Clinical lactation studies and the role of pharmacokinetic modeling and simulation in predicting drug exposures in breastfed infants

Selection of a suitable animal model to evaluate secretion of drugs in the human milk: a systematic approach

Lack of data on drug secretion in human milk is a concern for safe use of drugs during postpartum.Clinical studies are often difficult to perform; despite substantial improvements in computational methodologies such as physiologically based pharmacokinetic modelling, there is limited clinical data to validate such models for many drugs.Various factors that are likely to impact milk to plasma ratio were identified. A literature search was performed to gather available data on milk composition, total volume of milk produced per day, milk pH, haematocrit, and renal blood flow and glomerular filtration rate in various animal models.BLAST nucleotide and protein tools were used to evaluate the similarities between humans and animals in the expression and predominance of selected drug transporters, metabolic enzymes, and blood proteins.A multistep analysis of all the potential variables affecting drug secretion was considered to identify most appropriate animal model. The practicality of using the animal in a lab setting was also considered.Donkeys and goats were identified as the most suitable animals for studying drug secretion in milk and future studies should be performed in goats and donkeys to validate the preliminary observations.

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.

Machine Learning Models for Prediction of Xenobiotic Chemicals with High Propensity to Transfer into Human Milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via the transfer of environmental chemicals from maternal exposome is a significant concern for infant health. The milk to plasma concentration (M/P) ratio is a critical metric that quantifies the extent to which these chemicals transfer from maternal plasma into breast milk, impacting infant exposure. Machine learning-based predictive toxicology models can be valuable in predicting chemicals with a high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated data set, to date, of 375 chemicals with known milk-to-plasma concentration (M/P) ratios. Our support vector machine (SVM)-based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and an external test data set. Specifically, the SVM-based classifier on (internal) test data achieved a classification accuracy of 77.33%, a specificity of 84%, a sensitivity of 64%, and an F-score of 65.31%. When evaluated on an external test data set, our SVM-based classifier is found to be generalizable with a sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in the earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. Overall, this study attests to the immense potential of predictive computational toxicology models in characterizing the myriad of chemicals in the human exposome.

Supplementing clinical lactation studies with PBPK modeling to inform drug therapy in lactating mothers: Prediction of primaquine exposure as a case example

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.

Concentrations of remdesivir and GS-441524 in human milk from lactating individuals diagnosed with COVID-19

IMPACT

Findings from this study provide further reassuring evidence that infant exposure through human milk received from lactating individuals who require treatment with remdesivir is negligible.

The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics

Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.

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.

Guide de prescription psychiatrique pendant la grossesse, le postpartum et l’allaitement

Perinatal psychopharmacology is an emerging specialty that is gradually developing alongside perinatal psychiatry. The management of psychiatric disorders during the perinatal period is a challenge for perinatal practitioners due to the multiple changes occurring during this crucial period. This little-known specialty still suffers from inappropriate considerations on the impact of psychotropic treatments on the mother and the infant during pregnancy and postpartum, which can promote a deficiency in perinatal psychic care. However, the risks associated with insufficient management of mental health are major, impacting both the mental and physical health of the mother and the infant. In this paper, we propose a perinatal psychopharmacology prescription guide based on available scientific evidence and international and national recommendations. We thus propose a decision-making process formalized on simple heuristics in order to help the clinician to prescribe psychotropic drugs during the perinatal period.

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.

Presence of Five Chemotherapeutic Drugs in Breast Milk as a Guide for the Safe Use of Chemotherapy During Breastfeeding: Results From a Case Series

Little is known about infant's safety of chemotherapy during breastfeeding where evidence is limited to a few case reports. This lack of knowledge has led to a general tendency to advise against breastfeeding during cytotoxic therapy despite the overwhelming benefits that breastfeeding offers to both the mothers and their children. In this case series, the presence of five chemotherapies in breast milk was determined. The aim was to obtain insight into the presence of these drugs in breast milk to inform and help clinicians in making informed decisions for women who want to breastfeed. Three patients collected 24-hour samples of breast milk every day for 1, 2, or 3 weeks after chemotherapy, 210 in total. After determination of drug concentrations, the infant daily dose, relative daily infant dose (RID%) and cumulative RID were calculated. Cumulative RIDs in patients varied from 10% to values lower than 1%. Rich data allowed us to design a table which gives predictions on the amount of days that breast milk has to be discarded to reach cumulative RIDs below 5, 1, and 0.1% for each compound. For cyclophosphamide, paclitaxel, and carboplatin, cumulative RIDs below 1 or 0.1% are reached if breast milk is discarded for 1-3 days after administration. This might suggest that breastfeeding in between cycles is an option. However, other pharmacological parameters should also be taken into consideration. For doxorubicin, also the levels of the active metabolite doxorubicinol need quantification. Similarly, breastfeeding during treatment with cisplatin might give substantial exposure and we advise caution.

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.

A Review of Quantitative Instruments for Understanding Breastfeeding Dynamics

Breastfeeding, as a unique behavior of the postpartum period and an ideal source of nourishment, is profoundly impacted by the physiology and behavior of both mothers and infants. For more than three-quarters of a century, there has been an ongoing advancement of instruments that permit insight into the complex process of latching during breastfeeding, which includes coordinating sucking, swallowing, and breathing. Despite the available methodologies for understanding latching dynamics, there continues to be a large void in the understanding of infant latching and feeding. The causes for many breastfeeding difficulties remain unclear, and until a clearer understanding of the mechanics involved is achieved, the struggle will continue in the attempts to aid infants and mothers who struggle to breastfeed. In this review, the history of development for the most prominent tools employed to analyze breastfeeding dynamics is presented. Additionally, the importance of the most advanced instruments and systems used to understand latching dynamics is highlighted and how medical practitioners utilize them is reported. Finally, a controversial argument amongst pediatric otolaryngolo gists concerning breastfeeding difficulties is reviewed and the urgent need for quantification of latching dynamics in conjunction with milk removal rate through prospective controlled studies is discussed.

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.

Drug development research in pregnant and lactating women

Pregnant and lactating women are considered "therapeutic orphans" because they generally have been excluded from clinical drug research and the drug development process owing to legal, ethical, and safety concerns. Most medications prescribed for pregnant and lactating women are used "off-label" because most of the clinical approved medications do not have appropriate drug labeling information for pregnant and lactating women. Medications that lack human safety data on use during pregnancy and lactation may pose potential risks for adverse effects in pregnant and lactating women as well as risks of teratogenic effects to their unborn and newborn babies. Federal policy requiring the inclusion of women in clinical research and trials led to considerable changes in research design and practice. Despite more women being included in clinical research and trials, the inclusion of pregnant and lactating women in drug research and clinical trials remains limited. A recent revision to the "Common Rule" that removed pregnant women from the classification as a "vulnerable" population may change the culture of drug research and drug development in pregnant and lactating women. This review article provides an overview of medications studied by the Obstetric-Fetal Pharmacology Research Units Network and Centers and describes the challenges in current obstetrical pharmacology research and alternative strategies for future research in precision therapeutics in pregnant and lactating women. Implementation of the recommendations of the Task Force on Research Specific to Pregnant Women and Lactating Women can provide legislative requirements and opportunities for research focused on pregnant and lactating women.

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women’s health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and SCOPUS. Search terms included pregnancy and AI. Research articles and book chapters were included, while conference papers, editorials and notes were excluded from the review. Included papers focused on pregnancy and AI methods, and pertained to pharmacologic interventions. We identified 376 distinct studies from our queries. A final set of 31 papers were included for the review. Included papers represented a variety of pregnancy concerns and multidisciplinary applications of AI. Few studies relate to pregnancy, AI, and pharmacologics and therefore, we review carefully those studies. External validation of models and techniques described in the studies is limited, impeding on generalizability of the studies. Our review describes how AI has been applied to address maternal health, throughout the pregnancy process: preconception, prenatal, perinatal, and postnatal health concerns. However, there is a lack of research applying AI methods to understand how pharmacologic treatments affect pregnancy. We identify three areas where AI methods could be used to improve our understanding of pharmacological effects of pregnancy, including: (a) obtaining sound and reliable data from clinical records (15 studies), (b) designing optimized animal experiments to validate specific hypotheses (1 study) to (c) implementing decision support systems that inform decision-making (11 studies). The largest literature gap that we identified is with regards to using AI methods to optimize translational studies between animals and humans for pregnancy-related drug exposures.