Exploring the potential of computer simulation models in drug testing and biomedical research: a systematic review

Abstract

Introduction

The 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.

Methods

A 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.

Results

Computational 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.

Discussion

Simulation-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 Registration

identifier, INPLASY2024110028.