Drug-induced nephrotoxicity causes acute kidney injury and chronic kidney diseases, and is a major reason for late-stage failures in the clinical trials of new drugs. Therefore, early, pre-clinical prediction of nephrotoxicity could help to prioritize drug candidates for further evaluations, and increase the success rates of clinical trials. Recently, an in vitro model for predicting renal-proximal-tubular-cell (PTC) toxicity based on the expression levels of two inflammatory markers, interleukin (IL)-6 and -8, has been described. However, this and other existing models mostly use linear and manually determined thresholds to predict nephrotoxicity.  Here, we report a systematic comparison of the performances of three supervised classifiers, namely support vector machine (SVM), k-nearest-neighbor and naive Bayes classifiers, in predicting PTC toxicity based on IL-6 and -8 expression levels. Using a dataset of human primary PTCs treated with 41 well-characterized compounds that are toxic or not toxic to PTC, we found that SVM classifiers based on radial-basis-function kernels have the highest cross-validated classification performance (mean accuracy=83.05%, sensitivity=83.29%, and specificity=82.78%). Furthermore, we also found that IL-8 is more predictive than IL-6, but a combination of both markers gives higher classification accuracies. Finally, we also show that our SVM classifiers trained automatically on the whole dataset have higher mean accuracy than a previous threshold-based classifier constructed for the same dataset (92.52% vs. 81.80%).  Our results suggest that a SVM classifier based on these two markers can be used to automatically predict drug-induced PTC toxicity.