The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

In breastfeeding mothers, managing medical conditions presents unique challenges, particularly concerning medication use and breastfeeding practices. The transfer of drugs into breast milk and subsequent exposure to nursing infants raises important considerations for drug safety and efficacy. Modeling approaches are increasingly employed to predict infant exposure levels, crucial for assessing drug safety during breastfeeding. Physiologically‐based pharmacokinetic (PBPK) modeling provides a valuable tool for predicting drug exposure in lactating individuals and their infants. This tutorial offers an overview of PBPK modeling in lactation research, covering key concepts, prediction approaches, and best practices for model development and application. We delve into milk composition dynamics and its influence on drug transfer into breast milk, addressing modeling considerations, knowledge gaps, and future research directions. Practical examples and case studies illustrate PBPK modeling application in lactation studies. We demonstrate how prediction algorithms for Milk‐to‐Plasma (M/P) ratios within a PBPK framework can support scenarios lacking clinical lactation data or extend the utility of available lactation clinical data to support further untested clinical scenarios. This tutorial aims to assist researchers and clinicians in understanding and applying PBPK modeling to understand and support clinical scenarios in breastfeeding mothers. Advances in PBPK modeling techniques, along with ongoing research on lactation physiology and drug disposition, promise further insights into drug transfer during lactation.

The antiretroviral drug efavirenz remains widely used in children and mothers during breastfeeding in tuberculosis‐endemic areas. Evaluating the safety of efavirenz during breastfeeding requires an understanding of its pharmacokinetics (PKs) in breast milk, its exposure in the breastfed infant, and the potential influence of polymorphisms in drug disposition genes. The interplay of these factors between the mother and the nursing infant is a complex scenario that can be readily investigated using physiologically‐based PK (PBPK) modeling. A verified PBPK model for efavirenz describing the CYP3A4‐ and CYP2B6‐mediated auto‐induction during multiple dosing was reported previously and was applied in this study to predict the exposure of efavirenz in vulnerable populations, including children (down to the age of 3 months), mothers, and breastfeeding infants, accounting for the various CYP2B6 genotypes. Predicted pharmacokinetic parameters for mothers, breastfeeding infants, and children aged ≥ 3 months were reasonably consistent with observed data, irrespective of CYP2B6 genotype. The clinically significant trend toward higher infant efavirenz exposure from GG/GG to TT/TT composite maternal/infant CYP2B6 genotypes was captured reasonably well by the PBPK model. Thereafter, simulations were performed to determine the adequacy of the current World Health Organization (WHO; ≥ 3 years) and the US Food and Drug Administration (FDA; ≥ 3 months) weight‐based dosing regimens for efavirenz in children according to CYP2B6 genotype. The findings of this study indicate that PBPK models can be used in designing studies in vulnerable populations and providing guidance on optimal doses based on developmental physiology and pharmacogenetics.

Rifampicin induces both P‐glycoprotein (P‐gp) and cytochrome P450 3A4 (CYP3A4) through regulating common nuclear receptors (e.g., pregnane X receptor). The interplay of P‐gp and CYP3A4 has emerged to be an important factor in clinical drug–drug interactions (DDIs) with P‐gp–CYP3A4 dual substrates and requires qualitative and quantitative understanding. Although physiologically based pharmacokinetic (PBPK) modeling has become a widely accepted approach to assess DDIs and is able to reasonably predict DDIs caused by CYP3A4 induction and P‐gp induction individually, the predictability of PBPK models for the effect of simultaneous P‐gp and CYP3A4 induction on P‐gp‐CYP3A4 dual substrates remains to be systematically evaluated. In this study, we used a PBPK modeling approach for the assessment of DDIs between rifampicin and 12 drugs: three sensitive P‐gp substrates, seven P‐gp–CYP3A4 dual substrates, and two P‐gp–CYP3A4 dual substrates and inhibitors. A 3.5‐fold increase of intestinal P‐gp abundance was incorporated in the PBPK models to account for rifampicin‐mediated P‐gp induction at steady state. The simulation results showed that accounting for P‐gp induction in addition to CYP3A4 induction improved the prediction accuracy of the area under the concentration‐time curve and maximum (peak) plasma drug concentration ratios compared with considering CYP3A4 induction alone. Furthermore, the interplay of relevant drug‐specific parameters and its impact on the magnitude of DDIs were evaluated using sensitivity analysis. The PBPK approach described herein, in conjunction with robust in vitro and clinical data, can help in the prospective assessment of DDIs involving other P‐gp and CYP3A4 dual substrates. The database reported in the present study provides a valuable aid in understanding the combined effect of P‐gp and CYP3A4 induction during drug development.

Tuberculosis (TB) remains a global health problem and there is an ongoing effort to develop more effective therapies and new combination regimes that can reduce duration of treatment. The purpose of this study was to demonstrate utility of a physiologically-based pharmacokinetic modeling approach to predict plasma and lung concentrations of 11 compounds used or under development as TB therapies (bedaquiline [and N-desmethyl bedaquiline], clofazimine, cycloserine, ethambutol, ethionamide, isoniazid, kanamycin, linezolid, pyrazinamide, rifampicin, and rifapentine). Model accuracy was assessed by comparison of simulated plasma pharmacokinetic parameters with healthy volunteer data for compounds administered alone or in combination. Eighty-four percent (area under the curve [AUC]) and 91% (maximum concentration [C_max]) of simulated mean values were within 1.5-fold of the observed data and the simulated drug-drug interaction ratios were within 1.5-fold (AUC) and twofold (C_max) of the observed data for nine (AUC) and eight (C_max) of the 10 cases. Following satisfactory recovery of plasma concentrations in healthy volunteers, model accuracy was assessed further (where patients’ with TB data were available) by comparing clinical data with simulated lung concentrations (9 compounds) and simulated lung: plasma concentration ratios (7 compounds). The 5th–95th percentiles for the simulated lung concentration data recovered between 13% (isoniazid and pyrazinamide) and 88% (pyrazinamide) of the observed data points (Am J Respir Crit Care Med, 198, 2018, 1208; Nat Med, 21, 2015, 1223; PLoS Med, 16, 2019, e1002773). The impact of uncertain model parameters, such as the fraction of drug unbound in lung tissue mass (fu_mass), is discussed. Additionally, the variability associated with the patient lung concentration data, which was sparse and included extensive within-subject, interlaboratory, and experimental variability (as well interindividual variability) is reviewed. All presented models are transparently documented and are available as open-source to aid further research.