Effects of platinum-based anticancer drugs on the trace element profile of liver and kidney tissue from mice

Abstract

BackgroundPlatinum-based anticancer drugs are relatively successful chemotherapeutic agents, which can cause significant elemental changes in key organs and are known for undesirable side effects, such as nephrotoxicity (damage to the kidneys). Objectives and methodsInductively coupled plasma mass spectrometry (ICP-MS) and traditional statistical tools such as two-sample Student’s t-test and Pearson’s correlation analysis are used to evaluate the effects of different investigational Pt-based anticancer drugs on the elemental constitution of kidneys and liver of mice. Principal component analysis is used to uncover relationships in element concentration and potential correlations between those and clinical effects. Random forest importance is used to identify elements most associated with the drug’s maximum tolerated doses (MTDs). ResultsStrong negative correlations between Pt and both Cu (−0.814) and Zn (−0.784) in kidneys were observed for one of the Pt-acridine anticancer agents evaluated (Drug C). Strong positive correlations were observed between Cu in both kidneys (0.834) and liver (0.756) with Zn in liver for the same compound. Cisplatin administration correlates to higher concentrations of Ca, Cu, Rb and Zn in liver. Calcium and Mo in kidneys and Pt and Zn in liver are the features most associated with MTDs. ConclusionsThe results indicate that the Pt-based agents investigated are major modulators of ion homeostasis in excretory organs, which most likely contributes to their systemic toxicity and limits their efficacy. A better understanding of subtle patterns and correlations among elements in key organs may provide deeper insights into the mechanisms of action and ultimately contribute for better, safer drugs. To achieve this goal, researchers involved in cancer drug development may leverage the high sensitivity and high sample throughput of ICP-MS, and the capabilities of modern statistical tools to extract relevant information from a large dataset.