficacy Trials**](https://www.dfda.earth/historical-evidence-supporting-decentralized-efficacy-trials) diff --git a/public/globalSolutions/dfda/SUMMARY.md b/public/globalSolutions/dfda/SUMMARY.md new file mode 100644 index 00000000..26eb17c4 --- /dev/null +++ b/public/globalSolutions/dfda/SUMMARY.md @@ -0,0 +1,23 @@ +# Table of contents + +* [💊 The Decentralized FDA](README.md) +* [📘 Historical Evidence Supporting Decentralized Efficacy Trials](historical-evidence-supporting-decentralized-efficacy-trials.md) +* ☠️ Problems With the Current Model + * [🥫 No Data on Unpatentable Molecules](problems/no-data-on-unpatentable-molecules.md) + * [💰 Clinical Research is Expensive](problems/clinical-research-is-expensive.md) + * [🥸 Trials Often Aren't Representative of Real Patients](problems/trials-often-arent-representative-of-real-patients.md) + * [❓ We Know Next to Nothing](problems/page-1.md) + * [⏱️ Deaths Due to US Regulatory "Drug Lag"](problems/deaths-due-to-us-regulatory-drug-lag.md) + * [🙈 Negative Results are Never Published](problems/negative-results-are-never-published.md) + * [🎭 Conflicts of Interest](problems/conflicts-of-interest.md) + * [🗓️ No Long-Term Outcome Data](problems/no-long-term-outcome-data.md) + * [📃 Lack of Incentive to Discover the Full Range of Applications for Off-Patent Treatments](problems/lack-of-incentive-to-discover-the-full-range-of-applications-for-off-patent-treatments.md) + * [🤒 People With Rare Disease are Severely Punished](problems/people-with-rare-disease-are-severely-punished.md) + * [🔮 Pre-Determining Clinical Endpoints Requires Psychic Powers](pre-determining-clinical-endpoints-requires-psychic-powers.md) +* [🎯 Benefits of a Decentralized Model](2-solution.md) + * [🎭 More Cures and Less Lifelong Attempts at Masking Symptoms](more-cures-and-less-lifelong-attempts-at-masking-symptoms.md) + * [🧐 Greater Competitive Innovation and Fewer Monopolies](greater-competitive-innovation-and-fewer-monopolies.md) + * [👀 Lower Costs of Validated Observational Research for Efficacy](lower-costs-of-validated-observational-research-for-efficacy.md) + * [📈 Impact of Innovative Medicines on Life Expectancy](2-solution/impact-of-innovative-medicines-on-life-expectancy.md) + * [🌎 Cost Savings from Decentralized Clinical Trials](cost-savings-from-decentralized-clinical-trials.md) +* [📖 References](12-references.md) diff --git a/public/globalSolutions/dfda/components/browser-extension/browser-extension.md b/public/globalSolutions/dfda/components/browser-extension/browser-extension.md new file mode 100644 index 00000000..a69cbb12 --- /dev/null +++ b/public/globalSolutions/dfda/components/browser-extension/browser-extension.md @@ -0,0 +1,15 @@ +# Browser Extension + +By using the Browser Extension, you can track your mood, symptoms, or any outcome you want to optimize in a fraction of a second using a unique popup interface. + +![Chrome Extension](https://static.crowdsourcingcures.org/dfda/components/browser-extension/browser-extension.png) + +# 💻 Code + +For development, you need to copy manifest-chrome.json into manifest.json and then load the [apps/dfda-1/public/app/public](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/public/app/public) folder as an unpacked extension in Chrome. + +[👉 Code here...](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/public/app/public) + +# 🛟 Support + +If you have any questions or need help, please [create an issue](https://github.com/FDA-AI/FDAi/issues/new) instead of emailing us so that others can benefit from the discussion. diff --git a/public/globalSolutions/dfda/components/clinipedia/clinipedia.md b/public/globalSolutions/dfda/components/clinipedia/clinipedia.md new file mode 100644 index 00000000..a884068b --- /dev/null +++ b/public/globalSolutions/dfda/components/clinipedia/clinipedia.md @@ -0,0 +1,23 @@ +# The Wikipedia of Clinical Research + +![clinipedia_globe_circle.png](https://static.crowdsourcingcures.org/dfda/components/clinipedia/clinipedia_globe_circle.png) + +The Clinipedia wiki contains the aggregate of all available data on the effects of every food, drug, supplement, and medical intervention on human health. It requires the following features: + + - **Knowledge Base:** Inspiration could be taken from the Psychonaut Wiki. It's a modified version of MediaWiki with additional quantitative metadata storage regarding the pharmacokinetics of various substances. This could be expanded to document the quantitative effects of every factor on specific health outcomes. + - **Editing Authorization:** A robust authorization mechanism to maintain content integrity and trustworthiness. + - **AI Agent-Powered Research:** Leverage autonomous AI agents to efficiently conduct research by aggregating all available data on factors and outcome populate the wiki. + - **Data Silos Directory:** Compile a comprehensive directory of existing data sources, facilitating integration with the Digital Twin Safe. + - **Reputation Scoring:** Develop a transparent and reliable reputation-weighted voting system for intervention approval. + - **Comparative Policy Analysis** - Aggregate existing approval and certification data from existing national regulatory bodies. For instance, numerous countries have outlawed the use of glyphosate which can harm the gut microbiome. This data could be used to generate a comparative policy analysis to inform the FDA's decision-making process. + - **Food and Drug Outcome Labels** - Ultimately, the most useful output of a decentralized FDA would be [Outcome Labels](../data-analysis/outcome-labels/outcome-labels.md) list the degree to which the product is likely to improve or worsen specific health outcomes or symptoms. These are derived from real-world data (RWD) and subject to Futarchical-weighted review by the board members of the FDAi. [👉 Learn More](../data-analysis/outcome-labels/outcome-labels.md) + - **Publish Meta-Analyses** - Generate meta-analyses from all completed studies at ClinicalTrials.gov + - **Certification of Intervention Manufacturers/Sources** via a Decentralized Web of Trust derived from end-user data and reviews traced back using an NFT-tracked supply chain + - **Intervention Ranking** - Elevate the most promising yet little/known or researched treatments + - **Decentralized Clinical Trial Coordination and Protocols** - Not only would this increase knowledge but also access and availability of new and innovated treatments to those who need them urgently. + +**Potential Implementations, Components or Inspiration** +- [Psychonaut Wiki](https://psychonautwiki.org/wiki/Psychoactive_substance_index) - A modified version of MediaWiki with additional quantitative metadata storage regarding the pharmacokinetics of various substances. +- [Journal of Citizen Science](https://studies.crowdsourcingcures.org/) - Published outcome labels based on analysis of real-world data (RWD) from the [Crowdsourcing Cures](https://crowdsourcingcures.org/). + +![outcome-labels.png](https://static.crowdsourcingcures.org/dfda/components/outcome-labels/outcome-labels.png) diff --git a/public/globalSolutions/dfda/components/data-analysis/data-analysis.md b/public/globalSolutions/dfda/components/data-analysis/data-analysis.md new file mode 100644 index 00000000..e2566d54 --- /dev/null +++ b/public/globalSolutions/dfda/components/data-analysis/data-analysis.md @@ -0,0 +1,135 @@ +--- +title: Causal Inference and Optimal Daily Value Determination +description: How to quantify treatment effects from challenging sparse, irregular time series data with missing values. +published: true +tags: [causal inference, data analysis, treatment effects, time series] +--- + +# 📈 Data Analysis + +The Analytics Engine performs temporal precedence accounting, longitudinal data aggregation, erroneous data filtering, unit conversions, ingredient tagging, and variable grouping to quantify correlations between symptoms, treatments, and other factors. + +It then pairs every combination of variables and identifies likely causal relationships using correlation mining algorithms in conjunction with a pharmacokinetic model. The algorithms first identify the onset delay and duration of action for each hypothetical factor. It then identifies the optimal daily values for each factor. + +![](https://static.crowdsourcingcures.org/dfda/components/data-analysis/data-analysis.png) + +# Determining Treatment Effects from Sparse and Irregular Time Series Data + +## Introduction + +Analyzing the effects of a treatment based on observational time series data is a common need in many domains like medicine, psychology, and economics. However, this analysis often faces several key challenges: + +- The data is **sparse** - there are limited number of observations. +- The data is **irregular** - observations are not at regular time intervals. +- There is **missing data** - many timepoints have no observation. +- The **onset delay** of the treatment effect is unknown. It may take time to appear. +- The **duration** of the treatment effect is unknown. It may persist after cessation. +- Both **acute** (short-term) and **cumulative** (long-term) effects need to be analyzed. +- **Causality** and **statistical significance** need to be established rigorously. +- The **optimal dosage** needs to be determined to maximize benefits. + +This article provides a comprehensive methodology to overcome these challenges and determine whether a treatment makes an outcome metric better, worse, or has no effect based on sparse, irregular time series data with missingness. + +## Data Preprocessing + +Before statistical analysis can begin, the data must be preprocessed: + +- **Resample** the time series to a regular interval if needed while preserving original timestamps. This allows handling missing data. For example, resample to 1 measurement per day. +- **Do not** do interpolation or forward fill to estimate missing values. This introduces incorrect data. Simply exclude those time periods from analysis. +- **Filter out** any irrelevant variances like daily/weekly cycles. For example, detrend the data. + +Proper preprocessing sets up the data for robust analysis. + +## Statistical Analysis Methodology + +With cleaned data, a rigorous methodology can determine treatment effects: + +### Segment Data +First, split the data into three segments: + +- **Pre-treatment** - Period before treatment began +- **During treatment** - Period during which treatment was actively administered +- **Post-treatment** - Period after treatment ended + +This enables separate analysis of the acute and cumulative effects. + +### Acute Effects Analysis + +To analyze **acute** effects, compare the 'during treatment' segment vs the 'pre-treatment' segment: + +- Use interrupted time series analysis models to determine causality. +- Apply statistical tests like t-tests to determine significance. +- Systematically test different onset delays by shifting the 'during treatment' segment start time back incrementally. Account for unknown onset. +- Systematically test excluding various amounts of time after treatment cessation to account for effect duration. +- Look for acute improvements or decrements right after treatment begins based on the models. + +### Cumulative Effects Analysis + +To analyze **cumulative** effects, build regression models between the outcome variable and the cumulative treatment dosage over time: + +- Use linear regression, enforcing causality constraints. +- Apply statistical tests like F-tests for significance. +- Systematically test excluding various amounts of time after treatment cessation to account for effect duration. +- Look for long-term improvements or decrements over time based on the regression models. + +### Overall Effect Determination + +Combine the acute and cumulative insights to determine the overall effect direction and statistical significance. + +For example, acute worsening but long-term cumulative improvement would imply an initial side effect but long-term benefits. Lack of statistical significance would imply no effect. + +### Optimization + +To determine the **optimal dosage**, incrementally adjust the daily dosage amount in the models above. Determine the dosage that minimizes the outcome variable in both the acute and cumulative sense. + +## Analysis Pipeline + +Absolutely, given your constraints and requirements, here's a refined methodology: + +1. **Data Preprocessing**: + - **Handling Missingness**: Exclude rows or time periods with missing data. This ensures the analysis is grounded in actual observations. + - **Standardization**: For treatments with larger scales, standardize values to have a mean of 0 and a standard deviation of 1. This will make regression coefficients more interpretable, representing changes in symptom severity per standard deviation change in treatment. + +2. **Lagged Regression Analysis**: + - Evaluate if treatment on previous days affects today's outcome, given the discrete nature of treatment. + - Examine up to a certain number of lags (e.g., 30 days) to determine potential onset delay and duration. + - Coefficients represent the change in symptom severity due to a one unit or one standard deviation change in treatment, depending on whether standardization was applied. P-values indicate significance. + +3. **Reverse Causality Check**: + - Assess if symptom severity on previous days predicts treatment intake. This helps in understanding potential feedback mechanisms. + +4. **Cross-UserVariableRelationship Analysis**: + - Analyze the correlation between treatment and symptom severity across various lags. + - This aids in understanding potential onset delays and durations of effect. + +5. **Granger Causality Tests**: + - Test if past values of treatment provide information about future values of symptom severity and vice-versa. + - This test can help in determining the direction of influence. + +6. **Moving Window Analysis** (for cumulative effects): + - Create aggregated variables representing the sum or average treatment intake over windows (e.g., 7 days, 14 days) leading up to each observation. + - Use these in regression models to assess if cumulative intake over time affects symptom severity. + +7. **Optimal Dosage Analysis**: + - Group data by discrete dosage levels. + - Calculate the mean symptom severity for each group. + - The dosage associated with the lowest mean symptom severity suggests the optimal intake level. + +8. **Control for Confounders** (if data is available): + - If data on potential confounding variables is available, incorporate them in the regression models. This helps in isolating the unique effect of the treatment. + +9. **Model Diagnostics**: + - After regression, check residuals for normality, autocorrelation, and other potential issues to validate the model. + +10. **Interpretation**: + - Consistency in findings across multiple analyses strengthens the case for a relationship. + - While no single test confirms causality, evidence from multiple methods can offer a compelling case. + +By adhering to this methodology, you will be working with actual observations, minimizing the introduction of potential errors from imputation. The combination of lagged regression, Granger causality tests, and moving window analysis will provide insights into both acute and cumulative effects, onset delays, and optimal treatment dosages. + +## Conclusion + +This rigorous methodology uses interrupted time series analysis, regression modeling, statistical testing, onset/duration modeling, and optimization to determine treatment effects from sparse, irregular observational data with missingness. It establishes causality and significance in both an acute and cumulative sense. By finding the optimal dosage, it provides actionable insights for maximizing the benefits of the treatment. + + +![](https://static.crowdsourcingcures.org/dfda/components/data-analysis/discovery-scatterplots.png) diff --git a/public/globalSolutions/dfda/components/data-collection/data-collection.md b/public/globalSolutions/dfda/components/data-collection/data-collection.md new file mode 100644 index 00000000..dc2cfbb9 --- /dev/null +++ b/public/globalSolutions/dfda/components/data-collection/data-collection.md @@ -0,0 +1,34 @@ +# 📲 Data Collection + +![](https://static.crowdsourcingcures.org/dfda/components/data-collection/data-collection.png) + +Data collection can be done using wearable sensors, third-party applications, and client applications. + +![Diagram showing data collection ranging from human and public data +sources ultimately ending up at the aggregation layer(30)](https://static.crowdsourcingcures.org/dfda/components/data-collection/data-collection-flow-chart.png) + +### Data Types + +Ultimately, the system should be able to handle the following data types: + +* Omics data (e.g., genomics, proteomics, metabolomics, etc.) +* Image and physiological data (e.g., CT, PET/SPECT, sMRI, fMRI, rMRI, DTI, EEG, MEG, ultrasound, cellular level imaging, multi-electrode recording, etc.) +* Clinical data (e.g., lab tests, pathology, imaging, diagnosis, electronic health records, etc.) +* Multiscale data (genomic, epigenomic, subcellular, cellular, network, organ, systems, organism, population levels) +* Multiplatform data (desktop, cloud-based storage, etc.) +* Data from multiple research areas and diseases (e.g., common inflammatory pathways in cancer, obesity, immune diseases, and neurodegenerative diseases) +* Data with special considerations (e.g., sparse data, heterogeneous data, very large or very small datasets) +* Human-computer interfaces and visualization + +### Automated Data Acquisition + +At the present time, it requires a great deal of effort and diligence on the part of the self-tracker to gather all the data required to identify the triggers of mental illness and quantify the effectiveness of different treatments. Tracking one’s mood, diet, sleep, activity, and medication intake can be extremely time-consuming. The present invention automatically pulls data from a number of data sources (adding more all the time). + +The data sources would include: + +* Biometric Devices: that could measure vital signs and biomarkers +* Purchase Records: Data regarding consumption of foods and supplements could be automatically collected by and inferred from receipts or other financial aggregation services like Mint.com. +* Auditory Records: Voice recognition may be used to quantify emotion through conscious verbal expression, spectral analysis of the magnitudes of different frequencies of speech would probably be a better means of quantifying unconscious human affect and thus providing more accurate data for the machine learning process. CommonSense is a cloud-based platform for sensor data. +* Visual Affect Data via Web-Cameras: By tracking hundreds of points on the subjects’ faces, InSight can accurately capture emotional states. +* Prescription Records: Microsoft HealthVault can automatically collect lab results, prescription history, and visit records from a growing list of labs, pharmacies, hospitals, and clinics. + diff --git a/public/globalSolutions/dfda/components/data-collection/notifications.md b/public/globalSolutions/dfda/components/data-collection/notifications.md new file mode 100644 index 00000000..30d0c039 --- /dev/null +++ b/public/globalSolutions/dfda/components/data-collection/notifications.md @@ -0,0 +1,13 @@ +## Notifications with Action Buttons + +Web and mobile push notifications with action buttons allow you to easily record treatments and symptoms without opening the app. + +These are available on Android, iOS, and desktop browsers. + +![web notification action buttons](https://static.crowdsourcingcures.org/dfda/components/data-collection/web-notification-action-buttons.png) + +## Related Code + +- [Service Worker to Display Notifications](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/public/app/public/firebase-messaging-sw.js) +- [Push Notification Data Model](/apps/dfda-1/app/Slim/Model/Notifications/PushNotificationData.php) +- [Notification Sender Job](/apps/dfda-1/app/PhpUnitJobs/Reminders/PushNotificationsJob.php) diff --git a/public/globalSolutions/dfda/components/data-import/data-import.md b/public/globalSolutions/dfda/components/data-import/data-import.md new file mode 100644 index 00000000..dd7258fa --- /dev/null +++ b/public/globalSolutions/dfda/components/data-import/data-import.md @@ -0,0 +1,28 @@ +# 🕸 Data Import + +![import-data-connectors-mhealth-integrations.png](https://static.crowdsourcingcures.org/dfda/components/data-import/import-data-connectors-mhealth-integrations.png) + +The Connector Framework imports and normalizes data on all quantifiable aspects of human existence (sleep, mood, medication, diet, exercise, etc.) from dozens of applications and devices including: + +- [Rescuetime](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/RescueTimeConnector.php) – Productivity and Time Tracking +- [WhatPulse](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/WhatPulseConnector.php) – Keystroke and Mouse Behaviour +- [Oura](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/OuraConnector.php) – Sleep Duration, Sleep Quality, Steps, Physical Activity +- [Pollution](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/WeatherConnector.php) – Air Quality, Noise Level, Particulate Matter +- [Netatmo](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/NetatmoConnector.php) – Ambient Temperature, Humidity, Air Quality +- [Fitbit](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/FitbitConnector.php) – Sleep Duration, Sleep Quality, Steps, Physical Activity +- [Withings](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/WithingsConnector.php) – Blood Pressure, Weight, Environmental CO2 Levels, Ambient Temperature +- [Weather](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/WeatherConnector.php) – Local Humidity, Cloud Cover, Temperature +- [Facebook](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/FacebookConnector.php) – Social Interaction, Likes +- [GitHub](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/GithubConnector.php) – Productivity and Code Commits +- [MyFitnessPal](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/MyFitnessPalConnector.php) – Food and Nutrient Intake +- [MoodPanda](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/MoodPandaConnector.php) – Basic Reported Mood +- [MoodScope](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/MoodscopeConnector.php) – Detailed Reported Mood +- [Sleep as Android](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/SleepAsAndroidConnector.php) – Snoring, Deep Sleep, Reported Sleep Rating +- [RunKeeper](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/RunKeeperConnector.php) – Physical Activity +- [MyNetDiary](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors/MyNetDiaryConnector.php) – Food and Nutrient Intake, Vital Signs + + +![integrations-screenshot.png](https://static.crowdsourcingcures.org/dfda/components/data-import/integrations-screenshot.png) + +## Related Code +- [Connectors](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/DataSources/Connectors) diff --git a/public/globalSolutions/dfda/components/data-silo-gateway-api-nodes/data-silo-api-gateways.md b/public/globalSolutions/dfda/components/data-silo-gateway-api-nodes/data-silo-api-gateways.md new file mode 100644 index 00000000..6fdb2d3d --- /dev/null +++ b/public/globalSolutions/dfda/components/data-silo-gateway-api-nodes/data-silo-api-gateways.md @@ -0,0 +1,22 @@ +## Data Silo API Gateway Nodes + +![dfda-gateway-api-node-silo.png](https://static.crowdsourcingcures.org/dfda/components/data-silo-gateway-api-nodes/dfda-gateway-api-node-silo.png) + +FDAi Gateway API Nodes make it easy for data silos, such as hospitals and digital health apps, to let people export and save their data locally in their [PersonalFDA Nodes](../../home.md#2-personalfda-nodes). + +### Requirements + - **OAuth2 Protected API:** Provides a secure, OAuth2-protected API for people to easily access their data. + - **Personal Access Token Management** - Individuals should be able to create labeled access tokens that they can use to access their data. They should be able to label their access tokens and monitor the usage of each token. They should also be able to revoke access tokens at any time and set an expiration date. + - **Developer Portal:** Developer portal for data silos to easily register and manage their 3rd party application, so they can allow users to share data with their application. + - **OpenAPI Documentation:** Provide OpenAPI documentation for the API, making it easy for data silos to integrate. + - **Software Development Kits (SDKs):** Provide SDKs for popular programming languages to make it easy for developers to integrate the API into their applications. + - **Data Encryption:** Implement robust encryption protocols to safeguard sensitive health data. + - **HIPAA and GDPR Compliance:** Ensure compliance with HIPAA and GDPR privacy regulations. + - **Multiple Data Format Options:** Provide multiple data format options for data export, including CSV, JSON, and XML. + - **Data Structure Options:** Client applications should be able to request should be able to request data in various formats such as FHIR, HL7, and the Common Data Model (CDM). + +### Potential Implementations, Components or Inspiration + +There's a monolithic implementation of this in [apps/dfda-1](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1). + +Please make a pull request and add links to any other open-source projects that could better fulfill this role. diff --git a/public/globalSolutions/dfda/components/decision-support-notifications/decision-support-notifications.md b/public/globalSolutions/dfda/components/decision-support-notifications/decision-support-notifications.md new file mode 100644 index 00000000..5ba837b1 --- /dev/null +++ b/public/globalSolutions/dfda/components/decision-support-notifications/decision-support-notifications.md @@ -0,0 +1,38 @@ +--- +title: Decision Support Notifications +description: Get notifications telling you the most important thing (MIT) you can do at any given time to optimize your health and happiness. +cover: notifications-screenshot-slide.png +coverY: 203.93538913362704 +--- + +# 🤖 Decision Support Notifications + +![notifications-screenshot-slide.png](https://static.crowdsourcingcures.org/dfda/components/decision-support-notifications/notifications-screenshot-slide.png) + +## What is it? + +Decision Support Notifications are a way to get notifications telling you the most important thing (MIT) you can do at any given time to optimize your health and happiness. + +## How does it work? + +Decision Support Notifications work by using a combination of machine learning and control theory to predict the future and optimize your health and happiness. + +Time-series data on food, drugs, and symptoms is fed into a machine learning model that which input values produce the most significant improvements in symptom severity ratings or biomarker levels. + +The system monitors your body and environment in real-time, and then identifies when the most significant input factors are outside their optimal ranges. When this happens, the system sends you a notification telling you what you can do to optimize your health and happiness. + +## References + +1. [SunilDeshpande\_S2014\_ETD.pdf (asu.edu)](https://keep.lib.asu.edu/\_flysystem/fedora/c7/114023/Deshpande\_asu\_0010E\_14022.pdf) +2. [LocalControl: An R Package for Comparative Safety and Effectiveness Research | Journal of Statistical Software (jstatsoft.org)](https://www.jstatsoft.org/article/view/v096i04) +3. [bbotk: A brief introduction (r-project.org)](https://cran.r-project.org/web/packages/bbotk/vignettes/bbotk.html) +4. [artemis-toumazi/dfpk (github.com)](https://github.com/artemis-toumazi/dfpk) +5. [miroslavgasparek/MPC\_Cancer: Model Predictive Control for the optimisation of the tumour treatment through the combination of the chemotherapy and immunotherapy. (github.com)](https://github.com/miroslavgasparek/MPC\_Cancer) +6. [Doubly Robust Learning — econml 0.12.0 documentation](https://econml.azurewebsites.net/spec/estimation/dr.html) +7. [A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention (nih.gov)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4167895/) +8. [The promise of machine learning in predicting treatment outcomes in psychiatry - Chekroud - 2021 - World Psychiatry - Wiley Online Library](https://onlinelibrary.wiley.com/doi/full/10.1002/wps.20882) +9. [CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence - Agata Blasiak, Jeffrey Khong, Theodore Kee, 2020 (sagepub.com)](https://journals.sagepub.com/doi/full/10.1177/2472630319890316) +10. [Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation (aimspress.com)](https://www.aimspress.com/article/id/2665) +11. [Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks (nips.cc)](https://papers.nips.cc/paper/2018/hash/56e6a93212e4482d99c84a639d254b67-Abstract.html) +12. [Estimating counterfactual treatment outcomes over time through adversarially balanced representations | OpenReview](https://openreview.net/forum?id=BJg866NFvB) +13. [https://dash.harvard.edu/bitstream/handle/1/37366470/AGUILAR-SENIORTHESIS-2019.pdf?isAllowed=y\&sequence=1](https://dash.harvard.edu/bitstream/handle/1/37366470/AGUILAR-SENIORTHESIS-2019.pdf?isAllowed=y\&sequence=1) diff --git a/public/globalSolutions/dfda/components/digital-twin-safe/digital-twin-safe.md b/public/globalSolutions/dfda/components/digital-twin-safe/digital-twin-safe.md new file mode 100644 index 00000000..5b38d8f0 --- /dev/null +++ b/public/globalSolutions/dfda/components/digital-twin-safe/digital-twin-safe.md @@ -0,0 +1,39 @@ +--- +title: 👨‍🤝‍👨 The Digital Twin Safe 🔒 +description: A little house for your digital twin. 🏡 +tags: [projects, data sharing, data storage] +--- + +![human-file-system-banner-logo](https://user-images.githubusercontent.com/2808553/180306571-ac9cc741-6f34-4059-a814-6f8a72ed8322.png) + +Import data from all your apps and wearables, so you can centrally own, control, and share all your digital exhaust. + +# Requirements + +- **Data Import** from all your apps and wearables, so you can centrally own, control, and share all your digital exhaust. +- **Central ownership** and control over your digital health data. +- **Data Sharing Controls:** Seamless sharing of data with health apps through our secure iframe messaging system. Full control over data sharing. +- **Quantum-Resistant Data Encryption** to safeguard sensitive health data. +- **Sync to Between Trusted Devices** like your phone or computer or a family member's device to avoid data loss in the case of device failure. +- **Multifactorial and Biometric Security** because, let's face it, your password is going to get hacked or lost. + +# Possible Technologies and Frameworks +- **[Obsidian](https://obsidian.md/)** is a personal knowledge base based on the [Electron](https://www.electronjs.org/) framework. It could be a good foundation for a Digital Twin Safe because it has a: + - open-source license + - plugin architecture that could be used to implement a variety of features in a modular way + - built-in peer to peer sync feature + - robust encryption system + - and can be built for desktop and mobile +- **[Electron](https://www.electronjs.org/)** is a lower-level framework for creating native applications with web technologies like JavaScript, HTML, and CSS. The main benefit of Electron for the Digital Twin Safe is that it allows you to create cross-platform desktop applications using web technologies. +- **[Expo](https://expo.io/)** is a set of tools and services built around React Native and native platforms that help you develop, build, deploy, and quickly iterate on iOS, Android, and web apps from the same JavaScript/TypeScript codebase. + +# Potential Implementations, Components or Inspiration +- [Modified Gnosis Safe](/digital-twin-safe) +- [Weavechain](https://weavechain.com/) +- [Crowdsourcing Cures App](https://app.crowdsourcingcures.org/app/public/#/app/intro) + +## Demo + +[![digital-twin-safe-screenshot-home](https://user-images.githubusercontent.com/2808553/200402565-72bc85a3-deb2-4f1a-a9b1-bde108e63d87.png)](https://safe.FDAi.earth?access_token=demo) + +Demo available at [safe.FDAi.earth](https://safe.FDAi.earth?access_token=demo) diff --git a/public/globalSolutions/dfda/components/human-ai-collective-intelligence-platform/desci-ai-dao.md b/public/globalSolutions/dfda/components/human-ai-collective-intelligence-platform/desci-ai-dao.md new file mode 100644 index 00000000..b255b1de --- /dev/null +++ b/public/globalSolutions/dfda/components/human-ai-collective-intelligence-platform/desci-ai-dao.md @@ -0,0 +1,125 @@ +--- +title: DeSci DAO AI Agent Framework +description: How to create a truly autonomous DAO that actually accelerates scientific progress +tags: [dao, ai, automation, desci] +--- + +# Problem: + +- **Coordination** - Lack of inter-DAO coordination leads to duplication of efforts and stalled progress + +- **Resource allocation** - Lack of intra-DAO coordination causes inefficient resource allocation + +- **Mindsets** - Competitive mindsets prevent open collaboration between members + +- **Prioritization** - No systemic way to prioritize high-impact tasks objectively + +# Goals: + +- **Templates** - Create an open-source AI DAO template to improve coordination, transparency and automation + +- **Duplication** - Reduce duplication of efforts between DAOs and members + +- **Prioritization** - Enable efficient task prioritization and resource allocation + +- **Onboarding** - Lower barriers to entry for new DAOs via templates + +# Benefits: + +- **Collaboration** - Improved collaboration and reduced duplicated efforts + +- **Efficiency** - More efficient resource allocation via AI optimization + +- **Transparency** - Increased transparency into DAO activities + +- **Automation** - Automation of administrative tasks to free up human capital + +- **Iteration** - Rapid iteration and improvement via feedback loops + +- **Interoperability** - Interoperability between DAOs using open-source agents + +# Approach: + +- **Framework** - Design flexible DAO framework with defined structure, roles, tools + +- **Objectives** - Set quantitative objectives and key results (OKRs) + +- **Agents** - Build AI agents for core functions like task management and communication + +- **Tools** - Implement with tools like Discord, GitHub, Snapshot + +- **Expand** - Expand agents via reinforcement learning and community contributions + +- **Infrastructure** - Transition to decentralized infrastructure over time + + +# Development Plan + +## Phase 1 – Design DAO Framework + +### Milestone 1: Finalize DAO structure and governance +- Draft constitution and decision-making procedures +- Define member roles and responsibilities +- Choose tools like Discord, GitHub, Notion + +### Milestone 2: Set DAO objectives and key results (OKRs) +- Define top-level utility function for DAO +- Set quarterly OKRs with measurable key results +- Establish processes to monitor KPIs + +### Milestone 3: Design AI architecture and integration +- Map agent roles to DAO OKRs +- Define agent utility functions, KPIs, and rewards +- Plan for connecting agents to tools and DAO members + +## Phase 2 – Build Agents with Learning + +### Milestone 4: Implement core agent capabilities +- Dialog, memory, tool integrations +- Initialize knowledge graphs +- Handoff to humans when necessary + +### Milestone 5: Create a project manager agent +- Set objectives around the shipping roadmap +- Track KPIs like issues closed, features delivered +- Learn from human feedback + +### Milestone 6: Build a communicator agent +- Objectives for engagement, transparency +- Measure KPIs like messages sent, sentiment +- Learn from member input + +### Milestone 7: Launch MVP agents +- Connect agents to GitHub, Discord, Ethereum +- Start tracking DAO and agent KPIs +- Open source codebase and documentation + +## Phase 3 – Iterate and Expand Intelligently + +### Milestone 8: Enhance conversational abilities +- Expand dialog trees based on feedback +- Improve NLP models via reinforcement learning +- Lower handoff rate by learning from experience + +### Milestone 9: Add accounting and recruiting agents +- Automate tasks like payments and vetting +- Set objectives around their specialized domains +- Continuously optimize KPIs with feedback loops + +### Milestone 10: Open-source templatized agents +- Abstract agents into reusable frameworks +- Allow community contributions and experimentation +- Share learnings across compounds + +## Phase 4 – Transition to Full Autonomy + +### Milestone 11: Move agents to decentralized infrastructure +- Store memory and models on IPFS/Filecoin +- Connect to blockchain identity and permissions +- Make constitution and transition plan immutable + +### Milestone 12: Hand over control to agents +- Agents amend constitution and govern fully autonomously +- Humans step into oversight role +- Value-aligned AI DAO becomes self-sustaining + diff --git a/public/globalSolutions/dfda/components/human-ai-collective-intelligence-platform/dfda-collaboration-framework.md b/public/globalSolutions/dfda/components/human-ai-collective-intelligence-platform/dfda-collaboration-framework.md new file mode 100644 index 00000000..2ea2e9a4 --- /dev/null +++ b/public/globalSolutions/dfda/components/human-ai-collective-intelligence-platform/dfda-collaboration-framework.md @@ -0,0 +1,51 @@ +--- +title: FDAi Collaboration and Collective Intelligence Framework +description: A framework for coordinating the efforts of the stakeholders in the FDAi ecosystem. +tags: [dfda, collaborationism, collective intelligence, coordination] +--- + +> **Note:** This document is a work in progress. We welcome contributions and encourage the inclusion of existing projects that align with this framework's objectives. Please [contact us](mailto:grants@crowdsourcingcures.org) if you'd like to implement any aspect of this framework. + +We are creating the FDAi Collaboration and Collective Intelligence Framework to serve as a central platform for facilitating cooperation, communication, and collaborative actions among stakeholders in the health sector. Its primary objective is to harness collective capabilities towards a shared vision of accelerated clinical discovery and improved health outcomes. + +**Desired Features of the Platform:** + +1. **Communication Channels:** Facilitate seamless interaction among stakeholders to build a community with shared knowledge and goals. +2. **Resource Sharing Mechanisms:** Enable the exchange of data, technologies, expertise, and other resources among partners. +3. **Collaborative Workspaces:** Provide dedicated tools and spaces for research, data analysis, and project development. +4. **Partnership Agreements:** Ensure clear formation and management of partnerships with defined roles and responsibilities. +5. **Project Management Tools:** Offer resources for effectively planning, tracking, and managing joint projects. +6. **Knowledge Repository:** Establish a centralized or federated database for collective knowledge, research findings, and best practices. +7. **Legal and Regulatory Guidance:** Offer guidance for navigating relevant legal and regulatory frameworks. +8. **Impact Tracking:** Implement tools for monitoring and evaluating the outcomes of collaborative projects. + +**Implementation Approach:** + +To establish the FDAi platform, we propose leveraging existing open-source tools to ensure cost-efficiency, rapid deployment, and an environment conducive to collaboration. Key steps include: + +1. **Tool Selection:** Identify open-source tools for communication, collaboration, data sharing, and decentralized governance. +2. **Infrastructure Setup:** Use platforms like Slack, GitHub, and Trello for core communication and project management. +3. **Governance and Data Sharing:** Employ blockchain tools for governance (e.g., Aragon) and explore data-sharing platforms like CKAN. +4. **Incentive Systems:** Implement tokenization and reward mechanisms using platforms like Ethereum and SourceCred. +5. **Legal and Knowledge Management:** Utilize platforms like Nextcloud for legal guidance and MediaWiki for knowledge sharing. +6. **Security and Privacy:** Strengthen the platform with security tools like OpenSSL and privacy solutions like GnuPG. +7. **Community Engagement:** Foster community interactions using forums like Discourse. +8. **Feedback and Support:** Implement feedback tools like LimeSurvey and offer technical support through open-source helpdesk solutions. +9. **Promotion of Open Source Culture:** Encourage active community contributions to the open-source tools used. + +**Overcoming Competitive Challenges:** + +To address the competitive nature inherent in human and organizational behavior, the platform emphasizes: + +1. **Shared Goals and Vision:** Align stakeholders around collective success. +2. **Incentive Structures:** Reward collaboration and mutual support. +3. **Transparency and Trust:** Ensure openness and accountability in all operations. +4. **Decentralized Governance:** Adopt a fair and consensus-driven decision-making model. +5. **Resource Pooling:** Share resources and benefits equitably among participants. +6. **Open Innovation:** Promote knowledge sharing and cross-organizational collaboration. +7. **Conflict Resolution:** Implement effective mechanisms for dispute resolution. +8. **Cultural Change:** Encourage a shift from competition to collaboration through education and storytelling. +9. **Collaborative Metrics:** Focus on evaluation criteria that highlight collective efforts and impact. + +By integrating these elements, the FDAi initiative aims to foster a collaborative ecosystem that accelerates innovation in health, aligning with the mission of maximizing human lifespan and minimizing net suffering. + diff --git a/public/globalSolutions/dfda/components/human-file-system-protocol/human-file-system-protocol.md b/public/globalSolutions/dfda/components/human-file-system-protocol/human-file-system-protocol.md new file mode 100644 index 00000000..ff3100a2 --- /dev/null +++ b/public/globalSolutions/dfda/components/human-file-system-protocol/human-file-system-protocol.md @@ -0,0 +1,54 @@ +--- +title: Human File System +description: The Human File System Protocol SDK is an innovative suite of interoperable software libraries, meticulously designed to facilitate the creation of user-access controlled digital twins on the blockchain. +tags: [data-aggregation, data sharing, data storage] +--- + +## The Human File System Protocol SDK + +**A Simple API for Patient-Controlled Health Data Aggregation, Sharing, and Monetization** + +A set of interoperable software libraries that can be used independently to create user-access controlled digital twins on the blockchain. + +The libraries can be used independently, but will all be included in the HumanFS SDK. + +### The Need for a Human File System Protocol + +There are 350k health apps containing various types of symptom and factor data. However, the isolated data's relatively useless in all these silos. To figure out how to actually minimize/avoid chronic disease, all the factor data needs to be combined with the outcome data. + +**Web2 Problem** + +The web2 solution to combining all this data is a nightmare of + +1. creating thousands of OAuth2 data connectors +2. running a bunch of importer cron jobs on AWS. + +**Web3 Solution** + +User auth/databases/key management/access control/3rd party OAuth tokens abstracted away by a single, easy-to-use API + +i.e. + +Pain Tracking App A: + +`db.collections.create('Arthritis Severity', timeSeriesData);` + +Diet-Tracking App B: + +`let timeSeriesData = db.collections.get('Arthritis Severity');` + +⇒ Making it possible for Diet-Tracking App B (and/or Pain Tracking App A) to easily analyze the relationship between dietary factors and Arthritis Severity using causal inference predictive model control recurrent neural networks. + +# 📚 Libraries Used + +[Data Storage, Authorization and Sharing](https://github.com/yash-deore/sshr-hackfs) - Lit Programmable Key Pairs (PKPs) for access control over protected health information (PHI) with data storage on Ceramic. XMTP (Extensible Message Transport Protocol) is an open protocol and network for secure, private messaging between patients and physicians. + +### Relevant Libraries +- [Zero Knowledge Unique Patient Identifier Key in a Soul Bound NFT](https://app.dework.xyz/hackfs-dhealth-colle/suggestions?taskId=ff0c50bf-3c11-4076-8c9c-18d8c46ecf05) - For patients, owning an NFT of their medical data would be like creating a sentry to guard that personal information. The NFT would act as a gatekeeper, tracking who requested access, who was granted access, and when—and record all those actions publicly. +- [Federated Learning](https://app.dework.xyz/hackfs-dhealth-colle/suggestions?taskId=f25f12a9-7e3d-4488-85f7-023f95f75dfe) - Fluence to perform decentralized analysis of human generated data from applications and backends on peer-to-peer networks +- [Proof of Humanity](https://app.dework.xyz/hackfs-dhealth-colle/suggestions?taskId=db1092b9-91b4-4352-999a-f088ffefd6c8) - The Proof of Attendance Protocol for Sybil Resistant data collection, ensuring that robots aren't selling fake health data. +- [Reward open-source health innovation](https://app.dework.xyz/hackfs-dhealth-colle/suggestions?taskId=7261a8d8-f1ad-493c-a41c-b70a36507763) - Valist to reward public good open-source health technology innovations using Software License NFTs and proof of open-source contribution. +- [Querying specific health data](https://app.dework.xyz/hackfs-dhealth-colle/suggestions?taskId=3a546a7f-2aa6-43a1-8dda-08c5a62c83b4) - Tableland for querying the HumanFS for specific data types and time periods. +- [NFT Health Data Marketplace](https://app.dework.xyz/hackfs-dhealth-colle/main-space-477/projects/nft-health-data-mark) - NFTPort for minting data sets that can be sold to pharmaceutical companies in a health data marketplace. +- [On-Chain Analytics](https://app.dework.xyz/hackfs-dhealth-colle/suggestions?taskId=0114d499-36ff-4451-9d1a-e870c753e155) - Covalent for Health Data NFT marketplaces, On-Chain Analytics / Dashboards, Health Data Wallets, Health Data Asset tracking, and ROI for NFT generation and sales. + diff --git a/public/globalSolutions/dfda/components/no-code-app-builder/no-code-app-builder.md b/public/globalSolutions/dfda/components/no-code-app-builder/no-code-app-builder.md new file mode 100644 index 00000000..eaba8cd3 --- /dev/null +++ b/public/globalSolutions/dfda/components/no-code-app-builder/no-code-app-builder.md @@ -0,0 +1,22 @@ +# No-Code Health App Builder + +## Overview + +The No-Code Health Data App Builder is an innovative platform designed to simplify and streamline the process of creating health data collection, aggregation, and analysis applications. This powerful tool allows users, regardless of their technical background, to build custom apps tailored to their health data management needs. + +## Features + +Based on the provided screenshots, the No-Code Health Data App Builder includes features such as: + +1. **Easy-to-Use Interface**: A user-friendly interface that allows for easy navigation and app creation without any coding skills. + +2. **Customizable Data Links**: Users can link various health data sources for aggregation and analysis. This might include fitness trackers, medical records, dietary information, and more. + +3. **Flexible Design Options**: The builder offers a range of customization options to create a personalized app experience. + +4. **Data Visualization Tools**: Integrated tools to help visualize and analyze health data effectively. + +5. **Automated Data Collection and Aggregation**: Automated processes for collecting and aggregating data from multiple sources. + +6. **Privacy and Security**: Ensuring user data is kept secure and private. + diff --git a/public/globalSolutions/dfda/components/observational-studies/observational-studies.md b/public/globalSolutions/dfda/components/observational-studies/observational-studies.md new file mode 100644 index 00000000..170af780 --- /dev/null +++ b/public/globalSolutions/dfda/components/observational-studies/observational-studies.md @@ -0,0 +1,31 @@ +# 📑 Observational Studies Plugin + +- [Overview](#overview) +- [Impact](#impact) +- [Study Index](#study-index) +- [Overall Mood Mega Study](#overall-mood-mega-study) +- [Models](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/Studies) +- [Front-End Views](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/Studies) + +## Overview + +Observational studies are a type of study in which researchers observe the behavior of a group of people and measure outcomes. Observational studies do not involve any intervention or manipulation of the subjects, and therefore cannot establish causality. However, they are powerful tools for collecting data to help answer important scientific questions. Observational studies can be used to describe the distribution of a variable among a population, to identify relationships between two or more variables, and to identify risk factors for a disease or other health outcome. Observational studies can be conducted using either a retrospective or a prospective study design. + +## Impact + +- Clinicians and those suffering from chronic conditions will have access to the personalized effectiveness rates of treatments and the percent likelihood of root causes. +- Researchers will be able to use the data to identify the factors that most influence any given aspect of health. +- Anyone wanting to optimize any quantifiable aspect of their life is able to search and see a list of the products that are most effective at helping the average user achieve a particular health and wellness goal. For instance, if one wishes to improve one’s sleep efficiency, go to our site and search for “sleep efficiency”, where one is able to select from the list of products that most affect sleep efficiency. +- The Personal FDA will be able to use the data to identify the factors that most influence any given aspect of health. + +## Study Index + +![studies](https://static.crowdsourcingcures.org/dfda/components/observational-studies/studies-cropped.jpg) + +## Overall Mood Mega Study + +![(overall-mood-predictors](https://static.crowdsourcingcures.org/dfda/components/observational-studies/overall-mood-predictors.jpeg) + +## Tests + +[PHPUnit on Staging Environment](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/tests/StagingUnitTests/C/Studies) diff --git a/public/globalSolutions/dfda/components/optimiton-ai-agent/optomitron-ai-agent.md b/public/globalSolutions/dfda/components/optimiton-ai-agent/optomitron-ai-agent.md new file mode 100644 index 00000000..b31a660b --- /dev/null +++ b/public/globalSolutions/dfda/components/optimiton-ai-agent/optomitron-ai-agent.md @@ -0,0 +1,23 @@ +# Optimitron AI Agent + +Optimitron is an AI agent that lives in your PersonalFDA node that uses causal inference to estimate how various factors affect your health. + +![data-import-and-analysis.gif](../../images/data-import-and-analysis.gif) + +Optimitron is an AI assistant that asks you about your symptoms and potential factors. Then she applies pharmacokinetic predictive analysis to inform you of the most important things you can do to minimize symptom severity. + +[![Click Here for Demo Video](../../images/optimitron-ai-assistant.png)](https://youtu.be/hd50A74o8YI) + +[Or Try the Prototype Here](https://demo.curedao.org/app/public/#/app/chat) + +#### Data Analysis + +Currently, we've implemented causal inference analysis of sparse time series data that takes into account onset delays and other factors. + +![causal-inference-vertical.svg](https://static.crowdsourcingcures.org/dfda/components/data-analysis/causal-inference-vertical.svg) + +We're working on implementing a more robust pharmacokinetic predictive model control recurrent neural network. + +Ideally, Optimitron AI agent will be able to further improve the precision and accuracy of the real-time recommendations over time by leveraging reinforcement learning and community contributions. + +[👉 Learn more about the data analysis here...](../data-analysis/data-analysis.md) diff --git a/public/globalSolutions/dfda/components/outcome-labels/outcome-labels.md b/public/globalSolutions/dfda/components/outcome-labels/outcome-labels.md new file mode 100644 index 00000000..e51878ee --- /dev/null +++ b/public/globalSolutions/dfda/components/outcome-labels/outcome-labels.md @@ -0,0 +1,35 @@ +# 🏷 Outcome Labels + +![outcome-labels-plugin.png](https://static.crowdsourcingcures.org/dfda/components/outcome-labels/outcome-labels.png) + +Currently, all foods carry nutrition labels such as this one: + +![](outcome-labels.md) + +But how useful is it to the average person to know the amount of Riboflavin in something? The purpose of nutritional labels is to help individuals make choices that will improve their health and prevent disease. + +Telling the average person the amount of riboflavin in something isn’t going to achieve this. This is evidenced by the fact that these labels have existed for decades and during this time, we’ve only seen increases in most diseases they were intended to reduce. + +We have created a new and improved **Outcomes Label** that instead lists the degree to which the product is likely to improve or worsen specific health outcomes or symptoms. We currently have generated Outcome Labels for thousands of foods, drugs, and nutritional supplements that can be found at [studies.crowdsourcingcures.org](https://studies.crowdsourcingcures.org). These labels are derived from the analysis of 10 million data points anonymously donated by over 10,000 study participants via the app at [app.crowdsourcingcures.org](https://app.crowdsourcingcures.org). + +![](https://crowdsourcingcures.org/wp-content/uploads/2021/05/nutrition-facts-vs-outcome-labels-melatonin-1024x592.png) + +## Data Quantity + +We've collected over 10 million data points on symptom severity and influencing factors from over 10,000 people. Predictive machine learning algorithms are applied to reveal effectiveness and side effects of treatments and the degree to which hidden dietary and environmental improve or exacerbate chronic illnesses. + +These analytical results have been used to publish 90,000 studies on the effects of various treatments and food ingredients on symptom severity. + +![](https://static.crowdsourcingcures.org/video/johnny-5-need-input.gif) + +Although 10 million data points sound like a lot, currently, the usefulness and accuracy of these Outcome Labels are currently limited. This is due to the fact there are only a few study participants have donated data for a particular food paired with a particular symptom. In observational research such as this, a very large number of participants are required to cancel out all the errors and coincidences that can influence the data for a single individual. + +For instance, someone with depression may have started taking an antidepressant at the same time they started seeing a therapist. Then, if their depression improves, it’s impossible to know if the improvement was a result of the antidepressant, the therapist, both, or something else. These random factors are known as confounding variables. However, random confounding factors can cancel each other out when looking at large data sets. This is why it’s important to collect as much data as possible. + +## Data Sources + +Several types of data are used to derive the Outcome Labels: + +1. **Individual Micro-Level Data** – This could include data manually entered or imported from other devices or apps at [safe.dfda.earth](http://safe.dfda.earth), This could also include shopping receipts for foods, drugs, or nutritional supplements purchased and insurance claim data. +2. **Macro-Level Epidemiological Data** – This includes the incidence of various diseases over time combined with data on the amounts of different drugs or food additives. This is how it was initially discovered that smoking caused lung cancer. With macro-level data, it’s even harder to distinguish correlation from causation. However, different countries often enact different policies that can serve as very useful natural experiments. For instance, 30 countries have banned the use of glyphosate. If the rates of Alzheimer’s, autism, and depression declined in these countries and did not decline in the countries still using glyphosate, this would provide very powerful evidence regarding its effects. Unfortunately, there is no global database that currently provides easy access to the incidence of these conditions in various countries over time and the levels of exposure to various chemicals. +3. **Clinical Trial Data** – This is the gold standard with regard to the level of confidence that a factor is truly the cause of an outcome. However, it’s also the most expensive to collect. As a result, clinical trials are often very small (less than 50 people). Exclusion criteria in trials often prevent study participants from being representative of real patients. There are ethical considerations that prevent us from running trials that have any risk of harm to participants. Due to the expense involved we have very few trials run on anything other than a molecule that can be patented and sold as a drug. diff --git a/public/globalSolutions/dfda/components/personal-fda-nodes/personal-fda-nodes.md b/public/globalSolutions/dfda/components/personal-fda-nodes/personal-fda-nodes.md new file mode 100644 index 00000000..1b8740c7 --- /dev/null +++ b/public/globalSolutions/dfda/components/personal-fda-nodes/personal-fda-nodes.md @@ -0,0 +1,21 @@ +## 2. PersonalFDA Nodes + +PersonalFDA Nodes are applications that can run on your phone or computer. They import, store, and analyze your data to identify how various factors affect your health. They can also be used to share anonymous analytical results with the [Clinipedia FDAi Wiki](../clinipedia/clinipedia.md) in a secure and privacy-preserving manner. + +PersonalFDA Nodes are composed of two components, a Digital Twin Safe and an AI agent called Optimitron (or some better name) that uses causal inference to estimate how various factors affect your health. + +### 2.1. Digital Twin Safe + +![digital-twin-safe.png](https://static.crowdsourcingcures.org/dfda/components/digital-twin-safe/digital-twin-safe.png) + +A local application for self-sovereign import and storage of personal data. + +**👉 [Learn More or Contribute to Digital Twin Safe](../digital-twin-safe/digital-twin-safe.md)** + +### 2.2. Optimitron AI Agent + +![data-import-and-analysis.gif](../../images/data-import-and-analysis.gif) + +Optimitron is an AI agent that lives in your PersonalFDA node that uses causal inference to estimate how various factors affect your health. + +**👉 [Learn More](../optimiton-ai-agent/optomitron-ai-agent.md)** diff --git a/public/globalSolutions/dfda/components/predictor-search-engine/predictor-search-engine.md b/public/globalSolutions/dfda/components/predictor-search-engine/predictor-search-engine.md new file mode 100644 index 00000000..d8873a21 --- /dev/null +++ b/public/globalSolutions/dfda/components/predictor-search-engine/predictor-search-engine.md @@ -0,0 +1,47 @@ +# 🔎 Predictor Search Engine Plugin + +Aggregated user data is used to determine the factors that most influence any given aspect of health, powering the QM Search Engine. + +Anyone wanting to optimize any quantifiable aspect of their life is able to search and see a list of the products that are most effective at helping the average user achieve a particular health and wellness goal. For instance, if one wishes to improve one’s sleep efficiency, go to our site and search for “sleep efficiency”, where one is able to select from the list of products that most affect sleep efficiency. + +Impact: Clinicians and those suffering from chronic conditions will have access to the personalized effectiveness rates of treatments and the percent likelihood of root causes. + +The search engine is available in a variety of layout settings. + +### Network Graph Style + +[Network Graph Demo](https://app.quantimo.do/variables/Overall%20Mood) + +![Network Graph Style](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/overall-mood-predictors-network-graph.png) + +### Embeddable Cards + +![Cards](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/predictor-search-cards.png) + +### Embeddable Simple List + +![Predictor Search Engine Plugin](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/predictor-search-no-background.png) + +### Study Cards in Ionic App + +[Study Cards in Ionic App Demo](https://web.quantimo.do/dev/src/ionic/src/index.html#/app/predictors/Overall%20Mood) + +![Study Cards in Ionic App](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/overall-mood-predictors.png) + +### Nutrition Facts Style + +[Nutrition Facts Style Demo](https://app.quantimo.do/variables/Overall%20Mood) + +![Nutrition Facts Style](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/mood-predictors-nutrition-facts-style.png) + +### Bar Chart Style + +[Bar Chart Demo](https://app.quantimo.do/variables/Overall%20Mood) + +![Bar Chart Style](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/mood-predictors-bar-chart.png) + +### Sankey Chart Style + +[Sankey Chart Demo](https://app.quantimo.do/variables/Overall%20Mood) + +![Sankey Chart Style](https://static.crowdsourcingcures.org/dfda/components/predictor-search-engine/overall-mood-predictors-flow-sankey-chart.png) diff --git a/public/globalSolutions/dfda/components/root-cause-analysis-reports/root-cause-analysis-reports.md b/public/globalSolutions/dfda/components/root-cause-analysis-reports/root-cause-analysis-reports.md new file mode 100644 index 00000000..36d892c9 --- /dev/null +++ b/public/globalSolutions/dfda/components/root-cause-analysis-reports/root-cause-analysis-reports.md @@ -0,0 +1,23 @@ +# Root Cause Analysis Reports + +![factors-slide.png](https://static.crowdsourcingcures.org/dfda/components/root-cause-analysis-reports/factors-slide.png) + +A half billion are suffering from autoimmune diseases like irritable bowel disease, multiple sclerosis, Crohn's disease, psoriasis, and fibromyalgia. +Another half a billion people are suffering from depression and other mental illnesses. +At least one person will have committed suicide by the time you read this paragraph. (Hopefully, it won't be as a result of reading this paragraph.) Instead, we believe death is due to massive failures in our current systems of clinical research and diagnosis. +Currently, it costs as much as \$48,000 per subject in a Phase III clinical trial. That has resulted in very little data on the effects of any factors that aren’t patentable. + +Furthermore, something that most of these chronic conditions have in common is that they can be exacerbated or improved by hundreds of factors during daily life. They can be affected by the hundreds of chemicals you consume through your diet, treatments you've been prescribed, micronutrient intake, nutritional supplements, the amount of time spent in various sleep stages, the type, duration, and intensity of physical activity, social interaction, and even the weather! + +Unfortunately, the human mind evolved to survive in a world without data millions of years ago on the African savanna. As a result, our brains are only capable of processing 7 numbers at a given time. +Brains don’t have the bandwidth or statistical processing power required to analyze the massive amount of longitudinal data, time delays, and durations of action that has to be taken into account to determine the influence of each of these factors on the individual’s symptoms. +The good news is that new devices and apps can automate the process of collecting all of this, data and my brain can process millions of numbers! +I love to eat up all this data and analyze it and determine, which of these hundreds of factors have the strongest relationships to the symptom or outcome of interest. +Thus, I automatically generate reports intended to help you and your physician gain insights into the root causes and effective solutions to help you minimize your symptoms. + + +* [HTML Example](1398-root-cause-analysis.html) +* [Model and Analytics Code](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/app/Reports/RootCauseAnalysis.php) +* [Front-End View Code](https://github.com/FDA-AI/FDAi/tree/develop/apps/dfda-1/resources/views/root-cause-content.blade.php) + +[Generate your own!](https://app.curedao.org/) diff --git a/public/globalSolutions/dfda/cost-savings-from-decentralized-clinical-trials.md b/public/globalSolutions/dfda/cost-savings-from-decentralized-clinical-trials.md new file mode 100644 index 00000000..c67aa923 --- /dev/null +++ b/public/globalSolutions/dfda/cost-savings-from-decentralized-clinical-trials.md @@ -0,0 +1,14 @@ +--- +description: >- + Decentralized Clinical Trials Can Achieve Net Financial Benefits of 5X to 14X, + Due to Reduced Trial Timelines and Other Factors +--- + +# 🌎 Cost Savings from Decentralized Clinical Trials + +* **Net financial benefit:** In phase II studies, the typical DCT deployment for a clinical trial resulting in a one to three month time savings yields a net benefit that is up to five times greater than the upfront investment required. In phase III studies, a similar time savings yields a net benefit that is up to [14 times greater](https://www.businesswire.com/news/home/20220113005740/en/New-Study-Decentralized-Clinical-Trials-Can-Achieve-Net-Financial-Benefits-of-5X-to-14X-Due-to-Reduced-Trial-Timelines-and-Other-Factors) than the upfront investment required. +* **Shorter clinical trial times**: Cycle time reductions associated with DCT deployments had a substantially greater impact on net financial benefits than any other factor. Nearly 85 percent of all clinical trials will experience some sort of delay, with the financial impact of [$600,000 to $8 million](https://www.businesswire.com/news/home/20220113005740/en/New-Study-Decentralized-Clinical-Trials-Can-Achieve-Net-Financial-Benefits-of-5X-to-14X-Due-to-Reduced-Trial-Timelines-and-Other-Factors) per day of delay. Faster trial completion through decentralized methodologies can drive significant cost savings. +* **Lower screening failure rates**: Less than 5 percent of the U.S. population participates in clinical research, and up to [50 percent of trials are not completed](https://www.businesswire.com/news/home/20220113005740/en/New-Study-Decentralized-Clinical-Trials-Can-Achieve-Net-Financial-Benefits-of-5X-to-14X-Due-to-Reduced-Trial-Timelines-and-Other-Factors) because of insufficient enrollment. DCTs shift the paradigm to enable greater patient participation, reduced time and travel burden, faster screening, more convenient consent and enrollment, and in some cases, remote delivery of an intervention and the measurement of outcomes. +* **Fewer protocol amendments:** Protocol amendments often cause delays and dramatically increase the costs of developing new therapies. The potential for fewer research sites in a DCT leads to fewer institutional review boards and a corresponding reduction in regulatory costs and increased flexibility around protocol changes. +* In phase II studies, the typical decentralized clinical trial (DCT) deployment produced a [400%](https://github.com/cure-dao/docs/blob/main/assets/financial-benefits-of-decentralized-trials.pdf) return on investment in terms of trial cost reductions. +* In phase III studies, decentralization produced a [1300%](https://github.com/cure-dao/docs/blob/main/assets/financial-benefits-of-decentralized-trials.pdf) return on investment. diff --git a/public/globalSolutions/dfda/dfda-wide-text-logo-white-background.png b/public/globalSolutions/dfda/dfda-wide-text-logo-white-background.png new file mode 100644 index 00000000..63096e26 Binary files /dev/null and b/public/globalSolutions/dfda/dfda-wide-text-logo-white-background.png differ diff --git a/public/globalSolutions/dfda/greater-competitive-innovation-and-fewer-monopolies.md b/public/globalSolutions/dfda/greater-competitive-innovation-and-fewer-monopolies.md new file mode 100644 index 00000000..47165641 --- /dev/null +++ b/public/globalSolutions/dfda/greater-competitive-innovation-and-fewer-monopolies.md @@ -0,0 +1,21 @@ +--- +description: High Cost of Development Favors Monopoly and Punishes Innovation +--- + +# 🧐 Greater Competitive Innovation and Fewer Monopolies + +There's another problem with the increasing costs of treatment development. In the past, a genius scientist could come up with a treatment, raise a few million dollars, and do safety testing. Now that it costs a billion dollars to get a drug to market, the scientist has to persuade one of a few giant drug companies that can afford it to buy his patent. + +Then the drug company has two options: + +**Option 1: Risk $1 billion on clinical trials** + +**Possibility A:** Drug turns out to be one of the 90% the FDA rejects. GIVE BANKER A BILLION DOLLARS. DO NOT PASS GO. + +**Possibility B:** Drug turns out to be one of the 10%, the FDA approves. Now it's time to try to recover that billion dollars. However, very few drug companies have enough money to survive this game. So, this company almost certainly already has an existing inferior drug on the market to treat the same condition. Hence, any profit they make from this drug will likely be subtracted from revenue from other drugs they've already spent a billion dollars on. + +**Option 2: Put the patent on the shelf** + +Do not take a 90% chance of wasting a billion dollars on failed trials. Do not risk making your already approved cash-cow drugs obsolete. + +What's the benefit of bringing better treatment to market if you're just going to lose a billion dollars? Either way, the profit incentive is entirely in favor of just buying better treatments and shelving them. diff --git a/public/globalSolutions/dfda/historical-evidence-supporting-decentralized-efficacy-trials.md b/public/globalSolutions/dfda/historical-evidence-supporting-decentralized-efficacy-trials.md new file mode 100644 index 00000000..6c9d166a --- /dev/null +++ b/public/globalSolutions/dfda/historical-evidence-supporting-decentralized-efficacy-trials.md @@ -0,0 +1,112 @@ +--- +description: >- + Large scale efficacy-trials based on real-world evidence have historically led + to better health outcomes than current pharmaceutical industry-driven + randomized controlled trials. +--- + +# 📘 Historical Evidence Supporting Decentralized Efficacy Trials + +There is compelling historical evidence suggesting that large scale efficacy-trials based on real-world evidence have ultimately led to better health outcomes than current pharmaceutical industry-driven randomized controlled trials. + +For over 99% of recorded human history, the average human life expectancy has been around 30 years. + +![historical life expectancy](.gitbook/assets/life-expectancy-historical.jpg) + +**1893 – The Advent of Safety and Efficacy Trials** + +In the late nineteenth and early twentieth century, clinical objectivity grew. The independent peer-reviewed Journal of the American Medical Association (JAMA) was founded in 1893. It would gather case reports from the 144,000 physicians members of the AMA on the safety and effectiveness of drugs. The leading experts in the area of a specific medicine would review all the case reports and compile them into a study, listing side effects and the conditions for which a drug was or was not effective. If a medicine were found to be safe, JAMA would give its seal of approval for the conditions where it was found to be effective. + +The adoption of this system of crowdsourced, observational, objective, and peer-reviewed clinical research was followed by a sudden shift in the growth of human life expectancy. After over 10,000 years of almost no improvement, we suddenly saw a strangely linear 4-year increase in life expectancy every single year. + +**1938 – The FDA Requires Phase 1 Safety Trials** + +A drug called Elixir sulfanilamide caused over [100 deaths](https://www.fda.gov/files/about%20fda/published/The-Sulfanilamide-Disaster.pdf) in the United States in 1937. + +Congress [reacted](https://en.wikipedia.org/wiki/Elixir\_sulfanilamide) to the tragedy by requiring all new drugs to include: + +> "adequate tests by all methods reasonably applicable to show whether or not such drug is safe for use under the conditions prescribed, recommended, or suggested in the proposed labeling thereof." + +These requirements evolved to what is now called the [Phase 1 Safety Trial](https://en.wikipedia.org/wiki/Phase\_1\_safety\_trial). + +This consistent four-year/year increase in life expectancy remained unchanged before and after the new safety regulations. + +![Fda safety trials life expectancy](.gitbook/assets/fda-safety-trials-life-expectancy.png) + +This suggests that the regulations did not have a large-scale positive or negative impact on the development of life-saving interventions. + +#### **1950**'s – Thalidomide **Causes Thousands of Birth Defects Outside US** + +Thalidomide was first marketed in Europe in [1957](https://en.wikipedia.org/wiki/Thalidomide) for morning sickness. While it was initially thought to be safe in pregnancy, it resulted in thousands of horrific congenital disabilities. + +Fortunately, the existing FDA safety regulations prevented any birth defects in the US. Despite the effectiveness of the existing US regulatory framework in protecting Americans, newspaper stories such as the one below created a strong public outcry for increased regulation. + +![Thalidomide](.gitbook/assets/thalidomide.jpg) + +#### 1962 – New **Efficacy Regulations Reduce the Amount and Quality of Efficacy Data Collected** + +As effective **safety** regulations were already in place, the government instead responded to the Thalidomide disaster by regulating **efficacy** testing via the 1962 Kefauver Harris Amendment. Before the 1962 regulations, it cost a drug manufacturer an average of $74 million (2020 inflation-adjusted) to develop and test a new drug for safety before bringing it to market. Once the FDA had approved it as safe, efficacy testing was performed by the third-party American Medical Association. Following the regulation, trials were instead to be conducted in small, highly-controlled trials by the pharmaceutical industry. + +**Reduction in Efficacy Data** + +The 1962 regulations made these large real-world efficacy trials illegal. Ironically, even though the new regulations were primarily focused on ensuring that drugs were effective through controlled FDA efficacy trials, they massively reduced the quantity and quality of the efficacy data that was collected for several reasons: + +* New Trials Were Much Smaller +* Participants Were Less Representative of Actual Patients +* They Were Run by Drug Companies with Conflicts of Interest Instead of the 3rd Party AMA + +**Reduction in New Treatments** + +The new regulatory clampdown on approvals immediately reduced the production of new treatments by 70%. + +![](.gitbook/assets/new-treatments-per-year-2.png) + +**Explosion in Costs** + +Since the abandonment of the former efficacy trial model, costs have exploded. Since 1962, the cost of bringing a new treatment to market has gone from [$74 million](https://publications.parliament.uk/pa/cm200405/cmselect/cmhealth/42/4207.htm) to over [$1 billion](https://publications.parliament.uk/pa/cm200405/cmselect/cmhealth/42/4207.htm) US dollars (2020 inflation-adjusted). + +![Cost to Get a New Drug to Market](.gitbook/assets/cost-to-develop-a-new-drug.png) + +{% content-ref url="greater-competitive-innovation-and-fewer-monopolies.md" %} +[greater-competitive-innovation-and-fewer-monopolies.md](greater-competitive-innovation-and-fewer-monopolies.md) +{% endcontent-ref %} + +{% content-ref url="more-cures-and-less-lifelong-attempts-at-masking-symptoms.md" %} +[more-cures-and-less-lifelong-attempts-at-masking-symptoms.md](more-cures-and-less-lifelong-attempts-at-masking-symptoms.md) +{% endcontent-ref %} + +{% content-ref url="1-introduction-and-challenges/people-with-rare-disease-are-severely-punished.md" %} +[people-with-rare-disease-are-severely-punished.md](problems/people-with-rare-disease-are-severely-punished.md) +{% endcontent-ref %} + +{% content-ref url="1-introduction-and-challenges/deaths-due-to-us-regulatory-drug-lag.md" %} +[deaths-due-to-us-regulatory-drug-lag.md](problems/deaths-due-to-us-regulatory-drug-lag.md) +{% endcontent-ref %} + +**Increase in Patent Monopoly** + +Industry agitation surrounding the “drug lag” finally led to the modification of the drug patenting system in the Drug Price Competition and Patent Term Restoration Act of 1984. This further extended the life of drug patents. Thus, Kefauver's amendments ultimately made drugs more expensive by granting longer monopolies. + +**Decreased Ability to Determine Comparative Efficacy** + +The placebo-controlled, randomized controlled trial helped researchers gauge the efficacy of an individual drug. However, it makes the determination of comparative effectiveness much more difficult. + +**Slowed Growth in Life Expectancy** + +From 1890 to 1960, there was a linear 4-year increase in human lifespan every year. This amazingly linear growth rate had followed millennia with a flat human lifespan of around 28 years. Following this new 70% reduction in the pace of medical progress, the growth in human lifespan was immediately cut in half to an increase of 2 years per year. + +![Average Life Expectancy Over Time](.gitbook/assets/real-world-evidence-in-efficacy-clinical-trials-vs-rcts.png) + +**Diminishing Returns?** + +One might say, “It seems more likely — or as likely — to me that drug development provides diminishing returns to life expectancy.” However, diminishing returns produce a slope of exponential decay. It may be partially responsible, but it’s not going to produce a sudden change in the linear slope of a curve a linear as life expectancy was before and after the 1962 regulations. + +![What diminishing returns would look like](.gitbook/assets/diminishing-returns.png) + +**Correlation is Not Causation** + +You might say, "I don't know how much the efficacy regulations contribute to or hampers public health. I do know that correlation does not necessarily imply causation." However, a correlation plus a logical mechanism of action is the least bad method we have for inferring the most likely significant causal factor for an outcome (i.e., life expectancy). Assuming most likely causality based on temporal correlation is the entire basis of a clinical research study and the scientific method generally. + +{% content-ref url="2-solution/impact-of-innovative-medicines-on-life-expectancy.md" %} +[impact-of-innovative-medicines-on-life-expectancy.md](2-solution/impact-of-innovative-medicines-on-life-expectancy.md) +{% endcontent-ref %} diff --git a/public/globalSolutions/dfda/lower-costs-of-validated-observational-research-for-efficacy.md b/public/globalSolutions/dfda/lower-costs-of-validated-observational-research-for-efficacy.md new file mode 100644 index 00000000..103a53f1 --- /dev/null +++ b/public/globalSolutions/dfda/lower-costs-of-validated-observational-research-for-efficacy.md @@ -0,0 +1,26 @@ +--- +description: >- + Observational real-world evidence-based studies have several advantages over + randomized, controlled trials, including lower cost, increased speed of + research, and a broader range of patients. +--- + +# 👀 Lower Costs of Validated Observational Research for Efficacy + +**Meta-Analyses Support of Real-World Evidence** + +Observational real-world evidence-based studies have several advantages over randomized, controlled trials, including lower cost, increased speed of research, and a broader range of patients. However, concern about inherent bias in these studies has limited their use in comparing treatments. Observational studies have been primarily used when randomized, controlled trials would be impossible or unethical. + +However, [meta-analyses](https://www.nejm.org/doi/full/10.1056/NEJM200006223422506) found that: + +> when applying modern statistical methodologies to observational studies, the results are generally **not quantitatively or qualitatively different** from those obtained in randomized, controlled trials. + +**Mortality Observational Studies** + +![Mortality Observational Studies](.gitbook/assets/observational-vs-randomized-effect-sizes.png) + +**Observational Studies for Various Outcomes** + +![Observational Studies for Various Outcomes](.gitbook/assets/observational-vs-randomized-trial-effect-sizes.png) + +**** diff --git a/public/globalSolutions/dfda/more-cures-and-less-lifelong-attempts-at-masking-symptoms.md b/public/globalSolutions/dfda/more-cures-and-less-lifelong-attempts-at-masking-symptoms.md new file mode 100644 index 00000000..1ed57f18 --- /dev/null +++ b/public/globalSolutions/dfda/more-cures-and-less-lifelong-attempts-at-masking-symptoms.md @@ -0,0 +1,13 @@ +--- +description: >- + High Costs Punish Finding Cures Over Masking Symptoms Since Cures Are Far Less + Profitable Than Lifetime Treatments +--- + +# 🎭 More Cures and Less Lifelong Attempts at Masking Symptoms + +If the new treatment is a permanent cure for the disease, replacing a lifetime of refills with a one-time purchase would be economically disastrous for the drug developer. With a lifetime prescription, a company can recover its costs over time. Depending on the number of people with the disease, one-time cures would require a massive upfront payment to recover development costs. + +How is there any financial incentive for medical progress at all? + +Fortunately, there isn't a complete monopoly on treatment development. However, the more expensive it is to get a drug to market, the fewer companies can afford the upfront R\&D investment. So the drug industry inevitably becomes more monopolistic. Thus, there are more situations where the cost of trials for a superior treatment exceeds the profits from existing treatments. diff --git a/public/globalSolutions/dfda/pre-determining-clinical-endpoints-requires-psychic-powers.md b/public/globalSolutions/dfda/pre-determining-clinical-endpoints-requires-psychic-powers.md new file mode 100644 index 00000000..6fdfeac8 --- /dev/null +++ b/public/globalSolutions/dfda/pre-determining-clinical-endpoints-requires-psychic-powers.md @@ -0,0 +1,17 @@ +--- +description: >- + Current Regulation Expects Drug Developers to Have Psychic Powers Needed to + Pre-Determine Very Specific Clinical Endpoints Before Collecting Data +--- + +# 🔮 Pre-Determining Clinical Endpoints Requires Psychic Powers + +When running an efficacy trial, the FDA expects that the drug developer has the psychic ability to predict which conditions a treatment will be most effective for in advance of collecting the human trial data. If it was possible to magically determine this without any trials, it would render efficacy trials completely pointless. + +In 2007, manufacturer Dendreon submitted powerful evidence attesting to the safety and efficacy of its immunotherapy drug Provenge, which targets prostate cancer. They were able to show that the drug resulted in a significant decline in deaths among its study population, which even persuaded the FDA advisory committee to weigh in on the application. But ultimately, the FDA rejected its application. + +The FDA was unmoved by the evidence, simply because Dendreon didn’t properly specify beforehand what its study was trying to measure. Efficacy regulations state that finding a decline in deaths is not enough. The mountains of paperwork must be filled out just so and in the correct order. It took three more years and yet another large trial before the FDA finally approved the life-saving medication. + +Due to all the additional costs imposed by the efficacy trial burden, Dendreon ultimately [filed for chapter 11 bankruptcy](https://www.targetedonc.com/view/dendreon-files-for-bankruptcy-provenge-still-available). + +In addition to the direct costs to companies, the extreme costs and financial risks imposed by efficacy trials have a huge chilling effect on investment in new drugs. If you're an investment adviser trying to avoid losing your client's retirement savings, you're much better off investing in a more stable company like a bomb manufacturer building products to intentionally kill people than a drug developer trying to save lives. So it's impossible to know all the treatments that never even got to an efficacy trial stage due to the effects of decreased investment due to the regulatory risks. diff --git a/public/globalSolutions/dfda/problems/clinical-research-is-expensive.md b/public/globalSolutions/dfda/problems/clinical-research-is-expensive.md new file mode 100644 index 00000000..a182c8fd --- /dev/null +++ b/public/globalSolutions/dfda/problems/clinical-research-is-expensive.md @@ -0,0 +1,18 @@ +--- +description: It costs $2.6 billion to bring a drug to market (including failed attempts). +--- + +# 💰 Clinical Research is Expensive + +#### The Cost of Clinical Research + +* It costs [$2.6 billion](https://www.semanticscholar.org/paper/Innovation-in-the-pharmaceutical-industry%3A-New-of-DiMasi-Grabowski/3275f31c072ac11c6ca7a5260bd535720f07df41) to bring a drug to market (including failed attempts). +* The process takes over 10 years. +* It costs [$36k](https://www.clinicalleader.com/doc/getting-a-handle-on-clinical-trial-costs-0001) per subject in Phase III clinical trials. + + + +![clinicalresearch.io](https://static.crowdsourcingcures.org/dfda/assets/cost-of-clinical-trials.png) + + + diff --git a/public/globalSolutions/dfda/problems/conflicts-of-interest.md b/public/globalSolutions/dfda/problems/conflicts-of-interest.md new file mode 100644 index 00000000..0a579b7a --- /dev/null +++ b/public/globalSolutions/dfda/problems/conflicts-of-interest.md @@ -0,0 +1,11 @@ +--- +description: >- + When billions of dollars in losses or gains are riding on the results of a + study, this will almost inevitably influence the results. +--- + +# 🎭 Conflicts of Interest + +Long-term randomized trials are extremely expensive to set up and run. When billions of dollars in losses or gains are riding on the results of a study, this will almost inevitably influence the results. For example, an analysis of beverage studies, [published in the journal PLOS Medicine,](https://web.archive.org/web/20211207021133/https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001578#s3) found that those funded by Coca-Cola, PepsiCo, the American Beverage Association, and the sugar industry were **five times** more likely to find no link between sugary drinks and weight gain than studies whose authors reported no financial conflicts. + +The economic survival of the pharmaceutical company is dependent on the positive outcome of the trial. While there's not a lot of evidence to support that there's any illegal manipulation of results, it leads to two problems: diff --git a/public/globalSolutions/dfda/problems/deaths-due-to-us-regulatory-drug-lag.md b/public/globalSolutions/dfda/problems/deaths-due-to-us-regulatory-drug-lag.md new file mode 100644 index 00000000..b69664a2 --- /dev/null +++ b/public/globalSolutions/dfda/problems/deaths-due-to-us-regulatory-drug-lag.md @@ -0,0 +1,47 @@ +--- +description: >- + A comparative analysis between countries suggests that delays in new + interventions cost anywhere from 21,000 to 120, 000 US lives per decade. +--- + +# ⏱ Deaths Due to US Regulatory "Drug Lag" + +**Delayed Life-Saving Treatments** + +One unanticipated consequence of the amendments was that the new burden of proof made the process of drug development both more expensive and much longer, leading to increasing drug prices and a “drug lag”. After that point, whenever they released some new cancer or heart medication that would save 50 thousand lives a year, it meant that over the previous ten years of trials, 500 people died because they didn't have access to the drug earlier. + +**Deaths Due to US Regulatory "Drug Lag"** + +A comparative analysis between countries suggests that delays in new interventions cost anywhere from [21,000 to 120, 000](https://www.fdareview.org/features/references/#gieringer85) US lives per decade. + +Deaths owing to drug lag have been numbered in the [hundreds of thousands](https://www.fdareview.org/features/references/#wardell78a). It's estimated that practolol, a drug in the beta-blocking family, could save ten thousand lives a year, if allowed in the United States. Although the FDA allowed the first beta-blocker, propranolol, in 1968, three years after that drug had been available in Europe, it waited until 1978 to allow propranolol to treat hypertension and angina pectoris, its most essential indications. Despite clinical evidence as early as 1974, only in 1981 did the FDA allow a second beta-blocker, timolol, to prevent a second heart attack. The agency’s withholding of beta-blockers was alone responsible for probably [tens of thousands of deaths](https://www.fdareview.org/features/references/#gieringer85). + +[Data](http://csdd.tufts.edu/databases) from the Tufts Center for the Study of Drug Development suggests that thousands of patients have died because of US regulatory delays relative to other countries, for new drugs and devices, including: + +* interleukin-2 +* Taxotere +* vasoseal +* ancrod +* Glucophage +* navelbine +* Lamictal +* ethyol +* photofrin +* rilutek +* citicoline +* panorex +* Femara +* ProStar +* omnicath + +Before US FDA approval, most of these drugs and devices had already been available in other countries for a year or longer. + +Following the 1962 increase in US regulations, one can see a divergence from Switzerland's growth in life expectancy, which did not introduce the same delays to availability. + +![US vs Swiss Life Expectancy](https://static.crowdsourcingcures.org/dfda/assets/us-swiss-life-expectancy-5.png) + +Perhaps it's a coincidence, but you can see an increase in drug approvals in the '80s. At the same time, the gap between Switzerland and the US gets smaller. Then US approvals go back down in the '90s, and the gap expands again. + +![US vs Swiss Life Expectancy](https://static.crowdsourcingcures.org/dfda/assets/us-swiss-life-expectancy-drug-approvals.png) + +**** diff --git a/public/globalSolutions/dfda/problems/lack-of-incentive-to-discover-the-full-range-of-applications-for-off-patent-treatments.md b/public/globalSolutions/dfda/problems/lack-of-incentive-to-discover-the-full-range-of-applications-for-off-patent-treatments.md new file mode 100644 index 00000000..c30a313f --- /dev/null +++ b/public/globalSolutions/dfda/problems/lack-of-incentive-to-discover-the-full-range-of-applications-for-off-patent-treatments.md @@ -0,0 +1,3 @@ +# 📃 Lack of Incentive to Discover the Full Range of Applications for Off-Patent Treatments + +There are roughly [10,000](https://www.washingtonpost.com/news/fact-checker/wp/2016/11/17/are-there-really-10000-diseases-and-500-cures/) known diseases afflicting humans, most of which (approximately 95%) are classified as “orphan” (rare) diseases. The current system requires that a pharmaceutical company predict a particular condition in advance of running clinical trials. If a drug is found to be effective for other diseases after the patent has expired, no one has the financial incentive to get it approved for another disease. diff --git a/public/globalSolutions/dfda/problems/negative-results-are-never-published.md b/public/globalSolutions/dfda/problems/negative-results-are-never-published.md new file mode 100644 index 00000000..5b59faa3 --- /dev/null +++ b/public/globalSolutions/dfda/problems/negative-results-are-never-published.md @@ -0,0 +1,19 @@ +--- +description: >- + A global database of treatments and outcomes could provide information that + could avoid massive waste on failed trials. +--- + +# 🙈 Negative Results are Never Published + +Selective publishing can prevent the rapid spread of beneficial treatments or interventions, but more commonly it means that bad news and failure of medical interventions go unpublished. Past analysis of clinical trials supporting new drugs approved by the FDA showed that just [43 percent of more than 900 trials on 90 new drugs](https://www.livescience.com/8365-dark-side-medical-research-widespread-bias-omissions.html) ended up being published. In other words, about 60 percent of the related studies remained unpublished even five years after the FDA had approved the drugs for market. That meant physicians were prescribing the drugs and patients were taking them without full knowledge of how well the treatments worked. + +This leads to a massive waste of money by other companies repeating the same research and going down the same dead-end streets that could have been avoided. + +**Cost Savings in Drug Development** + +Failed drug applications are expensive. A global database of treatments and outcomes could provide information that could avoid massive waste on failed trials. + +* A 10% improvement in predicting failure before clinical trials could save [$100 million](https://drugwonks.com/blog/the-dog-days-of-drug-approvals) in development costs. +* Shifting 5% of clinical failures from Phase III to Phase I reduces out-of-pocket costs by [$15 to $20 million](https://drugwonks.com/blog/the-dog-days-of-drug-approvals). +* Shifting failures from Phase II to Phase I would reduce out-of-pocket costs by [$12 to $21 million](https://drugwonks.com/blog/the-dog-days-of-drug-approvals). diff --git a/public/globalSolutions/dfda/problems/no-data-on-unpatentable-molecules.md b/public/globalSolutions/dfda/problems/no-data-on-unpatentable-molecules.md new file mode 100644 index 00000000..18aade9c --- /dev/null +++ b/public/globalSolutions/dfda/problems/no-data-on-unpatentable-molecules.md @@ -0,0 +1,16 @@ +--- +description: >- + We still know next to nothing about the long-term effects of 99.9% of the 4 + pounds of over 7,000 different synthetic or natural chemicals you consume + every day. +--- + +# 🥫 No Data on Unpatentable Molecules + +Under the current system of research, it costs [$41k](https://www.clinicalleader.com/doc/getting-a-handle-on-clinical-trial-costs-0001) per subject in Phase III clinical trials. As a result, there is not a sufficient profit incentive for anyone to research the effects of any factor besides a molecule that can be patented. + +![how much we know]() + +**Lack of Incentive to Discover the Full Range of Applications for Off-Patent Treatments** + +There are roughly [10,000](https://www.washingtonpost.com/news/fact-checker/wp/2016/11/17/are-there-really-10000-diseases-and-500-cures/) known diseases afflicting humans, most of which (approximately 95%) are classified as “orphan” (rare) diseases. The current system requires that a pharmaceutical company predict a particular condition in advance of running clinical trials. If a drug is found to be effective for other diseases after the patent has expired, no one has the financial incentive to get it approved for another disease. diff --git a/public/globalSolutions/dfda/problems/no-long-term-outcome-data.md b/public/globalSolutions/dfda/problems/no-long-term-outcome-data.md new file mode 100644 index 00000000..0d4654b7 --- /dev/null +++ b/public/globalSolutions/dfda/problems/no-long-term-outcome-data.md @@ -0,0 +1,11 @@ +--- +description: >- + Using centralized trials, it's not financially feasible to collect data on a + participant for years or decades. +--- + +# 🗓 No Long-Term Outcome Data + +Even if there is a financial incentive to research a new drug, there is no data on the long-term outcomes of the drug. The data collection period for participants can be as short as several months. Under the current system, it's not financially feasible to collect data on a participant for years or decades. So we have no idea if the long-term effects of a drug are worse than the initial benefits. + +For instance, even after controlling for co-morbidities, the Journal of American Medicine recently found that long-term use of Benadryl and other anticholinergic medications is associated with an [increased](https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2091745) risk for dementia and Alzheimer's disease. diff --git a/public/globalSolutions/dfda/problems/people-with-rare-disease-are-severely-punished.md b/public/globalSolutions/dfda/problems/people-with-rare-disease-are-severely-punished.md new file mode 100644 index 00000000..a2f19aec --- /dev/null +++ b/public/globalSolutions/dfda/problems/people-with-rare-disease-are-severely-punished.md @@ -0,0 +1,12 @@ +--- +description: >- + In the case of rare diseases, increasing the cost of treatment development to + over a billion makes it impossible to recover your investment from a small + number of patients. +--- + +# 🤒 People With Rare Disease are Severely Punished + +In the case of rare diseases, increasing the cost of treatment development to over a billion makes it impossible to recover your investment from a small number of patients. So rare disease patients suffer the most severe harm from the added regulatory burden on development. + +How high should the cost of drug development be on our list of human problems? Well, when something costs more, you get less of it. For people dying of cancer, the fact that we couldn't afford enough research to cure them is definitely at the top of their list of human problems. diff --git a/public/globalSolutions/dfda/problems/trials-often-arent-representative-of-real-patients.md b/public/globalSolutions/dfda/problems/trials-often-arent-representative-of-real-patients.md new file mode 100644 index 00000000..399f71b7 --- /dev/null +++ b/public/globalSolutions/dfda/problems/trials-often-arent-representative-of-real-patients.md @@ -0,0 +1,41 @@ +--- +description: >- + Phase III clinical trials are often designed to exclude a vast majority of the + population of interest. +--- + +# 🥸 Trials Often Aren't Representative of Real Patients + +External validity is the extent to which the results can be generalized to a population of interest. The population of interest is usually defined as the people the intervention is intended to help. + +Phase III clinical trials are often designed to exclude a vast majority of the population of interest. In these cases, the subjects of the drug trials are not representative of the prescribed recipients, once said drugs are approved. One investigation found that only [14.5%](https://www.ncbi.nlm.nih.gov/pubmed/14628985) of patients with major depressive disorder fulfilled eligibility requirements for enrollment in an antidepressant efficacy trial. + +As a result, the results of these trials are not necessarily generalizable to patients matching any of these criteria: + +* Suffer from multiple mental health conditions (e.g., post-traumatic stress disorder, generalized anxiety disorder, bipolar disorder, etc.) +* Engage in drug or alcohol abuse +* Suffer from mild depression (Hamilton Rating Scale for Depression (HAM-D) score below the specified minimum) +* Use other psychotropic medications + +These facts call into question the external validity of standard efficacy trials. + +Furthermore, patient sample sizes are very small. The number of subjects per trial on average: + +* [275](https://www.ncbi.nlm.nih.gov/books/NBK50886/) patients are sought per cardiovascular trial +* [20](https://www.ncbi.nlm.nih.gov/books/NBK50886/) patients per cancer trial +* [70](https://www.ncbi.nlm.nih.gov/books/NBK50886/) patients per depression trial +* [100](https://www.ncbi.nlm.nih.gov/books/NBK50886/) per diabetes trial + +![wellbutrin small sample size](https://static.crowdsourcingcures.org/dfda/assets/wellbutrin-effectiveness-small-sample-size.png) + +In the example in the graphic above, a drug is prescribed to millions of patients based on a study with only 36 subjects, where a representation of the general public is questionable. + +### **Solution: Collect Data on Actual Patients** + +In the real world, no patient can be excluded. Even people with a history of drug or alcohol abuse, people on multiple medications, and people with multiple conditions must be treated. Only through the crowdsourcing of this research, would physicians have access to the true effectiveness rates and risks for their real-world patients. + +The results of crowdsourced studies would exhibit complete and utter external validity, since the test subjects are identical to the population of interest. + +Furthermore, self-trackers represent a massive pool of potential subjects, dwarfing any traditional trial cohort. Diet tracking is the most arduous form of self-tracking. Yet, just one of the many available diet tracking apps, **MyFitnessPal,** has 30 million users. + +Tracking any variable in isolation is nearly useless in that it cannot provide the causal which can be derived from combining data streams. Hence, this 30 million user cohort is a small fraction of the total possible [stratifiable](https://en.wikipedia.org/wiki/Stratified\_sampling) base. diff --git a/public/globalSolutions/dfda/problems/we-know-nothing.md b/public/globalSolutions/dfda/problems/we-know-nothing.md new file mode 100644 index 00000000..923f3112 --- /dev/null +++ b/public/globalSolutions/dfda/problems/we-know-nothing.md @@ -0,0 +1,25 @@ +--- +description: We only know 0.000000002% of what is left to be known. +--- + +# ❓ We Know Next to Nothing + +We’re only 2 lifetimes from the use of the modern scientific method in medicine. Thus, it's only been applied for 0.0001% of human history. The more clinical research studies we read, the more we realize we don’t know. Nearly every study ends with the phrase "more research is needed". We know basically nothing at this point compared to what will eventually be known about the human body. + +There are over [7,000](https://www.washingtonpost.com/news/fact-checker/wp/2016/11/17/are-there-really-10000-diseases-and-500-cures/) known diseases afflicting humans. + +![](https://static.crowdsourcingcures.org/dfda/assets/rare-diseases.jpg) + +There are as many untested compounds with drug-like properties as there are [atoms in the solar system](https://www.nature.com/articles/549445a) (166 billion). + +![]() + +If you multiply the number of molecules with drug-like properties by the number of diseases, that's 1,162,000,000, 000,000 combinations. So far we've studied [21,000 compounds](https://www.centerwatch.com/articles/12702-new-mit-study-puts-clinical-research-success-rate-at-14-percent). + +That means we only know 0.000000002% of what is left to be known. + +The currently highly restrictive, overly cautious method of clinical research prevents us from knowing more faster. + +We’re at the very beginning of thousands or millions of years of systematic discovery. So it’s unlikely that this decline in lifespan growth is the result of diminishing returns due to our running out of things to discover. + +However, to validate the theory that large-scale real-world evidence can produce better health outcomes requires further validation of this method of experimentation. That's the purpose of deFDA. diff --git a/public/globalSolutions/dfda/right-to-trial-act-1.md b/public/globalSolutions/dfda/right-to-trial-act-1.md new file mode 100644 index 00000000..f2081913 --- /dev/null +++ b/public/globalSolutions/dfda/right-to-trial-act-1.md @@ -0,0 +1,200 @@ +# RIGHT TO TRIAL ACT + +## SECTION 1. SHORT TITLE AND FINDINGS + +### 1.1 Title +This Act may be cited as the "Right to Trial Act" + +### 1.2 Core Problems This Act Solves + +Today's healthcare system is broken because: +* Life-saving treatments are blocked by FDA delays averaging 7-12 years +* 97% of patients are excluded from clinical trials, denying them access to promising treatments +* Drug development costs average $2.6B, driving companies to focus on expensive treatments +* 80% of R&D goes to drugs costing over $100k/year rather than affordable alternatives +* Terminal patients wait 4+ years for breakthrough therapy approvals +* US approvals lag 3-5 years behind Europe and Asia +* Only 5% of healthcare spending goes to preventive care, despite every $1 spent saving $3 in future costs +* The system ignores real-world evidence about which treatments actually work + +### 1.3 The Solution + +This Act: +* Guarantees every person's right to try any treatment that passes basic safety testing +* Creates a free, open platform that eliminates billions in unnecessary trial costs +* Rewards companies for developing actual cures and prevention +* Removes artificial barriers blocking access to treatments that work +* Measures and rewards real-world results through comprehensive data collection +* Establishes an AI-powered system (FDAi) to continuously analyze treatment outcomes + +### 1.4 Economic Impact + +This Act will: +* Cut clinical trial costs by over 90% through free open infrastructure +* Save over $2 trillion annually by incentivizing disease prevention +* Reduce time-to-market by years through universal trial participation +* Creat multi-billion dollar rewards for companies that develop actual cures +* Enable real price competition through global access and transparent outcomes + +## SECTION 2. OPEN SOURCE GLOBAL DECENTRALIZED TRIAL PLATFORM + +### 2.1 Transforming Safety and Efficacy Testing + +A free public decentralized trial platform will: +* Replace traditional Phase 1-4 trials with continuous real-world evidence collection +* Enable more efficient safety testing through remote monitoring and rapid signal detection +* Track safety and effectiveness automatically across the entire healthcare system +* Generate better evidence faster through universal participation +* Enable rapid identification of which treatments work best for whom + +### 2.2 Universal Participation +The platform enables: +* Broader testing with more diverse participants +* Any patient to participate +* Home and remote participation through telemedicine +* Patient monitoring through mobile devices and apps +* Automated outcome tracking through electronic health records and other data sources +* Direct patient reporting of experiences and results +* Continuous collection of real-world evidence + +### 2.3 FDAi: Autonomous Agent +The FDAi continuously: +* Analyzes all available research and patient data +* Quantifies positive and negative effects of all: + * Pharmaceutical drugs + * Food products and ingredients + * Dietary patterns + * Treatment combinations +* Provides early warning of potential safety issues +* Identifies optimal treatments for specific patient profiles +* Monitors population-level health outcomes +* Tracks food-drug interactions and dietary impacts + +## SECTION 3. UNIVERSAL ACCESS TO TREATMENTS + +### 3.1 Breaking Down Barriers +After basic safety testing, any patient can: +* Join trials for the most promising treatments +* Participate from home or any location +* Access treatments from any country +* Use telemedicine or local providers +* Share their results to help others + +### 3.2 No More Artificial Restrictions +The FDA and states cannot: +* Block informed patient access to treatments +* Force patients to travel for treatment +* Prevent doctors from offering treatments +* Ban importing of medicines from other countries +* Interfere with home or local treatment options +* Block telemedicine access + +### 3.3 Empowering Doctors +All healthcare providers can: +* Offer proven treatments anywhere +* Import treatments for patients +* Provide care at home +* Use remote monitoring +* Cross state lines to help patients +* Share results through the platform with patient consent + +## SECTION 4. FREE OPEN SOURCE TRIAL PLATFORM + +### 4.1 Replacing Expensive Systems +A decentralized trial platform will: +* Handle all trial data collection +* Track real-world outcomes +* Monitor safety automatically +* Analyze what works best +* Connect patients and doctors +* Share results globally +* Automate trial recruitment, monitoring, and analysis + +### 4.2 Eliminating Unnecessary Costs +The platform eliminates expenses for: +* Trial software and systems +* Patient monitoring tools +* Data collection and storage +* Analysis and reporting +* Compliance tracking +* Security infrastructure + +### 4.3 Better Data, Better Decisions +The platform automatically: +* Matches similar patients +* Identifies optimal treatments based on real-world outcomes +* Spots potential problems early +* Shows comparative effectiveness +* Compares treatment costs +* Helps patients find optimal care + +### 4.4 Open To Everyone +Anyone can: +* Access aggregated and anonymized trial data +* Build new analysis tools to integrate with the platform +* Create patient apps +* Improve the platform +* Add new features + +### 4.5 Global Collaboration +The platform supports: +* Recognition of international safety data +* Harmonized global reporting standards +* Cross-border research collaboration +* Transparent pricing across regions +* International treatment access + +## SECTION 5. FINANCIAL INCENTIVES + +### 5.1 Removing Barriers To Treatment Development and Accessibility +* No user fees imposed on those developing new treatments +* Congressional funding for platform maintenance +* Free importation of lower-cost treatments + +### 5.2 Healthcare Savings Sharing Program + +The program incentivizes manufacturers to develop and provide affordable preventative treatments by sharing the resulting healthcare cost savings: + +* Manufacturers receive 50% of verified healthcare savings when their treatments: + * Prevent diseases or health conditions + * Reduce long-term healthcare costs + * Improve health outcomes + +* Key benefits: + * Encourages development of preventative treatments (like klotho gene therapy) + * Motivates low pricing since wider patient adoption = more shared savings + * Creates sustainable funding through actual cost reductions + * Aligns manufacturer profits with public health outcomes + +* Implementation: + * Savings calculated from anonymized healthcare data stored by the dFDA + * Independent verification of cost reductions + +## SECTION 6. BENEFITS OVER CURRENT SYSTEM + +### 6.1 Cheaper +* Eliminates billions in redundant trial infrastructure costs +* Removes expensive middlemen and administrative overhead +* Enables global price competition through open access +* Shares cost savings from prevention back to developers +* Reduces healthcare spending through earlier intervention +* Cuts development costs by over 90% through shared platform +* Eliminates duplicate safety testing across regions + +### 6.2 Faster +* Removes years of administrative delays +* Enables immediate trial participation for interested patients +* Automates patient matching and enrollment +* Provides real-time safety and efficacy monitoring +* Accelerates treatment optimization through AI analysis +* Eliminates redundant approval processes across countries +* Enables rapid iteration based on real-world evidence + +### 6.3 Better +* Includes all willing patients instead of just 3% +* Generates real-world evidence across diverse populations +* Identifies optimal treatments for specific patient profiles +* Catches safety issues earlier through comprehensive monitoring +* Enables continuous improvement through global collaboration +* Aligns profit incentives with actual health outcomes +* Democratizes access to promising treatments \ No newline at end of file diff --git a/public/globalSolutions/dfda/right-to-trial-act-2.md b/public/globalSolutions/dfda/right-to-trial-act-2.md new file mode 100644 index 00000000..b9bda445 --- /dev/null +++ b/public/globalSolutions/dfda/right-to-trial-act-2.md @@ -0,0 +1,227 @@ +# Right to Trial Act + +## Section 1. Short Title and Findings + +### 1.1 Title +Cited as the "Right to Trial Act." + +### 1.2 Core Findings +1. **Current System Failures** + - FDA approval process delays access to life-saving treatments by 7-12 years on average. + - Only 3% of patients qualify for traditional clinical trials due to strict eligibility criteria, leaving most without access to new treatments. + - Real-world outcome data (actual patient results) is collected for less than 10% of approved drugs. + - 97% of patients are excluded from clinical trials due to strict eligibility criteria. + - Geographic restrictions force patients to travel hundreds of miles to trial sites. + - $2.6B average cost of drug development drives 10-100x markup in consumer prices. + - Terminal patients wait 4+ years for breakthrough therapy approvals. + - Treatments available in Europe/Asia take 3-5 additional years for US approval. + - Companies invest 80% of R&D in drugs costing >$100k/year vs. affordable alternatives. + - Only 5% of healthcare spending goes to preventive care despite 3x return on investment (meaning every $1 spent saves $3 in future costs). + - Less than 10% of approved drugs have comprehensive real-world outcome data. +2. **Solution Framework** + - Enable all patients to participate in decentralized trials (studies conducted remotely from patients' locations) from home, hospitals, or any care setting. + - Transform clinical trials into transparent, real-world data collection that includes all willing patients. + - Guarantee treatment access across geographic locations through telemedicine and home delivery. + - Create financial incentives for treatments that improve outcomes and reduce healthcare costs. + - Build a comprehensive database of patient experiences to inform treatment decisions. + - Refocus FDA on safety verification, data quality, and public health protection rather than access restriction. +3. **Establishment of Decentralized FDA (dFDA) Protocol** + - The **Decentralized FDA (dFDA)** cryptographic protocol and framework is established to automate decentralized clinical research, store and analyze data, and publish aggregated and anonymized outcome labels quantifying all positive and negative effects of foods and drugs. + +## Section 2. Patient and Provider Access Rights + +### 2.1 Universal Right to Treatment +Patients may access treatments that: +- Completed Phase 1 safety testing (initial human trials focusing on safety and side effects). +- Have published safety data. +- Are registered for outcome tracking. +- Feature accurate labeling and manufacturing standards. + +### 2.2 Access Protection +FDA and states cannot restrict: +- Treatment choices post safety verification. +- Treatment administration locations. +- Doctor prescribing rights. +- Manufacturing meeting safety standards. +- Importing from qualified facilities. +- Home-based delivery and remote monitoring. +- Telemedicine prescribing. + +### 2.3 Provider and International Access Rights +Providers may: +- Prescribe any registered treatment. +- Source from any registered manufacturer. +- Import treatments for patients. +- Engage in outcome tracking. +- Deliver home-based care and use telemedicine. +- Monitor remotely and operate across state lines. + +## Section 3. dFDA + +### 3.1 Establishment +The dFDA shall: +- Replace restricted trials with inclusive tracking. +- Support participation from any location. +- Enable home-based monitoring. +- Track real-world outcomes and healthcare costs. +- Facilitate treatment comparisons and reward calculations. + +### 3.2 Universal Data Collection +Providers must report: +- Patient conditions using standardized ICD-11 codes and SNOMED CT terminology +- Treatments administered, including: + - Drug name, dosage, frequency, and duration + - Treatment protocols followed + - Combination therapies used +- Health outcomes measured by: + - Clinical measurements and lab results + - Standardized outcome scales (e.g., PROMIS measures) + - Survival rates and disease progression metrics +- Adverse events categorized by: + - Severity (using CTCAE v5.0 criteria) + - Causality assessment + - Resolution and interventions required +- Treatment costs including: + - Direct medication costs + - Administration costs + - Associated care costs +- Quality of life measures using validated tools: + - SF-36 Health Survey + - EQ-5D questionnaire + - Disease-specific QoL instruments +- Care delivery methods including: + - In-person clinical visits + - Telemedicine consultations + - Home healthcare services + - Emergency or urgent care utilization + - Specialized treatment facilities + +### 3.3 Inclusive Design +The dFDA must support: +- Remote data entry and mobile apps. +- Home monitoring devices and telemedicine integration. +- Patient-reported outcomes and caregiver input. +- Multiple languages and accessibility features. + +### 3.4 Public Access +- De-identified data available through: + - Public web portal + - Downloadable datasets + - Interactive visualization tools +- RESTful API access with: + - OAuth 2.0 authentication + - Rate limiting controls + - Comprehensive documentation + - Versioning support + - Data validation endpoints +- Mobile applications supporting: + - iOS and Android platforms + - Offline data collection + - Push notifications + - Accessibility features +- Cost comparison tools featuring: + - Treatment price transparency + - Insurance coverage information + - Alternative therapy comparisons + - Regional cost variations +- Research access tools including: + - Statistical analysis packages + - Machine learning capabilities + - Cohort selection tools + - Custom query builders + +### 3.5 Privacy Protection +- De-identification standards following: + - HIPAA Safe Harbor method +- Patient consent requirements: + - Granular data sharing options + - Revocation mechanisms +- Personal data access rights including: + - Complete medical records + - Data export capabilities + - Audit trail access + - Correction mechanisms +- Distributed data architecture: + - Local node requirements + - Synchronization protocols + - Backup procedures + - Disaster recovery plans +- Security measures including: + - End-to-end AES-256 encryption + - Multi-factor authentication + - Access logging + - Intrusion detection + - Regular security audits + +### 3.1.1 FDAi: Autonomous AI Agent + +#### Definition and Purpose +The **FDAi** (Food and Drug Administration Intelligence) is an autonomous AI agent designed to continually collect and analyze healthcare research data. It will enhance the capabilities of the dFDA (Decentralized Food and Drug Administration) by providing real-time insights into treatment outcomes and patient experiences. + +#### Functionality +- **Data Collection**: FDAi will autonomously gather data from various sources, including clinical trials, patient reports, and existing research studies. +- **Patient Interaction**: FDAi can contact patients to collect data on symptoms, treatments, dietary habits, and other relevant health information through secure communication channels. +- **Meta-Analyses**: The AI will combine results from all available studies and data sources to identify trends, effectiveness, and safety profiles of treatments, contributing to evidence-based decision-making. + +#### Benefits +- **Improved Data Accuracy**: Continuous data collection minimizes gaps and biases in research data. +- **Enhanced Patient Engagement**: By directly contacting patients, FDAi fosters a more active role in their health management. +- **Real-Time Insights**: The ability to analyze data in real-time allows for quicker adjustments to treatment protocols and better patient outcomes. + +## Section 4. Cost and Price Transparency + +### 4.1 International Cooperation +- **Recognition of International Safety Data:** Accept equivalent international Phase 1 safety data. +- **Harmonized Reporting Standards:** Align with international data reporting. +- **Cross-Border Collaboration:** Foster partnerships among global researchers, manufacturers, and providers. + +### 4.2 Price Transparency and Cost Reduction Measures + +#### a) Public Access +All pricing information must be accessible through the dFDA for patient and provider comparison. + +### 4.3 Enhancing Competition +#### a) Facilitating Generic Entry +- Streamline approval for generic drugs (identical copies of brand-name medications) after demonstrating safety and basic effectiveness. +- Implement abbreviated pathways to reduce time and costs. + +#### b) Allow Importation of Low-Cost Treatments +- Allow importation of treatments already approved in other countries (parallel importation) that meet U.S. standards to increase competition and reduce costs. + +## Section 8. Savings Sharing Program + +### 8.1 Purpose +Incentivize pharmaceutical companies to develop treatments that prevent disability and reduce government medical costs, promoting public health and economic efficiency. + +### 8.2 Definitions +1. **Savings Sharing Program:** A financial incentive system that rewards pharmaceutical companies for developing treatments that prevent disability and reduce government medical costs. +2. **Cost Savings:** Measurable reductions in government medical expenses, including: + - Fewer hospital stays + - Reduced long-term care needs + - Lower disability-related costs +3. **Evaluation Period:** A five-year monitoring period after treatment approval to measure its impact on public health and costs. +4. **Independent Assessment Body (IAB):** An external organization that evaluates how well treatments work and calculates their cost savings to the healthcare system. + +### 8.3 Eligibility Criteria +Treatments must: +1. **Approval and Registration:** + - FDA-approved under this Act. + - Registered with the dFDA. +2. **Demonstrated Impact:** + - Evidence of preventing disability or reducing costs through: + - Randomized controlled trials + - Real-world evidence studies + - Health economic analyses + - Public health significance determined by: + - Disease prevalence (>1 in 10,000 population) + - Mortality rate impact + - Quality of life improvement + - Economic burden reduction + - Healthcare resource utilization + +### 8.4 Calculation of Rewards +#### a) Cost Savings Assessment +IAB annually assesses savings using the dFDA data, healthcare expenditure reports, and other sources, including hospitalizations, long-term care, and disability benefits. + +#### b) Reward Calculation +Rewards are a percentage of verified savings. Specifically, the program incentivizes low-cost treatments by offering a 50% savings sharing reward per patient. This means that for every patient treated, 50% of the cost savings achieved is rewarded to the manufacturer. Lowering the cost of treatments not only increases accessibility and patient uptake but also enhances long-term income for manufacturers through higher volume sales. diff --git a/public/img/placeholder-image.png b/public/img/placeholder-image.png new file mode 100644 index 00000000..f8899cb6 Binary files /dev/null and b/public/img/placeholder-image.png differ