Artificial intelligence - driven toxicogenomics for predictive environmental and pharmaceutical safety.
4 affiliations
- 1Department of Biomedical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa, Saudi Arabia.
- 2Basic Medical Sciences Department, College of Medicine, Dar Al Uloom University, Riyadh, Saudi Arabia.
- 3Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
- 4Department of Pharmacology, Kathmandu Medical College, Kathmandu, Nepal.
Abstract
Toxicogenomics integrates high-throughput genomic technologies with toxicology to evaluate molecular responses to chemical and pharmaceutical exposures. The increasing volume and complexity of multi-omics data necessitate advanced computational approaches capable of extracting meaningful and predictive insights. In this context, artificial intelligence (AI) and machine learning (ML) techniques-including support vector machines, random forests, and deep learning architectures-are increasingly employed for early toxicity prediction and risk assessment, with reported improvements in predictive accuracy and sensitivity relative to conventional toxicological approaches. This paper explores the role of AI-driven toxicogenomics in enhancing predictive environmental and pharmaceutical safety. It highlights the application of ML-based and data-driven models for the integrative analysis of transcriptomic, proteomic, and metabolomic datasets to identify toxicity-associated biomarkers, elucidate mechanisms of action, and predict adverse drug and environmental effects prior to clinical manifestation. Notably, AI-driven models have demonstrated significant improvements in prediction accuracy, sensitivity, and scalability compared to traditional toxicological approaches, reducing reliance on animal testing and accelerating decision-making processes. Furthermore, the paper discusses the significance of AI-based toxicogenomics in drug development, environmental contaminant screening, and regulatory toxicology, while addressing key challenges such as data heterogeneity, model interpretability, and regulatory acceptance. The convergence of computer science and toxicogenomics represents a robust framework for next-generation predictive toxicology, facilitating safer pharmaceutical development, more precise environmental risk assessment, and evidence-based public health and regulatory decision-making.
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BibTeX
@article{islam2026artificial,
title = {{Artificial intelligence - driven toxicogenomics for predictive environmental and pharmaceutical safety.}},
author = {Mohammed Monirul Islam and Sibghatullah Muhammad Ali Sangi and Girish Meravanige and Sreeharsha Nagaraja and Muhammad Shahzad Chohan and Md Khalid Anwer and Ashesh Gurung},
journal = {Frontiers in genetics},
year = {2026},
month = oct,
doi = {10.3389/fgene.2026.1858338},
url = {https://europepmc.org/article/MED/42923112},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence - driven toxicogenomics for predictive environmental and pharmaceutical safety.
AU - Mohammed Monirul Islam
AU - Sibghatullah Muhammad Ali Sangi
AU - Girish Meravanige
AU - Sreeharsha Nagaraja
AU - Muhammad Shahzad Chohan
AU - Md Khalid Anwer
AU - Ashesh Gurung
PY - 2026
DA - 2026/10/08
JO - Frontiers in genetics
DO - 10.3389/fgene.2026.1858338
UR - https://europepmc.org/article/MED/42923112
AB - Toxicogenomics integrates high-throughput genomic technologies with toxicology to evaluate molecular responses to chemical and pharmaceutical exposures. The increasing volume and complexity of multi-omics data necessitate advanced computational approaches capable of extracting meaningful and predictive insights. In this context, artificial intelligence (AI) and machine learning (ML) techniques-including support vector machines, random forests, and deep learning architectures-are increasingly employed for early toxicity prediction and risk assessment, with reported improvements in predictive accuracy and sensitivity relative to conventional toxicological approaches. This paper explores the role of AI-driven toxicogenomics in enhancing predictive environmental and pharmaceutical safety. It highlights the application of ML-based and data-driven models for the integrative analysis of transcriptomic, proteomic, and metabolomic datasets to identify toxicity-associated biomarkers, elucidate mechanisms of action, and predict adverse drug and environmental effects prior to clinical manifestation. Notably, AI-driven models have demonstrated significant improvements in prediction accuracy, sensitivity, and scalability compared to traditional toxicological approaches, reducing reliance on animal testing and accelerating decision-making processes. Furthermore, the paper discusses the significance of AI-based toxicogenomics in drug development, environmental contaminant screening, and regulatory toxicology, while addressing key challenges such as data heterogeneity, model interpretability, and regulatory acceptance. The convergence of computer science and toxicogenomics represents a robust framework for next-generation predictive toxicology, facilitating safer pharmaceutical development, more precise environmental risk assessment, and evidence-based public health and regulatory decision-making.
SN - 1664-8021
ER -