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Matrix metalloproteinase inhibitors enhance the efficacy of frontline drugs against Mycobacterium tuberculosis.

ABSTRACT: Mycobacterium tuberculosis (Mtb) remains a grave threat to world health with emerging drug resistant strains. One prominent feature of Mtb infection is the extensive reprogramming of host tissue at the site of infection. Here we report that inhibition of matrix metalloproteinase (MMP) activity by a panel of small molecule inhibitors enhances the in vivo potency of the frontline TB drugs isoniazid (INH) and rifampicin (RIF). Inhibition of MMP activity leads to an increase in pericyte-covered blood vessel numbers and appears to stabilize the integrity of the infected lung tissue. In treated mice, we observe an increased delivery and/or retention of frontline TB drugs in the infected lungs, resulting in enhanced drug efficacy. These findings indicate that targeting Mtb-induced host tissue remodeling can increase therapeutic efficacy and could enhance the effectiveness of current drug regimens.

SUBMITTER: Xu Y

PROVIDER: S-EPMC5919409 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Matrix metalloproteinase inhibitors enhance the efficacy of frontline drugs against Mycobacterium tuberculosis.

Xu Yitian Y Wang Lihua L Zimmerman Matthew D MD Chen Kai-Yuan KY Huang Lu L Fu Dah-Jiun DJ Kaya Firat F Rakhilin Nikolai N Nazarova Evgeniya V EV Bu Pengcheng P Dartois Veronique V Russell David G DG Shen Xiling X

PLoS pathogens 20180426 4

Mycobacterium tuberculosis (Mtb) remains a grave threat to world health with emerging drug resistant strains. One prominent feature of Mtb infection is the extensive reprogramming of host tissue at the site of infection. Here we report that inhibition of matrix metalloproteinase (MMP) activity by a panel of small molecule inhibitors enhances the in vivo potency of the frontline TB drugs isoniazid (INH) and rifampicin (RIF). Inhibition of MMP activity leads to an increase in pericyte-covered bloo ...[more]

PMID: 29698476

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Project description:BackgroundMycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains.ResultsThis study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach.ConclusionIn this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Dataset Information

Matrix metalloproteinase inhibitors enhance the efficacy of frontline drugs against Mycobacterium tuberculosis.

Publications

Matrix metalloproteinase inhibitors enhance the efficacy of frontline drugs against Mycobacterium tuberculosis.

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