In silico-based high-throughput screen for discovery of novel combinations for tuberculosis treatment.

Ragini Singh, Vasanthi Ramachandran, Radha Shandil, Sreevalli Sharma, Swati Khandelwal, Malancha Karmarkar, Naveen Kumar, Suresh Solapure, Ramanatha Saralaya, Robert Nanduri, Vijender Panduga, Jitendar Reddy, K R Prabhakar, Swaminathan Rajagopalan, Narasimha Rao, Shridhar Narayanan, Anand Anandkumar, V Balasubramanian, Santanu Datta

文献索引：Antimicrob. Agents Chemother. 59 , 5664-74, (2015)

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摘要

There are currently 18 drug classes for the treatment of tuberculosis, including those in the development pipeline. An in silico simulation enabled combing the innumerably large search space to derive multidrug combinations. Through the use of ordinary differential equations (ODE), we constructed an in silico kinetic platform in which the major metabolic pathways in Mycobacterium tuberculosis and the mechanisms of the antituberculosis drugs were integrated into a virtual proteome. The optimized model was used to evaluate 816 triplets from the set of 18 drugs. The experimentally derived cumulative fractional inhibitory concentration (∑FIC) value was within twofold of the model prediction. Bacterial enumeration revealed that a significant number of combinations that were synergistic for growth inhibition were also synergistic for bactericidal effect. The in silico-based screen provided new starting points for testing in a mouse model of tuberculosis, in which two novel triplets and five novel quartets were significantly superior to the reference drug triplet of isoniazid, rifampin, and ethambutol (HRE) or the quartet of HRE plus pyrazinamide (HREZ). Copyright © 2015, Singh et al.