Emerging Human-Based Alternative Technologies as Replacements for Preclinical Animal Testing
DOI:
https://doi.org/10.55006/biolsciences.2025.5402Keywords:
AI Computational modeling, Non-animal clinical trials, Alternatives for animal testingAbstract
This review aims to explore recent advancements in human-based methodologies that are reshaping the landscape of preclinical research. It focuses on evaluating the scientific validity, translational relevance, and regulatory recognition of emerging non-animal platforms. By analyzing these approaches, the article seeks to demonstrate their potential in enhancing predictive accuracy, ethical responsibility, and the overall efficiency of biomedical research and drug development. Most predominantly used methods include Artificial intelligence models, in-vitro models, in-silico models, organ-on-chip systems, and other innovative technologies. Human organoids-on-chips (OrgOCs) combine human organoids (HOs) technology and microfluidic organs-on-chips (OOCs). HOs are related to biological analysis and genetic manipulation while OOCs can simulate external characteristics of organs like living tissue, OrgOCs served as 3D organotypic living models allowing them to recapitulate critical tissue-specific properties and predict human responses. Virtual screening, molecular docking, QSAR modeling, AI/ML based clinical computational models are some of the tools used in building non-animal modelling. Animal testing requirement of FDA will be replaced using above range of approaches in a laboratory setting. Implementation of the regimen shall begin for investigational new drug (IND) applications, where inclusion of NAMs data is encouraged, as outlined in road map guidance document. In the future, computational approaches have the potential to catapult us into the realm of customized treatment, where individual differences are methodically examined leading to transformed drug development. Virtual screening and computer-based trials are emerging as ways to speed up drug development while reducing expenses. is critical to balance innovation with ethical data handling. In essence, the future of drug design is being charted by the dynamic interplay of computational prowess and biological insight, heralding a new era of targeted, efficient, and personalized therapeutics.
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