Abstract

Knowledge derived from genome sequences of humans and pathogens has the potential to accelerate diagnosis, prognosis, and cure of disease. We have moved quickly into an era of precision medicine, not only in familial diseases where a mutation in a human gene is important, but also for understanding somatic mutations in cancer. Equally important, the genome sequences of mycobacterial bacterial pathogens in tuberculosis, cystic fibrosis, and leprosy, can give clues about the choice of protein targets in drug discovery. These include those of existing drugs, and the design of new medicines to combat the increasing occurrence of drug resistance.

Structure-guided approaches, both in academia and large pharma, have informed drug discovery for nearly six decades. Over the past 24 years, fragment-based structure-guided techniques have proved effective in lead discovery for cancer, particularly in Astex, a company I cofounded in 1999. In parallel in my academic laboratory in Cambridge University we have used these techniques to target mycobacterial infections not only for tuberculosis, but also for mycobacterial infections of patients with cystic fibrosis and leprosy.

Computational approaches, using both statistical and machine learning methods, have proved to be of major complementary value, particularly in understanding ligand binding and mechanisms of drug resistance. These have led to new ideas about repurposing and redesigning drugs for tuberculosis and other mycobacterial infections.