Development of a robust QSAR model to predict the affinity of pyrrolidine analogs for dipeptidyl peptidase IV (DPP- IV)

Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management

Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.

Application of machine learning methods for the development of antidiabetic drugs

Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development, which opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Dual Inhibition of DPP-4 and Cholinesterase Enzymes by the Phytoconstituents of the Ethanolic Extract of Prosopis cineraria Pods: Therapeutic Implications for the Treatment of Diabetes-associated Neurological Impairments

BACKGROUND

Insulin resistance causes decreased uptake of glucose which promotes the susceptibility of type 2 associated neurological impairments.

METHODS

The study was aimed to evaluate the inhibition potential of the ethanolic extract of Prosopis cineraria (EPC) pods against DPP-4 and cholinesterase enzymes by in-vitro, in-vivo and in-silico assessments. The present study consists of in vivo studies on a diabetes-induced rat model by HOMA (Homeostasis model assessment) and related parameters, in vitro studies through the DPP-4 enzyme assay and cholinesterase assays using Ellman's reaction. The in-silico studies were conducted by the molecular docking of Cinerin C with targeted enzymes. The phytochemical characterization of the extract was demonstrated through LCMS studies. The antioxidant studies on the extract were performed by FRAP and TEAC assays.

RESULTS

The extract showed 64.8% maximum inhibition of DPP-4, 34.91% inhibition of AChE and 74.35% inhibition of BuChE. The antioxidant capacity of the extract was observed to be 847.81±16.25μM Fe2+ equivalent in the FRAP assay and 0.40 ± 0.08 mmol/l of Trolox equivalent in the TEAC assay. The in vivo study showed competent glycaemic control against significant HOMA IR (1.5), HOMA % β (26.5) and HOMA % S (68.8) as well as pancreatic cell mass proliferation. The insilico analysis also revealed positive interactions of Cinerin C with targeted enzymes (DPP4 and cholinesterase).

CONCLUSION

It can be concluded that the phytoconstituents of Prosopis cineraria pod extract can be significantly considered in neuropharmacology to resolve insulin resistance-induced neurological complications as it showed inhibition against DPP-4, AChE and BuChE target enzymes.

In-Silico QSAR Modelling of Predicted Rho Kinase Inhibitors Against Cardio Vascular Diseases

BACKGROUND

Rho-kinase is an essential downstream target of GTP-binding protein RhoA, and plays a crucial role in the calcium-sensitization pathway. Rho-kinase pathway is critically involved in phosphorylation state of myosin light chain, leading to increased contraction of smooth muscles. Inhibition of this pathway has turned out to be a promising target for several indications such as cardiovascular diseases, glaucoma and inflammatory diseases.

METHODS

The present work focuses on a division-based 2D quantitative structure-activity relationship (QSAR) analysis along with a docking study to predict structural features that may be essential for the enhancement of selectivity and potency of the target compounds. Furthermore, a set of indoles and azaindoles were also projected based on the regression equation as novel developments. Molecular docking was applied for exploring the binding sites of the newly predicted set of compounds with the receptor.

RESULTS

Results of the docked conformations suggested that introduction of non-bulky and substituted groups in the hinge region of ROCK-II ATP binding pocket would improve the activity by decreasing the bulkiness or length of the compounds.

CONCLUSION

ADME studies were performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.

A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on Qint2 , QLGO2 , QBoot2 , MSEtrain , Qext2 , MSEtest , Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher Qint2 of 0.957, QLGO2 of 0.951, QBoot2 of 0.954, Qext2 of 0.938, and lower MSEtrain and MSEtest compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.

SVMDLF: A novel R-based Web application for prediction of dipeptidyl peptidase 4 inhibitors

Dipeptidyl peptidase 4 (DPP4) is a well-known target for the antidiabetic drugs. However, currently available DPP4 inhibitor screening assays are costly and labor-intensive. It is important to create a robust in silico method to predict the activity of DPP4 inhibitor for the new lead finding. Here, we introduce an R-based Web application SVMDLF (SVM-based DPP4 Lead Finder) to predict the inhibitor of DPP4, based on support vector machine (SVM) model, predictions of which are confirmed by in vitro biological evaluation. The best model generated by MACCS structure fingerprint gave the Matthews correlation coefficient of 0.87 for the test set and 0.883 for the external test set. We screened Maybridge database consisting approximately 53,000 compounds. For further bioactivity assay, six compounds were shortlisted, and of six hits, three compounds showed significant DPP4 inhibitory activities with IC₅₀ values ranging from 8.01 to 10.73 μm. This application is an OpenCPU server app which is a novel single-page R-based Web application for the DPP4 inhibitor prediction. The SVMDLF is freely available and open to all users at http://svmdlf.net/ocpu/library/dlfsvm/www/ and http://www.cdri.res.in/svmdlf/.

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.

Identification of Structurally Diverse Antimicrobials Through Sequential Application of Pharmacophore Modeling, Virtual Screening, Molecular Docking and In Vitro Microbiological Assay

Dihydrofolate reductase enzyme has been an attractive biological target for the design and development of antimicrobials. Considering this, we have attempted to identify novel dihydrofolate reductase inhibitors through our well-defined in silico and in vitro work flow. An accurate and predictive pharmacophore model comprising of one hydrogen bond acceptor, two hydrophobic and one ring aromatic was developed and utilized as a query to search the National Cancer Institute and Maybridge database leading to retrieval of various compounds which were filtered on the basis of estimated activity, fit value and Lipinski's violation. Selected hits NSC3423, KM09759, NSC391, NSC2091 and HTS00630 were subjected to docking studies which resulted into visualization of potential interaction capabilities of hits in line to pharmacophoric features. The identified hits were evaluated for in vitro antimicrobial potential, and the results revealed that among all the five hits, NSC3423 is the most potent compound with activity against E. coli, P. aeruginosa, S. aureus, B. substilis, A. niger and F. oxysporum. On the other hand, KM09759, NSC391, NSC2091 and HTS00630 showed varying degree of activities against gram-positive, gram-negative and fungal strains.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

Predicting the DPP-IV inhibitory activity pIC₅₀ based on their physicochemical properties

The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.