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Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach

An extensive use of pharmaceuticals and the widespread practices of their erroneous disposal measures have made these products contaminants of emerging concern (CEC). Especially, active pharmaceutical ingredients (APIs) are ubiquitously detected in surface water and soil, mainly in the aquatic compa...

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Autores principales: Kar, Supratik, Roy, Kunal, Leszczynski, Jerzy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120680/
https://www.ncbi.nlm.nih.gov/pubmed/29934904
http://dx.doi.org/10.1007/978-1-4939-7899-1_19
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author Kar, Supratik
Roy, Kunal
Leszczynski, Jerzy
author_facet Kar, Supratik
Roy, Kunal
Leszczynski, Jerzy
author_sort Kar, Supratik
collection PubMed
description An extensive use of pharmaceuticals and the widespread practices of their erroneous disposal measures have made these products contaminants of emerging concern (CEC). Especially, active pharmaceutical ingredients (APIs) are ubiquitously detected in surface water and soil, mainly in the aquatic compartment, where they do affect the living systems. Unfortunately, there is a huge gap in the availability of ecotoxicological data on pharmaceuticals’ environmental behavior and ecotoxicity which force EMEA (European Medicines Agency) to release guidelines for their risk assessment. In silico modeling approaches are vital tools to exploit the existing information to rapidly emphasize the potentially most hazardous and toxic pharmaceuticals and prioritize the most environmentally hazardous ones for focusing further on their experimental studies. The quantitative structure–activity relationship (QSAR) models are capable of predicting missing properties for toxic end-points required to prioritize existing, or newly synthesized chemicals for their potential hazard. This chapter reviews the information regarding occurrence and impact of pharmaceuticals and their metabolites in the environment along with their persistence, environmental fate, risk assessment, and risk management. A bird’s eye view about the necessity of in silico methods for fate prediction of pharmaceuticals in the environment as well as existing successful models regarding ecotoxicity of pharmaceuticals are discussed. Available toxicity endpoints, ecotoxicity databases, and expert systems frequently used for ecotoxicity predictions of pharmaceuticals are also reported. The overall discussion justifies the requirement to build up additional in silico models for quick prediction of ecotoxicity of pharmaceuticals economically, without or involving only limited animal testing.
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spelling pubmed-71206802020-04-06 Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach Kar, Supratik Roy, Kunal Leszczynski, Jerzy Computational Toxicology Article An extensive use of pharmaceuticals and the widespread practices of their erroneous disposal measures have made these products contaminants of emerging concern (CEC). Especially, active pharmaceutical ingredients (APIs) are ubiquitously detected in surface water and soil, mainly in the aquatic compartment, where they do affect the living systems. Unfortunately, there is a huge gap in the availability of ecotoxicological data on pharmaceuticals’ environmental behavior and ecotoxicity which force EMEA (European Medicines Agency) to release guidelines for their risk assessment. In silico modeling approaches are vital tools to exploit the existing information to rapidly emphasize the potentially most hazardous and toxic pharmaceuticals and prioritize the most environmentally hazardous ones for focusing further on their experimental studies. The quantitative structure–activity relationship (QSAR) models are capable of predicting missing properties for toxic end-points required to prioritize existing, or newly synthesized chemicals for their potential hazard. This chapter reviews the information regarding occurrence and impact of pharmaceuticals and their metabolites in the environment along with their persistence, environmental fate, risk assessment, and risk management. A bird’s eye view about the necessity of in silico methods for fate prediction of pharmaceuticals in the environment as well as existing successful models regarding ecotoxicity of pharmaceuticals are discussed. Available toxicity endpoints, ecotoxicity databases, and expert systems frequently used for ecotoxicity predictions of pharmaceuticals are also reported. The overall discussion justifies the requirement to build up additional in silico models for quick prediction of ecotoxicity of pharmaceuticals economically, without or involving only limited animal testing. 2018-03-20 /pmc/articles/PMC7120680/ /pubmed/29934904 http://dx.doi.org/10.1007/978-1-4939-7899-1_19 Text en © Springer Science+Business Media, LLC, part of Springer Nature 2018 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Kar, Supratik
Roy, Kunal
Leszczynski, Jerzy
Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
title Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
title_full Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
title_fullStr Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
title_full_unstemmed Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
title_short Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
title_sort impact of pharmaceuticals on the environment: risk assessment using qsar modeling approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120680/
https://www.ncbi.nlm.nih.gov/pubmed/29934904
http://dx.doi.org/10.1007/978-1-4939-7899-1_19
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