Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
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Volume 10 Number 1 2026
REVIEWS (Open Access)
Comprehensive Review of Foundation Toxicity Models Integrating In Vivo, In Vitro, and Chemical Knowledge for Unified Risk Prediction
Tajmin Khanam 1*, Lilufar Yeasmin 1, Mst. Farzina Akter 1
Integrative Biomedical Research 10 (1) 1-14 https://doi.org/10.25163/biomedical.10110733
Submitted: 07 March 2026 Revised: 22 April 2026 Accepted: 06 May 2026 Published: 08 May 2026
Abstract
Toxicology, perhaps more than many scientific disciplines, seems to be standing at a quiet but decisive turning point. For decades, the field relied heavily on in vivo experimentation—robust and deeply informative, yet increasingly constrained by ethical concerns, cost, and, not insignificantly, questions of human relevance. As chemical exposure grows in both scale and complexity, these traditional approaches begin to feel, if not outdated, then at least insufficient. This review explores the emergence of “foundation toxicity models,” an evolving class of integrative frameworks that attempt to bring together in vivo observations, in vitro mechanistic insights, and chemical structural knowledge into unified predictive systems. At the core of this transition lies a shift from observation to anticipation. Advances in quantitative high-throughput screening, artificial intelligence, and multi-omics technologies have made it possible to deconstruct toxicity into measurable biological perturbations. Mechanistic frameworks such as Adverse Outcome Pathways provide a conceptual scaffold, linking molecular initiating events to organism-level outcomes. Meanwhile, computational approaches—from classical QSAR models to deep learning and graph-based architectures—are increasingly capable of capturing the nonlinear complexity inherent in biological systems. And yet, the path forward is not entirely smooth. Challenges related to data heterogeneity, model interpretability, and the persistent difficulty of in vitro-to-in vivo extrapolation remain significant. Still, there is a sense—perhaps cautious, but growing—that these limitations are not insurmountable. Rather, they represent transitional constraints within a rapidly evolving paradigm. Taken together, the convergence of experimental and computational toxicology suggests a future in which toxicity is not merely detected after the fact, but predicted in advance. Foundation toxicity models, in this context, do not simply extend existing methods—they begin to redefine how chemical risk is understood, evaluated, and, ultimately, prevented.
Keywords: Predictive toxicology; Adverse Outcome Pathways; qHTS; Artificial intelligence; IVIVE
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