<i>In Silico</i>
            -Based High-Throughput Screen for Discovery of Novel Combinations for Tuberculosis Treatment

Rg, Singh; Ramachandran, Vasanthi; Shandil, Radha; Sharma, Sreevalli; Khandelwal, Swati; Karmarkar, Malancha; Kumar, Naveen; Solapure, Suresh; Saralaya, Ramanatha; Naeije, Robert; Panduga, Vijender; Reddy, Jitendar; Prabhakar, K. R.; Rajagopalan, Swaminathan; Rao, N. S.; Narayanan, Shridhar; Anandkumar, Anand; Balasubramanian, V.; Datta, Sumana

doi:10.1128/aac.05148-14

Cited by 9 publications

(10 citation statements)

References 60 publications

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“…INDIGO-MTB outperforms existing strategies in terms of throughput. The largest studies of MTB have so far analyzed up to 200 unique drug combinations (36). Here, we have estimated outcomes for 13,366 pairwise and 721,764 three-way combinations of 164 drugs with significant accuracy based on our prospective validation.…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Jaipalli

Larkins-Ford

et al. 2019

mBio

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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.

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Section: Discussionmentioning

confidence: 99%

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Jaipalli

Larkins-Ford

et al. 2019

mBio

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“…INDIGO-MTB outperforms existing strategies in terms of throughput. The largest studies in MTB have so far analyzed up to two hundred unique drug combinations (37). Here, we have estimated outcomes for 13,366 pairwise and 721,764 three-way combinations of 164 drugs with significant accuracy based on our prospective validation.…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

Jaipalli

Larkins-Ford

et al. 2019

Preprint

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22 23 Classification: BIOLOGICAL SCIENCES: Microbiology; Biophysics and Computational Biology 24 25 ABSTRACT 34 35 The rapid spread of multi-drug resistant strains has created a pressing need for new drug 36 regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new 37 regimens has been challenging due to the slow growth of the pathogen M. tuberculosis (MTB), 38 coupled with large number of possible drug combinations. Here we present a computational 39 model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-40 TB drugs after screening in silico over 1 million potential drug combinations using MTB drug 41 transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key 42 transcriptional regulator of multiple drug interactions, and we confirmed experimentally that 43 Rv1353c up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. 44 Retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that 45 synergistic combinations were significantly more efficacious than antagonistic combinations (p-46 value = 1 x 10 -4 ) based on the percentage of patients with negative sputum cultures after 8 47 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug 48 combinations and is also applicable to other bacterial pathogens. 49 50 IMPORTANCE 51 52 Multi-drug combination therapy is an important strategy for treating tuberculosis, the world's 53 deadliest bacterial infection. Long treatment durations and growing rates of drug resistance 54 have created an urgent need for new approaches to prioritize effective drug regimens. Hence, 55 we developed a computational model called INDIGO-MTB, which identifies synergistic drug 56 regimens from an immense set of possible drug combinations using pathogen response 57 transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB 58 was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB 59 correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also 60identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, 61which could be targeted for rationally enhancing drug synergy. 62 63 64 65 66 67 68 69 70 71 131 gene associations that are correlated with synergy and antagonism. In the example above, MTB 132 upregulation of both gene 1 and gene 3 in response to the drugs measured in monotherapy is predictive 133 of antagonism when the drugs are combined. By perturbing individual genes and known targets of 134 Transcription Factors (TFs) in the model, we can infer the impact of individual gene and TF activity 135 respectively on drug interactions and subsequently engineer interaction outcomes.136 137 138

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“…In a separate study, we have demonstrated the importance of low pH screens by selecting PZA-hybrid molecules in-vitro [ 69 ]. Recently, PZA was also reported to enhance the cidality of various combinations of existing drugs [ 70 ]and is an integral component of emerging novel combinations in the clinic like PAMZ (PA-824, Moxi, PZA), in STAND (Shortening Treatment by Advancing New Drugs, www.clinicaltrial.com ) clinical trial. Our findings unequivocally suggest that anti-TB activity in the low pH environment may be predictive of in-vivo tuberculocidality, especially for the acute phase of infection.…”

Section: Discussionmentioning

confidence: 99%

Unravelling the Secrets of Mycobacterial Cidality through the Lens of Antisense

Kaur¹,

Datta²,

Shandil³

et al. 2016

PLoS ONE

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One of the major impediments in anti-tubercular drug discovery is the lack of a robust grammar that governs the in-vitro to the in-vivo translation of efficacy. Mycobacterium tuberculosis (Mtb) is capable of growing both extracellular as well as intracellular; encountering various hostile conditions like acidic milieu, free radicals, starvation, oxygen deprivation, and immune effector mechanisms. Unique survival strategies of Mtb have prompted researchers to develop in-vitro equivalents to simulate in-vivo physiologies and exploited to find efficacious inhibitors against various phenotypes. Conventionally, the inhibitors are screened on Mtb under the conditions that are unrelated to the in-vivo disease environments. The present study was aimed to (1). Investigate cidality of Mtb targets using a non-chemical inhibitor antisense-RNA (AS-RNA) under in-vivo simulated in-vitro conditions.(2). Confirm the cidality of the targets under in-vivo in experimental tuberculosis. (3). Correlate in-vitro vs. in-vivo cidality data to identify the in-vitro condition that best predicts in-vivo cidality potential of the targets. Using cidality as a metric for efficacy, and AS-RNA as a target-specific inhibitor, we delineated the cidality potential of five target genes under six different physiological conditions (replicating, hypoxia, low pH, nutrient starvation, nitrogen depletion, and nitric oxide).In-vitro cidality confirmed in experimental tuberculosis in BALB/c mice using the AS-RNA allowed us to identify cidal targets in the rank order of rpoB>aroK>ppk>rpoC>ilvB. RpoB was used as the cidality control. In-vitro and in-vivo studies feature aroK (encoding shikimate kinase) as an in-vivo mycobactericidal target suitable for anti-TB drug discovery. In-vitro to in-vivo cidality correlations suggested the low pH (R = 0.9856) in-vitro model as best predictor of in-vivo cidality; however, similar correlation studies in pathologically relevant (Kramnik) mice are warranted. In the acute infection phase for the high fidelity translation, the compound efficacy may also be evaluated in the low pH, in addition to the standard replication condition.

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In Silico -Based High-Throughput Screen for Discovery of Novel Combinations for Tuberculosis Treatment

Cited by 9 publications

References 60 publications

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

Unravelling the Secrets of Mycobacterial Cidality through the Lens of Antisense

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