02615nas a2200301   4500000000100000000000100001008004100002260001500043653002500058653002400083653002400107653002700131653002300158653002200181653002700203100001700230700001200247700002300259700002000282700002200302700001800324700001900342245011900361856009500480490000700575520171700582022001402299       2025                            d  c2025-09-2410aAI in drug discovery10aanimal alternatives10aBiomedical Research10acomputational modeling10aregulatory science10asimulation models10atranslational research1 aRahul Mittal1 aAlan Ho1 aHarini Adivikolanu1 aMuskaan Sawhney1 aJoana R. N. Lemos1 aMannat Mittal1 aKhemraj Hirani00aExploring the potential of computer simulation models in drug testing and biomedical research: a systematic review  uhttps://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1644907/full0 v163 aIntroductionThe growing limitations of animal models in drug testing and biomedical research, including ethical concerns, high costs, and poor translational relevance to human biology, have driven increasing interest in computational simulation models. These models encompass in silico approaches, pharmacokinetic/pharmacodynamic frameworks, molecular simulations, and organ-on-chip technologies, offering greater precision in replicating human physiological and pathological processes.MethodsA systematic review was conducted to examine the role of computational simulation models as alternatives to traditional animal-based research. Relevant literature on their applications, predictive accuracy, translational value, and alignment with ethical research practices was analyzed.ResultsComputational models were found to bridge critical gaps in predictive accuracy and translational relevance, supporting drug development pipelines, reducing late-stage failures, and enhancing opportunities for personalized medicine. Additionally, their capacity to reduce reliance on animal models aligns with global ethical initiatives promoting humane and sustainable research practices.DiscussionSimulation-based approaches represent a transformative opportunity for biomedical research. While their potential to reshape drug development and improve health outcomes is evident, challenges such as standardization, scalability, and regulatory integration remain. Addressing these barriers will be essential to fully realize the potential of computational simulation models in replacing or reducing animal testing and advancing human-centered biomedical innovation.Systematic Review Registrationidentifier, INPLASY2024110028.  a1663-9812