Quantitative structure-activity relationships for kinetic parameters of polycyclic aromatic hydrocarbon biotransformation.

Environ Toxicol Chem

Environmental Engineering Division, Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, Texas 77843-3136, USA.

Published: July 2008

Quantitative structure-activity relationships (QSARs) were developed for three Monod-type parameters--qmax, Ks, and qmax/Ks--that express the kinetics of polycyclic aromatic hydrocarbon (PAH) biotransformation by Sphingomonas paucimobilis strain EPA505. The training sets contained high-quality experimental values of the kinetic parameters for 20 unsubstituted and methylated PAHs as well as values of 41 meaningful molecular descriptors. A genetic function approximation algorithm was used to develop the QSARs. Statistical evaluation of the developed QSARs showed that the relationships are statistically significant and satisfy the assumptions of linear-regression analysis. The Organization for Economic Co-operation and Development principles for (Q)SAR validation were followed to evaluate the developed QSARs, which showed that the QSARs are valid. The QSARs contain spatial, spatial and electronic, topological, and thermodynamic molecular descriptors. Whereas spatial descriptors were essential in explaining biotransformation kinetics, electronic descriptors were not. Mechanistic interpretation of the QSARs resulted in evidence that is consistent with the hypothesis of membrane transport as being the rate-limiting process in PAH biotransformation by strain EPA505. The present study demonstrates the value of QSAR not only as a predictive tool but also as a framework for understanding the mechanisms governing biodegradation at the molecular level.

Download full-text PDF

Source
http://dx.doi.org/10.1897/07-498.1DOI Listing

Publication Analysis

Top Keywords

quantitative structure-activity
8
structure-activity relationships
8
kinetic parameters
8
polycyclic aromatic
8
aromatic hydrocarbon
8
pah biotransformation
8
strain epa505
8
molecular descriptors
8
developed qsars
8
qsars
7

Similar Publications

Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates.

View Article and Find Full Text PDF

The TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.

Database (Oxford)

January 2025

Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.

The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.

View Article and Find Full Text PDF

Agonists of insect hormones, namely molting hormone (MH) and juvenile hormone (JH), disrupt the normal growth of insects and can be employed as insecticides that are harmless to vertebrates. In this study, a series of experiments and computational analyses were conducted to rationally design novel insect hormone agonists. Syntheses and quantitative structure-activity relationship (QSAR) analyses of two MH agonist chemotypes, imidazothiadiazoles and tetrahydroquinolines, revealed that the structural factors important for the ligand-receptor interactions are significantly different between these chemotypes.

View Article and Find Full Text PDF

Minimal study focused on the association between mixed pollutants in atmospheric particulate matter (PM) and their reproductive health risks. Utilizing a novel quantitative structure-activity relationship (QSAR) integrated machine learning algorithms, we evaluated the mixed reproductive health risks associated with phthalates (PAEs) and organophosphates (OPEs) exposure by assessing the affinities of these compounds binding to estrogen receptors (ER) and androgen receptors (AR). The mixed toxicity equivalent factor (TEF) and mixed toxicity equivalent quantity (TEQ) by the QSAR model were all smaller than the sum TEF and TEQ of individual PAEs and OPEs, which may be due to the antagonistic effect of PAEs and OPEs monomers on reproductive toxicity.

View Article and Find Full Text PDF

Background: Our research highlights the synthesis of newer antimalarial compounds using molecular modeling studies.

Objective: The study investigates a series of isocryptolepine derivatives from previous literature, focusing on their biological activities as antimalarial agents.

Methods: Computational methods such as molecular docking and QSAR were employed to gain insights into the interaction between the synthesized compounds and the target enzyme PfDHFR-TS.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!