In silico design of human IMPDH inhibitors using pharmacophore mapping and molecular docking approaches

Synthetic 1,2,4-triazole-3-carboxamides Induce Cell Cycle Arrest and Apoptosis in Leukemia Cells

BACKGROUND

A number of studies demonstrate the efficacy of ribavirin against various cancer types in in vitro and in vivo models. However, ribavirin induces the development of multiple side effects, suggesting a high demand for ribavirin analogues with improved therapeutic indexes.

OBJECTIVE

This study was focused on the analysis of ribavirin, its aglycon 1,2,4-triazole-3-carboxamide, and several of its derivatives activities in blood cancer cells in vitro.

METHODS

Four 1,2,4-triazole-3-carboxamide derivatives were designed and synthesized. Antiproliferative effects were evaluated in chronic myeloid leukemia cells К562 and acute lymphoblastic leukemia cells CCRF-SB as well as in the cells of whole blood mononuclear fraction of healthy volunteers by cell counting using the trypan blue exclusion method. Cell cycle distribution and apoptosis under the influence of the compounds were analyzed by flow cytometry with PI staining, and then apoptosis data were confirmed by Western blot analysis for PARP1 and caspase-3 cleavage.

RESULTS

We demonstrated the significant antiproliferative effect of 5-(tetrahydropyran-2-yl)-1,2,4-triazole-3- carboxamide and 1-(tetrahydropyran-2-yl)-1,2,4-triazol-3-carboxamide in leukemia cell lines in vitro in comparison to non-transformed monocytes, providing the rationale for further studies of 1,2,4-triazole-3-carboxamide derivatives as anti-leukemia drugs.

CONCLUSION

These results implied that the 1,2,4-triazole-3-carboxamide derivatives exhibited their antiproliferative activities by induction of cell cycle arrest. Consequently, 5-(tetrahydropyran-2-yl)-1,2,4-triazole-3-carboxamide and 1-(tetrahydrofuran-2-yl)-1,2,4-triazol-3-carboxamide may present antimetabolites with potential anticancer efficacy.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

Pharmacophore Modelling and Virtual Screening Studies for the Discovery of Natural Product-Based PDE 3/4 Dual Inhibitors for COPD

Background:

Chronic Obstructive Pulmonary Disorder (COPD) is a chronic and progressive lung disease with a steady increase in prevalence over the recent years. Current treatment options of COPD are aimed at symptomatic relief without the ability to cure COPD, and certain corticosteroid treatments cause patients to be susceptible to infections. Newer studies have hinted that PDE3/4 dual inhibitors may produce a higher efficacy and better safety profile compared to current alternatives. These novel inhibitors may potentially improve the control of COPD exacerbation without increasing the risk of infections. Thus, our study aims to identify and refine natural compounds with PDE3/4 dual inhibitory activities through molecular modelling techniques.

Method:

A two-sided approach through ligand-based and structure-based pharmacophore modelling was employed, followed by virtual screening and molecular docking to identify lead compounds with PDE3/4 dual inhibition activity.

Results:

Pharmacophore based screening of Universal Natural Products Database (UNPD) resulted in identification of one compound for each pharmacophore model, namely UNPD1558 and UNPD139455, with high binding affinities towards both PDE3B and PDE4B. The two compounds were subsequently docked with PDE3B and PDE4B to study their interactions with the active site residues. Structural modifications of the compounds were proposed based on the docking results, to optimise their binding affinity and physicochemical properties.

Conclusion:

Compound 25a4 and compound 28, which were designed based on the structures of UNPD1558 and UNPD139455, respectively, showed improved binding affinity for both PDE3B and PDE4B. These lead compounds showed promising results as drug candidates and their PDE3/4 dual inhibitory properties should be further investigated through in vivo and in vivo studies.

In Silico Exploration of Novel Tubulin Inhibitors: A Combination of Docking and Molecular Dynamics Simulations, Pharmacophore Modeling, and Virtual Screening

Microtubules play a critical role in mitosis and cell division and are regarded as an excellent target for anticancer therapy. Although microtubule-targeting agents have been widely used in the clinical treatment of different human cancers, their clinical application in cancer therapy is limited by both intrinsic and acquired drug resistance and adverse toxicities. In a previous work, we synthesized compound 9IV-c, ((E)-2-(3,4-dimethoxystyryl)-6,7,8-trimethoxy-N-(3,4,5-trimethoxyphenyl)quinoline-4-amine) that showed potent activity against multiple human tumor cell lines, by targeting spindle formation and/or the microtubule network. Accordingly, in this study, to identify potent tubulin inhibitors, at first, molecular docking and molecular dynamics studies of compound 9IV-c were performed into the colchicine binding site of tubulin; then, a pharmacophore model of the 9IV-c-tubulin complex was generated. The pharmacophore model was then validated by Güner-Henry (GH) scoring methods and receiver operating characteristic (ROC) analysis. The IBScreen database was searched by using this pharmacophore model as a screening query. Finally, five retrieved compounds were selected for molecular docking studies. These efforts identified two compounds (b and c) as potent tubulin inhibitors. Investigation of pharmacokinetic properties of these compounds (b and c) and compound 9IV-c displayed that ligand b has better drug characteristics compared to the other two ligands.

Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes- such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

Design, synthesis, and structure activity relationship analysis of new betulinic acid derivatives as potent HIV inhibitors

Prior modification of betulinic acid (1), a natural product lead with promising anti-HIV activity, produced 3-O-(3',3'-dimethylsuccinyl)betulinic acid (bevirimat, 3), the first-in-class HIV maturation inhibitor. After 3-resistant variants were found during Phase I and IIa clinical trials, further modification of 3 produced 4 with improved activity against wild-type and 3-resistant HIV-1. In continued efforts to optimize 1, 63 final products have now been designed, synthesized, and evaluated for anti-HIV-1 replication activity against HIV-1_NL4-3 infected MT-4 cell lines. Five known and 21 new derivatives were as or more potent than 3 (EC₅₀ 0.065 μM), while eight new derivatives were as or more potent than 4 (EC₅₀ 0.019 μM). These derivatives feature expanded structural diversity and chemical space that may improve the antiviral activity and address the growing resistance crisis. Structure-Activity Relationship (SAR) correlations were thoroughly analyzed, and a 3D Quantitative SAR model with high predictability was constructed to facilitate further rational design and development of new potent derivatives.

Molecular docking, 3D-QSAR, and molecular dynamics simulations of thieno[3,2-b]pyrrole derivatives against anticancer targets of KDM1A/LSD1

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which has been proposed as a promising target for anticancer drug development. Extensive research on LSD1 inhibitors has been performed since its discovery. In order to get more information for lead identification and optimization, we carried out a molecular modeling study on a set of 43 thieno[3,2-b]pyrrole competitive inhibitors of LSD1 using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations. Based on the co-crystallized conformer-based alignment (CCBA) method, 3D-QSAR model of thieno[3,2-b]pyrrole derivatives as LSD1 inhibitors was established. The significant statistics (q² = 0.595, r² = 0.959, r²_pred = 0.846) of the 3D-QSAR indicated the good predictive power and statistical reliability of this model. Based on the corresponding contour maps six LSD1 inhibitors were designed and their activities were predicted by 3D-QSAR model. Meanwhile, molecular docking was performed to simulate the probable binding modes between ligands and LSD1 protein. The molecular interactions mainly contributions to the binding affinity for LSD1 inhibitions were further supplemented by 100 ns MD simulations and binding free energy calculation.

Computational investigation reveals Picrasidine C as selective PPARα lead: binding pattern, selectivity mechanism and ADME/tox profile

Natural products and their derivatives have been recognized as an important source of therapeutic agents for many years. Previously we isolated a dimeric β-carboline-type alkaloid Picrasidine C from the root of Picrasma quassioides as subtype-selective peroxisome proliferator-activated receptor α (PPARα) agonist. In order to modify this natural product for better affinity and druggability, we investigated a series of properties exhibited by Picrasidine C, such as its binding mode with PPARα, the selectivity mechanism over PPARγ, as well as ADME/Tox profile through computational methods including sequence alignment, molecular docking, pharmacophore modeling and molecular dynamics simulations. The detailed information of binding pattern and affinity for Picrasidine C elucidated here will be valuable for chemical modification. Besides, the steric hindrance of residue Phe363 in PPARγ pocket was speculated as the main isoform selectivity mechanism for Picrasidine C, which would be helpful for the design of selective derivatives. ADME/Tox prediction was conducted to avoid potential undesirable pharmacokinetic properties for reducing the risk of failure. Finally, novel skeletons were derived from lead compound by core hopping method, validated through molecular dynamic simulations and MM-GBSA calculation. In short, the information obtained from computational strategy would be valuable for us to find more potent, safe and selective PPARα agonists during structural optimization.

Quinone-thioether metabolites of hydroquinone play a dual role in promoting a vicious cycle of ROS generation: in vitro and in silico insights

Humans are exposed to hydroquinone (HQ) via diet, smoking, occupation, and even via inhalation of polluted air. Given its preferential distribution in kidney and liver, the impact of biotransformation on the nephrotoxicity and hepatotoxicity of HQ was evaluated. Indeed, HQ and its metabolites, benzoquinone, and quinone-thioethers (50, 100, 200, and 400 μM) all induced ROS-dependent cell death in both HK-2, a human kidney proximal epithelial cell line, and THLE-2, a human liver epithelial cell line, in a concentration-dependent manner. For a deeper insight into the biological mechanism of ROS stimulation, the bioinformatics database was reviewed. Intriguingly, 163 proteins were currently reported to form co-crystal complex with benzoquinone analogs, a large proportion of which are closely related to ROS generation. After a thorough assessment of the interaction affinity and binding energy, three key mitochondrial proteins that are particularly involved in electric transport, namely, cytochrome BC1, succinate dehydrogenase, and sulfide:quinone oxidoreductase, were highlighted for further verification. Their binding affinity and the action pattern were explored and validated by molecular docking and molecular dynamics simulations. Remarkably, quinone-thioether metabolites of HQ afforded high affinity to the above proteins that purportedly cause a surge in the generation of ROS. Therefore, HQ can be further converted into quinone-thioethers, which on one hand can function as substrates for redox cycling, and on the other hand may afford high affinity with key proteins evolved in mitochondrial electron transport system, leading to a vicious cycle of ROS generation. The combined data provide a prospective insight into the mechanisms of ROS motivation, expanding HQ-mediated toxicology profiles.

Novel glitazones as PPARγ agonists: molecular design, synthesis, glucose uptake activity and 3D QSAR studies

BACKGROUND

An alarming requirement for finding newer antidiabetic glitazones as agonists to PPARγ are on its utmost need from past few years as the side effects associated with the available drug therapy is dreadful. In this context, herein, we have made an attempt to develop some novel glitazones as PPARγ agonists, by rational and computer aided drug design approach by implementing the principles of bioisosterism. The designed glitazones are scored for similarity with the developed 3D pharmacophore model and subjected for docking studies against PPARγ proteins. Synthesized by adopting appropriate synthetic methodology and evaluated for in vitro cytotoxicity and glucose uptake assay. Illustrations about the molecular design of glitazones, synthesis, analysis, glucose uptake activity and SAR via 3D QSAR studies are reported.

RESULTS

The computationally designed and synthesized ligands such as 2-(4-((substituted phenylimino)methyl)phenoxy)acetic acid derivatives were analysed by IR, ¹H-NMR, ¹³C-NMR and MS-spectral techniques. The synthesized compounds were evaluated for their in vitro cytotoxicity and glucose uptake assay on 3T3-L1 and L6 cells. Further the activity data was used to develop 3D QSAR model to establish structure activity relationships for glucose uptake activity via CoMSIA studies.

CONCLUSION

The results of pharmacophore, molecular docking study and in vitro evaluation of synthesized compounds were found to be in good correlation. Specifically, CPD03, 07, 08, 18, 19, 21 and 24 are the candidate glitazones exhibited significant glucose uptake activity. 3D-QSAR model revealed the scope for possible further modifications as part of optimisation to find potent anti-diabetic agents.

Computational Insight into Protein Tyrosine Phosphatase 1B Inhibition: A Case Study of the Combined Ligand- and Structure-Based Approach

Protein tyrosine phosphatase 1B (PTP1B) is an attractive target for treating cancer, obesity, and type 2 diabetes. In our work, the way of combined ligand- and structure-based approach was applied to analyze the characteristics of PTP1B enzyme and its interaction with competitive inhibitors. Firstly, the pharmacophore model of PTP1B inhibitors was built based on the common feature of sixteen compounds. It was found that the pharmacophore model consisted of five chemical features: one aromatic ring (R) region, two hydrophobic (H) groups, and two hydrogen bond acceptors (A). To further elucidate the binding modes of these inhibitors with PTP1B active sites, four docking programs (AutoDock 4.0, AutoDock Vina 1.0, standard precision (SP) Glide 9.7, and extra precision (XP) Glide 9.7) were used. The characteristics of the active sites were then described by the conformations of the docking results. In conclusion, a combination of various pharmacophore features and the integration information of structure activity relationship (SAR) can be used to design novel potent PTP1B inhibitors.

In silico study of subtilisin-like protease 1 (SUB1) from different Plasmodium species in complex with peptidyl-difluorostatones and characterization of potent pan-SUB1 inhibitors

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Homology models of four SUB1 orthologues from P. falciparum species were produced.

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We analyzed the binding mode of our previous difluorostatone inhibitors to six SUB1.

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In vitro activity of our difluorostatone-based inhibitors was correctly predicted.

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We derived a structure-based pan-SUB1 pharmacophore, and validated it in silico.

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We confirmed that development of pan-SUB1 inhibitors is a feasible task.

Plasmodium falciparum subtilisin-like protease 1 (SUB1) is a novel target for the development of innovative antimalarials. We recently described the first potent difluorostatone-based inhibitors of the enzyme ((4S)-(N-((N-acetyl-l-lysyl)-l-isoleucyl-l-threonyl-l-alanyl)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (1) and (4S)-(N-((N-acetyl-l-isoleucyl)-l-threonyl-l-alanylamino)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (2)). As a continuation of our efforts towards the definition of the molecular determinants of enzyme-inhibitor interaction, we herein propose the first comprehensive computational investigation of the SUB1 catalytic core from six different Plasmodium species, using homology modeling and molecular docking approaches. Investigation of the differences in the binding sites as well as the interactions of our inhibitors 1,2 with all SUB1 orthologues, allowed us to highlight the structurally relevant regions of the enzyme that could be targeted for developing pan-SUB1 inhibitors. According to our in silico predictions, compounds 1,2 have been demonstrated to be potent inhibitors of SUB1 from all three major clinically relevant Plasmodium species (P. falciparum, P. vivax, and P. knowlesi). We next derived multiple structure-based pharmacophore models that were combined in an inclusive pan-SUB1 pharmacophore (SUB1-PHA). This latter was validated by applying in silico methods, showing that it may be useful for the future development of potent antimalarial agents.