CoMFA based de novo design of Pyrrolidine Carboxamides as Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis

Identification of Novel Inhibitor of Enoyl-Acyl Carrier Protein Reductase (InhA) Enzyme in Mycobacterium tuberculosis from Plant-Derived Metabolites: An In Silico Study

Mycobacterium tuberculosis (M.tb.) enoyl-acyl carrier protein (ACP) reductase (InhA) is validated as a useful target for tuberculosis therapy and is considered an attractive enzyme to drug discovery. This study aimed to identify the novel inhibitor of the InhA enzyme, a potential target of M.tb. involved in the type II fatty acid biosynthesis pathway that controls mycobacterial cell envelope synthesis. We compiled 80 active compounds from Ruta graveolens and citrus plants belonging to the Rutaceae family for pharmacokinetics and molecular docking analyses. The chemical structures of the 80 phytochemicals and the 3D structure of the target protein were retrieved from the PubChem database and RCSB Protein Data Bank, respectively. The evaluation of druglikeness was performed based on Lipinski’s Rule of Five, while the computed phytochemical properties and molecular descriptors were used to predict the ADMET of the compounds. Amongst these, 11 pharmacokinetically-screened compounds were further examined by performing molecular docking analysis with an InhA target using AutoDock 4.2. The docking results showed that gravacridonediol, a major glycosylated natural alkaloid from Ruta graveolens, might possess a promising inhibitory potential against InhA, with a binding energy (B.E.) of −10.80 kcal/mole and inhibition constant (Ki) of 600.24 nM. These contrast those of the known inhibitor triclosan, which has a B.E. of −6.69 kcal/mole and Ki of 12.43 µM. The binding efficiency of gravacridonediol was higher than that of the well-known inhibitor triclosan against the InhA target. The present study shows that the identified natural compound gravacridonediol possesses drug-like properties and also holds promise in inhibiting InhA, a key target enzyme of M.tb.

Cell wall permeability assisted virtual screening to identify potential direct InhA inhibitors of Mycobacterium tuberculosis and their biological evaluation

The arising cases of isoniazid-resistance have motivated research interests toward new class of molecules known as direct InhA inhibitors. Here, a combine approach of shape-based pharmacophore and descriptor-based 2D QSAR was used to identify the potential direct InhA inhibitors. The approach is duly assisted with in vitro testing and molecular dynamics simulations. A combination of empirical parameters was derived to use as a filter for cell wall permeability while 2D QSAR was used as another filter to predict the biological activity. Both filters were applied to prioritize the molecules for biological evaluation against anti-TB activity. It led to 6 potential molecules which showed > 90% inhibition of H37Rv strain of Mycobacterium tuberculosis in BACTEC assay. Further, MMGBSA binding free energy of identified molecules was compared with available highly potent molecule, 5-hexyl-2-(2-methylphenoxy) phenol (IC₅₀ = 5nM) using molecular dynamics simulations. It showed two molecules with comparatively higher affinity toward InhA as compared to potent molecule. It indicated the candidature of identified molecules to be further considered in anti-TB drug development pipeline.

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents.

QSAR based therapeutic management of M. tuberculosis

Mycobacterium tuberculosis is responsible for severe mortality and morbidity worldwide but, under-developed and developing countries are more prone to infection. In search of effective and wide-spectrum anti-tubercular agents, interdisciplinary approaches are being explored. Of the several approaches used, computer based quantitative structure activity relationship (QSAR) have gained momentum. Structure-based drug design and discovery implies a combined knowledge of accurate prediction of ligand poses with the good prediction and interpretation of statistically validated models derived from the 3D-QSAR approach. The validated models are generally used to screen a small combinatorial library of potential synthetic candidates to identify hits which further subjected to docking to filter out compounds as novel potential emerging drug molecules to address multidrug-resistant tuberculosis. Several newer models are integrated to QSAR methods which include different types of chemical and biological data, and simultaneous prediction of pharmacological activities including toxicities and/or other safety profiles to get new compounds with desired activity. In the process, several newer molecules have been identified which are now being assessed for their clinical efficacy. Present review deals with the advances made in the field highlighting overall future prospects of the development of anti-tuberculosis drugs.

Analyzing the substitution effect on the CoMFA results within the framework of density functional theory (DFT)

Though QSAR was originally developed in the context of physical organic chemistry, it has been applied very extensively to chemicals (drugs) which act on biological systems, in this idea one of the most important QSAR methods is the 3D QSAR model. However, due to the complexity of understanding the results it is necessary to postulate new methodologies to highlight their physical-chemical meaning. In this sense, this work postulates new insights to understand the CoMFA results using molecular quantum similarity and chemical reactivity descriptors within the framework of density functional theory. To obtain these insights a simple theoretical scheme involving quantum similarity (overlap, coulomb operators, their euclidean distances) and chemical reactivity descriptors such as chemical potential (μ), hardness (ɳ), softness (S), electrophilicity (ω), and the Fukui functions, was used to understand the substitution effect. In this sense, this methodology can be applied to analyze the biological activity and the stabilization process in the non-covalent interactions on a particular molecular set taking a reference compound.

3D-QSAR and molecular modeling studies on 2,3-dideoxy hexenopyranosid-4-uloses as anti-tubercular agents targeting alpha-mannosidase

Ligand-based and structure-based methods were applied in combination to exploit the physicochemical properties of 2,3-dideoxy hex-2-enopyranosid-4-uloses against Mycobacterium tuberculosis H37Rv. Statistically valid 3D-QSAR models with good correlation and predictive power were obtained with CoMFA steric and electrostatic fields (r(2) = 0.797, q(2) = 0.589) and CoMSIA with combined steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (r(2) = 0.867, q(2) = 0.570) based on training set of 33 molecules with predictive r(2) of 0.808 and 0.890 for CoMFA and CoMSIA respectively. The results illustrate the requirement of optimal alkyl chain length at C-1 position and acceptor groups along hydroxy methyl substituent of C-6 to enhance the anti-tubercular activity of the 2,3-dideoxy hex-2-enopyranosid-4-uloses while any substitution at C-3 position exert diminishing effect on anti-tubercular activity of these enulosides. Further, homology modeling of M. tuberculosis alpha-mannosidase followed by molecular docking and molecular dynamics simulations on co-complexed models were performed to gain insight into the rationale for binding affinity of selected inhibitors with the target of interest. The comprehensive information obtained from this study will help to better understand the structural basis of biological activity of this class of molecules and guide further design of more potent analogues as anti-tubercular agents.

Elucidating the structural basis of diphenyl ether derivatives as highly potent enoyl-ACP reductase inhibitors through molecular dynamics simulations and 3D-QSAR study

Diphenyl ether derivatives are good candidates for anti-tuberculosis agents that display a promising potency for inhibition of InhA, an essential enoyl-acyl carrier protein (ACP) reductase involved in fatty acid biosynthesis pathways in Mycobacterium tuberculosis. In this work, key structural features for the inhibition were identified by 3D-QSAR CoMSIA models, constructed based on available experimental binding properties of diphenyl ether inhibitors, and a set of four representative compounds was subjected to MD simulations of inhibitor-InhA complexes for the calculation of binding free energies. The results show that bulky groups are required for the R1 substituent on the phenyl A ring of the inhibitors to favor a hydrophobic pocket formed by residues Phe149, Met155, Pro156, Ala157, Tyr158, Pro193, Met199, Val203, Leu207, Ile215, and Leu218. Small substituents with a hydrophilic property are required at the R3 and R4 positions of the inhibitor phenyl B rings to form hydrogen bonds with the backbones of Gly96 and Met98, respectively. For the R2 substituent, small substituents with simultaneous hydrophilic or hydrophobic properties are required to favor the interaction with the pyrophosphate moiety of NAD(+) and the methyl side chain of Ala198, respectively. The reported data provide structural guidance for the design of new and potent diphenyl ether-based inhibitors with high inhibitory activities against M. tuberculosis InhA.

Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach

Mycobacterium tuberculosis InhA (MtInhA) is an attractive enzyme to drug discovery efforts due to its validation as an effective biological target for tuberculosis therapy. In this work, two different virtual-ligand-screening approaches were applied in order to identify new InhA inhibitors' candidates from a library of ligands selected from the ZINC database. First, a 3-D pharmacophore model was built based on 36 available MtInhA crystal structures. By combining structure-based and ligand-based information, four pharmacophoric points were designed to select molecules able to satisfy the binding features of MtInhA substrate-binding cavity. The second approach consisted of using four well established docking programs, with different search algorithms, to compare the binding mode and score of the selected molecules from the aforementioned library. After detailed analyses of the results, six ligands were selected for in vitro analysis. Three of these molecules presented a satisfactory inhibitory activity with IC50 values ranging from 24 (±2) μM to 83 (±5) μM. The best compound presented an uncompetitive inhibition mode to NADH and 2-trans-dodecenoyl-CoA substrates, with Ki values of 24 (±3) μM and 20 (±2) μM, respectively. These molecules were not yet described as antituberculars or as InhA inhibitors, making its novelty interesting to start efforts on ligand optimization in order to identify new effective drugs against tuberculosis having InhA as a target. More studies are underway to dissect the discovered uncompetitive inhibitor interactions with MtInhA.

Computational models for tuberculosis drug discovery

The search for small molecules with activity against Mycobacterium tuberculosis increasingly uses -high-throughput screening and computational methods. Previously, we have analyzed recent studies in which computational tools were used for cheminformatics. We have now updated this analysis to illustrate how they may assist in finding desirable leads for tuberculosis drug discovery. We provide our thoughts on strategies for drug discovery efforts for neglected diseases.

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.

In Silico Design and Evaluation of Carboxylesterase Inhibitors

Carboxylesterases (CEs) are important enzymes that catalyze biological detoxification, hydrolysis of certain pesticides, and metabolism of many esterified drugs. The development of inhibitors for CE has many potential uses, including increasing drug lifetime and altering biodistrubution; reducing or abrogating toxicity of metabolized drugs; and reducing pest resistance to insecticides. In this review, we discuss the major classes of known mammalian CE inhibitors and describe our computational efforts to design new scaffolds for development of novel, selective inhibitors. We discuss several strategies for in silico inhibitor development, including structure docking, database searching, multidimensional quantitative structure activity analysis (QSAR), and a newly-used approach that uses QSAR combined with de novo drug design. While our research is focused on design of specific inhibitors for human intestinal carboxylesterase (hiCE), the methods described are generally applicable to inhibitors of other enzymes, including CE from other tissues and organisms.

3D-QSAR studies of arylcarboxamides with inhibitory activity on InhA using pharmacophore-based alignment

Enoyl acyl carrier protein reductase (InhA) is a promising target for the development of antituberculosis drugs. The InhA-bound conformation of an indole-5-amide inhibitor (Genz 10850) (PDB code: IP44) was used to build a pharmacophore model by LigandScout. This model was then successfully used to identify the bioactive conformation and align 40 structurally diverse arylcarboxamide derivatives. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on arylcarboxamides-based InhA inhibitors based on pharmacophore alignment. The best prediction was obtained with CoMSIA model combining steric and electrostatic fields (, r(2) = 0.972). The model was validated by an external test set, which gave a good predictive value (). Graphical interpretation of the results revealed important structural features of the zarylcarboxamides related to the active site of InhA. The results may be exploited for further design and virtual screening for some novel InhA inhibitors.

Discovery of potential new InhA direct inhibitors based on pharmacophore and 3D-QSAR analysis followed by in silico screening

This study develops an efficient approach for discovering new InhA direct inhibitors in theory. The InhA-bound conformation of a pyrrolidine carboxamide inhibitor was used to build a pharmacophore model. This model with feature-shape query was successfully used to identify and align the bioactive conformations of pyrrolidine carboxamide analogues and screen SPECS database. A statistically valid 3D-QSAR with good results (r(2)(cv)=0.660 and r(2)=0.962) was obtained. From database screening, 30 hits were selected and identified as potential leads, which exhibit good estimated activities by 3D-QSAR model. Docking studies were carried out on two representative hits to analyze their interactions with InhA. Also, the interactions between existing pyrazole inhibitors and InhA were explored based on the pharmacophore model.