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.