Toxicodynamics and Human Health Risks of Metal Nanoparticles: A Review of AI/ML-Based Predictive Nanotoxicology

Metal-based nanoparticles (MNPs) are the most extensively explored advanced nanostructures for biomedical applications. These MNPs, have unique intrinsic properties. These properties modify particle characteristics and functionalization, making them suitable for applications in biomedicine, cancer imaging, and other therapeutics. With the exceeding production of MNPs annually, toxic chemical and solvents used for synthesis of MNPs are causing various health implications. This review discusses MNPs effects on human exposure, biokinetics, and toxicodynamics alongside developments in artificial intelligence (AI) and machine learning (ML) for predictive toxicology. In vitro and in vivo mechanistic study reports organ-specific accumulation of MNPs, particularly in liver, kidney, and brain, with potential crossing of the blood brain and placental barriers leading to oxidative stress, ROS production, mitochondrial dysfunction, and genotoxicity. The review also presents integrating omics approach and AI frameworks to attain safe nanoparticle development and human health risk assessment.