A data-driven approach to predict the in vitro dissolution time of sustained-release tablets using raw material databases and machine learning algorithms

Abstract

Tablets are the most typical dosage forms of pharmaceutical inventions. Sustained-release (SR) tablet formulations are designed to release the drug gradually in the bloodstream and often require less frequent dosing. Current strategies to optimize sustained-release tablet dissolution time still rely on the traditional approach, which is time-consuming and expensive. In the present context, we have demonstrated alternate machine learning and deep learning models through the TPOT AutoML platform. Six machine learning (ML) models were compared to improve the methodology for dissolution time prediction, particularly the decision tree regressor (DTR), gradient boost regressor (GBR), random forest regressor (RFR), extra tree regressor (ETR), XGBoost regressor (XGBR), and deep learning (DL). The obtained results indicated that machine learning methods are convincing in speculating the dissolution time, especially the random forest regressor, but upon hypertuning of the deep neural network, the deep learning model with a 10-fold cross-validation scheme demonstrated superior predictive performance with an NRMSE of 8% and an R2 of 0.92. The major essentials affecting the dissolution time of SR tablets were explained using the SHAP method.