Smart Pharmaceutics: Integrating Machine Learning into Controlled Drug Release Systems
Rehan Haider, Zeba Ahmed, Geetha Kumari Das · Medical and Pharmaceutical Journal · 2025
Background: The concept of controlled drug-release systems is based on a fundamental principle of modern pharmaceutics, aimed at achieving sustained therapeutic effects with the lowest possible systemic side effects. The development of conventional controlled-release systems, despite significant advances in formulation science, remains largely empirical and is both time-consuming and labor-intensive. Over the past few years, machine learning (ML) has enabled a novel approach to improving drug delivery systems by enabling data-driven prediction and drug development. Objective: This paper examines the use of ML algorithms in designing and optimizing drug delivery systems that are under control. Methods: A set of 200 polymer-drug formulations was studied with supervised learning models, artificial neural networks (ANNs), support vector machines (SVMs), and random forest (RF) models. All models were trained to predict the 24-hour release efficacy of drugs based on formulation parameters, including polymer type, molecular weight, pH, solubility, and temperature. Results: ANN model proved to be most predictive (R 2 = 0.96, RMSE = 0.08), better than SVM (R 2 = 0.92) and RF (R 2 = 0.89). Conclusion: Analysis of the importance of features showed that polymer molecular weight and environmental pH are the most significant factors that determine release kinetics. The findings emphasize ML's capacity to reduce experimental work, improve prediction accuracy, and accelerate formulation development. Altogether, the paper highlights the transformative capabilities of machine learning in pharmaceutics, including the integration of experimental and computational approaches to develop more efficient, accurate, and patient-specific controlled-release systems.