Problem definition: To approve a novel drug therapy, the U.S. Food and Drug Administration (FDA) requires clinical trial evidence demonstrating efficacy with 2.5% statistical significance, although the agency often uses regulatory discretion when interpreting these standards. Factors including disease severity, prevalence, and availability of existing therapies are qualitatively considered; yet, current guidelines fail to systematically consider such characteristics in approval decisions. Academic/practical relevance: In making approval decisions, the FDA weighs the health benefits of introducing life-saving therapies against the potential risks of approving ineffective or harmful drugs. Tailoring approval standards to individual diseases could improve treatment options for patients with few alternatives and further incentivize pharmaceutical companies to invest in neglected diseases. Methodology: Using a novel queueing framework, we analyze the FDA’s drug approval process to incorporate disease-specific factors and obsolescence—newer drugs replacing older formulas—through a set of pre-emptive M/M/1/1 queues. Based on public data encompassing all registered U.S. clinical trials and FDA-approved drugs, we estimate model parameters for three high-burden diseases (breast cancer, human immunodeficiency virus (HIV), and hypertension) and solve for the optimal policy to maximize net life-years gained following FDA approval. Results: The optimal policy relaxes approval standards for diseases with long trial duration, high attrition, or low research and development intensity. Results indicate that a more lenient policy is warranted for drugs targeting breast cancer or hypertension, and a more stringent policy is recommended for HIV, relative to the FDA’s existing policy. If pharmaceutical firms respond to the new standards by submitting more drugs for approval—leading to an endogenous clinical trial initiation rate—the FDA’s optimal policy modestly decreases for breast cancer and hypertension, with minimal change for HIV. Managerial implications: Our study offers a transparent, quantitative framework that could help the FDA develop disease-specific approval guidelines based on underlying disease-related severity, prevalence, and characteristics of the drug development process and existing market.

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