Multivariate Regression Modeling of Environmental and Biological Risk Factors in Toxicology

Abstract

The field of toxicology is undergoing a paradigm shift from single-chemical assessment toward the comprehensive evaluation of complex environmental mixtures and their interactions with biological systems. This study explores the application of multivariate regression modeling as an integrative framework for assessing environmental and biological risk factors. It synthesizes theoretical foundations, statistical methodologies, and emerging technologies used to analyze high-dimensional exposure data, including Weighted Quantile Sum regression, regularized regression techniques, and survival analysis models such as the Cox Proportional Hazards Model. The research highlights the importance of incorporating biological susceptibility through -omics technologies and New Approach Methodologies (NAMs), enabling mechanistic insights into toxicity pathways. Additionally, it addresses key statistical challenges such as multicollinearity and confounding, and emphasizes the role of causal inference tools like Directed Acyclic Graphs and Structural Equation Modeling. The study further examines the integration of artificial intelligence and machine learning in predictive toxicology and discusses evolving regulatory frameworks that support non-animal testing approaches. Overall, this work demonstrates that multivariate regression modeling provides a robust and scalable approach for advancing precision toxicology and improving risk assessment in complex real-world exposure scenarios.