Machine Learning Modeling of D-Optimal Design Data for Metformin Hydrochloride Orally Disintegrating Tablets
Abstract
Abstract Metformin hydrochloride orally disintegrating tablets require an appropriate balance between mechanical strength and rapid disintegration, particularly because the drug is administered at a relatively high dose and has unfavorable taste characteristics. The present study applied machine learning to published D-optimal design data for metformin hydrochloride orally disintegrating tablets to evaluate whether formulation and process variables could predict powder-flow and tablet-performance responses. The dataset contained 30 experimental runs with four inputs: co-processed metformin hydrochloride ratio, co-processed excipient ratio, compression pressure, and excipient brand. Five responses were modeled separately: angle of repose, Hausner’s ratio, Carr’s index, hardness, and disintegration time. Polynomial Ridge Regression, Random Forest, Extra Trees, Gradient Boosting, Support Vector Regression, Gaussian Process Regression, and k-nearest neighbors were compared using leave-one-out cross-validation. Model performance was assessed using mean absolute error, root mean square error, and cross-validated R². Gaussian Process Regression was the best model for Hausner’s ratio, Carr’s index, hardness, and disintegration time, while Support Vector Regression performed best for angle of repose. Tablet hardness and disintegration time were predicted with the strongest performance, with R² values of 0.840 and 0.858, respectively. In contrast, angle of repose was weakly predicted,…
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