Background: Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic tool used to predict how a drug moves through the body by incorporating real human physiology, including organ sizes, blood flows, tissue compositions, and enzyme activities. It has been widely employed to estimate drug exposure in different populations with organ impairment, genotype variabilities, and physiological variations. Metoclopramide is an antiemetic and prokinetic agent that is subject to CYP2D6 polymorphism. The study aims to develop PBPK models for several CYP2D6 variants to predict changes in the pharmacokinetic (PK) behavior of metoclopramide. Methods: To conduct this study, a literature review was conducted, and the retrieved physicochemical, biochemical, and PK data were integrated into PK-Sim to develop a PBPK model. Initially, a non-genotype-specific model was developed and extrapolated to genotype-based models. The models were verified using a Visual Predicted Check (VPC), mean predicted-to-observed ratio (R_pre/obs) values, and mean relative deviation (MRD). Results: The simulated profiles were aligned with the reported data, and all the predicted and observed PK parameters were comparable, as the R_pre/obs values were within the 0.5–2 range and MRD values were <2. Moreover, an increasing trend in AUC_0–∞ was observed across CYP2D6*wt/*wt, CYP2D6*wt/*10, CYP2D6*10/*10, and CYP2D6*5/*10, with approximately 1.63-, 2.64-, and 2.88-fold increases compared with the CYP2D6*wt/*wt genotype. Conclusions: The models have adequately estimated the PK behavior of metoclopramide across different CYP2D6 variants. These models might be helpful for populations with diverse CYP2D6 genotypes in dose optimization.

Background: Physiologically based pharmacokinetic (PBPK) modeling is an established approach used in recent years for estimating drug disposition in challenging clinical scenarios where in vivo studies are difficult to perform. Metoclopramide is a benzamide derivative, widely indicated for its antiemetic and prokinetic actions. This study aims to develop a PBPK model for metoclopramide in infants to predict its systemic exposure in this population. Methods: To develop the model, a detailed literature review was conducted, and the required data related to the drug, human physiology, and published clinical studies were retrieved. After that, all information was integrated into the PK-Sim software, and the model was initially developed in adults to create a base; subsequently, it was extrapolated to infants. After successful development, these models were verified visually and numerically using visual predictive checks (VPCs), mean predicted-to-observed ratios (R_pre/obs), average fold error (AFE), and mean relative deviation (MRD). Results: All simulated profiles were consistent with observed data; computed AFE and R_pre/obs for key pharmacokinetic (PK) parameters, including area under the concentration–time curve from 0 to t (AUC_0–t), maximum plasma concentration (C_max), and clearance (CL), fell within an acceptable two-fold error range. Moreover, the MRD values for all profiles were <2, showing promising agreement between the reported and predicted datasets. Conclusions: The current model provides a robust framework to estimate the PK of metoclopramide in infants, which may help with dose individualization in this vulnerable population.