Machine Learning-Based Prediction of Drug Degradation Kinetics under Forced Degradation Conditions Signal Detection, and Real-World Data Analytics

Abstract

Drug degradation is a critical factor influencing the safety, efficacy, and shelf life of pharmaceutical products. Forced degradation studies are widely used in pharmaceutical analysis to evaluate the stability of active pharmaceutical ingredients (APIs) under stress conditions such as heat, light, oxidation, and hydrolysis. However, traditional degradation studies require extensive experimental work and long analysis times. In recent years, machine learning (ML) techniques have emerged as powerful tools to predict degradation of kinetics using experimental and stability data. Machine learning algorithms can analyze complex relationships among variables such as temperature, pH, humidity, and exposure time, enabling accurate prediction of degradation behavior. This article discusses the application of machine learning approaches in predicting drug degradation kinetics under forced degradation conditions. The use of algorithms such as artificial neural networks, support vector machines, random forests, and gradient boosting models can improve prediction accuracy and reduce experimental workload. Integration of machine learning with pharmaceutical stability studies can accelerate drug development, improve quality control, and support regulatory compliance in pharmaceutical industries.