Inferring drug interactions in M. abscessus using chemo-genomics and orthology

P.I.: David Sherman PhD
Professor and Chair, Microbiology

Nontuberculous mycobacteria (NTM) of the Mycobacterium abscessus complex (MABSC) are increasingly recognized as an infectious threat to those with cystic fibrosis (CF) or other underlying lung disease. MABSC organisms exhibit intrinsic resistance to most antibiotics, multidrug regimens are empirically derived and outcomes are generally poor. To rationally design improved multidrug regimens for MABSC, we will adapt a recently developed experimentally grounded in silico modelling approach called INDIGO, which employs chemogenomic drug response data to predict cumulative efficacy of drug combinations. From transcriptomes of bacteria responding to single agents, we can rapidly and accurately predict synergy and antagonism of all the possible drug combinations. Here we will apply INDIGO to MABSC, predict new efficacious drug combinations and then validate those predictions on clinically relevant CF patient isolates. If successful, this work could facilitate the rapid and rational development of new therapeutic regimens for pernicious pathogens of CF patients.