Abstract
The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10-4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world's deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.
Highlights
The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year
Here we extend an in silico tool that we recently created— inferring drug interactions using chemogenomics and orthology (INDIGO) [14]—to predict synergy/antagonism in combinations of two or more drugs
The INDIGO approach requires a list of drug-gene associations and known drug-drug interaction data as input for building a chemical-genetic model of drug interactions
Summary
The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. We developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. New approaches have been developed to increase throughput of checkerboard assays, either by reducing the number of doses required or by using computational optimization to find optimal doses [4,5,6] Even with these developments, the enormous and expanding number of potential drug combinations renders regimen optimization by comprehensive experimental testing infeasible. A feedback-based approach was recently used to determine the optimal dosing of multidrug regimens [4, 5] This approach still requires hundreds of dose-specific measurements for training the algorithm, all of which must be redone whenever a new agent is under consideration. Drugs with similar targets can have both synergistic and antagonistic outcomes [14]
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