title: Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels.
abstract: BACKGROUND
  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 usually use linear and manually determined thresholds to predict nephrotoxicity. Automated machine learning algorithms may improve these models, and produce more accurate and unbiased predictions.
  
  
  RESULTS
  Here, we report a systematic comparison of the performances of four supervised classifiers, namely random forest, support vector machine, 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 random forest classifiers have the highest cross-validated classification performance (mean balanced accuracy = 87.8%, sensitivity = 89.4%, and specificity = 85.9%). Furthermore, we also found that IL-8 is more predictive than IL-6, but a combination of both markers gives higher classification accuracy. Finally, we also show that random forest classifiers trained automatically on the whole dataset have higher mean balanced accuracy than a previous threshold-based classifier constructed for the same dataset (99.3% vs. 80.7%).
  
  
  CONCLUSIONS
  Our results suggest that a random forest classifier can be used to automatically predict drug-induced PTC toxicity based on the expression levels of IL-6 and -8.
authors.forename: Ran
authors.initials: R
authors.lastname: Su
authors.forename: Yao
authors.initials: Y
authors.lastname: Li
authors.forename: Daniele
authors.initials: D
authors.lastname: Zink
authors.forename: Lit-Hsin
authors.initials: LH
authors.lastname: Loo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D006801:Humans; D015850:Interleukin-6 / Q000378:metabolism*; D016209:Interleukin-8 / Q000378:metabolism*; D007674:Kidney Diseases / Q000139:chemically induced*; D007687:Kidney Tubules, Proximal / Q000187:drug effects* / Q000378:metabolism*; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D012372:ROC Curve; D060388:Support Vector Machine
chemical_list: D015850:Interleukin-6; D016209:Interleukin-8; D004364:Pharmaceutical Preparations