Structure based virtual screening of MDPI database: discovery of structurally diverse and novel DPP-IV inhibitors

Insights from in silico exploration of major curcumin analogs targeting human dipeptidyl peptidase IV

The goal of this work is to use a variety of in-silico techniques to identify anti-diabetic agents against DPP-IV enzyme from five main curcumin analogues. To produce the successful molecules, five main curcumin analogues were docked into the active site of DPP-IV enzyme. In comparison to the control molecule (Saxagliptin, -6.9 kcal/mol), all the compounds have the highest binding affinity (-7.6 to -7.7 kcal/mol) for the DPP-IV enzyme. These compounds underwent further testing for studies on drug-likeness, pharmacokinetics, and acute toxicity to see the efficacy and safety of compounds. To assess the stability of the docking complex and the binding posture identified during the docking experiment, our study got THC as the lead compound, which was then exposed to 200 ns of molecular dynamic simulation and PCA analysis. Additionally, DFT calculations were conducted to determine the thermodynamic, molecular orbital, and electrostatic potential characteristics of lead compound. Overall, the lead chemical has shown strong drug-like properties, is non-toxic, and has a sizable affinity for the DPP-IV enzyme.Communicated by Ramaswamy H. Sarma.

Pyrazoline scaffold: hit identification to lead synthesis and biological evaluation as antidiabetic agents

Background: Mining of novel scaffolds as potential DPP-IV inhibitors for future development of potential candidates as antidiabetic agents to address global issues. Methodology: The identified hit KB-10 from a previously reported study was taken as a lead for designing a library of analogues and screened initially based on in silico parameters and docking score. A series of selected (2[4-(1-acetyl-5-phenyl-4,5-dihydro-1H-pyrazol-3-yl)phenoxy]-1-phenylethanone derivatives were synthesized and evaluated through in vitro studies. Compounds KB-23, KB-22 and KB-06 were found to be as potent, with IC₅₀ values of 0.10 μM, 0.12 μM and 0.35 μM, respectively. They also showed promising antihyperglycemic potential in in vivo studies (oral glucose tolerance tests) in Wistar rats. Conclusion: This work establishes pyrazoline analogues KB-23, KB-22 and KB-06 as promising starting points for the development of potential antidiabetic agents.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

BACKGROUND

In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents.

RESULTS

To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction.

CONCLUSION

Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.

Anti-glycemic potential of benzophenone thio/semicarbazone derivatives: synthesis, enzyme inhibition and ligand docking studies

Inhibition of dipeptidyl peptidase-IV (DPP-IV) has been identified as a promising approach for the treatment of type 2 diabetes mellitus (T2DM). Therefore, development of DPP-IV inhibitors with new chemical scaffold is of utmost importance to medicinal chemistry. In the present study, we identified benzophenone thio- and semicarbazone scaffolds as novel DPP-IV inhibitors. For that purpose, benzophenone thio- and semicarbazone were synthesized through a 2-step reaction. These newly synthetic derivatives were characterized by different spectroscopic techniques, including HREI-MS and NMR. whereas stereochemistry of the iminic bond was predicted by NOESY experiments. Thio- and semicarbazones derivatives were evaluated for their DPP-IV inhibitory potential and found to exhibit a good to moderate enzyme inhibitory activity. Most active and non-cytotoxic derivatives were further evaluated for their DPP-IV inhibitory potential in in cellulo model. The binding sites as well as affinity of active compounds for DPP- IV enzyme were predicted by in silico studies, and compared to a standard drug, sitagliptin. Pharmacophore studies of thio- and semicarbazones derivatives 1-29 suggest that substitution of aryl group, particularly a lipophilic substituents at C-4″ of benzene ring, and a hydroxyl at C-4' strongly influenced the DPP-IV inhibitory activity. Compound 9 showed the highest inhibitory activity (IC₅₀ = 15.0 ± 0.6 µM), whereas compounds 10, 17, 12, 14 and 23 showed a moderate activity with IC₅₀ values in the range of 28.9-39.2 µM. This study identifies thio- and semicarbazones as new classes of DPP-IV inhibitors which may translate into safe and effective therapeutics for a better management of type 2 diabetes.Communicated by Ramaswamy H. Sarma.

Drug discovery approaches targeting the incretin pathway

Incretin pathway plays an important role in the development of diabetes medications. Interventions in DPP-4 and GLP-1 receptor have shown remarkable efficacy in experimental and clinical studies and imperatively become one of the most promising therapeutic approaches in the T2DM drug discovery pipeline. Herein, we analyzed the actionmechanismsof DPP-4 and GLP-1 receptor targeting the incretin pathway in T2DM treatment. We gave an insight into the structural requirements for the potent DPP-4 inhibitors and revealed a classification of DPP-4 inhibitors by stressing on the binding modes of these ligands to the enzyme. We then reviewed the drug discovery strategies for the development of peptide and non-peptide GLP-1 receptor agonists (GLP-1 RAs). Furthermore, the drug design strategies for DPP-4 inhibitors and GLP-1R agonists were detailed accurately. This review might provide an efficient evidence for the highly potent and selective DPP-4 inhibitors and the GLP-1 RAs, as novel medicines for patients suffering from T2DM.

Cheminformatics in the Identification of Drug Classes for the Treatment of Type 2 Diabetes

Computer-Aided Drug Design has developed into a powerful suite of methods that complement experimental approaches to the identification of new pharmacologically active compounds. In particular, virtual screening has become a standard tool for lead identification. Diverse examples of the application of virtual screening applied to T2DM target proteins have been reported. While several of these indicate successful identification of new lead compounds from synthetic chemical and natural product databases, many of them have been performed on a small scale and with limited validation. Careful study design and collaboration with cheminformaticians and computational chemists will enable these approaches to fulfil their potential for T2DM.

Identification of novel selective Mtb-DHFR inhibitors as antitubercular agents through structure-based computational techniques

Inhibition of dihydrofolate reductase from Mycobacterium tuberculosis-dihydrofolate reductase (Mtb-DHFR) has emerged as a promising approach for the treatment of tuberculosis. To identify novel Mtb-DHFR inhibitors, structure-based virtual screening (SBVS) of the Molecular Diversity Preservation International (MolMall) database was performed using Glide against the Mtb-DHFR and h-DHFR enzymes. On the basis of SBVS, receptor fit, drug-like filters, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, 16 hits were selected and tested for their antitubercular activity against the H₃₇ R_V strain of M. tuberculosis. Five compounds showed promising activity with compounds 11436 and 15275 as the most potent hits with IC₅₀ values of 0.65 and 12.51 μM, respectively, against the H₃₇ R_V strain of M. tuberculosis. The two compounds were further tested in the Mtb-DHFR and h-DHFR enzymatic assay for selectivity and were found to be three- to eight-fold selective towards Mtb-DHFR over h-DHFR with minimum inhibitory concentration values of 5.50, 73.89 µM and 42.00, 263.00 µM, respectively. In silico simulation studies also supported the stability of the protein-ligand complex formation. The present study demonstrates the successful utilization of in silico SBVS tools for the identification of novel and potential Mtb-DHFR inhibitors and compound 11436 ((2,4-dihydroxyphenyl)(3,4,5-trihydroxyphenyl)methanone) as a potential lead for the development of novel Mtb-DHFR inhibitors.

Dipeptidyl peptidase IV inhibitors as a potential target for diabetes: patent review (2015-2018)

INTRODUCTION

Dipeptidyl peptidase 4 (DPP-4) belongs to the family of serine proteases and is involved in the degradation of GLP-1 and GIP hormones, which enhance the production and release of insulin. Targeting DPP-4 inhibitors is increasingly being considered as promising paradigms to treat type 2 diabetes mellitus and therefore DPP-4 inhibitors are being considered as promising antidiabetic drugs.

AREAS COVERE

This review provides an overview of published patents describing natural and synthetic DPP-4 inhibitors from January 2015 to December 2018.

EXPERT OPINION

A fair number of new synthetic and natural DPP-4 inhibitors have been reported in the last four years which describe the progress in the development of various heterocyclic scaffolds or heterocyclic hybrid compounds. As a result of this, many marketed DPP-4 inhibitors that have been approved by the appropriate governing bodies during the past decade, have been introduced as inhibitors. Molecular hybridization is an emerging idea in medicinal chemistry and therefore hybrid compounds of DPP-4 inhibitors with other DPP-4 inhibitors or with antidiabetic drugs should be formulated for a comprehensive evaluation. More detailed pharmacovigilance of DPP-4 inhibitors is required because this will address the pancreas-related adverse events as well as their impact on cardiovascular outcomes via long-term studies.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

DPP-IV Inhibitory Potentials of Flavonol Glycosides Isolated from the Seeds of Lens culinaris: In Vitro and Molecular Docking Analyses

Dipeptidyl peptidase IV (DPP-IV), a new target for the treatment of type 2 diabetes mellitus, degrades incretins such as glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide. DPP-IV inhibitors shorten the inactivation of GLP-1, permitting the incretin to stimulate insulin release, thereby combating hyperglycemia. In our ongoing search for new DPP-IV inhibitors from medicinal plants and foods, three flavonol glycosides (1–3) were isolated from the seeds of Lens culinaris Medikus (Fabaceae) and tested for their DPP-IV–inhibitory activity. We demonstrated for the first time, that compounds 1–3 inhibited DPP-IV activity in a concentration-dependent manner in our in vitro bioassay system. In addition, molecular docking experiments of compounds 1–3 within the binding pocket of DPP-IV were conducted. All investigated compounds readily fit within the active sites of DPP-IV, in low-energy conformations characterized by the flavone core structure having optimal electrostatic attractive interactions with the catalytic triad residues of DPP-IV.

Activity and selectivity cliffs for DPP-IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC₅₀ ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.

Gold Nanoparticle-Based Colorimetric and Electrochemical Methods for Dipeptidyl Peptidase-IV Activity Assay and Inhibitor Screening

We presented the colorimetric and electrochemical methods for determination of the dipeptidyl peptidase-IV (DPP-IV) activity and screening of its inhibitor using gold nanoparticle (AuNP) as the probe. In the colorimetric assay, the substrate peptide with a sequence of Arg-Pro-Arg induced the aggregation and color change of AuNPs, whereas cleavage of the peptide by DPP-IV prevented the aggregation of AuNPs. Furthermore, the aggregation of AuNPs in the solution was easily initiated on a solid/liquid (electrode/electrolyte) surface, which induced a decrease in the electron-transfer resistance. However, once the peptide was clipped by DPP-IV, the assembly of AuNPs on electrode surface was prevented. Consequently, a higher electron-transfer resistance was observed. The colorimetric and electrochemical assays allowed for the determination of DPP-IV with the detection limits of 70 μU/mL and 0.55 μU/mL, respectively. Meanwhile, the proposed methods were used to determine DPP-IV inhibitor with satisfactory results. Both the colorimetric and electrochemical methods are simple, rapid and sufficiently sensitive for DPP-IV activity assay and inhibitor screening. The results also demonstrated that the AuNP-based colorimetric assay could be converted into an enhanced surface tethered electrochemical assay with improving sensitivity. The simple detection principle may be extended to the design of other peptidases biosensors with easy manipulation procedures.

Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones

In this study, the dipeptidyl peptidase-IV (DPP-IV) inhibition activities of a series of novel aminomethyl-piperidones were investigated by molecular docking studies and modeled by quantitative structure-activity relationship (QSAR) methodology. Molecular docking studies were used to find the best conformations of the studied molecules in the active site of DPP-IV protein. Then the best docking-derived conformation for each molecule was applied for calculating the molecular descriptors. Multiple linear regression (MLR) and Levenberg-Marquardt artificial neural network (LM-ANN) were conducted on descriptors derived by docking. The results of these models revealed the superiority of LM-ANN model over MLR which showed the nonlinear relationship between the selected molecular descriptors and DPP-IV inhibition activities of studied molecules. The correlation coefficient (R) and standard error (SE) of ANN model were 0.983 and 0.103 for the training set and 0.966 and 0.168 for the external test set. These results showed a close agreement between the experimental and calculated values of pIC₅₀ which demonstrated the robustness of LM-ANN model in modeling of aminomethyl-piperidones. Applicability domain analysis and sensitivity analysis were applied on the obtained models. This study gives useful information for further experimental studies on DPP-IV inhibitors. The results of this work reveal the applicability of hybrid docking-QSAR methodology in ligand-protein studies.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.