Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient prediction of query compounds. However, the reliability of the predictions from QSARs derived from small datasets is often questionable from a statistical point of view. This is due to the presence of a larger number of descriptors as compared to the number of training compounds, which reduces the degree of freedom of the developed model. To reduce the overall prediction error for a particular QSAR model, we have proposed here the computation of the novel Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. We have reduced the number of modeling descriptors in a supervised manner by partitioning them into K classes (K = 2 here) depending on the higher mean normalized values of the descriptors to a particular response class, thus preventing the loss of chemical information. A scatter plot of the data points using the values of two ARKA descriptors (ARKA_2 vs. ARKA_1) can potentially identify activity cliffs, less confident data points, and less modelable data points. We have used here five representative environmentally relevant endpoints (skin sensitization, earthworm toxicity, milk/plasma partitioning, algal toxicity, and rodent carcinogenicity of hazardous chemicals) with graded responses to which the ARKA framework was applied for classification modeling. On comparing the performance of the models generated using conventional QSAR descriptors and the ARKA descriptors, the prediction quality of the models derived from ARKA descriptors was found, based on multiple graded-data validation metrics-derived decision criteria, much better than the models derived from QSAR descriptors signifying the potential of ARKA descriptors in ecotoxicological classification modeling of small data sets. Additionally, this holds true for the Read-Across approach as well, since the Read-Across predictions using ARKA descriptors supersede the predictions generated from QSAR descriptors. For the ease of users, a Java-based expert system has been developed that computes the ARKA descriptors from the input of QSAR descriptors.

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.

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.

Health-Relevant Phenotypes in the Offspring of Mice Given CAR Activators Prior to Pregnancy

Hepatic induction in response to drugs and environmental chemicals affects drug therapies and energy metabolism. We investigated whether the induction is transmitted to the offspring. We injected 3-day- and 6-week-old F0 female mice with TCPOBOP, an activator of the nuclear receptor constitutive androstane receptor (CAR, NR1I3), and mated them 1-6 weeks afterward. We detected in the offspring long-lasting alterations of CAR-mediated drug disposition, energy metabolism, and lipid profile. The transmission to the first filial generation (F1) was mediated by TCPOBOP transfer from the F0 adipose tissue via milk, as revealed by embryo transfer, crossfostering experiments, and liquid chromatography-mass spectrometry analyses. The important environmental pollutant PCB153 activated CAR in the F1 generation in a manner similar to TCPOBOP. Our findings indicate that chemicals accumulating and persisting in adipose tissue may exert liver-mediated, health-relevant effects on F1 offspring simply via physical transmission in milk. Such effects may occur even if treatment has been terminated far ahead of conception. This should be considered in assessing developmental toxicity and in the long-term follow-up of offspring of mothers exposed to both approved and investigational drugs, and to chemicals with known or suspected accumulation in adipose tissue.

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.

Evaluating the influence of half-life, milk:plasma partition coefficient, and volume of distribution on lactational exposure to chemicals in children

Women are exposed to multiple environmental chemicals, many of which are known to transfer to breast milk during lactation. However, little is known about the influence of the different chemical-specific pharmacokinetic parameters on children's lactational dose. Our objective was to develop a generic pharmacokinetic model and subsequently quantify the influence of three chemical-specific parameters (biological half-life, milk:plasma partition coefficient, and volume of distribution) on lactational exposure to chemicals and resulting plasma levels in children. We developed a two-compartment pharmacokinetic model to simulate lifetime maternal exposure, placental transfer, and lactational exposure to the child. We performed 10,000 Monte Carlo simulations where half-life, milk:plasma partition coefficient, and volume of distribution were varied. Children's dose and plasma levels were compared to their mother's by calculating child:mother dose ratios and plasma level ratios. We then evaluated the association between the three chemical-specific pharmacokinetic parameters and child:mother dose and level ratios through linear regression and decision trees. Our analyses revealed that half-life was the most influential parameter on children's lactational dose and plasma concentrations, followed by milk:plasma partition coefficient and volume of distribution. In bivariate regression analyses, half-life explained 72% of child:mother dose ratios and 53% of child:mother level ratios. Decision trees aiming to identify chemicals with high potential for lactational exposure (ratio>1) had an accuracy of 89% for child:mother dose ratios and 84% for child:mother level ratios. Our study showed the relative importance of half-life, milk:plasma partition coefficient, and volume of distribution on children's lactational exposure. Developed equations and decision trees will enable the rapid identification of chemicals with a high potential for lactational exposure.

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

OBJECTIVES

The transfer of xenobiotic compounds into human breast milk has raised serious concerns in the last few years. The present study is aimed to assess whether simple physicochemical properties exert significant impact on human breast milk transfer of drugs and chemicals.

METHODS

A large data set of 375 xenobiotic compounds with available experimental milk to plasma (M/P) ratios was systematically compiled from the literature and explored with their physicochemical properties being further analyzed with respect to their extent to transfer into breast milk.

RESULTS

Xenobiotic compounds with increased breast milk transfer (M/P ≥ 1) were characterized by enhanced lipophilicity and decreased molecular size (p < 0.05). Enhanced polarity and hydrogen bonding capacity were more frequently observed in xenobiotic compounds with reduced breast milk transfer (p < 0.0001). Xenobiotic compounds presenting increased positive charge at pH 7.4 were characterized by enhanced breast milk transfer (p < 0.001). Xenobiotic compounds presenting increased negative charge at pH 7.4 were characterized by decreased breast milk transfer (p < 0.001).

CONCLUSIONS

The present study supports evidence that simple physicochemical properties related with lipophilicity, polarity, molecular size and ionization status exert significant impact on drugs and chemicals transport into human breast milk.

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.

Antibiotics and Breastfeeding

During the breastfeeding period, bacterial infections can occur in the nursing mother, requiring the use of antibiotics. A lack of accurate information may lead health care professionals and mothers to suspend breastfeeding, which may be unnecessary. This article provides information on the main antibiotics that are appropriate for clinical use and the interference of these antibiotics with the infant to support medical decisions regarding the discontinuation of breastfeeding. We aim to provide information on the pharmacokinetic factors that interfere with the passage of antibiotics into breast milk and the toxicological implications of absorption by the infant. Publications related to the 20 most frequently employed antibiotics and their transfer into breast milk were evaluated. The results demonstrate that most antibiotics in clinical use are considered suitable during breastfeeding; however, the pharmacokinetic profile of each drug must be observed to ensure the resolution of the maternal infection and the safety of the infant.