In Vitro-In Vivo Correlation Using In Silico Modeling of Physiological Properties, Metabolites, and Intestinal Metabolism

Recently, pharmaceutical research has focused on in vitro-in vivo correlation as a novel challenge, and in silico modeling has been an important component. As in silico models are highly representative of practical use, regulatory agencies such as the US Food and Drug Administration and European Medicines Agency have recognized and utilized in silico modeling as a useful tool; this allows pharmaceutical organizations to use Physiologically Based Pharmacokinetic (PBPK) models for decision-making, which may aid the financial efficiency of a clinical trial. However, some studies have shown differences of up to approximately 40% in pharmacokinetic parameters such as area under the curve or maximum serum concentration between observed and simulated data.Gastroplus™ was used to demonstrate current PBPK simulation. 46 research papers were compared with each other's applications of PBPK simulation.To improve the accuracy of simulation, additional factors may need to be considered, such as precise volume of gastrointestinal sections, specific metabolism of the target drug, and physicochemical data of drug metabolites. Furthermore, the results of these simulations would be extremely valuable to the relevant applications. Simulation programs using Advanced Compartmental Absorption and Transit (ACAT)/PBPK modeling could be a powerful tool for companies performing pre-clinical experiments, and could provide a solution for the ethical issues and economic constraints of clinical trials.If in silico modeling produced more precise results that could closely match clinical data, it could be more readily used to screen drug pharmacodynamics in bodily systems, and the efficiency of clinical trials would be improved. However, simulation programs are currently limited in their accuracy of pharmacodynamic predictions. In developing new drugs, pharmaceutical companies should address this issue in order to improve in silico/PBPK modeling in the future.