Development of quantitative structure-activity relationship models to predict potential nephrotoxic ingredients in traditional Chinese medicines

The broad use of traditional Chinese medicines (TCMs) and the accompanied incidences of kidney injury have attracted considerable interest in investigating the responsible toxic ingredients. It is challenging to evaluate toxicity of TCMs since they contain complex mixtures of phytochemicals. Quantitative structure-activity relationship (QSAR) is an efficient tool to predict toxicity and QSAR study on TCMs-induced nephrotoxicity remains lacked. We developed QSAR models using three datasets of 609 compounds: natural products, drugs, and mixed (contained both kinds of data) datasets. Each dataset was used for modelling by utilizing artificial neural networks (ANN) and support vector machines (SVM) algorithms separately. Both internal and external validations were performed on each model. Six QSAR models were developed and yielded reliable performance in the internal validation. For external validation, 30 ingredients in the TCMs were predicted well by the natural product models (accuracy: ANN 96.7%, SVM 93.3%). The mixed models (accuracy: ANN 76.7%, SVM 66.7%) showed a better performance than the drug models (accuracy: ANN 50%, SVM 53.3%). Particularly, natural product models produced the most reliable results. It has the application not only on screening the nephrotoxic ingredients in TCMs, but it is also helpful at prioritizing the subsequent toxicity testing of natural products. •Nephrotoxicity is one of the most common toxic effects in traditional Chinese medicines.•Three datasets consisting of natural products, drugs, and mixed (contained both kinds of data) were used for QSAR modelling.•Models constructed using natural product or mixed datasets showed enhanced predictive power.•Natural product-based models produced the most rational results and showed the highest applicable.