Prediction of human fetal–maternal blood concentration ratio of chemicals

• Main Authors: Chia-Chi Wang, Pinpin Lin, Che-Yu Chou, Shan-Shan Wang, Chun-Wei Tung
• Format: Article
• Language: English
• Published: PeerJ Inc. 2020-07-01
• Series: PeerJ
• Subjects: Transplacental transfer; Machine learning; logFM; Developmental toxicity
• Online Access: https://peerj.com/articles/9562.pdf

Background The measurement of human fetal-maternal blood concentration ratio (logFM) of chemicals is critical for the risk assessment of chemical-induced developmental toxicity. While a few in vitro and ex vivo experimental methods were developed for predicting logFM of chemicals, the obtained experimental results are not able to directly predict in vivo outcomes. Methods A total of 55 chemicals with logFM values representing in vivo fetal-maternal blood ratio were divided into training and test datasets. An interpretable linear regression model was developed along with feature selection methods. Cross-validation on training dataset and prediction on independent test dataset were conducted to validate the prediction model. Results This study presents the first valid quantitative structure-activity relationship model following the Organisation for Economic Co-operation and Development (OECD) guidelines based on multiple linear regression for predicting in vivo logFM values. The autocorrelation descriptor AATSC1c and information content descriptor ZMIC1 were identified as informative features for predicting logFM. After the adjustment of the applicability domain, the developed model performs well with correlation coefficients of 0.875, 0.850 and 0.847 for model fitting, leave-one-out cross-validation and independent test, respectively. The model is expected to be useful for assessing human transplacental exposure.