Predictive analysis of first Abbreviated New Drug Application submission for new chemical entities based on machine learning methodology.

Generic drug products are approved by the US Food and Drug Administration (FDA) through Abbreviated New Drug Applications (ANDAs). The ANDA review and approval involves multiple offices across the FDA. Forecasting ANDA submissions can critically inform resource allocation and workload management. In this work, we used machine learning (ML) methodologies to predict the time to first ANDA submissions referencing new chemical entities following their earliest lawful ANDA submission dates. Drug product information, regulatory factors, and pharmacoeconomic factors were used as modeling inputs. The random survival forest ML method, as well as the conventional Cox model, was used for ANDA submission predictions. The ML method outperformed the conventional Cox regression model in predictive performance that was adequately assessed by both internal and external validations. In conclusion, it can potentially serve as an effective forecasting tool for strategic workload and research planning for generic applications.