Abstract

The goal of building a hybrid computational model that predicts drug–nutrient interactions in human metabolic networks is to increase our understanding of how biochemical pathways, therapeutic outcomes and metabolic homeostasis respond to pharmaceutical compounds and nutrients in the diet. Drug–nutrient interactions are emerging as a key factor impacting the efficacy and toxicity/safety of certain drugs, particularly in chronic disease and multiple drugs or nutrient deficiency patients. Traditional approaches to detecting such interactions are labour intensive and costly, and difficult to account for the complexity of interacting metabolic pathways. The current study seeks to develop an integrated computational framework for predicting and evaluating the complex metabolic interactions between drugs and nutrients using systems biology, machine learning, network analysis, and mathematical modeling. The model is modular and integrates various biological information such as gene profiles, metabolic pathway databases, nutrient composition data, pharmacokinetic parameters and enzyme interaction networks. The metabolic network is represented as a graph, with biochemical reactions and molecular dependencies of human physiology encoded as its nodes and edges, respectively. The hybrid model is highly predictive in the simulation and can help to identify high risk drug–nutrient combinations that may result in adverse metabolic effects or lowered therapeutic effectiveness. In addition, the study explores how the outcomes of interactions differ depending on individual factors (such as age, metabolism, diet, genetic polymorphisms). The findings show the potential of using hybrid computational intelligence for enhancing precision medicine and personal nutrition. The model could be a valuable resource for clinicians, pharmacologists, and health-care researchers, enabling them to optimize therapeutic approaches, minimize drug side effects, and personalize treatment plans. Furthermore, the Research framework aims to facilitate the development of smart health-care systems that are able to integrate nutritional and pharmacological information for decision-making. This model could be expanded in the future to include real-time patient monitoring data, deep learning architectures, and comprehensive clinical validation, further improving the reliability of prediction and its applicability in today's health-care environments.