Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model

AbstractINTRODUCTIONIdentification of drug combinations that could be effective in Alzheimer’s treatment is made difficult by the number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources.METHODSEstimates of the efficacy of the same drug combinations were derived from a clinical dataset and from pre-clinical data, in the form of a computational model of neuroinflammation. Standard linear regression was used to show that the two sets of estimates were correlated, and to rule out possible confounds.RESULTSThe ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications.DISCUSSIONTen combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future clinical and pre-clinical studies.

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

mBio ◽

10.1128/mbio.02627-19 ◽

2019 ◽

Vol 10 (6) ◽

Cited By ~ 6

Author(s):

Shuyi Ma ◽

Suraj Jaipalli ◽

Jonah Larkins-Ford ◽

Jenny Lohmiller ◽

Bree B. Aldridge ◽

...

Keyword(s):

Clinical Trials ◽

Computational Model ◽

Drug Combinations ◽

Broth Culture ◽

Multiple Drug ◽

Content Type ◽

Drug Synergy ◽

Drug Regimens

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.

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

10.1101/800334 ◽

2019 ◽

Author(s):

Shuyi Ma ◽

Suraj Jaipalli ◽

Jonah Larkins-Ford ◽

Jenny Lohmiller ◽

Bree B. Aldridge ◽

...

Keyword(s):

Clinical Trials ◽

Computational Model ◽

Drug Combinations ◽

Broth Culture ◽

Multiple Drug ◽

P Value ◽

Drug Synergy ◽

Drug Regimens

ABSTRACTThe rapid spread of multi-drug 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 M. tuberculosis (MTB), coupled with 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 over 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 up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. 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.IMPORTANCEMulti-drug 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, which identifies synergistic drug regimens from an immense set of possible drug combinations using 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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