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Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity
Abstract Active Pharmaceutical Ingredients (APIs) are recognized as Contaminants of Emerging Concern (CEC) since they are detected in the environment in increasing amount, mainly in aquatic compartment, where they may be hazardous for wildlife. The huge lack of experimental data for a large number of end-points requires tools able to quickly highlight the potentially most hazardous and toxic pharmaceuticals, focusing experiments on the prioritized compounds. In silico tools, like QSAR (Quantitative Structure-Activity Relationship) models based on structural molecular descriptors, can predict missing data for toxic end-points necessary to prioritize existing, or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute toxicity of APIs in key organisms of the three main aquatic trophic levels, i.e. algae, Daphnia and two species of fish, were developed using the QSARINS software. These Multiple Linear regressions - Ordinary Least Squares (MLR-OLS) models are based on theoretical molecular descriptors calculated by free PaDEL-Descriptor software and selected by Genetic Algorithm. The models are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for a large set of APIs without experimental data. Then predictions were processed by Principal Component Analysis (PCA) and a trend, driven by the combination of toxicities for all the studied organisms, was highlighted. This trend, named Aquatic Toxicity Index (ATI), allowed the raking of pharmaceuticals according to their potential toxicity upon the whole aquatic environment. Finally a QSAR model for the prediction of this Aquatic Toxicity Index (ATI) was proposed to be applicable in QSARINS for the screening of existing APIs for their potential hazard and the a priori chemical design of not environmentally hazardous APIs.
Graphical abstract Display Omitted
Highlights QSAR models to predict the ecotoxicity of pharmaceuticals in the three main aquatic trophic levels Ranking of APIs according to their potential cumulative toxicity for aquatic environment Aquatic Toxicity Index model to predict the aquatic toxicity of new APIs from molecular structure
Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity
Abstract Active Pharmaceutical Ingredients (APIs) are recognized as Contaminants of Emerging Concern (CEC) since they are detected in the environment in increasing amount, mainly in aquatic compartment, where they may be hazardous for wildlife. The huge lack of experimental data for a large number of end-points requires tools able to quickly highlight the potentially most hazardous and toxic pharmaceuticals, focusing experiments on the prioritized compounds. In silico tools, like QSAR (Quantitative Structure-Activity Relationship) models based on structural molecular descriptors, can predict missing data for toxic end-points necessary to prioritize existing, or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute toxicity of APIs in key organisms of the three main aquatic trophic levels, i.e. algae, Daphnia and two species of fish, were developed using the QSARINS software. These Multiple Linear regressions - Ordinary Least Squares (MLR-OLS) models are based on theoretical molecular descriptors calculated by free PaDEL-Descriptor software and selected by Genetic Algorithm. The models are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for a large set of APIs without experimental data. Then predictions were processed by Principal Component Analysis (PCA) and a trend, driven by the combination of toxicities for all the studied organisms, was highlighted. This trend, named Aquatic Toxicity Index (ATI), allowed the raking of pharmaceuticals according to their potential toxicity upon the whole aquatic environment. Finally a QSAR model for the prediction of this Aquatic Toxicity Index (ATI) was proposed to be applicable in QSARINS for the screening of existing APIs for their potential hazard and the a priori chemical design of not environmentally hazardous APIs.
Graphical abstract Display Omitted
Highlights QSAR models to predict the ecotoxicity of pharmaceuticals in the three main aquatic trophic levels Ranking of APIs according to their potential cumulative toxicity for aquatic environment Aquatic Toxicity Index model to predict the aquatic toxicity of new APIs from molecular structure
Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity
Sangion, Alessandro (author) / Gramatica, Paola (author)
Environmental International ; 95 ; 131-143
2016-08-16
13 pages
Article (Journal)
Electronic Resource
English
APIs , Active Pharmaceutical Ingredients , AD , applicability domain , ATI , Aquatic Toxicity Index , PBT , Persistence, Bioaccumulation and Toxicity , CAS , Chemical Abstract Service , CCC , concordance correlation coefficient , CEC , Contaminants of Emerging Concern , ECOSAR , Ecological Structure Activity Relationships , E-State , electrotopological state , ERA , Environmental Risk Assessment , EE2 , estrogen ethinyl estradiol , EMEA , European Medicines Agency , CSTEE , European Union Commission's Scientific Committee on Toxicity, Ecotoxicity and Environment , GA-VSS , Genetic Algorithm Variable Subset Selection , MLR , Multiple Linear Regression , NCCOS , National Centre for Coastal Ocean Science , NOAA , National Oceanic and Atmospheric Administration , ORe , Ordered by Response , OSt , Ordered by Structure , OLS , Ordinary Least Square , OECD , Organization for Economic Cooperation and Development , PCA , Principal Component Analysis , PCs , Principal Components , QSARINS , QSAR-INSubria , QSAR , Quantitative Structure Activity Relationship , QSTR , Quantitative Structure Toxicity Relationship , Rnd , Random selection , RMSE , Root Mean Squared of Errors , SMILES , Simplified Molecular Input Line Entry System , US-EPA , United States - Environmental Protection Agency , WWTPs , waste water treatment plants , Pharmaceuticals , Ecotoxicity , Ranking
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