# Food contaminants: mechanisms of toxicity, computational assessment, and mitigation

## TL;DR

This review explores how food contaminants harm health, using computational methods to replace animal testing and improve risk assessment.

## Contribution

The paper highlights the integration of computational methods with experimental data for predicting and mitigating food contaminant toxicity.

## Key findings

- Computational methods like MD simulations and PBPK modeling are effective for assessing contaminant toxicity.
- In silico approaches support ethical alternatives to animal testing in toxicology.
- Predictive models combining computational and experimental data improve risk mitigation strategies.

## Abstract

Understanding the toxicological mechanisms of food contaminants is critical for assessing risks to human health. This review comprehensively examines their adverse effects, tracing the pathway from molecular initiation to systemic organ-level damage. A central focus is placed on the growing trust on computational methods as ethical and practical alternatives to traditional animal testing. The discussion encompasses a multi-scale assessment, detailing atomic-level interactions through Density Functional Tight Binding Molecular docking and Molecular Dynamics (MD) simulations, analyses of toxicity pathway, and prediction of systemic fate using Physiologically Based Pharmacokinetic (PBPK) modeling. We further explore how these in silico insights are integrated with experimental data to build predictive models, including Quantitative Structure-Activity Relationship and machine learning frameworks. Ultimately, this review aims to inform the development of effective strategies for mitigating contaminant risks, thereby advancing public health objectives and supporting the 3Rs principles (Replacement, Reduction, and Refinement) in toxicological science.