Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR

Authors: Maeshima T, Yoshida S, Watanabe M, Itagaki F
Publication: Pharm Res
Keywords: admet predictor, artificial intelligence, machine learning, neural networks, pregnancy, structure-activity relationship
Software: ADMET Predictor®

Abstract

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/PAUC). 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/PAUC ≥ 1 and M/PAUC < 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/PAUC data were obtained for 173 compounds, while 230 compounds only had M/Pnon-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/PAUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.

By Tae Maeshima, Shin Yoshida, Machiko Watanabe & Fumio Itagaki