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/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.

Effect of antipsychotics on breast tumors by analysis of the Japanese Adverse Drug Event Report database and cell-based experiments.

BACKGROUND: Since antipsychotics induce hyperprolactinemia via the dopamine D2 receptor, long-term administration may be a risk factor for developing breast tumors, including breast cancer. On the other hand, some antipsychotic drugs have been reported to suppress the growth of breast cancer cells in vitro. Thus, it is not clear whether the use of antipsychotics actually increases the risk of developing or exacerbating breast tumors. The purpose of this study was to clarify the effects of antipsychotic drugs on the onset and progression of breast tumors by analyzing an adverse event spontaneous reporting database and evaluating the proliferation ability of breast cancer cells. METHODS: Japanese Adverse Drug Event Report database (JADER) reports from April 2004 to April 2019 were obtained from the Pharmaceuticals and Medical Devices Agency (PMDA) website. Reports of females only were analyzed. Adverse events included in the analysis were hyperprolactinemia and 60 breast tumor-related preferred terms. The reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC) were used to detect signals. Furthermore, MCF-7 cells were treated with haloperidol, risperidone, paliperidone, sulpiride, olanzapine and blonanserin, and cell proliferation was evaluated by WST-8 assay. RESULTS: In the JADER analysis, the IC signals of hyperprolactinemia were detected with sulpiride (IC, 3.73; 95% CI: 1.81-5.65), risperidone (IC, 3.69; 95% CI: 1.71-5.61), and paliperidone (IC, 4.54; 95% CI: 2.96-6.12). However, the IC signal of breast tumors was not observed with any antipsychotics. In cell-based experiments, MCF-7 cells were treated with six antipsychotics at concentrations of 2 and 32 µM, and none of the drugs showed any growth-promoting effects on MCF-7 cells. On the other hand, blonanserin markedly suppressed the growth of MCF-7 cells at a concentration of 32 µM, and the effect was concentration dependent. CONCLUSIONS: Analysis of the JADER using the IC did not show breast tumor signals due to antipsychotic drugs. In in vitro experiments, antipsychotics did not promote MCF-7 cell proliferation whereas blonanserin suppressed MCF-7 cell growth. Further research on the effects of blonanserin on the onset and progression of breast tumor is expected.