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Developing Quantitative Structure Toxicity Relationship (QSTR) Models using Relevant Physico-chemical Descriptors for Predicting Chemical Toxicity

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posted on 2024-06-02, 23:39 authored by Zeeshan Arif
Predictive toxicology is a multidisciplinary approach to chemical toxicity assessment that employs a variety of non-animal testing methods to predict a chemical's effects on biological systems. Computational chemistry investigates the properties of atoms, molecules and reactions at the atomistic or electronic level. One of the most effective methods of modern quantum chemistry is the density functional theory (DFT) based on the Hohenberg-Kohn theorem. The project aims to theoretically investigate structural properties and interactions between various chemicals with biomolecules using quantum mechanics (QM), DFT, chemoinformatics and QSTR-based approaches. The thesis presents a comprehensive investigation for a series of chloro- and fluoropyrroles using DFT-based descriptors to elucidate physicochemical properties and their relevance to reactivity, charge transfer, site selectivity, and toxicity. Aquatic-quantitative structure toxicity relationship (Aqua-QSTR) models for predicting chemical toxicity in aquatic organisms is developed using the ECT descriptor in the subsequent study. Aqua-QSTR studies were carried out for Fathead minnow and Tetrahymena pyriformis using linear regression (LR), machine learning-based random forest regression (RFR), and support vector regression (SVR) methods. The utilization of computational approaches to profile the nature of the diverse set of chemical compounds (applications in fuel, food additives, and flammable products) and their risk to human health and the environment was undertaken in the latter study. Expert and statistical-based methods were carried out to predict various toxicity endpoints and utilized charge transfer analysis for 119 chemical compounds to understand the toxicity. A Daphnia magna EC50 (effective concentration) and Fathead minnow LC50 (lethal concentration) QSTR models were used to assess the environmental and ecological fate of compounds. A systematic investigation was carried out in the next study on ZnO clusters and Au (111) surface with embedded tyrosine molecule heterostructures to extract the structural and electronic properties for developing quantitative structure-toxicity relationships using quantum chemical DFT reactivity descriptors. The nature of ZnO NPs clusters’ reactivity was recorded and explored by studying frontier molecular orbitals, Mulliken atomic charges and molecular electrostatic potential surface. We herein also investigate the nature of the interaction between tyrosine and the Au (111) surface together with the bonding mechanism and their electronic interactions by calculating important surface characteristics such as the charge density difference, density of states, Bader charge analysis, and adsorption energies. Further, we discuss an in silico and DFT-based study which examines the impact of Pesticides Active Substances (PAS) (used as insecticides, acaricides, herbicides, fungicides and plant growth regulators) on human health and their binding with Muscarinic and Nicotinic acetylcholine receptors (AChRs). The chemical library was further screened for in silico ADMET, TOPKAT-based predictions (Rat Oral-LD50, Skin sensitization, Ames mutagenicity), molecular docking and charge transfer analysis against their target AChRs. An effort has been made to develop relevant chemical reactivity descriptors and the applications of these descriptors towards the prediction of toxicity, mechanism, biological activities, other properties, and recognition/identification of potential reactive sites of chemical interfaces. DFT-based quantum chemical calculations have been utilized for obtaining global reactivity descriptors and demonstrate the usefulness of the quantitative structure-toxicity relationship (QSTR) models for predictive toxicology. Interfacing with predictive analytics, this research provides a quality computational model for the toxicity prediction of chemicals. The developed model shall provide insight into designing safer chemical alternatives and risk management.

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Doctorate by Research

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© Zeeshan Arif 2024

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Science

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