Discussions

Biosimulation refers to computer-based mathematical modeling and simulation techniques that predict how biological systems, drugs, and diseases behave under various conditions. This powerful computational approach uses mathematical equations representing physiological processes, drug pharmacokinetics, pharmacodynamics, and disease progression to create virtual representations of biological phenomena. By integrating diverse data from molecular, cellular, organ, and whole-body levels, biosimulation enables researchers to conduct virtual experiments, test hypotheses, and make predictions that would be impractical, unethical, or impossible to perform in living subjects. The technology has become indispensable in modern pharmaceutical research and development.

Biosimulation applications span the drug development pipeline. During drug discovery, molecular modeling predicts how candidate compounds interact with biological targets, guiding optimization before synthesis. Pharmacokinetic/pharmacodynamic (PK/PD) modeling predicts drug absorption, distribution, metabolism, and elimination, along with concentration-effect relationships, informing dose selection and regimen design. Physiologically-based pharmacokinetic (PBPK) models incorporate anatomical and physiological parameters to predict drug behavior across diverse populations, including pediatric, geriatric, pregnant, and disease-specific groups. Clinical trial simulation optimizes study designs, sample sizes, and endpoint selection, potentially reducing trial failures and accelerating development timelines. Virtual populations enable exploration of drug responses across genetic variations and disease severities.

Benefits include reduced reliance on animal testing, decreased clinical trial costs and durations, improved dose selection accuracy, enhanced understanding of drug mechanisms, and better prediction of drug-drug interactions and safety concerns. Regulatory agencies increasingly accept biosimulation evidence supporting drug applications. Systems pharmacology approaches integrate multiple biological scales, from genes to organs, providing holistic understanding of drug effects. Challenges include model validation, parameter uncertainty, computational complexity, and biological variability. As computational power increases and biological knowledge expands, biosimulation continues evolving toward more predictive, mechanistic models supporting precision medicine and accelerating therapeutic innovation.