Randomized controlled trial data for successful new drug application for rare diseases in the United States

Abstract

Abstract Background Randomized controlled trial (RCT) data have important implications in drug development. However, the feasibility and cost of conducting RCTs lower the motivation for drug development, especially for rare diseases. We investigated the potential factors associated with the need for RCTs in the clinical data package for new drug applications for rare diseases in the United States (US). This study focused on 233 drugs with orphan drug designations approved in the US between April 2001 and March 2021. Univariable and multivariable logistic regression analyses were conducted to investigate the association between the presence or absence of RCTs in the clinical data package for new drug applications. Results Multivariable logistic regression analysis showed that the severity of the disease outcome (odds ratio [OR] 5.63, 95% confidence interval [CI] 2.64–12.00), type of drug usage (odds ratio [OR] 2.95, 95% confidence interval [CI] 1.80–18.57), and type of primary endpoint (OR 5.57, 95% CI 2.57–12.06) were associated with the presence or absence of RCTs. Conclusions Our results indicated that the presence or absence of RCT data in the clinical data package for successful new drug application in the US was associated with three factors: severity of disease outcome, type of drug usage, and type of primary endpoint. These results highlight the importance of selecting target diseases and potential efficacy variables to optimize orphan drug development.

Keywords

• Rare diseases
• Orphan drugs
• Clinical trials
• Randomization
• Efficacy endpoint
• Logistic regression analysis