OR34
Exploring in silico drug repurposing to develop new therapeutic alternatives for tuberculosis
L Rodrigues(1) M Martins(2) P Cravo(1) M Viveiros(1)
1:Global Health and Tropical Medicine, GHTM, LA-REAL, Instituto de Higiene e Medicina Tropical, IHMT, Universidade NOVA de Lisboa, UNL, Lisboa, 1349-008, Portugal; 2:Department of Microbiology, Moyne Institute of Preventive Medicine, School of Genetics and Microbiology, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland
Energy metabolism, particularly the oxidative phosphorylation pathway, has emerged as a novel target pathway in tuberculosis (TB) drug discovery. Inhibitors of bacterial energy metabolism may interfere with several physiological processes, including the activity of efflux pumps involved in drug resistance. In this work, we used a drug repurposing strategy to find drugs that target energy metabolism and membrane transporters in Mycobacterium tuberculosis. A list of M. tuberculosis proteins involved in energy metabolism and membrane transport was compiled using the TDR Targets and Mycobrowser databases. Sequence similarity screenings using DrugBank and STITCH 5.0 databases predicted 69 targets associated with 245 approved drugs. Functional regions comparison between the approved drug targets and M. tuberculosis targets resulted in 18 potential targets, such as succinate dehydrogenases, sodium-potassium ATPases and NADH-dehydrogenases, that are expected to interact with 23 approved drugs. Some examples are thiabendazole, deslanoside, valproic acid and doxorubicin. Preliminary in vitro testing, using M. smegmatis as a model, showed that doxorubicin presented the lowest minimum inhibitory concentration (MIC, 0.8 mg/L), while valproic acid promoted a four-fold MIC reduction for clarithromycin. This work has the potential to benefit TB drug discovery by finding drugs that may serve as lead compounds for the development of new therapeutic strategies against TB and multidrug resistant TB and by introducing a new approach that can increase the probability of identifying effective drugs and decrease the bottlenecks of conventional drug discovery.
