Literature DB >> 24239825

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

Ester Papa1, Leon van der Wal2, Jon A Arnot3, Paola Gramatica4.   

Abstract

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated.
© 2013.

Keywords:  Biotransformation rate; Chemical prioritization; Consensus modeling; Metabolic half-life; QSAR; Risk assessment

Mesh:

Substances:

Year:  2013        PMID: 24239825     DOI: 10.1016/j.scitotenv.2013.10.068

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  7 in total

1.  Risk-Based Chemical Ranking and Generating a Prioritized Human Exposome Database.

Authors:  Fanrong Zhao; Li Li; Yue Chen; Yichao Huang; Tharushi Prabha Keerthisinghe; Agnes Chow; Ting Dong; Shenglan Jia; Shipei Xing; Benedikt Warth; Tao Huan; Mingliang Fang
Journal:  Environ Health Perspect       Date:  2021-04-30       Impact factor: 9.031

2.  Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk.

Authors:  Li Li; Alessandro Sangion; Frank Wania; James M Armitage; Liisa Toose; Lauren Hughes; Jon A Arnot
Journal:  Environ Health Perspect       Date:  2021-12-09       Impact factor: 9.031

3.  A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals.

Authors:  Jiaqi Wang; Tom M Nolte; Stewart F Owen; Rémy Beaudouin; A Jan Hendriks; Ad M J Ragas
Journal:  Environ Sci Technol       Date:  2022-04-26       Impact factor: 11.357

4.  In vitro biotransformation assays using fish liver cells: Comparing rainbow trout and carp hepatocytes.

Authors:  Ina Bischof; Jon A Arnot; Heinrich Jürling; Georg Knipschild; Christian Schlechtriem; Anna Schauerte; Helmut Segner
Journal:  Front Toxicol       Date:  2022-09-23

5.  Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening.

Authors:  Sean P Collins; Tara S Barton-Maclaren
Journal:  Front Toxicol       Date:  2022-09-20

6.  In vitro metabolism of pesticides and industrial chemicals in fish.

Authors:  Toshiyuki Katagi
Journal:  J Pestic Sci       Date:  2020-02-20       Impact factor: 2.529

7.  QSARINS-Chem standalone version: A new platform-independent software to profile chemicals for physico-chemical properties, fate, and toxicity.

Authors:  Nicola Chirico; Alessandro Sangion; Paola Gramatica; Linda Bertato; Ilaria Casartelli; Ester Papa
Journal:  J Comput Chem       Date:  2021-05-11       Impact factor: 3.376

  7 in total

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