Executive summary: The author argues that rising clinical trial costs and inefficiencies are a major, tractable bottleneck to biomedical progress, and curates a reading list supporting the “Clinical Trial Abundance” view that expanding and improving trials could accelerate innovation.
Key points:
The author suggests “clinical trial abundance” could be an EA cause area because disease burden remains high and increasing the pace of progress seems tractable.
Drug development costs have risen ~80x since the 1950s to around $1B per approved drug, leading to fewer drugs, avoidance of risky bets, and worse outcomes for patients.
Historical shifts moved the field from small, fast, sometimes unethical trials to large, slow, prediction-heavy preclinical pipelines that take many years.
“Eroom’s Law” describes declining R&D efficiency, potentially driven by factors like higher standards of care, risk-averse regulators, excessive spending, and overreliance on predictive preclinical research.
The author is uncertain about some strong claims in this literature, such as constant clinical trial success rates over 50 years.
Clinical Trial Abundance advocates argue that neither deregulation alone nor AI will solve drug development, because human trials remain essential for testing efficacy.
Trials are inefficient partly because they are treated as bespoke projects rather than standardized engineering processes, and industry risk aversion has causes beyond regulation.
The goal is not just more trials but tighter feedback loops where trials improve understanding of disease, not just filter drugs.
Regulatory uncertainty (e.g., opaque approval criteria) may drive inefficiency more than strict rules, leading firms to over-test and avoid risk; proposed solutions include publishing data from failed trials.
Some countries (e.g., Australia) run faster, cheaper early-phase trials due to lighter approval requirements, lower GMP standards, and financial incentives, without clear safety tradeoffs.
Proposed reforms include streamlining consent, using human challenge trials, increasing transparency (e.g., FDA letters), and treating current norms as historically contingent rather than optimal.
The broader movement involves researchers, policymakers, and patient advocates (e.g., 1DaySooner) working on policy frameworks and practical reforms to expand and improve clinical trials.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: The author argues that rising clinical trial costs and inefficiencies are a major, tractable bottleneck to biomedical progress, and curates a reading list supporting the “Clinical Trial Abundance” view that expanding and improving trials could accelerate innovation.
Key points:
The author suggests “clinical trial abundance” could be an EA cause area because disease burden remains high and increasing the pace of progress seems tractable.
Drug development costs have risen ~80x since the 1950s to around $1B per approved drug, leading to fewer drugs, avoidance of risky bets, and worse outcomes for patients.
Historical shifts moved the field from small, fast, sometimes unethical trials to large, slow, prediction-heavy preclinical pipelines that take many years.
“Eroom’s Law” describes declining R&D efficiency, potentially driven by factors like higher standards of care, risk-averse regulators, excessive spending, and overreliance on predictive preclinical research.
The author is uncertain about some strong claims in this literature, such as constant clinical trial success rates over 50 years.
Clinical Trial Abundance advocates argue that neither deregulation alone nor AI will solve drug development, because human trials remain essential for testing efficacy.
Trials are inefficient partly because they are treated as bespoke projects rather than standardized engineering processes, and industry risk aversion has causes beyond regulation.
The goal is not just more trials but tighter feedback loops where trials improve understanding of disease, not just filter drugs.
Regulatory uncertainty (e.g., opaque approval criteria) may drive inefficiency more than strict rules, leading firms to over-test and avoid risk; proposed solutions include publishing data from failed trials.
Some countries (e.g., Australia) run faster, cheaper early-phase trials due to lighter approval requirements, lower GMP standards, and financial incentives, without clear safety tradeoffs.
Proposed reforms include streamlining consent, using human challenge trials, increasing transparency (e.g., FDA letters), and treating current norms as historically contingent rather than optimal.
The broader movement involves researchers, policymakers, and patient advocates (e.g., 1DaySooner) working on policy frameworks and practical reforms to expand and improve clinical trials.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.