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Predictive Analysis for First Submission of Generic Drug Application for Orphan Drug Products Using Random Survival
Robert Hopefl1, Jing Wang1, Abhinav Ram Mohan1
1Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Clinical and Translational Science
|October 5, 2025
Summary
Developing generic orphan drugs (ODPs) can lower costs for rare disease patients. This study identified key factors influencing abbreviated new drug applications (ANDAs) for generic ODPs, using machine learning to predict submissions and inform strategies for increased availability.
Area of Science:
- Pharmacoeconomics
- Regulatory Science
- Machine Learning in Drug Development
Background:
- Rare diseases affect small patient populations, leading to limited incentives for orphan drug product (ODP) development.
- The Orphan Drug Act of 1983 aimed to incentivize ODP development, but ODPs often incur higher treatment costs.
- Generic ODPs can enhance market competition and offer alternative treatments, benefiting patients.
Purpose of the Study:
- To identify factors influencing the initial submission of abbreviated new drug applications (ANDAs) for generic orphan drugs.
- To develop a predictive model for ANDA submissions of ODPs using machine learning.
Main Methods:
- Data collected from U.S. Food and Drug Administration (FDA) databases and IQVIA sales database.
- Included drug product information, regulatory factors, and pharmacoeconomic data.
- Random Survival Forest (RSF) machine learning model used for New Chemical Entities (NCEs) and non-NCEs, validated internally and externally.
Main Results:
- RSF model predicted ANDA submissions with C-indices of 0.675 ± 0.0261 for NCEs and 0.754 ± 0.0441 for non-NCEs.
- For NCE ODPs, sales data was the most important predictor of ANDA submission.
- For non-NCE ODPs, regulatory data, specifically the availability of product-specific guidances (PSGs), was most important.
Conclusions:
- Machine learning, particularly RSF, can predict ANDA submissions for ODPs, with varying key factors for NCEs and non-NCEs.
- Future data availability may enhance RSF model accuracy for predicting ODP ANDA submissions.
- Model-informed insights can guide strategies to promote ANDA submissions and increase generic ODP availability.
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