Investigation into the use of NMR-based bioinformatics in determining the composition and quality of immune supplements in Australia

The outbreak of the SARS-CoV-2 virus has brought prominence to the concept of immune health for individuals. A common means of attempting to do so is by incorporating immune supplements into everyday life. While immune supplements generally contain well-documented traditional herbs, knowledge about the quality and safety of these commercial products is minimal. In Australia, the Therapeutic Goods Administration (TGA) regulates and enforces advertising, labelling and compositional consistency of immune supplements; however, minimal pre-market assessment omits the potential harm and adulteration regularly cited in the literature. A multifaceted approach to these products’ overall safety and quality is essential in safeguarding human health. Following TGA guidelines, seventeen immune supplements were investigated for their labelling compliance with the Therapeutic Goods Order No. 92 for non-prescription medicines. Although systemic labelling non-compliance was observed throughout the products, this was not associated with their potential to cause harm. Thus, stringency in this area is not necessarily applicable to protecting consumers. More focus should be put on high throughput pharmacovigilance methods that examine immune supplements' compositional integrity and consistency. For this study, the composition of immune supplements was analysed via nuclear magnetic resonance (NMR) spectroscopy using metabolomics. NMR provides detailed ‘snap shots’ into the chemical profile of immune supplements that can be interpreted via multivariate statistics to indicate the consistency of products across numerous batches. Therefore, this thesis aims to provide an overview of the quality and safety of Australian immune supplements. At the same time, it is recognising the place of metabolomics in regulatory environments as a high throughput mechanism of quality assurance

An automated framework for NMR chemical shift calculations of small organic molecules

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries