Host-mycobacteria conflict: Immune responses of the host vs. the mycobacteria TLR2 and TLR4 ligands and concomitant host-directed therapy

Pattanaik, Kali Prasad; Sengupta, Srabasti; Jit, Bimal Prasad; Kotak, Riddhi; Sonawane, Avinash

doi:10.1016/j.micres.2022.127153

Cited by 11 publications

(16 citation statements)

References 161 publications

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“…A SAR analysis between two tetrahydrofuran-type phyto-constituents, beilschmin A (2.5 μg/mL) exhibited increased activity by presence of 4-methoxybenzo[d] [1,3]dioxole than 1,2,3-trimethoxybenzene B (7.5 μg/mL) (Figure 5A). From predicted drug-ability profiles (À 1.10 and À 0.64), both candidates had negative profiles belonging to the same toxicity class, IV.…”

Section: Structural Activity Relationship Analysismentioning

confidence: 99%

“…From predicted drug-ability profiles (À 1.10 and À 0.64), both candidates had negative profiles belonging to the same toxicity class, IV. Thus, 4-methoxybenzo[d] [1,3]dioxole has a relatively strong activity, but the entire moiety is not sufficient to produce a positive drug-ability score. Even though there are different substitutions in dihydrobenzofuran neolignans such as, licarin A and B, both showed anti-TB activity at 3.1 μg/mL.…”

Section: Structural Activity Relationship Analysismentioning

confidence: 99%

“…Even though there are different substitutions in dihydrobenzofuran neolignans such as, licarin A and B, both showed anti-TB activity at 3.1 μg/mL. Based on molecular docking and toxicity profiles, 2-methoxyphenol substituted licarin A obtained a comparatively higher docking score and belonged to toxicity class-V versus benzo[d] [1,3]dioxole substituted licarin B. However, both chemicals showed a negative drug-likeness score, which hindered their lead candidate status (Figure 5B).…”

Section: Structural Activity Relationship Analysismentioning

confidence: 99%

“…[1] On the other hand, the failure of the front-line anti-TB drugs, isoniazid (INH), rifampicin (RIF), ethambutol (EMB), pyrazinamide (PZA) and streptomycin (STM), the second-line anti-TB drugs, moxifloxacin (MXF), kanamycin (KAN), ethionamide (ETA) and para-aminosalicylic acid (PAS) including the combined directly observed treatment short-course (DOTS) against emerged multidrug-resistant (MDR)-TB and extensively drug-resistant (XDR TB strains to create a pandemic situation and one types of threatening to global communities. [2,3] From the last decade three anti-TB drugs, bedaquiline (BDQ), delamanid (DLM) and pretomanid (PMD), have been introduced as empiric anti-TB treatments. [2,4] As a result, the development of a newer anti-TB drug is slower than drug resistance development.…”

Section: Introductionmentioning

confidence: 99%

“…Approximately 10 million newly diagnosed TB cases (nearly 1.1 million children, 3.2 million women and 5.7 million men) have been reported each year, of which up to 8 million freshly diagnosed TB cases are reported from African and Asian nations [1] . On the other hand, the failure of the front‐line anti‐TB drugs, isoniazid (INH), rifampicin (RIF), ethambutol (EMB), pyrazinamide (PZA) and streptomycin (STM), the second‐line anti‐TB drugs, moxifloxacin (MXF), kanamycin (KAN), ethionamide (ETA) and para ‐aminosalicylic acid (PAS) including the combined directly observed treatment short‐course (DOTS) against emerged multidrug‐resistant (MDR)‐TB and extensively drug‐resistant (XDR TB strains to create a pandemic situation and one types of threatening to global communities [2,3] …”

Section: Introductionmentioning

confidence: 99%

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Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug‐Able Anti‐TB Phytochemicals: A Cost‐Effective Platform for Natural Product‐Based Drug Discovery

Swain

Hussain

2022

Chemistry & Biodiversity

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Based on extensive experimental studies, a huge number of phytochemicals showed potential activity against tuberculosis (TB) at a lower minimum inhibitory concentration (MIC) and fewer toxicity profiles. However, these promising drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles. Thus, early drug-prospective analyses are required at the primary stage to accelerate natural-productbased drug discovery with limited resources and time. In the present study, we have selected seventy-three potential anti-TB phytochemicals (MIC value � 10 μg/mL) and assessed the drug-ability profiles using bioinformatics and combinatorial chemistry tools, systematically. Primarily, the molecular docking study was done against two putative drug targets, catalase-peroxidase enzyme (katG) and RNA polymerase subunit-β (rpoB) of Mycobacterium tuberculosis (Mtb) using AutoDock 4.2 software. Further, assessed the drug-ability score from Molsoft, toxicity profiles from ProTox, pharmacokinetics from SwisADME, hierarchical cluster analysis (HCA) by ChemMine tools and frontier molecular orbitals (FMOs) with Avogadro and structural activity relationships (SAR) analysis with ChemDraw 18.0 software. Above analyses indicated that, lower MIC exhibited anti-TB phytochemicals, abietane, 12-demethylmulticaulin exhibited poor docking and drug-ability scores, while tiliacorinine, 2-nortiliacorinine showed higher binding energy and drug-ability profiles. Overall, tiliacorinine, 2-nortiliacorinine, 7α-acetoxy-6β-hydroxyroyleanone (AHR), (2S)-naringenin and isovachhalcone were found as the most active and drug-able anti-TB candidates from 73 candidates. Phytochemicals are always a vital source of mainstream drugs, but the MIC value of a phytochemical is not sufficient for it to be promoted. An ideal drugability profile is therefore essential for achieving clinical success, where advanced bioinformatics tools help to assess and analyse that profile. Additionally, several natural pharmacophores found in existing anti-TB drugs in SAR analyses also provide crucial information for developing potential anti-TB drug. As a conclusion, combined bioinformatics and combinatorial chemistry are the most effective strategies to locate potent-cum-drug-able candidates in the current drug-development module.

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Section: Structural Activity Relationship Analysismentioning

confidence: 99%

Section: Structural Activity Relationship Analysismentioning

confidence: 99%

Section: Structural Activity Relationship Analysismentioning

confidence: 99%