Advances in predictive neurotoxicology: human-relevant models, mechanistic understanding, and translational safety evaluation

The field of predictive neurotoxicology is rapidly evolving in response to the increasing need for more accurate methods to anticipate neurotoxicity risks posed by pharmaceuticals and environmental chemicals. Despite considerable advancements, the complex architecture and functions of the nervous system pose ongoing challenges to both mechanistic understanding and translational modeling, particularly given the limitations of traditional animal-based methods. Recent evidence highlights the advantages of human-relevant in vitro systems, including hiPSC-derived neural cultures, organoids, and microphysiological platforms. These models offer greater physiological relevance, helping to bridge the translational gap between preclinical findings and clinical outcomes. Emerging computational approaches such as machine learning and multi-omics integration have further propelled the field, enabling improved prediction and mechanistic insights. Nonetheless, the ability to reliably translate in vitro and in silico data into actionable safety signals for humans remains constrained by gaps in validation, standardization, and regulatory acceptance. Ongoing debates concern the best practices for establishing adverse outcome pathways, integrating diverse datasets, and ensuring reproducibility, which collectively underscore the need for continued innovation and critical assessment in this field.This Research Topic aims to spotlight recent advances and foster new research at the intersection of human-relevant neurotoxicity modeling, mechanistic toxicology, and translational risk assessment. The objective is to both stimulate methodological innovation and support the development of predictive frameworks that can inform safety decisions across drug discovery, chemical development, and regulatory evaluation. Researchers are invited to address key questions such as: How can next-generation in vitro and microphysiological models be further optimized or validated for neurotoxicity prediction? What are the cutting-edge approaches to elucidate adverse outcome pathways and key neurotoxic events? How can computational and experimental datasets be harmonized for better translational outcomes? Emphasis will be placed on contributions that bridge in vitro findings to in vivo and clinical settings, including advancements in validation, reproducibility, and reporting of uncertainty.This Research Topic covers the development, application, and benchmarking of methodologies relevant to predictive neurotoxicology, with a focus on translational and mechanistically-informed approaches. Submissions should remain within the scope of neurotoxicity prediction, mechanistic elucidation, or their integration into translational or regulatory contexts. We welcome articles addressing, but not limited to, the following themes:• Human-relevant in vitro and 3D neurotoxicity models, including hiPSC-based, organoid, and microphysiological systems• Functional and phenotypic readouts such as neuronal network activity, high-content imaging, and behavioral correlations• Mechanistic toxicology covering synaptic function, glial roles, neuroinflammation, excitotoxicity, mitochondrial dysfunction, and cell death pathways• Computational, systems, and integrative approaches for neurotoxicity prediction (e.g., machine learning, omics, PBPK modeling)• Validation strategies, benchmarking, regulatory considerations, and data standardization• Case studies that demonstrate translational success from in vitro or in silico predictions to in vivo or clinical safety outcomesAppendix: Article TypesOriginal Research, Review, Mini Review, Methods, Perspective, and Brief Research Report. Topic editors: Honey Goel, Bruna Puty Source: https://www.frontiersin.org/research-topics/82574/advances-in-predictive-neurotoxicology-human-relevant-models-mechanistic-understanding-and-translational-safety-evaluation