AI-integrated human-relevant alternatives to animal experimentation in drug development: Toward a predictive translational ecosystem, from in vitro and in silico models to clinical and real-world evidence.

Authors

Prashant N Amale, Dhirendra H Tiwari

Published in

Alternatives to laboratory animals : ATLA. Pages 2611929261492225. Sep 25, 2026. Epub Sep 25, 2026.

Abstract

Animal experimentation has long been associated with biomedical research and drug development. However, ethical concerns, limited translatability to humans and high clinical attrition rates (> 90%) necessitate a transition toward human-relevant approaches. Although diverse in vitro, in silico and artificial intelligence (AI)-enabled methodologies have emerged, their integration into a unified, regulatory-acceptable framework remains limited. This review proposes an AI-centred, multi-tier decision-support framework, to enable a phased shift toward animal-reduced and, ultimately, animal-free drug development. Human-relevant platforms (e.g. organoids, microphysiological systems (MPS) and 3D bioprinting), are evaluated for their potential to reproduce selected aspects of human pharmacokinetics, toxicity and disease phenotypes. AI-driven strategies, including pharmacokinetic/pharmacodynamic modelling, quantitative systems pharmacology, digital twins and virtual clinical simulations, support the prediction of individualised responses with multi-omics, imaging and real-world data. Special emphasis is placed on neuropharmacology, where AI-integrated human neural models enhance mechanistic understanding and therapeutic optimisation. Human-based approaches, such as microdosing, non-invasive imaging and real-world evidence, provide validation pathways and strengthen translational reliability. Despite progress, challenges related to technical limitations, data integration, cost and regulatory acceptance persist. To address these gaps, we propose the 'AI-orchestrated Human-Relevant Translational Ecosystem' (AI-HRTE), a conceptual framework integrating experimental, computational and clinical data, to support iterative, AI-assisted decision-making. While this framework offers a structured approach to enhance predictive modelling and reduce animal use in line with the Three Rs principles (i.e. replacement, reduction and refinement), its effectiveness remains hypothetical and requires prospective validation.

PMID:
42788857
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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