Predicting idiopathic toxicity of cisplatin by a pharmacometabonomic approach

• Published in: Kidney international Vol. 79; no. 5; pp. 529 - 537
• Main Authors: Kwon, Hyuk Nam, Kim, Mina, Wen, He, Kang, Sunmi, Yang, Hye-ji, Choi, Myung-Joo, Lee, Hee Seung, Choi, DalWoong, Park, In Suh, Suh, Young Ju, Hong, Soon-Sun, Park, Sunghyouk
• Format: Journal Article
• Language: English
• Published: Basingstoke Elsevier Inc 01.03.2011; Nature Publishing Group; Elsevier Limited
• Subjects: Animals; anticancer; Antineoplastic Agents - toxicity; Biological and medical sciences; cisplatin; Cisplatin - toxicity; Indexing in process; Kidney - drug effects; Kidney - metabolism; Magnetic Resonance Spectroscopy; Male; Medical sciences; Metabolomics; Multivariate Analysis; Nephrology. Urinary tract diseases; pharmacometabonomics; prediction; Rats; Rats, Sprague-Dawley; Reactive Oxygen Species - metabolism; toxicity; toxicity; prediction; pharmacometabonomics; cisplatin; anticancer; Antineoplastic agent; Alkylating agent; Nephrology; Toxicity; Idiopathic; Cisplatin; Urology; Platinum II Complexes
• ISSN: 0085-2538; 1523-1755
• DOI: 10.1038/ki.2010.440

Cisplatin has been one of the most widely used anticancer agents, but its nephrotoxicity remains a dose-limiting complication. Here, we evaluated the idiopathic nature and the predose prediction of cisplatin-induced nephrotoxicity using a nuclear magnetic resonance (NMR)-based pharmacometabonomic approach. Cisplatin produced serious toxic responses in some animals (toxic group), but had little effect in others (nontoxic group), as judged by hematological and histological results. The individual metabolic profiles, assessed by urine NMR spectra, showed large differences between the post-administration profiles of the two groups, indicating the relevance of the NMR approach. Importantly, multivariate analysis of the NMR data showed that the toxic and nontoxic groups can be differentiated based on the pretreatment metabolite profiles. Leave-one-out analysis, performed to evaluate the practical performance of our approach, gave a 66% accuracy rate in predicting toxic responses based on the pretreatment metabolite profiles. Hence, we provide a working model that can explain the idiopathic toxicity mechanism based on marker metabolites found by NMR analysis consistent with tissue NADH measurements. Thus, a pharmacometabonomic approach using pretreatment metabolite profiles may help expedite personalized chemotherapy of anticancer drugs.