Modeling and analysis of transport in the mammary glands

A literature review of drug transport mechanisms during lactation

Despite the benefits of breastfeeding, lactating mothers who take prescribed medications may discontinue breastfeeding due to concerns associated with infant drug exposure in breastmilk. Consolidating the current knowledge of drug transport to breastmilk may inform understanding of the safety of medication use during lactation. This literature review summarizes the mechanisms of drug transport to breastmilk, details the physicochemical drug properties that may alter the extent of passive transport, and describes the expressional changes in mammary drug transporters that may affect active transport. During the period of 20 July 2023 to 11 August 2023, PubMed® was searched to identify journal articles pertinent to the mechanisms of drug transport from maternal plasma to breastmilk and the expression of mammary drug transporters during lactation. From the 28 studies included in this review, four mechanisms were identified for transporting drugs from maternal plasma to breastmilk: passive transport, active transport, lipid co-transport, and transcytosis. The lactational expression of 20 drug transporters was further summarized, with 9 transporters demonstrating downregulated expression during lactation and 11 transporters demonstrating upregulated expression during lactation. Understanding the mechanisms of drug transport to breastmilk may aid in estimating infant drug exposure, developing physiologically based pharmacokinetic (PBPK) models that describe drug transfer, and initiating clinical drug development programs in the lactating population.

Drugs in Human Milk Part 1: Practical and Analytical Considerations in Measuring Drugs and Metabolites in Human Milk

Human milk is a remarkable biofluid that provides essential nutrients and immune protection to newborns. Breastfeeding women consuming medications could pass the drug through their milk to neonates. Drugs can be transferred to human milk by passive diffusion or active transport. The physicochemical properties of the drug largely impact the extent of drug transfer into human milk. A comprehensive understanding of the physiology of human milk formation, composition of milk, mechanisms of drug transfer, and factors influencing drug transfer into human milk is critical for appropriate selection and use of medications in lactating women. Quantification of drugs in the milk is essential for assessing the safety of pharmacotherapy during lactation. This can be achieved by developing specific, sensitive, and reproducible analytical methods using techniques such as liquid chromatography coupled with mass spectrometry. The present review briefly discusses the physiology of human milk formation, composition of human milk, mechanisms of drug transfer into human milk, and factors influencing transfer of drugs from blood to milk. We further expand upon and critically evaluate the existing analytical approaches/assays used for the quantification of drugs in human milk.

Developing an In Vitro to In Vivo Extrapolation (IVIVE) Model to Predict Human Milk-to-Plasma Drug Concentration Ratios

Determining the amount of drug transferred into human milk is critical for benefit-risk analysis of taking medication while breastfeeding. In this study, we developed an in vitro and in vivo extrapolation (IVIVE) model to predict human milk/plasma (M/P) drug concentration ratios. Drug unionized fractions at pH 7.0 (F_ni,7.0) and 7.4 (F_ni,7.4), drug fractions unbound in human plasma (f_up) and milk (f_um), and in vitro cell permeability in both directions (efflux ratio, ER) were incorporated into the IVIVE model. A multiple regression E_max model was chosen to predict f_um from f_up and polar surface area (PSA). A total of 97 drugs with experimental ER from Caco-2 cells were used to test the IVIVE model. The M/P ratios predicted by the IVIVE model had a 1.93-fold geometric mean fold error (GMFE) and 72% of predictions were within two-fold error (Pw2FE), which were superior to the performance of previously reported five models. The IVIVE model showed a reasonable prediction accuracy for passive diffusion drugs (GMFE = 1.71-fold, Pw2FE = 82%, N = 50), BCRP substrates (BCRP: GMFE = 1.91-fold, Pw2FE = 60%, N = 5), and substrates of P-gp and BCRP (GMFE = 1.74-fold, Pw2FE = 75%, N = 8) and a lower prediction performance for P-gp substrates (GMFE = 2.51-fold, Pw2FE = 55%, N = 22). By fitting the observed M/P ratios of 39 P-gp substrates, an optimized ER (1.61) was generated to predict the M/P ratio of P-gp substrates using the developed IVIVE model. Compared with currently available in vitro models, the developed IVIVE model provides a more accurate prediction of the drug M/P ratio, especially for passive diffusion drugs. The model performance is expected to be further improved when more experimental f_um and ER data are available.

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.

forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

MOTIVATION

A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This 'n≪p' property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relationships between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection.

RESULTS

To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data.

AVAILABILITY AND IMPLEMENTATION

The method is available at https://github.com/yunchuankong/forgeNet.

CONTACT

tianwei.yu@emory.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mother-child transfer rates of organohalogen compounds up to four years of age

BACKGROUND

Breastfed children absorb persistent and toxic chemicals such as organohalogen compounds (OHCs) during the entire lactation period. Nursing is a main contributor to the burden of these pollutants in the first years of life, hence further assessments on the OHC load processes are needed.

OBJECTIVES

To identify the determinants of OHC increase in children at four years of age, considering concentration gains, maternal venous concentrations and breastfeeding time.

METHODS

Concentrations of 19 organochlorine compounds (OCs) and 14 polybrominated diphenyl ethers (PBDEs) were analyzed in maternal venous (n = 466), cord blood (n = 326) and children venous serum at four years of age (n = 272) in the Asturias INMA cohort representing the Spanish general population. Data were evaluated considering the socio-demographic and individual information collected at recruitment and follow up surveys, as well as the OHC physical-chemical constants.

RESULTS

The four years-old children concentration gains of the most abundant OHCs showed strong correlations (R² = 0.65-0.93) with the maternal concentrations during pregnancy and lactation period. The child gain/maternal transfer rates of most correlated pollutants were similar.

DISCUSSION

Between 65 and 93% of the variance of OCs in four years-old children was explained by the maternal concentrations during pregnancy and the lactation period. The compounds with log(Kow) > 3.7 (hydrophobic) showed analogous child gain/maternal transfer rates indicating similar processes of membrane lipid dissolution and passive diffusion from the epithelial cells into the milk. Molecular weight of these pollutants did not influence on these rates. Compounds with low log(Koa) such as hexachlorobenzene are more volatile and less retained, involving lower child gain/maternal transfer rates. These results may be useful to anticipate the increase of the concentrations of OCs in children using the maternal concentration of these compounds during pregnancy and the planned lactation period and to implement prophylactic measures in mothers with high venous pollutant concentrations.

Drugs in Lactation

One impediment to breastfeeding is the lack of information on the use of many drugs during lactation, especially newer ones. The principles of drug passage into breastmilk are well established, but have often not been optimally applied prospectively. Commonly used preclinical rodent models for determining drug excretion into milk are very unreliable because of marked differences in milk composition and transporters compared to those of humans. Measurement of drug concentrations in humans remains the gold standard, but computer modeling is promising. New FDA labeling requirements present an opportunity to apply modeling to preclinical drug development in place of conventional animal testing for drug excretion into breastmilk, which should improve the use of medications in nursing mothers.

Modeling drug passage into human milk

Breastfeeding has positive health consequences for both the breastfed infant and the nursing mother.(1,2) Although information on drug use during lactation is available through sites such as LactMed,(3) available information is often incomplete. Unlike pregnancy, in which large numbers of pregnant women need to be studied to assure safety, measurement of drug concentrations in breastmilk in a relatively few subjects can provide valuable information to assess drug safety. This article reviews methods of measuring and predicting drug passage into breastmilk.