DRUG FORMULATION SYSTEM FOR GENERICS USING OPTIMIZATION-BASED DOSAGE CALIBRATION

This paper reports a Drug Formulation System to Generics with Optimization-Based Dosage Calibration to hasten the creation of bioequivalent generic drugs. The methodology uses constrained multi-objective Bayesian Optimization (BO) to maximize probability of bioequivalence (P(BE)) and at the same time meet the critical quality attributes e.g., dissolution, friability and hardness of the tablet. The system uses BoTorch (including Ax), a PyTorch-based optimization library, to combine mechanistic modeling with surrogate learning to minimize the need to rely on trial-and-error experimentation. It has been demonstrated that the BO framework can reach P(BE) ≥ 0.90 in much fewer optimization cycles than both the conventional response surface methodology and genetic algorithms and also delivers better constraint satisfaction and computational efficiency. The proposed system can provide an uncertainty-sensitive formulation design solution that offers a regulatory-conformant avenue to decrease development time, decrease the cost of experiments, and improve reliability in generic drug approvals.