| description | emoji | title | number | textFollowingNumber | tags | published | editor | date | dateCreated |
|---|---|---|---|---|---|---|---|---|---|
You and Everyone You Love Will Suffer and Die. |
💀 |
Problems We Can Solve with a Decentralized FDA |
150k |
people die every day from possibly preventable degenerative diseases |
global-health, chronic-diseases, preventable-deaths, healthcare-spending |
true |
markdown |
2025-02-11T13:38:21.578Z |
2025-02-11T13:38:21.578Z |
There are over 2 billion people suffering from chronic diseases.
Additionally, 150,000 people die every single day by possibly preventable degenerative diseases. For perspective, this is equivalent to:
Is throwing more money at the existing healthcare system the solution?
Since 2014, healthcare spending per person has been increasing faster than ever before.
Despite this additional spending, life expectancy has actually been declining.
The current system of clinical research, diagnosis, and treatment is failing the billions of people are suffering from chronic diseases.
It takes over 10 years and 2.6 billion dollars to bring a drug to market (including failed attempts). It costs $41k per subject in Phase III clinical trials.
- It costs $2.6 billion to bring a drug to market (including failed attempts).
- The process takes over 10 years.
- It costs $36k per subject in Phase III clinical trials.
This high cost leads to the following problems:
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 different synthetic or natural chemicals you consume every day.
Under the current system of research, it costs $41k 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.
Lack of Incentive to Discover the Full Range of Applications for Off-Patent Treatments
There are roughly 10,000 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.
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 risk for dementia and Alzheimer disease.
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, 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:
Negative Results are Never Published
Pharmaceutical companies that sponsor research often report only “positive” results, leaving out the non-findings or negative findings where a new drug or procedure may have proved more harmful than helpful. 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 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.
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 designed to exclude a vast majority of the population of interest. In other words, 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% 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 patients are sought per cardiovascular trial
- 20 patients per cancer trial
- 70 patients per depression trial
- 100 per diabetes trial
In the example in 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.
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 crowd-sourced 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 base.
There are more than 350,000 health apps. Mobile health app development costs $425,000 on average.
Most of these have a ton of overlap in functionality representing $157,500,000,000 wasted on duplication of effort.
If this code was freely shared, everyone could build on what everyone else had done. Theoretically, this could increase the rate of progress by 350,000 times.
The obstacle has been the free-rider problem. Software Developers that open source their code give their closed-source competitors an unfair advantage. This increases their likelihood of bankruptcy even higher than the 90% failure rate they already faced.
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 will die while waiting for treatment.
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.
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.
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.
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.
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.
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 known diseases afflicting humans.
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There are as many untested compounds with drug-like properties as there are atoms in the solar system (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.
That means we only know 0.000000002% of what is left to be known.
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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 dFDA.