Data-Driven Machine Learning Surrogate Models for Predicting Dissolution and Gel Viscosity in Hot-Melt Extruded Polymeric Formulations: A Quality-by-Design Framework
Abstract: Hot-melt extrusion (HME) is a solvent-free, continuous manufacturing technology that is increasingly central to the production of modified-release and abuse-deterrent oral dosage forms. The relationships among formulation composition, process conditions and critical quality attributes such as drug dissolution and gel viscosity are complex, nonlinear and costly to resolve by experimentation alone. This study develops and benchmarks tree-based machine-learning surrogate models, namely XGBoost, Random Forest and Gradient Boosting, for predicting the 30-min aqueous dissolution of hot-melt extruded polyethylene oxide/hydroxypropyl methylcellulose/carbomer matrices containing pseudoephedrine, and characterizes gel viscosity through exploratory regression. On a small curated dataset (42 records; five features), Random Forest achieved the best held-out performance (R2 = 0.485), with pseudoephedrine drug loading identified as the dominant predictor. Five-fold hyperparameter optimization did not materially improve held-out accuracy, and negative cross-validated R2 values revealed the variance inherent to small samples. Integrating a second data source (96 usable records; 17 features) yielded a markedly stronger optimized model (R2 = 0.984; RMSE = 3.56%), although this gain partly reflects target leakage through the 60-min dissolution endpoint. SHAP analysis attributed predictive power to drug content uniformity and the 60-min readout, and residual diagnostics of the single-source model showed no systematic bias. These results establish a reusable, interpretable, quality-by-design workflow for surrogate modeling in pharmaceutical process engineering and highlight the safeguards needed before such models are used for formulation decisions.
Keywords: hot-melt extrusion; machine learning; quality by design; dissolution prediction; SHAP
How to Cite?: Mohammad Ali Riaz, "Data-Driven Machine Learning Surrogate Models for Predicting Dissolution and Gel Viscosity in Hot-Melt Extruded Polymeric Formulations: A Quality-by-Design Framework", Volume 15 Issue 11, November 2026, International Journal of Science and Research (IJSR), Pages: 288-296, https://www.ijsr.net/getabstract.php?paperid=SR261106054406, DOI: https://dx.doi.org/10.21275/SR261106054406