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Concerns over gestational effects on the disposition of drugs has highlighted the need for a better understanding of drug distribution and elimination during pregnancy. This study aimed at predicting maternal drug kinetics using a physiologically based pharmacokinetic (PBPK) modelling approach focusing on the observed gestational changes in three important Cytochrome P450 metabolizing enzymes, namely, CYP1A2, CYP2D6 and CYP3A4 at different gestational weeks (GWs). The Pregnancy PBPK model within the Simcyp Simulator V19 was used to predict the pharmacokinetics of sensitive probes to these enzymes; namely caffeine, theophylline, metoprolol, propranolol, paroxetine, midazolam, nifedipine and rilpivirine. PBPK model predictions were compared against clinical data collated from multiple studies for each compound to cover a wide spectrum of gestational ages. Pregnancy PBPK model predictions were within 2-fold error and indicated that CYP1A2 activity is approximately 0.70, 0.44 and 0.30 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. On the other hand, CYP2D6 activity increases by 1.36, 2.16 and 3.10 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. Likewise, CYP3A4 activity increases by 1.25, 1.75 and 2.32 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. The enzymes activity have been qualified throughout pregnancy. Quantified changes in drug dosing are most relevant during the third trimester, especially for drugs that are mainly eliminated by CYP1A2, CYP2D6 and CYP3A4 enzymes. The provided functions describing the continuous changes to the activity of these enzymes during pregnancy are important when modelling long term pharmacokinetic studies where longitudinal modelling or time-varying covariates are used.

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.

Background Developmental physiology can alter pharmacotherapy in preterm populations. Because of ethical and clinical constraints in studying this vulnerable age group, physiologically based pharmacokinetic models offer a viable alternative approach to predicting drug pharmacokinetics and pharmacodynamics in this population. However, such models require comprehensive information on the changes of anatomical, physiological and biochemical variables, where such data are not available in a single source. Objective The objective of this study was to integrate the relevant physiological parameters required to build a physiologically based pharmacokinetic model for the preterm population. Methods Published information on developmental preterm physiology and some drug-metabolising enzymes were collated and analysed. Equations were generated to describe the changes in parameter values during growth. Results Data on organ size show different growth patterns that were quantified as functions of bodyweight to retain physiological variability and correlation. Protein binding data were quantified as functions of age as the body weight was not reported in the original articles. Ontogeny functions were derived for cytochrome P450 1A2, 3A4 and 2C9. Tissue composition values and how they change with age are limited. Conclusions Despite the limitations identified in the availability of some tissue composition values, the data presented in this article provide an integrated resource of system parameters needed for building a preterm physiologically based pharmacokinetic model.

Background Preterm neonates are usually not part of a traditional drug development programme, however they are frequently administered medicines. Developing modelling and simulation tools, such as physiologically based pharmacokinetic (PBPK) models that incorporate developmental physiology and maturation of drug metabolism, can be used to predict drug exposure in this group of patients, and may help to optimize drug dose adjustment. Objective The aim of this study was to assess and verify the predictability of a preterm PBPK model using compounds that undergo diverse renal and/or hepatic clearance based on the knowledge of their disposition in adults. Methods A PBPK model was developed in the Simcyp Simulator V17 to predict the pharmacokinetics (PK) of drugs in preterm neonates. Drug parameters for alfentanil, midazolam, caffeine, ibuprofen, gentamicin and vancomycin were collated from the literature. Predicted PK parameters and profiles were compared against the observed data. Results The preterm PBPK model predicted the PK changes of the six compounds using ontogeny functions for cytochrome P450 (CYP) 1A2, CYP2C9 and CYP3A4 after oral and intravenous administrations. For gentamicin and vancomycin, the maturation of renal function was able to predict the exposure of these two compounds after intravenous administration. All PK parameter predictions were within a twofold error criteria. Conclusion While the developed preterm model for the prediction of PK behaviour in preterm patients is not intended to replace clinical studies, it can potentially help with deciding on first-time dosing in this population and study design in the absence of clinical data.

Pharmacotherapy during lactation often lacks reliable drug safety data, resulting in delayed treatment or early cessation of breastfeeding. tools, such as physiologically-based pharmacokinetic (PBPK) models, can help to bridge this knowledge gap. To increase the accuracy of these models, it is essential to account for the physiological changes that occur throughout the postpartum period. This study aimed to collect and analyze data on the longitudinal changes in various physiological parameters that can affect drug distribution into breast milk during lactation. Following meta-analysis of the collated data, mathematical functions were fitted to the available data for each parameter. The best-performing functions were selected through numerical and visual diagnostics. The literature search identified 230 studies, yielding a dataset of 36,689 data points from 20,801 postpartum women, covering data from immediately after childbirth to 12 months postpartum. Sufficient data were obtained to describe postpartum changes in maternal plasma volume, breast volume, cardiac output, glomerular filtration rate, haematocrit, human serum albumin, alpha-1-acid glycoprotein, milk pH, milk volume, milk fat, milk protein, milk water content, and daily infant milk intake. Although data beyond 7 months postpartum were limited for some parameters, mathematical functions were generated for all parameters. These functions can be integrated into lactation PBPK models to increase their predictive power and better inform medication efficacy and safety for breastfeeding women.

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.

Over the last 3 decades, death rates and disability-adjusted life years due to suboptimal breastfeeding reduced by approximately 80%, reflecting its relevant health benefit (Zhu et al., 2025). In addition to these medicine specific observations, and somewhat merging the Research Topic on computational techniques with case series reporting, Monfort et al. reported on a “Milk4baby decision tree approach”, describing a workflow for pragmatic and contextualized method selection on how to assess safety of infant systemic medicine exposure through human milk in clinical trials or care (Monfort et al.). [...]based on the newly reported information on how to improve computational techniques, on DRPs in lactating women, and on drug-specific or generic approaches to assess safety of drug exposure during lactation contributes to this overarching aim to convert the ‘information desert’ into a more sustainable environment for all stakeholders involved. World Health Organization(2025b).WHO/UNICEF discussion paper: the extension of the 2025 maternal, infant and young child nutrition targets to 2030.Available online at:https://data.unicef.org/resources/who-unicef-discussion-paper-nutrition-targets/(Accessed July 21, 2025).

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.

Evaluating the safety of primaquine (PQ) during breastfeeding requires an understanding of its pharmacokinetics (PKs) in breast milk and its exposure in the breastfed infant. Physiologically‐based PK (PBPK) modeling is primed to assess the complex interplay of factors affecting the exposure of PQ in both the mother and the nursing infant. A published PBPK model for PQ describing the metabolism by monoamine oxidase A (MAO‐A; 90% contribution) and cytochrome P450 2D6 (CYP2D6; 10%) in adults was applied to predict the exposure of PQ in mothers and their breastfeeding infants. Plasma exposures following oral daily dosing of 0.5 mg/kg in the nursing mothers in a clinical lactation study were accurately captured, including the observed ranges. Reported infant daily doses based on milk data from the clinical study were used to predict the exposure of PQ in breastfeeding infants greater than or equal to 28 days. On average, the predicted exposures were less than or equal to 0.13% of the mothers. Furthermore, in simulations involving neonates less than 28 days, PQ exposures remain less than 0.16% of the mothers. Assuming that MAO‐A increases slowly with age, the predicted relative exposure of PQ remains low in neonates (<0.46%). Thus, the findings of our study support the recommendation made by the authors who reported the results of the clinical lactation study, that is, that when put into context of safety data currently available in children, PQ should not be withheld in lactating women as it is unlikely to cause adverse events in breastfeeding infants greater than or equal to 28 days old.

Optimal dosing in pregnant and lactating women requires an understanding of the pharmacokinetics in the mother, fetus, and breastfed infant. Physiologically‐based pharmacokinetic (PBPK) modeling can be used to simulate untested scenarios and hence supplement clinical data to support dosing decisions. A PBPK model for the antiretroviral dolutegravir (mainly metabolized by UGT1A1) was verified using reported exposures in non‐pregnant healthy volunteers, pregnant women, and the umbilical cord, lactating mothers, and breastfed neonates. The model was then applied to predict the impact of UGT1A1 phenotypes in extensive (EM), poor (PM), and ultra‐rapid metabolizers (UM). The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 PMs was 1.6‐fold higher than in EMs. The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 UMs mothers was 1.3‐fold lower than in EMs. The predicted mean systemic and umbilical vein concentrations were in excess of the dolutegravir IC90 at 17, 28, and 40 gestational weeks, regardless of UGT1A1 phenotype, indicating that the standard dose of dolutegravir (50 mg q.d., fed state) is generally appropriate in late pregnancy, across UGT1A1 phenotypes. Applying the model in breastfed infants, a 1.5‐, 1.7‐, and 2.2‐fold higher exposure in 2‐day‐old neonates, 10‐day‐old neonates, and infants who were UGT1A1 PMs, respectively, compared with EMs of the same age. However, it should be noted that the exposure in breastfed infants who were UGT1A1 PMs was still an order of magnitude lower than maternal exposure with a relative infant daily dose of <2%, suggesting safe use of dolutegravir in breastfeeding women.