Simple physicochemical properties related with lipophilicity, polarity, molecular size and ionization status exert significant impact on the transfer of drugs and chemicals into human breast milk

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.

Micro problems with macro consequences: accumulation of persistent organic pollutants and microplastics in human breast milk and in human milk substitutes

Industrial activities provide a modern human lifestyle with advances and comforts in every field. However, such scenario has brought several negative issues. Persistent organic pollutants (POPs) and a growing plastic usage together with the degradation byproducts, namely microplastics (MPs), are current environmental problems present in every ecosystem, disturbing all forms of life. POPs and MPs are also found in human consumption products including animal and vegetal derivatives, human milk substitutes, and in human breast milk. To date, it is currently unknown what are the effects of MPs and POPs when ingested during the first and most important stage for health programming of the offspring, the first 1000 days of life. Here, we add epidemiological and clinical evidence supporting major sources of POPs and MPs in the ecosystem; and we will precisely describe the effect of POP and MP accumulation in animal- or plant-based infant formulas and human breast milk, modulating health outcomes in the newborn. This review provides a rational to incentive the POP and MP identification in human breast milk and human milk substitutes for avoiding susceptibility to negative health outcomes for the newborn.

Raman Microspectroscopy Detection and Characterisation of Microplastics in Human Breastmilk

The widespread use of plastics determines the inevitable human exposure to its by-products, including microplastics (MPs), which enter the human organism mainly by ingestion, inhalation, and dermal contact. Once internalised, MPs may pass across cell membranes and translocate to different body sites, triggering specific cellular mechanisms. Hence, the potential health impairment caused by the internalisation and accumulation of MPs is of prime concern, as confirmed by numerous studies reporting evident toxic effects in various animal models, marine organisms, and human cell lines. In this pilot single-centre observational prospective study, human breastmilk samples collected from N. 34 women were analysed by Raman Microspectroscopy, and, for the first time, MP contamination was found in 26 out of 34 samples. The detected microparticles were classified according to their shape, colour, dimensions, and chemical composition. The most abundant MPs were composed of polyethylene, polyvinyl chloride, and polypropylene, with sizes ranging from 2 to 12 µm. MP data were statistically analysed in relation to specific patients’ data (age, use of personal care products containing plastic compounds, and consumption of fish/shellfish, beverages, and food in plastic packaging), but no significant relationship was found, suggesting that the ubiquitous MP presence makes human exposure inevitable.

Introducing of modeling techniques in the research of POPs in breast milk - A pilot study

This study used advanced statistical and machine learning methods to investigate organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in breast milk, assuming that in a complex biological mixture, the pollutants emitted from the same source or with similar properties are statistically interrelated and possibly exhibit non-linear dynamics. The elaborated analyses such as Unmix source apportionment characterized individual source groups, while guided regularized random forest indicated the pollutant dependence on the ortho-chlorine atom attached to the congener's phenyl ring and mother's age. Mutual associations among PCBs were further discussed, but the results implied they were mostly not related to child delivery. PCB congeners -153, -180, -170, -118, -156, -105, and -138 appeared to be compounds of the outmost importance for mutual prediction with reference to their interrelations regarding chemical structure and metabolic processes in the mother's body. Finally, machine learning methods, which provided prediction relative errors lower than 30% and correlation coefficients higher than 0.90, suggested a possible strong non-linear relationship among the pollutants and consequently, the complexity of their pathways in the breast milk.

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.