Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity.
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| Title: | Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity. |
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| Authors: | Sangion, Alessandro1, Gramatica, Paola1 paola.gramatica@uninsubria.it |
| Source: | Environment International. Oct2016, Vol. 95, p131-143. 13p. |
| Subjects: | Drug toxicity, Hazardous wastes, Drugs & the environment, QSAR models, Regression analysis |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Environment International is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 118029466 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sangion%2C+Alessandro%22">Sangion, Alessandro</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gramatica%2C+Paola%22">Gramatica, Paola</searchLink><relatesTo>1</relatesTo><i> paola.gramatica@uninsubria.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment+International%22">Environment International</searchLink>. Oct2016, Vol. 95, p131-143. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Drug+toxicity%22">Drug toxicity</searchLink><br /><searchLink fieldCode="DE" term="%22Hazardous+wastes%22">Hazardous wastes</searchLink><br /><searchLink fieldCode="DE" term="%22Drugs+%26+the+environment%22">Drugs & the environment</searchLink><br /><searchLink fieldCode="DE" term="%22QSAR+models%22">QSAR models</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environment International is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.envint.2016.08.008 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 131 Subjects: – SubjectFull: Drug toxicity Type: general – SubjectFull: Hazardous wastes Type: general – SubjectFull: Drugs & the environment Type: general – SubjectFull: QSAR models Type: general – SubjectFull: Regression analysis Type: general Titles: – TitleFull: Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sangion, Alessandro – PersonEntity: Name: NameFull: Gramatica, Paola IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 01604120 Numbering: – Type: volume Value: 95 Titles: – TitleFull: Environment International Type: main |
| ResultId | 1 |
