Identification of novel electron transport chain inhibitors and chemical validation of combination strategies against Mycobacterium tuberculosis

Media 15 Mar 2023
23
laura jeffreys

Laura Jeffreys, BSc, PhD Post-Doctoral Research Associate
Centre for Drugs and Diagnostics, Department of Tropical Disease Biology, LSTM, UK
Thursday 21st July 2022, 12.00–13.00, Wolfson Rm 8, LSTM
Laura.jeffreys@lstmed.ac.uk

Speaker: Laura Jeffreys is a protein biochemist who focuses on determining the mechanism of action of compounds of interest. Her PhD focused on developing high-value oxy-pharmaceuticals using a bacterial P450 at the Manchester Institute of Biotechnology (MIB) within the University of Manchester. Moving on to industry she worked for a year in preclinical trials to assess host damage to a range of fragrance, herbicide and pharmaceutical compounds before returning to academia at LSTM. During her time at LSTM she has worked on drug discovery for Mycobacterium tuberculosis and SARS-CoV-2. She was awarded a grant for her own research and is currently undertaking a DCF project to elucidate the mechanisms of action of compounds against SARS-CoV-2.

Bedaquiline was the first electron transport chain inhibitor designed to target M. tuberculosis and was initially approved for the treatment of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis in 2012. However, despite recent advances clinical resistance has developed for bedaquiline and during the COVID-19 pandemic the number of deaths caused by tuberculosis (TB) increased for the first time in 10 years, hampering efforts to achieve the WHO TB aims by 2050. Laura will be sharing the results from her recently submitted paper which is the culmination of over 10 years of TB work at LSTM. Her manuscript describes the identification of novel electron transport chain inhibitors and combination therapies with other compounds such bedaquiline and telacebec (Q203). To achieve these results a variety of techniques were utilised using in vitro, in vivo and in silico models.