Literature DB >> 26874337

QSARs for estimating intrinsic hepatic clearance of organic chemicals in humans.

Alessandra Pirovano1, Stefan Brandmaier2, Mark A J Huijbregts3, Ad M J Ragas4, Karin Veltman5, A Jan Hendriks3.   

Abstract

Quantitative structure-activity relationships (QSARs) were developed to predict the in vitro clearance (CLINT) of xenobiotics metabolised in human hepatocytes (118 compounds) and microsomes (115 compounds). Clearance values were gathered from the scientific literature and multiple linear models were built and validated selecting at most 6 predictors from a pool of over 2000 potential molecular descriptors. For the hepatocytes QSAR, the explained variance (Radj(2)) was 67% and the predictive ability (Rext(2)) was 62%. For the microsomes QSAR, Radj(2) was 50% and Rext(2) 30%. For both liver assays, the most important descriptor relates to electronic properties of the compound. Functional groups of fragments were useful to identify specific compounds that have a deviating reaction rate compared to the others, such as polychlorobiphenyls (PCBs) and organic amides which were poorly metabolised by hepatocytes and microsomes, respectively. For hepatocytes, clearance was predominantly determined by electronic characteristics, while size and shape characteristics were less important and partitioning properties were absent. This may suggest that uptake across the membrane and enzyme binding are not rate-limiting steps. Particularly for hepatocytes the QSAR statistics are encouraging, allowing application of the outcomes in in vitro to in vivo extrapolation.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biotransformation; Clearance (CL(INT)); Hepatocytes; Microsomes; Quantitative structure–activity relationship

Mesh:

Substances:

Year:  2016        PMID: 26874337     DOI: 10.1016/j.etap.2016.01.017

Source DB:  PubMed          Journal:  Environ Toxicol Pharmacol        ISSN: 1382-6689            Impact factor:   4.860


  4 in total

Review 1.  IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

Authors:  Xiaoqing Chang; Yu-Mei Tan; David G Allen; Shannon Bell; Paul C Brown; Lauren Browning; Patricia Ceger; Jeffery Gearhart; Pertti J Hakkinen; Shruti V Kabadi; Nicole C Kleinstreuer; Annie Lumen; Joanna Matheson; Alicia Paini; Heather A Pangburn; Elijah J Petersen; Emily N Reinke; Alexandre J S Ribeiro; Nisha Sipes; Lisa M Sweeney; John F Wambaugh; Ronald Wange; Barbara A Wetmore; Moiz Mumtaz
Journal:  Toxics       Date:  2022-05-01

2.  Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.

Authors:  Daniel E Dawson; Brandall L Ingle; Katherine A Phillips; John W Nichols; John F Wambaugh; Rogelio Tornero-Velez
Journal:  Environ Sci Technol       Date:  2021-04-15       Impact factor: 9.028

3.  Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

Authors:  Taeho Kim; Byoung Hoon You; Songhee Han; Ho Chul Shin; Kee-Choo Chung; Hwangseo Park
Journal:  Int J Mol Sci       Date:  2021-10-12       Impact factor: 5.923

4.  HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone.

Authors:  Mariusz Zapadka; Przemysław Dekowski; Bogumiła Kupcewicz
Journal:  Int J Mol Sci       Date:  2022-06-12       Impact factor: 6.208

  4 in total

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