How a Decentralized FDA Could Radically Accelerate Medical Progress

The current drug development and approval process, overseen by the Food and Drug Administration (FDA) in the United States, is lengthy, costly, and often inefficient. This article explores the potential impact of a decentralized FDA (dFDA) approach, quantifying the possible acceleration in medical discovery and the subsequent reduction in global suffering.

Current State of Drug Development

• Average time from discovery to market: 10-15 years [1]
• Average cost per drug: $1.3 billion to $2.8 billion [2]
• Success rate of clinical trials: 12% [3]

Acceleration Factors in a dFDA Model

1. Faster Trial Recruitment and Execution

   • Current recruitment time: 6-12 months [4]
   • Estimated dFDA recruitment time: 1-2 months
   • Time saved: 4-10 months per trial

2. Increased Number of Parallel Trials

   • Current: Approximately 280,000 registered clinical studies globally [5]
   • Estimated increase: 3-5x
   • Potential number of trials: 840,000 – 1,400,000

3. Real-World Data Utilization

   • Reduction in Phase 1 trial duration: 30-50%
   • Current Phase 1 duration: 1-2 years
   • Time saved: 4-12 months

4. Adaptive Trial Design

   • Efficiency increase: 30-50% [6]
   • Time saved in Phase 2 and 3: 1-2 years

5. Repurposing of Existing Drugs

   • Current repurposing studies: ~10% of all drug development [7]
   • Estimated increase: 2-3x
   • Potential repurposing studies: 20-30% of all drug development

Quantified Acceleration Estimates

Short-term (1-5 years):

• Overall acceleration: 40-60%
• Time from discovery to market: 4-9 years

Medium-term (5-10 years):

• Overall acceleration: 70-100%
• Time from discovery to market: 3-7.5 years

Long-term (10+ years):

• Overall acceleration: 100-150%
• Time from discovery to market: 2.5-6 years

Impact on Global Health Burden

To quantify the reduction in global suffering, we’ll use Disability-Adjusted Life Years (DALYs) as a metric. One DALY represents one lost year of healthy life.

Global DALYs (2019): 2.44 billion [8]

Estimated reduction in DALYs:

1. Infectious Diseases (Current DALYs: 400 million)

   • Acceleration: 200-300%
   • Potential DALY reduction: 200-300 million

2. Non-communicable Diseases (Current DALYs: 1.6 billion)

   • Acceleration: 100-150%
   • Potential DALY reduction: 400-600 million

3. Rare Diseases (Current DALYs: 100 million, estimated)

   • Acceleration: 150-200%
   • Potential DALY reduction: 50-75 million

Total potential DALY reduction: 650-975 million per year

This represents a 26.6-39.9% reduction in global health burden.

Economic Impact

1. Reduced Healthcare Costs

   • Current global healthcare spending: $8.3 trillion (2020) [9]
   • Estimated reduction: 10-20%
   • Potential savings: $830 billion – $1.66 trillion annually

2. Increased Productivity

   • Global GDP: $84.5 trillion (2020) [10]
   • Estimated productivity increase due to better health: 1-2%
   • Potential economic gain: $845 billion – $1.69 trillion annually

Challenges and Considerations

1. Data Quality and Integration

   • Investment needed in data standardization and AI analytics

2. Ethical Considerations

   • Robust frameworks required for patient safety and consent

3. Regulatory Adaptation

   • Time and resources needed to transform regulatory processes

4. Global Cooperation

   • International agreements necessary for data sharing and standard practices

Conclusion

A decentralized FDA approach could potentially accelerate medical progress by 100-150% in the long term, reducing the global health burden by 26.6-39.9%. This could translate to 650-975 million DALYs saved annually, representing a significant reduction in human suffering. The economic impact could be equally substantial, with potential annual benefits of $1.675 – $3.35 trillion when combining healthcare savings and productivity gains.

While these estimates are based on current data and reasonable projections, the actual impact may vary. The potential benefits are immense, but realizing them will require overcoming significant technical, ethical, and regulatory challenges.

References

[1] DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20-33.

[2] Wouters, O. J., McKee, M., & Luyten, J. (2020). Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA, 323(9), 844-853.

[3] Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273-286.

[4] Huang, G. D., Bull, J., McKee, K. J., Mahon, E., Harper, B., & Roberts, J. N. (2018). Clinical trials recruitment planning: A proposed framework from the Clinical Trials Transformation Initiative. Contemporary Clinical Trials, 66, 74-79.

[5] ClinicalTrials.gov. (2023). Trends, Charts, and Maps. https://clinicaltrials.gov/ct2/resources/trends

[6] Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., … & Jaki, T. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.

[7] Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., … & Pirmohamed, M. (2019). Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery, 18(1), 41-58.

[8] GBD 2019 Diseases and Injuries Collaborators. (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1204-1222.

[9] World Health Organization. (2022). Global Health Expenditure Database. https://apps.who.int/nha/database

[10] World Bank. (2021). GDP (current US$). https://data.worldbank.org/indicator/NY.GDP.MKTP.CD