Predictive Modeling of Interactions between Herbal and Conventional Medicines
Pooja Khurana, Ghanshyam Das Gupta, Deepak Kumar, Devendra Kumar · 2025
The co-administration of herbal remedies and conventional drugs is common in clinical practice, but it poses challenges due to potential interactions that can affect treatment outcomes and patient safety. Predictive modeling using mathematical approaches offers a promising solution for understanding and predicting herbal-drug interactions (HDIs). This chapter provides a comprehensive overview of the current state of research on predictive modeling of HDIs, focusing on mathematical approaches, methodologies, applications explored, challenges, and future directions in this field. The authors discuss mathematical models for predicting and characterizing HDIs including machine learning algorithms, pharmacokinetic and pharmacodynamic models, and network analysis approaches. Additionally, they examine the data sources, including clinical trials, pharmacological databases, and computational simulations, used to develop and validate predictive models, highlighting applications of predictive modeling in clinical decision support, drug development, and personalized medicine, along with challenges such as data integration, model validation, and clinical translation. Finally, they propose future directions for research, including developing standardized datasets, integrating multi-omics data, and implementing decision support systems in healthcare settings. By leveraging mathematical approaches, predictive modeling of HDIs holds great promise for improving medication safety, optimizing treatment regimens, and advancing personalized healthcare.