A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals. (15th June 2019)

• Record Type: Journal Article
• Authors: Acharya, Kishor; Werner, David; Dolfing, Jan; Barycki, Maciej; Meynet, Paola; Mrozik, Wojciech; Komolafe, Oladapo; Puzyn, Tomasz; Davenport, Russell J.
• Is Part Of: Water research. Volume 157(2019)
• Page Start: 181
• Page End: 190
• Publication Date: 2019-06-15
• Subjects: Biodegradation rates -- Molecular descriptors -- QSBR -- Quantitative structure activity relationships; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2019.03.086
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R 2 = 0.89, Q 2 loo = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation. Graphical abstract: Image 1 Highlights: QSBR models that can predict half-life of wide range of aromatic chemicals have been developed. Developed QSBR models provide insight into those factors affecting biodegradation. Developed QSBR models can be a tool of prioritising and classifying chemicals in PBT assessment.