top of page

Drug Discovery

We have been engaged for several years in antibiotic discovery targeting mycobacterial pathogens such as Mycobacterium tuberculosis, Mycobacterium abscessus, Mycobacterium leprae and Pseudomonas aeruginosa using target-based approaches, and successfully developed potent against several targets (1-6).


Our drug discovery pipeline has evolved over the years to include target validation methods that consider not just target essentially but experimentally derived vulnerability assessment of a target with tuneable CRISPR based methods, mutability assessment of protein sites with saturation mutagenesis and X-Chem fragment screening before any decision is made to develop compounds against a target.


Examples of our work are shown in figures 1 and 2.

Figure 1: Discovery of inhibitors against mycobacterial TrmD using a fragment-approach yielded active and on-target compounds against M. tuberculosis, M. abscessus and M. leprae (4,5). 

Figure 2: Discovery of inhibitors against M. abscessus PurC using a fragment-based approach produced potent inhibitors of the enzyme that are inactive against M. abscessus and M. tuberculosis (2).

Permeability enabled antibiotic discovery

One of the major barriers to antibiotic discovery is the lack of understanding of what properties compounds must have to permeate bacterial cells and avoid efflux and metabolism. Our own research illustrates this problem. While we have been successful at rationally developing high potency inhibitors against several cytoplasmic targets, using structure- and biophysical-guided approaches, the inhibitors show large drops in activity or are inactive when tested against the pathogen of interest with a single exception (1-5). 

Our current research focuses in developing intracellular predictive models for cell envelope permeability, intracellular retention, and metabolic stability of drug-like molecules for each of our pathogens of interest (M. tuberculosis, M. abscessus, M. leprae and P. aeruginosa) using machine learning methods, that can be used to enable target-centric rational antibiotic discovery using structure-guided approaches and to improve phenotypic library design and screens. 

The datasets required to develop the predictive models are generated using a high-throughput LC/MS based approach that enables us to screen libraries of tens of thousands of compounds to quantify intracellular quantities of these drug-like molecules and identify xenometabolites. 

Understanding bacterial drug metabolism to aid antibiotic discovery

From our research and that of others it is known that mycobacteria possess the metabolic capacity to transform diverse drug-like molecules. However, very little is know about what types of molecules are susceptible to mycobacterial metabolism. Our large drug-like molecule screens possess a wealth of xenometabolic information that we are exploring to improve antibiotic development. This can be achieved by designing molecules that avoid bacterial metabolism but also exploiting the same metabolism to deliver toxic “warheads” specifically to bacterial cells thus minimizing drug toxicity.


Improved delivery of antibiotics

The efficacy and safety of many antibiotics is limited by difficulties in achieving adequate exposure at different infection locales while keeping systemic concentrations at tolerable levels. It would therefore be advantageous to have specific delivery of antibiotics at the infection locales as this would minimize drug toxicity. We have previously demonstrated that such approach can improve the efficacy of nanoparticle-conjugated antibiotics against mycobacterial infections (Figure 3) (7). 

Picture 1.png

Figure 3: Nanobiotics show higher efficacy at clearing M. marinum than the free antibiotic in a zebrafish infection model (7). 

Our research focus in developing antibiotic conjugates that are activated upon contact with the pathogen and new pathogen specific cleavable pro-drugs.



1.         Thomas, S.E. et al. Structural Characterization of Mycobacterium abscessus Phosphopantetheine Adenylyl Transferase Ligand Interactions: Implications for Fragment-Based Drug Design. Front Mol Biosci 9, 880432 (2022).

2.         Charoensutthivarakul, S. et al. Development of Inhibitors of SAICAR Synthetase (PurC) from Mycobacterium abscessus Using a Fragment-Based Approach. ACS Infect Dis 8, 296-309 (2022).

3.         Acebron-Garcia-de-Eulate, M. et al. Discovery of Novel Inhibitors of Uridine Diphosphate-N-Acetylenolpyruvylglucosamine Reductase (MurB) from Pseudomonas aeruginosa, an Opportunistic Infectious Agent Causing Death in Cystic Fibrosis Patients. J Med Chem 65, 2149-2173 (2022).

4.         Thomas, S.E. et al. Fragment-based discovery of a new class of inhibitors targeting mycobacterial tRNA modification. Nucleic Acids Res 48, 8099-8112 (2020).

5.         Whitehouse, A.J. et al. Development of Inhibitors against Mycobacterium abscessus tRNA (m(1)G37) Methyltransferase (TrmD) Using Fragment-Based Approaches. J Med Chem 62, 7210-7232 (2019).

6.         Thomas, S.E. et al. Structure-guided fragment-based drug discovery at the synchrotron: screening binding sites and correlations with hotspot mapping. Philos Trans A Math Phys Eng Sci 377, 20180422 (2019).

7.         Batalha, I.L. et al. Polymeric nanobiotics as a novel treatment for mycobacterial infections. J Control Release314, 116-124 (2019).

bottom of page