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Environmental risk assessment of anti-cancer drugs and their transformation products: A focus on their genotoxicity characterization-state of knowledge and short comings

Journal

MUTATION RESEARCH-REVIEWS IN MUTATION RESEARCH
Volume 760, Issue -, Pages 18-35

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.mrrev.2014.02.001

Keywords

Risk assessment; Transformation product; Mutagenicity; Anti-cancer drug; Environment; Mixture toxicity

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Anti-cancer drugs are chemotherapeutic agents that are designed to kill or reduce proliferating cells. Often times, they interfere directly or indirectly with the cell's deoxyribonucleic acid (DNA). Some of these drugs can be detected in the ng/L concentration range in the aquatic environment and have the potential to be very persistent. Environmental risk assessment is available for only a few anti-cancer drugs, derived mainly from predicted data and excluding information on their metabolites and transformation products (TPs). Notably, there is no defined strategy for genotoxicity risk assessment of anti-cancer drugs, their metabolites and TPs in the environment. In fact, the presence of anti-cancer drugs in hospital and municipal wastewaters has not been clearly related to the genotoxic nature of these wastewaters. The few available studies that have sought to investigate the genotoxicity of mixtures derived from treating anti-cancer drugs prior to disposal seem to share the commonality of coupling analytical methods to measure concentration and genotoxic bioassays, namely the Ames test to monitor inactivation. Such limited studies on the environmental fate and effects of these drugs presents an area for further research work. Most importantly, there is a need to characterize the genotoxic effects of anti-cancer drugs towards aquatic organisms. Given current environmental risk assessment strategies, genotoxicity risk assessment of these drugs and their TPs would have to include a combination of appropriate analytical methods, genotoxicity bioassays, (bio) degradability and computer based prediction methods such as QSAR studies. (C) 2014 Elsevier B.V. All rights reserved.

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