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There is a risk of exposure to drugs in neonates during the lactation period due to maternal drug intake. The ability to predict drugs of potential hazards to the neonates would be useful in a clinical setting. This work aimed to evaluate the possibility of integrating milk‐to‐plasma (M/P) ratio predictive algorithms within the physiologically‐based pharmacokinetic (PBPK) approach and to predict milk exposure for compounds with different physicochemical properties. Drug and physiological milk properties were integrated to develop a lactation PBPK model that takes into account the drug ionization, partitioning between the maternal plasma and milk matrices, and drug partitioning between the milk constituents. Infant dose calculations that take into account maternal and milk physiological variability were incorporated in the model. Predicted M/P ratio for acetaminophen, alprazolam, caffeine, and digoxin were 0.83 ± 0.01, 0.45 ± 0.05, 0.70 ± 0.04, and 0.76 ± 0.02, respectively. These ratios were within 1.26‐fold of the observed ratios. Assuming a daily milk intake of 150 ml, the predicted relative infant dose (%) for these compounds were 4.0, 6.7, 9.9, and 86, respectively, which correspond to a daily ingestion of 2.0 ± 0.5 mg, 3.7 ± 1.2 µg, 2.1 ± 1.0 mg, and 32 ± 4.0 µg by an infant of 5 kg bodyweight. Integration of the lactation model within the PBPK approach will facilitate and extend the application of PBPK models during drug development in high‐throughput screening and in different clinical settings. The model can also be used in designing lactation trials and in the risk assessment of both environmental chemicals and maternally administered drugs.

Concerns over maternal and fetal drug exposures highlights the need for a better understanding of drug distribution into the fetus through the placental barrier. This study aimed to predict maternal and fetal drug disposition using physiologically based pharmacokinetic (PBPK) modelling. The detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 was used to predict the maternal and fetoplacental exposure of cefazolin, cefuroxime, and amoxicillin during pregnancy and at delivery. The mechanistic dynamic model includes physiological changes of the maternal, fetal, and placental parameters over the course of pregnancy. Placental kinetics were parametrized using permeability parameters determined from the physicochemical properties of these compounds. Then, the PBPK predictions were compared to the observed data.Fully bottom-up feto-placental PBPK models were developed for cefuroxime, cefazolin, and amoxicillin without any parameter fitting. Predictions in non-pregnant and in pregnant subjects fall within 2-fold of the observed values. Predictions matched observed PK data reported in 9 maternal (5 fetoplacental) studies for cefuroxime, 10 maternal (5 fetoplacental) studies for cefazolin, and 6 maternal (2 fetoplacental) studies for amoxicillin.Integration of the fetal and maternal system parameters within PBPK models, together with compound-related parameters used to calculate placental permeability facilitates and extends the applications of the maternal-placental-fetal PBPK model. The developed model can also be used for designing clinical trials and prospectively use for maternal/fetal risk assessment following maternally administered drugs or unintended exposure to environmental toxicants.

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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.

Ritonavir is a well-known CYP3A4 and CYP2D6 enzyme inhibitor, frequently used to assess the drug–drug interaction (DDI) liability of susceptible drugs. It is also used as a pharmacokinetic booster to increase exposure to CYP3A4 substrates. This study aimed to develop a mechanistic absorption and disposition model to describe exposure to ritonavir following oral dosing of the commercial amorphous solid dispersion tablet, Norvir, under fasted and fed conditions. A mechanistic description of ritonavir absorption from Norvir tablets may help to improve the design of DDI studies. Key parameters of amorphous ritonavir including free base solubility (solubility of the unbound, un-ionized species), bile micelle partition coefficients, formulation wetting/disintegration, and in vivo precipitation parameters were either obtained from the literature or estimated by modeling in vitro biopharmaceutic experiments. Based on variety of in vitro evidence, a main assumption of the model is that ritonavir does not form a crystalline precipitate while resident in the gastrointestinal tract. In the model, if simulated luminal concentration exceeds the amorphous solubility limit, then precipitation to an amorphous form is immediate. Simulated and observed C max and AUC0‑t parameters were well captured (within 1.5-fold) for both fasted and fed states in healthy volunteers. By accounting for luminal fluid viscosity differences in the different prandial states (affecting drug diffusivity) as well as the effect of drug free fraction on gut wall permeation rates, it was possible to explain the negative food effect observed for Norvir tablets in humans. In summary, a biopharmaceutic in vitro in vivo extrapolation approach provides confidence in (verification of) key input parameters of the physiologically-based pharmacokinetic ritonavir model which resulted in successful simulation of observed plasma profiles.

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