Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls

Abstract

The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.

Graphical Abstract