Quantum Chemical Methods and Their Application to the Molecular Initiating Event

Abstract

For many years, the field of toxicology has been plagued by the contentious ethical issue of animal testing. However, in the last two decades, predictive toxicology has increased its focus on alternative approaches, and regulatory settings now emphasise the importance of mechanistic information in performing chemical safety assessments. The adverse outcome pathway (AOP) is a framework that encourages the use of non-animal methods, and starts with an initial chemical-biological interaction called the molecular initiating event (MIE). The AOP aims to collate pre-existing knowledge on chemical, biological and physical events that link the MIE with an adverse outcome. To acquire this information, and to circumnavigate the issue of animal testing, a combination of in vitro, in chemico, and in silico methods can be used in AOP construction.

The MIE is often times a chemical reaction that can be probed using molecular modelling. In this thesis, the use of computational chemistry for building predictive models based on the MIE is explored. By using quantum chemical approaches to understand MIEs, intricate details about the steric and energetic interactions between toxicant and target can be examined. For three different toxicological endpoints (Chapter 4: Mutagenicity, Chapter 5: Acute Aquatic Toxicity and Chapter 6: Targeted Covalent Inhibition), a combined computational workflow (DFT, SQM and MM) is used to better understand the fundamental chemical reactivity in toxicologically relevant 1,4 Michael addition reactions. Additionally, in Chapter 7, we explore the use of semi-empirical quantum chemistry methods for the rapid prediction of toxicologically relevant DFT data. Throughout this work, substantial emphasis is placed on using intermediate structures for building predictive quantum chemical models that allow for rapid \textit{in silico} chemical safety assessments to be made.

Date of Award	2 Nov 2022
Original language	English
Awarding Institution	University of Bath
Supervisor	Matt Grayson (Supervisor) & Lee Bryant (Supervisor)