Felicity Crawshay-Williams

Department of Medicine

We do not yet understand how Mycobacterium tuberculosis (Mtb) handles exogenous chemicals, which has created two major scientific and clinical challenges. First, treatment failure rates remain high because we lack insight into how antibiotics interact and can be effectively combined. Second, structure-guided drug discovery has largely been abandoned because of our limited understanding of the rules governing chemical entry, efflux, and metabolism in Mtb. During Felicity’s PhD, she developed a novel high-throughput method to accurately assess chemical permeability and metabolism within Mtb. This work led to the identification of novel, highly effective combinations of existing antibiotics—now ready for clinical testing—and the development of machine learning algorithms to predict drug retention and metabolism, thereby enabling the rational elaboration of fragment hits.