Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors

Structural aspects of triazole derivatives as HSP90 inhibitors for the treatment of cancer: in silico studies

HSP90, one important class of chaperons has been intensively investigated as a promising and novel class of drug target for cancer therapy from the past few decades. A series of 2-((4-resorcinolyl)-5-aryl-1, 2, 3-triazol-1-yl) acetate derivatives were taken in the present study for the generation of pharmacophore based models, predictive 3 D-QSAR models, docking and ZINC screening studies against HSP90. The investigation included 30 ligands which emerged DHRRR_1 having survival score of 5.59 was found the most effective pharmacophore model. The generated third PLS factor includes a model with significant Q², R², and R² CV values as 0.62, 0.77, and 0.50, respectively. The molecular docking studies against HSP90 showed interactions with important amino acids such as GLY-97, ASN-106, THR-184, ASN-51, PHE-138 and SER-52 required for HSP90 inhibitory activity. According to the docking analysis compound 34 was the top scoring compound, had a docking score of -10.98 from the series and showed interactions with amino acids likeASP-93, GLY-97, AND ASP-102. Using pharmacophore characteristics, the virtual screening investigation was carried out and DHRRR_1 showed the potential ZINC compounds. The ZINC compounds ZINC72417069 and ZINC77522480 showed best XP docking scores (-8.205 and -7.103 consecutively) and the top-scoring compound ZINC72417069 displayed amino acid binding affinity with GLY-97, ASN-106, and THR-184 against HSP90, PDB ID: 2xjx. These ZINC compounds can be used as target for HSP90. The result of the study may further help to the scientist for the design and development of potential HSP90 inhibitors.

Discovery of new small-molecule cyclin-dependent kinase 6 inhibitors through computational approaches

Excessive cell proliferation due to cell cycle disorders is one of the hallmarks of breast cancer. Cyclin-dependent kinases (CDKs), which are involved in the transition of the cell cycle from G1 phase to S phase by combining CDKs with cyclin, are considered promising targets with broad therapeutic potential based on their critical role in cell cycle regulation. Pharmacological evidence has shown that abnormal cell cycle due to the overexpression of CDK6 is responsible for the hyperproliferation of cancer cells. Blocking CDK6 expression inhibits tumour survival and growth. Therefore, CDK6 can be regarded as a potential target for anticancer therapeutics. Thus, small molecules that can be considered CDK inhibitors have been developed into promising anticancer drugs. In this study, combined structure-based and ligand-based in silicon models were created to identify new chemical entities against CDK6 with the appropriate pharmacokinetic properties. The database used to screen drug-like compounds in this thesis was based on the best E-pharmacophore hypothesis and the best ligand-based drug hypothesis. As a result, 147 common compounds were identified by further molecular docking. Surprisingly, the in vitro evaluation results of 20 of those compounds showed that the two had good CDK6 inhibitory effects. The best compound was subjected to kinase panel screening, followed by molecular dynamic simulations. The 50-ns MD studies revealed the pivotal role of VAL101 in the binding of inhibitors to CDK6. Overall, the identification of two new chemical entities with CDK6 inhibitory activity demonstrated the feasibility and potential of the new method.

Tumor progression locus 2 (TPL2) in tumor-promoting Inflammation, Tumorigenesis and Tumor Immunity

Over the years, tumor progression locus 2 (TPL2) has been identified as an essential modulator of immune responses that conveys inflammatory signals to downstream effectors, subsequently modulating the generation and function of inflammatory cells. TPL2 is also differentially expressed and activated in several cancers, where it is associated with increased inflammation, malignant transformation, angiogenesis, metastasis, poor prognosis and therapy resistance. However, the relationship between TPL2-driven inflammation, tumorigenesis and tumor immunity has not been addressed. Here, we reconcile the function of TPL2-driven inflammation to oncogenic functions such as inflammation, proliferation, apoptosis resistance, angiogenesis, metastasis, immunosuppression and immune evasion. We also address the controversies reported on TPL2 function in tumor-promoting inflammation and tumorigenesis, and highlight the potential role of the TPL2 adaptor function in regulating the mechanisms leading to pro-tumorigenic inflammation and tumor progression. We discuss the therapeutic implications and limitations of targeting TPL2 for cancer treatment. The ideas presented here provide some new insight into cancer pathophysiology that might contribute to the development of more integrative and specific anti-inflammatory and anti-cancer therapeutics.

Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors

Pharmacophore modeling, molecular docking, and in silico ADME studies have been carried out to determine the binding mode and drug likeliness profile of Pyrrolidine derivatives as Dipeptidyl peptidase IV inhibitors. A five point pharmacophore model (AAADH_1) was generated using 96 compounds with IC₅₀ values ranging from 1.8 to 13000 nM. A statistically significant 3D-QSAR model was generated from the pharmacophore hypothesis. The model had a high correlation coefficient (R² - 0.92), cross validation coefficient (Q² - 0.776) and F value (F - 144) at 6 component Partial least square factor. Pearson r of 0.7124 indicated greater degree of confidence on the model. The accuracy and predictability of the generated model were checked by Enrichment factor, Receiver operating characteristic curves, area under curve, Boltzmann-enhanced discrimination of Receiver operating characteristic and the Robust initial enhancement. To identify novel and potent Dipeptidyl peptidase IV inhibitors, virtual screening was performed using the ligand and database screening. Considering the potent hit molecules on the basis of pharmacophore virtual screening, we have designed new molecules and further subjected to see the interaction with protein. The catalytic domain of Dipeptidyl peptidase IV enzyme in complex with Vildagliptin (PDB Code: 6B1E) was obtained from protein data bank with resolution 1.77 A°. Compound 75 showed the better binding (dock score -7.966) with protein than standard drug vildagliptin (Dock Score -6.554). The hits obtained on virtual screening of the database have provided new chemical starting points for design and development of novel Dipeptidyl peptidase IV inhibitory agents.Communicated by Ramaswamy H. Sarma.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

Computational design, synthesis and evaluation of new sulphonamide derivatives targeting HIV-1 gp120

Attachment of envelope glycoprotein gp120 to the host cell receptor CD4 is the first step during the human immunodeficiency virus-1 (HIV-1) entry into the host cells that makes it a promising target for drug design. To elucidate the crucial three dimensional (3D) structural features of reported HIV-1 gp120 CD4 binding inhibitors, 3D pharmacophores were generated and receptor based approach was employed to quantify these structural features. A four-partial least square factor model with good statistics and predictive ability was generated for the dataset of 100 molecules. To further ascertain the structural requirement for gp120-CD4 binding inhibition, molecular interaction studies of inhibitors with gp120 was carried out by performing molecular docking using Glide 5.6. Based on these studies, structural requirements were drawn and new molecules were designed accordingly to yield new sulphonamides derivatives. A water based green synthetic approach was adopted to obtain these compounds which were evaluated for their HIV-1 gp120 CD4 binding inhibition. The newly synthesized compounds exhibited remarkable activity (10-fold increase) when compared with the standard BMS 806. Further the stability of newly synthesized derivatives with HIV-1 gp120 was also investigated through molecular dynamics simulation studies. This provides a proof of concept for molecular modeling based design of new inhibitors for inhibition of HIV-1 gp120 CD4 interaction.

Identification of Hydroxamic Acid Based Selective HDAC1 Inhibitors: Computer Aided Drug Design Studies

Background:

Overexpression of Histone deacetylase 1 (HDAC1) is responsible for carcinogenesis by promoting epigenetic silence of tumour suppressor genes. Thus, HDAC1 inhibitors have emerged as the potential therapeutic leads against multiple human cancers, as they can block the activity of particular HDACs, renovate the expression of several tumour suppressor genes and bring about cell differentiation, cell cycle arrest and apoptosis.

Methods:

The present research work comprises atom-based 3D-QSAR, docking, molecular dynamic simulations and DFT (density functional theory) studies on a diverse series of hydroxamic acid derivatives as selective HDAC1 inhibitors. Two pharmacophoric models were generated and validated by calculating the enrichment factors with the help of the decoy set. The Four different 3D-QSAR models i.e., PLS (partial least square) model, MLR (multiple linear regression) model, Field-based model and GFA (Genetic function approximation) model were developed using ‘PHASE’ v3.4 (Schrödinger) and Discovery Studio (DS) 4.1 software and validated using different statistical parameters like internal and external validation.

Results and Discussion:

The results showed that the best PLS model has R2=0.991 and Q2=0.787, the best MLR model has R2= 0.993 and Q2= 0.893, the best Field-based model has R2= 0.974 and Q2= 0.782 and the best GFA model has R2= 0.868 and Q2= 0.782. Cross-validated coefficients, (rcv 2) of 0.967, 0.926, 0.966 and 0.829 was found for PLS model, MLR, Field based and GFA model, respectively, indicated the satisfactory correlativity and prediction. The docking studies were accomplished to find out the conformations of the molecules and their essential binding interactions with the target protein. The trustworthiness of the docking results was further confirmed by molecular dynamics (MD) simulations studies. Density Functional Theory (DFT) study was performed which promptly optimizes the geometry, stability and reactivity of the molecule during receptor-ligand interaction.

Conclusion:

Thus, the present research work provides spatial fingerprints which would be beneficial for the development of potent HDAC1 inhibitors.

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.

Macrophages in multiple myeloma: emerging concepts and therapeutic implications

Multiple myeloma, a clonal plasma cell malignancy, has long provided a prototypic model to study regulatory interactions between malignant cells and their microenvironment. Myeloma-associated macrophages have historically received limited scrutiny, but recent work points to central and non-redundant roles in myeloma niche homeostasis. The evidence supports a paradigm of complex, dynamic and often mutable interactions between macrophages and other cellular constituents of the niche. We and others have shown that macrophages support myeloma cell growth, viability and drug resistance through both contact-mediated and non-contact-mediated mechanisms. These tumor-beneficial roles have evolved in opposition to, or in parallel with, intrinsic pro-inflammatory and tumoricidal properties. Thus, simple blockade of protective "don't eat me" signals on the surface of myeloma cells leads to macrophage-mediated myeloma cell killing. Macrophages also enhance the tumor-supportive role of mesenchymal stem/stromal cells (MSCs) in the niche: importantly, this interaction is bidirectional, producing a distinct state of macrophage polarization that we termed "MSC-educated macrophages." The intriguing pattern of cross-talk between macrophages, MSCs and tumor cells highlights the myeloma niche as a dynamic multi-cellular structure. Targeted reprogramming of these interactions harbors significant untapped therapeutic potential, particularly in the setting of minimal residual disease, the main obstacle toward a cure.

A rational approach to selective pharmacophore designing: an innovative strategy for specific recognition of Gsk3β

We propose a novel cheminformatics approach that combines structure and ligand-based design to identify target-specific pharmacophores with well-defined exclusion ability. Our strategy includes the prediction of selective interactions, developing structure, and knowledge-based selective pharmacophore models, followed by database screening and molecular docking. This unique strategy was employed in addressing the off-target toxicity of Gsk3β and CDKs. The connections of Gsk3β in eukaryotic cell apoptosis and the extensive potency of Gsk3β inhibitors to block cell death have made it a potential drug-discovery target for many grievous human disorders. Gsk3β is phylogenetically very closely related to the CDKs, such as CDK1 and CDK2, which are suggested to be the off-target proteins of Gsk3β inhibitors. Here, we have employed novel computational approaches in designing the ligand candidates that are potentially inhibitory against Gsk3β, with well-defined the exclusion ability to CDKs. A structure-ligand -based selective pharmacophore was modeled. This model was used to retrieve molecules from the zinc database. The hits retrieved were further screened by molecular docking and protein–ligand interaction fingerprints. Based on these results, four molecules were predicted as selective Gsk3β antagonists. It is anticipated that this unique approach can be extended to investigate any protein–ligand specificity.

Pharmacophore generation and atom-based 3D-QSAR of N-iso-propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors

Background

Coronary heart disease continues to be the leading cause of mortality and a significant cause of morbidity and account for nearly 30% of all deaths each year worldwide. High levels of cholesterol are an important risk factor for coronary heart disease. The blockage of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. Herein, we describe the development of effective and robust pharmacophore model and the structure–activity relationship studies of 43N-iso-propyl pyrrole-based derivatives previously reported for HMG-CoA reductase inhibition.

Results

A 5-point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D quantitative structure–activity relationship analysis (3D-QSAR) model for the studied dataset. The obtained 3D-QSAR model has an excellent correlation coefficient value (r² = 0.96) along with good statistical significance as shown by high Fisher ratio (F = 143.2). The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (q² = 0.672). Further, pharmacophoric model was employed for virtual screening to identify four potential HMG-CoA reductase inhibitors.

Conclusions

The QSAR model suggests that electron-withdrawing character is crucial for the HMG-CoA reductase inhibitory activity. In addition to the electron-withdrawing character, hydrogen bond--donating groups, hydrophobic and negative ionic groups positively contribute to the HMG-CoA reductase inhibition. These findings provide a set of guidelines for designing compounds with better HMG-CoA reductase inhibitory potential.