Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model

Zuranolone Concentrations in the Breast Milk of Healthy, Lactating Individuals: Results From a Phase 1 Open-Label Study

PURPOSE/BACKGROUND

Zuranolone is a positive allosteric modulator of both synaptic and extrasynaptic γ-aminobutyric acid type A receptors and a neuroactive steroid approved as an oral, once-daily, 14-day treatment course for adults with postpartum depression in the United States. This study assessed zuranolone transfer into breast milk.

METHODS/PROCEDURES

Healthy, nonpregnant, lactating adult female participants received once-daily 30 mg zuranolone from day (D)1 through D5 in this phase 1 open-label study. The relative infant dose (RID; weight-adjusted proportion of the maternal dose in breast milk over 24 hours) for 30 mg zuranolone was assessed at D5. An RID for 50 mg zuranolone was estimated using a simulation approach across a range of infant ages and weights.

FINDINGS/RESULTS

Of 15 enrolled participants (mean age, 30.1 years), 14 completed the study. The mean RID for 30 mg zuranolone at D5 was 0.357%; the mean steady-state milk volume over D3 to D5 decreased from baseline by 8.3%. Overall unbound zuranolone in plasma was low (≤0.49%). Plasma concentrations peaked at D5 before decreasing in a biexponential manner. There was strong concordance between the temporal evolution of zuranolone concentrations in plasma and breast milk. The estimated mean RID for 50 mg zuranolone based on a milk intake of 200 mL/kg per day was 0.984%. All treatment-emergent adverse events reported by participants were mild, the most common being dizziness (n = 3).

IMPLICATIONS/CONCLUSIONS

Zuranolone transfer into the breast milk of healthy, nonpregnant, lactating adult female participants was low; the estimated RID for 50 mg zuranolone was <1%, well below the <10% threshold generally considered compatible with breastfeeding.

Transfer of the Dual Orexin Receptor Antagonist Daridorexant into Breast Milk of Healthy Lactating Women

The novel dual orexin receptor antagonist daridorexant was approved in 2022 for the treatment of adult patients with insomnia. The aim of this post-marketing study was to measure daridorexant and its major metabolites in breast milk and plasma of 10 healthy lactating subjects. This single-center, open-label study evaluated the transfer of the analytes into breast milk. A single dose of 50 mg was orally administered in the morning. Milk and blood samples were collected pre-dose and over a period of 72 h after dosing. The pharmacokinetics of daridorexant in milk and plasma were assessed including the cumulative amount and fraction of dose excreted, daily infant dose, and relative infant dose. Safety and tolerability were also investigated. All subjects completed the study. Daridorexant was rapidly absorbed into and distributed from plasma. Daridorexant and its major metabolites were measurable in breast milk. The cumulative total amount of daridorexant excreted over 72 h was 0.010 mg, which corresponds to 0.02% of the maternal dose. This corresponds to a mean daily infant dose of 0.009 mg/day and a relative infant dose of less than 0.22% over 24 h. The maternal safety profile was similar to that observed in previous studies. Low amounts of daridorexant and its metabolites were detected in the breast milk of healthy lactating women. Since the exposure and potential effects on the breastfed infant are unknown, a risk of somnolence or other depressant effects cannot be excluded.

Determining the exposure of maternal medicines through breastfeeding: the UmbrelLACT study protocol-a contribution from the ConcePTION project

INTRODUCTION

Breastfeeding is beneficial for the health of the mother and child. However, at least 50% of postpartum women need pharmacotherapy, and this number is rising due to the increasing prevalence of chronic diseases and pregnancies at a later age. Making informed decisions on medicine use while breastfeeding is often challenging, considering the extensive information gap on medicine exposure and safety during lactation. This can result in the unnecessary cessation of breastfeeding, the avoidance of pharmacotherapy or the off-label use of medicines. The UmbrelLACT study aims to collect data on human milk transfer of maternal medicines, child exposure and general health outcomes. Additionally, the predictive performance of lactation and paediatric physiologically based pharmacokinetic (PBPK) models, a promising tool to predict medicine exposure in special populations, will be evaluated.

METHODS AND ANALYSIS

Each year, we expect to recruit 5-15 breastfeeding mothers using pharmacotherapy via the University Hospitals Leuven, the BELpREG project (pregnancy registry in Belgium) or external health facilities. Each request and compound will be evaluated on relevance (ie, added value to available scientific evidence) and feasibility (including access to analytical assays). Participants will be requested to complete at least one questionnaire on maternal and child's general health and collect human milk samples over 24 hours. Optionally, two maternal and one child's blood samples can be collected. The maternal medicine concentration in human milk will be determined along with the estimation of the medicine intake (eg, daily infant dose and relative infant dose) and systemic exposure of the breastfed child. The predictive performance of PBPK models will be assessed by comparing the observed concentrations in human milk and plasma to the PBPK predictions.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee Research UZ/KU Leuven (internal study number S67204). Results will be published in peer-reviewed journals and presented at (inter)national scientific meetings.

TRIAL REGISTRATION NUMBER

NCT06042803.

Characterization of the US Food and Drug Administration Post-Marketing Commitments and Requirements for Pregnancy and Lactation

Enactment of the US Food and Drug Administration Amendments Act (FDAAA) in 2007 and the Pregnancy and Lactation Labeling Rule (PLLR) in 2015 are important milestones giving the FDA the authority to request studies in pregnant and lactating women. Our objective was to characterize trends of pregnancy and lactation-related postmarketing commitments (PMCs) and postmarketing requirements (PMRs) for new molecular entities approved by the FDA between 2000 and 2022. Approval letters of original New Drug Applications (NDAs, N = 488) for new molecular entities were obtained from the FDA website. NDAs with pregnancy and lactation-based PMCs/PMRs were identified, and data extracted. Data included: PMC/PMR timelines and attributes of requested study(ies) (type, design elements, and outcomes) when available. Fifty-nine NDAs included 92 PMCs/PMRs related to pregnancy and lactation. Forty-one NDAs had pregnancy-related PMRs/PMCs, 4 had lactation-related PMRs, and 14 had both. Most PMRs/PMCs were for nervous system medications (N = 33). Forty-seven NDAs specified safety data collection in infants in at least the first year of life. All pregnancy-related PMRs were issued after 2008, most PMCs (N = 8) were issued before 2008. Only one PMC requested a pharmacokinetic study in pregnant women. All lactation-related PMRs (N = 18) requested measurement of drug concentrations in breast milk with one also requiring measurement of maternal blood concentrations. Eighty-nine percent of lactation-related PMRs were requested after 2015. There was a steady increase in pregnancy and lactation-related PMRs following enactment of FDAAA and PLLR. Additions involved information collection pertaining to safety of the medication in pregnant and lactating women and children exposed to medications during pregnancy and breastfeeding.

Challenges Related to Acquisition of Physiological Data for Physiologically Based Pharmacokinetic (PBPK) Models in Postpartum, Lactating Women and Breastfed Infants-A Contribution from the ConcePTION Project

Physiologically based pharmacokinetic (PBPK) modelling is a bottom-up approach to predict pharmacokinetics in specific populations based on population-specific and medicine-specific data. Using an illustrative approach, this review aims to highlight the challenges of incorporating physiological data to develop postpartum, lactating women and breastfed infant PBPK models. For instance, most women retain pregnancy weight during the postpartum period, especially after excessive gestational weight gain, while breastfeeding might be associated with lower postpartum weight retention and long-term weight control. Based on a structured search, an equation for human milk intake reported the maximum intake of 153 mL/kg/day in exclusively breastfed infants at 20 days, which correlates with a high risk for medicine reactions at 2-4 weeks in breastfed infants. Furthermore, the changing composition of human milk and its enzymatic activities could affect pharmacokinetics in breastfed infants. Growth in breastfed infants is slower and gastric emptying faster than in formula-fed infants, while a slower maturation of specific metabolizing enzymes in breastfed infants has been described. The currently available PBPK models for these populations lack structured systematic acquisition of population-specific data. Future directions include systematic searches to fully identify physiological data. Following data integration as mathematical equations, this holds the promise to improve postpartum, lactation and infant PBPK models.

Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach

A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration-time curve (AUC(0-24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0-24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.

A systematic review on maternal-to-infant transfer of drugs through breast milk during the treatment of malaria, tuberculosis, and neglected tropical diseases

BACKGROUND

Exclusive breastfeeding of infants under 6 months of age is recommended by the World Health Organization. In 2021, over 300 million combined incident cases of malaria, tuberculosis, and neglected tropical diseases (NTDs) were reported, predominantly in low-income countries. For many of the drugs used as first-line treatments for these conditions, there is limited knowledge on infant exposure through breastfeeding with poorly understood consequences. This review summarized available knowledge on mother-to-infant transfer of these drugs to inform future lactation pharmacokinetic studies.

METHODOLOGY

A list of first-line drugs was generated from the latest WHO treatment guidelines. Using standard online databases, 2 independent reviewers searched for eligible articles reporting lactation pharmacokinetics studies and extracted information on study design, participant characteristics, and the mathematical approach used for parameter estimation. A third reviewer settled any disagreements between the 2 reviewers. All studies were scored against the standardized "ClinPK" checklist for conformity to best practices for reporting clinical pharmacokinetic studies. Simple proportions were used to summarize different study characteristics.

FINDINGS

The most remarkable finding was the scarcity of lactation pharmacokinetic data. Only 15 of the 69 drugs we listed had lactation pharmacokinetics fully characterized. Most studies enrolled few mothers, and only one evaluated infant drug concentrations. Up to 66% of the studies used non-compartmental analysis to estimate pharmacokinetic parameters rather than model-based compartmental analysis. Unlike non-compartmental approaches, model-based compartmental analysis provides for dynamic characterization of individual plasma and breast milk concentration-time profiles and adequately characterizes variability within and between individuals, using sparsely sampled data. The "ClinPK" checklist inadequately appraised the studies with variability in the number of relevant criteria across different studies.

CONCLUSIONS/SIGNIFICANCE

A consensus is required on best practices for conducting and reporting lactation pharmacokinetic studies, especially in neglected diseases such as malaria, tuberculosis, and NTDs, to optimize treatment of mother-infant pairs.

Safety of medicines during breastfeeding - from case report to modeling : A contribution from the ConcePTION project

INTRODUCTION

Despite many research efforts, current data on the safety of medicines during breastfeeding are either fragmented or lacking, resulting in restrictive labeling of most medicines. In the absence of pharmacoepidemiologic safety studies, risk estimation for breastfed infants is mainly derived from pharmacokinetic (PK) information on the medicine. This manuscript provides a description and a comparison of the different methodological approaches that can yield reliable information on medicine transfer into human milk and the resulting infant exposure.

AREA COVERED

Currently, most information on medicine transfer in human milk relies on case reports or traditional PK studies, which generate data that can hardly be generalized to the population. Some methodological approaches, such as population PK (popPK) and physiologically-based PK (PBPK) modeling, can be used to provide a more complete characterization of infant medicine exposure through human milk and simulate the most extreme situations, while decreasing the burden of sampling in breastfeeding women.

EXPERT OPINION

PBPK and popPK modeling are promising approaches to fill the gap of knowledge in medicine safety in breastfeeding, as illustrated with our escitalopram example.

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol

Characterization of infant drug exposure through human milk is important and underexplored. Because infant plasma concentrations are not frequently collected in clinical lactation studies, modeling and simulation approaches can integrate physiology, available milk concentrations, and pediatric data to inform exposure in breastfeeding infants. A physiologically based pharmacokinetic model was built for sotalol, a renally eliminated drug, to simulate infant drug exposure from human milk. Intravenous and oral adult models were built, optimized, and scaled to an oral pediatric model for a breastfeeding-relevant age group (<2 years). Model simulations captured the data that were put aside for verification. The resulting pediatric model was applied to predict the impacts of sex, infant body size, breastfeeding frequency, age, and maternal dose (240 and 433 mg) on drug exposure during breastfeeding. Simulations suggest a minimal effect of sex or frequency on total sotalol exposure. Infants in the 90th percentile in height and weight have predicted exposures ≈20% higher than infants of the same age in the 10th percentile due to increased milk intake. The simulated infant exposures increase throughout the first 2 weeks of life and are maintained at the highest concentrations in weeks 2-4, with a consistent decrease observed as infants age. Simulations suggest that breastfeeding infants will have plasma concentrations in the lower range observed in infants administered sotalol. With further validation on additional drugs, physiologically based pharmacokinetic modeling approaches could use lactation data to a greater extent and provide comprehensive information to support decisions regarding medication use during breastfeeding.

Generic Workflow to Predict Medicine Concentrations in Human Milk Using Physiologically-Based Pharmacokinetic (PBPK) Modelling-A Contribution from the ConcePTION Project

Women commonly take medication during lactation. Currently, there is little information about the exposure-related safety of maternal medicines for breastfed infants. The aim was to explore the performance of a generic physiologically-based pharmacokinetic (PBPK) model to predict concentrations in human milk for ten physiochemically diverse medicines. First, PBPK models were developed for "non-lactating" adult individuals in PK-Sim/MoBi v9.1 (Open Systems Pharmacology). The PBPK models predicted the area-under-the-curve (AUC) and maximum concentrations (C_max) in plasma within a two-fold error. Next, the PBPK models were extended to include lactation physiology. Plasma and human milk concentrations were simulated for a three-months postpartum population, and the corresponding AUC-based milk-to-plasma (M/P) ratios and relative infant doses were calculated. The lactation PBPK models resulted in reasonable predictions for eight medicines, while an overprediction of human milk concentrations and M/P ratios (>2-fold) was observed for two medicines. From a safety perspective, none of the models resulted in underpredictions of observed human milk concentrations. The present effort resulted in a generic workflow to predict medicine concentrations in human milk. This generic PBPK model represents an important step towards an evidence-based safety assessment of maternal medication during lactation, applicable in an early drug development stage.

Transfer of antibiotics and their metabolites in human milk: Implications for infant health and microbiota

Antibiotics are an essential tool for perinatal care. While antibiotics can play a life-saving role for both parents and infants, they also cause collateral damage to the beneficial bacteria that make up the host gut microbiota. This is especially true for infants, whose developing gut microbiota is uniquely sensitive to antibiotic perturbation. Emerging evidence suggests that disruption of these bacterial populations during this crucial developmental window can have long-term effects on infant health and development. Although most current studies have focused on microbial disruptions caused by direct antibiotic administration to infants or prenatal exposure to antibiotics administered to the mother, little is known about whether antibiotics in human milk may pose similar risks to the infant. This review surveys current data on antibiotic transfer during lactation and highlights new methodologies to assess drug transfer in human milk. Finally, we provide recommendations for future work to ensure antibiotic use in lactating parents is safe and effective for both parents and infants.

Infant Exposure to Antituberculosis Drugs via Breast Milk and Assessment of Potential Adverse Effects in Breastfed Infants: Critical Review of Data

Infants of mothers treated for tuberculosis might be exposed to drugs via breast milk. The existing information on the exposure of breastfed infants lacks a critical review of the published data. We aimed to evaluate the quality of the existing data on antituberculosis (anti-TB) drug concentrations in the plasma and milk as a methodologically sound basis for the potential risk of breastfeeding under therapy. We performed a systematic search in PubMed for bedaquiline, clofazimine, cycloserine/terizidone, levofloxacin, linezolid, pretomanid/pa824, pyrazinamide, streptomycin, ethambutol, rifampicin and isoniazid, supplemented with update references found in LactMed^®. We calculated the external infant exposure (EID) for each drug and compared it with the recommended WHO dose for infants (relative external infant dose) and assessed their potential to elicit adverse effects in the breastfed infant. Breast milk concentration data were mainly not satisfactory to properly estimate the EID. Most of the studies suffer from limitations in the sample collection, quantity, timing and study design. Infant plasma concentrations are extremely scarce and very little data exist documenting the clinical outcome in exposed infants. Concerns for potential adverse effects in breastfed infants could be ruled out for bedaquiline, cycloserine/terizidone, linezolid and pyrazinamide. Adequate studies should be performed covering the scenario in treated mothers, breast milk and infants.

Systematic review of physiologically based kinetic lactation models for transfer of xenobiotic compounds to milk

Lactational elimination has been described mathematically for nearly 50 years. Over 40 published articles, containing >50 physiologically based kinetic (PBK) lactation models were included in the systematic review. These PBK models described the lactational elimination of xenobiotic compounds in humans, rats, mice, and dairy cows and goats. A total of 78 compounds have been modelled, ranging from industrial chemicals, pesticides, to pain medication, antibiotics, and caffeine. Few models included several species or compounds, and models were thus generally not translational or generic. Three dairy cow models mechanistically described the intramammary disposition of pharmaceuticals after intramammary administration, including volume changes caused by milking, while empirically describing the remaining pharmacokinetics. The remaining models were semi- or whole body PBK models, describing long-term exposure of environmental pollutants, or short-term exposure of pharmaceuticals. The absolute majority described the disposition to the mammary gland or milk with perfusion limited compartments, but permeability limited models were available as well. With long-term exposure, models often included changes in milk volume and/or consumption by the offspring, and changes in body weight of offspring. Periodic emptying of the mammary gland, as with feeding or milking, was sparsely applied. Rodent models used similar physiological parameters, while values of physiological parameters applied in human models could range widely. When milk composition was included in the models, it most often included the fat content. The review gives an extensive overview of the applied functions and modelling strategies of PBK lactation models.

Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR

PURPOSE

Information on milk transferability of drugs is important for patients who wish to breastfeed. The purpose of this study is to develop a prediction model for milk-to-plasma drug concentration ratio based on area under the curve (M/P_AUC). The quantitative structure-activity/property relationship (QSAR/QSPR) approach was used to predict compounds involved in active transport during milk transfer.

METHODS

We collected M/P ratio data from literature, which were curated and divided into M/P_AUC ≥ 1 and M/P_AUC < 1. Using the ADMET Predictor® and ADMET Modeler™, we constructed two types of binary classification models: an artificial neural network (ANN) and a support vector machine (SVM).

RESULTS

M/P ratios of 403 compounds were collected, M/P_AUC data were obtained for 173 compounds, while 230 compounds only had M/P_non-AUC values reported. The models were constructed using 129 of the 173 compounds, excluding colostrum data. The sensitivity of the ANN model was 0.969 for the training set and 0.833 for the test set, while the sensitivity of the SVM model was 0.971 for the training set and 0.667 for the test set. The contribution of the charge-based descriptor was high in both models.

CONCLUSIONS

We built a M/P_AUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.

Physiologically based pharmacokinetic model to predict drug concentrations of breast cancer resistance protein substrates in milk

Many mothers need to take some medications during breastfeeding, which may carry a risk to breastfed infants. Thus, determining the amount of a drug transferred into breast milk is critical for risk-benefit analysis of breastfeeding. Breast cancer resistance protein (BCRP), an efflux transporter which usually protects the body from environmental and dietary toxins, was reported to be highly expressed in lactating mammary glands. In this study, we developed a mechanistic lactation physiologically based pharmacokinetic (PBPK) modeling approach incorporating BCRP mediated transport kinetics to simulate the concentration-time profiles of five BCRP drug substrates (acyclovir, bupropion, cimetidine, ciprofloxacin, and nitrofurantoin) in nursing women's plasma and milk. Due to the lack of certain physiological parameters and scaling factors in nursing women, we combine the bottom up and top down PBPK modeling approaches together with literature reported data to optimize and determine a set of parameters that are applicable for all five drugs. The predictive performance of the PBPK models was assessed by comparing predicted pharmacokinetic profiles and the milk-to-plasma (M/P) ratio with clinically reported data. The predicted M/P ratios for acyclovir, bupropion, cimetidine, ciprofloxacin, and nitrofurantoin were 2.48, 3.70, 3.55, 1.21, and 5.78, which were all within 1.5-fold of the observed values. These PBPK models are useful to predict the PK profiles of those five drugs in the milk for different dosing regimens. Furthermore, the approach proposed in this study will be applicable to predict pharmacokinetics of other transporter substrates in the milk.

A perspective on the current use of the phase distribution model for predicting milk-to-plasma drug concentration ratio

The phase distribution model, proposed by Atkinson and Begg in 1990, has been widely used for predicting breastmilk-to-plasma drug concentration ratio. However, misrepresentations of the equations have been noted in recent publications. In this perspective, we revisit the derivation of the equations and provide an R/Shiny interface for the model with a view to helping scientists in this field acquire in-depth understanding of the theoretical background and implementation of the model.

Application of a Physiologically Based Pharmacokinetic Approach to Predict Theophylline Pharmacokinetics Using Virtual Non-Pregnant, Pregnant, Fetal, Breast-Feeding, and Neonatal Populations

Perinatal pharmacology is influenced by a myriad of physiological variables that are changing dynamically. The influence of these covariates has not been assessed systemically. The objective of this work was to use theophylline as a model drug and to predict its pharmacokinetics before, during (including prediction of the umbilical cord level), and after pregnancy as well as in milk (after single and multiple doses) and in neonates using a physiological-based pharmacokinetic (PBPK) model. Neonatal theophylline exposure from milk consumption was projected in both normal term and preterm subjects. Predicted infant daily doses were calculated using theophylline average and maximum concentration in the milk as well as an estimate of milk consumption. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted theophylline concentrations in non-pregnant and pregnant populations at different gestational weeks were within 2-fold of the observations and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The PBPK model predicted an average cord-to-maternal plasma ratio of 1.0, which also agrees well with experimental observations. Predicted postpartum theophylline concentration profiles in milk were also in good agreement with observations with a predicted milk-to-plasma ratio of 0.68. For an infant of 2 kg consuming 150 ml of milk per day, the lactation model predicted a relative infant dose (RID) of 12 and 17% using predicted average (C_avg,ss) and maximum (C_max,ss) concentration in milk at steady state. The maximum RID of 17% corresponds to an absolute infant daily dose of 1.4 ± 0.5 mg/kg/day. This dose, when administered as 0.233 mg/kg every 4 h, to resemble breastfeeding frequency, resulted in plasma concentrations as high as 3.9 (1.9-6.8) mg/L and 2.8 (1.3-5.3) (5th-95th percentiles) on day 7 in preterm (32 GW) and full-term neonatal populations.

Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model

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.