3D-QSAR and molecular docking analysis of biphenyl amide derivatives as p38α mitogen-activated protein kinase inhibitors

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to spread globally with more than 172 million confirmed cases and 3.57 million deaths. Cyclic sulfonamide derivative is identified as a successful compound and showed anti-SARS-CoV-2 activity. In this study, the structure and activity relationships of 35 cyclic sulfonamide compound inhibitors are investigated by using three-dimensional quantitative structure-activity relationship (3D-QSAR) and holographic quantitative structure-activity relationship (HQSAR). Two models with good statistical parameters and reliable predictive ability are obtained from the same training set, including Topomer CoMFA (q2= 0.623,r2= 0.938,rpred2= 0.893) model and HQSAR (q2= 0.704,r2= 0.958,rpred2=0.779) model. The established models not only have good stability, but also show good external prediction ability for the test set. The contour and color code maps of the models provide a lot of useful information for determining the structural requirements which might affect the activity; this information paves the way for the design of four novel cyclic sulfonamide compounds, and predictes their pIC₅₀ values. We explore the interaction between the newly designed molecule and SARS-CoV-2 3CLpro by molecular docking. The docking results show that GLU166, GLN192, ALA194, and VAL186 may be the potential active residues of the SARS-CoV-2 inhibitor evaluated in this study. Finally, the oral bioavailability and toxicity of the newly designed cyclic sulfonamide compounds are evaluated and the results show that the four newly designed cyclic sulfonamide compounds have major ADMET properties and can be used as reliable inhibitors against COVID-19. These results may provide useful insights for the design of effective SARS-CoV-2 inhibitors.

Current Insights of Inhibitors of p38 Mitogen-Activated Protein Kinase in Inflammation

BACKGROUND

The inflammatory process is one of the mechanisms by which our body upholds us from pathogens such as parasites, bacteria, viruses, and other harmful microorganisms. Inflammatory stimuli activate many intracellular signaling pathways such as the nuclear factor-kB (NF-kB) pathway and three mitogen-activated protein kinase (MAPK) pathways, which are mediated through extracellular-signal regulated kinase (ERK), c-Jun N-terminal kinase (JNK) and p38. The p38 has evolved as an enticing target in treating many persistent inflammatory diseases. Hence, designing novel p38 inhibitors targeting MAPK pathways has acquired significance.

OBJECTIVE

Peruse to identify the lead target to discover novel p38MAPK inhibitors with different scaffolds having improved selectivity over the prototype drugs.

METHODS

Structure and the binding sites of p38MAPK were focused. Various scaffolds designed for inhibition and the molecules which have entered the clinical trials are discussed.

RESULTS

This review aspires to present the available information on the structure and the 3D binding sites of p38MAPK, various scaffolds designed for imidazole, urea, benzamide, azoles, quinoxaline, chromone, ketone as a potent p38MAPK inhibitors and their SAR studies and the molecules which have entered the clinical trials.

CONCLUSION

The development of successful selective p38MAPK inhibitors in inflammatory diseases is in progress despite all challenges. It was speculated that p38MAPK also plays an important role in treating diseases such as neuroinflammation, arterial inflammation, vascular inflammation, cancer and so on, which are posing the world with treatment challenges. In this review, clinical trials of drugs are discussed related to inflammatory and its related diseases. Research is in progress to design and develop novel p38MAPK inhibitors with minimal side effects.

Molecular Modeling Study for the Design of Novel Peroxisome Proliferator-Activated Receptor Gamma Agonists using 3D-QSAR and Molecular Docking

Type 2 diabetes is becoming a global pandemic disease. As an important target for the generation and development of diabetes mellitus, peroxisome proliferator-activated receptor γ (PPARγ) has been widely studied. PPARγ agonists have been designed as potential anti-diabetic agents. The advanced development of PPARγ agonists represents a valuable research tool for diabetes therapy. To explore the structural requirements of PPARγ agonists, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking studies were performed on a series of N-benzylbenzamide derivatives employing comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and surflex-dock techniques. The generated models of CoMFA and CoMSIA exhibited a high cross-validation coefficient (q²) of 0.75 and 0.551, and a non-cross-validation coefficient (r²) of 0.958 and 0.912, respectively. The predictive ability of the models was validated using external validation with predictive factor (r²_pred) of 0.722 and 0.682, respectively. These results indicate that the model has high statistical reliability and good predictive power. The probable binding modes of the best active compounds with PPARγ active site were analyzed, and the residues His323, Tyr473, Ser289 and Ser342 were found to have hydrogen bond interactions. Based on the analysis of molecular docking results, and the 3D contour maps generated from CoMFA and CoMSIA models, the key structural features of PPARγ agonists responsible for biological activity could be determined, and several new molecules, with potentially higher predicted activity, were designed thereafter. This work may provide valuable information in further optimization of N-benzylbenzamide derivatives as PPARγ agonists.

New molecular insights into the tyrosyl-tRNA synthase inhibitors: CoMFA, CoMSIA analyses and molecular docking studies

Drug resistance caused by excessive and indiscriminate antibiotic usage has become a serious public health problem. The need of finding new antibacterial drugs is more urgent than ever before. Tyrosyl-tRNA synthase was proved to be a potent target in combating drug-resistant bacteria. In silico methodologies including molecular docking and 3D-QSAR were employed to investigate a series of newly reported tyrosyl-tRNA synthase inhibitors of furanone derivatives. Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q 2 = 0.611, r 2pred  = 0.933, r 2m  = 0.954) and CoMSIA model (q 2 = 0.546, r 2pred  = 0.959, r 2m  = 0.923). Docking results, which correspond with CoMFA/CoMSIA contour maps, gave the information for interactive mode exploration. Ten new molecules designed on the basis of QSAR and docking models have been predicted more potent than the most active compound 3-(4-hydroxyphenyl)-4-(2-morpholinoethoxy)furan-2(5H)-one (15) in the literatures. The results expand our understanding of furanones as inhibitors of tyrosyl-tRNA synthase and could be helpful in rationally designing of new analogs with more potent inhibitory activities.

Application of Docking Methodologies in QSAR-Based Studies

The computational strategies permeate all aspects of drug discovery such as virtual screening techniques. Virtual screening can be classified into ligand based and structure based methods. The ligand based method such as Quantitative Structure Activity Relationship (QSAR) is used when a set of active ligand compounds is recognized and slight or no structural information is available for the receptors. In structure based drug design, the most widespread method is molecular docking. It is widely accepted that drug activity is obtained through the molecular binding of one ligand to receptor. In their binding conformations, the molecules exhibit geometric and chemical complementarity, both of which are essential for successful drug activity. The molecular docking approach can be used to model the interaction between a small drug molecule and a protein, which allow us to characterize the performance of small molecules in the binding site of target proteins as well as to clarify fundamental biochemical processes.

Prediction and evaluation of the lipase inhibitory activities of tea polyphenols with 3D-QSAR models

The extraordinary hypolipidemic effects of polyphenolic compounds from tea have been confirmed in our previous study. To gain compounds with more potent activities, using the conformations of the most active compound revealed by molecular docking, a 3D-QSAR pancreatic lipase inhibitor model with good predictive ability was established and validated by CoMFA and CoMISA methods. With good statistical significance in CoMFA (r²_cv = 0.622, r² = 0.956, F = 261.463, SEE = 0.096) and CoMISA (r²_cv = 0.631, r² = 0.932, F = 75.408, SEE = 0.212) model, we summarized the structure-activity relationship between polyphenolic compounds and pancreatic lipase inhibitory activities and find the bulky substituents in R₂, R₄ and R₅, hydrophilic substituents in R₁ and electron withdrawing groups in R₂ are the key factors to enhance the lipase inhibitory activities. Under the guidance of the 3D-QSAR results, (2R,3R,2′R,3′R)-desgalloyloolongtheanin-3,3′-O-digallate (DOTD), a potent lipase inhibitor with an IC50 of 0.08 μg/ml, was obtained from EGCG oxidative polymerization catalyzed by crude polyphenol oxidase. Furthermore, DOTD was found to inhibit lipid absorption in olive oil-loaded rats, which was related with inhibiting the activities of lipase in the intestinal mucosa and contents.

Study of novel pyrazolo[3,4-d]pyrimidine derivatives as selective TgCDPK1 inhibitors: molecular docking, structure-based 3D-QSAR and molecular dynamics simulation

We explored the structural features that have an impact on TgCDPK1 activity and TgCDPK1/Src selectivity by multi-computational methods with different statistical models.

Structure based virtual screening to identify selective phosphodiesterase 4B inhibitors

Phosphodiesterase 4 (PDE4), is a hydrolytic enzyme, is proposed as a promising target in asthma and chronic obstructive pulmonary disease. PDE4B selective inhibitors are desirable to reduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. To achieve this goal, ligand based pharmacophore modeling and molecular docking approach is employed. Pharmacophore hypotheses for PDE4B and PDE4D are generated using HypoGen algorithm. The best PDE4B pharmacophore hypothesis (Hypo1_PDE4B) consist of one hydrogen-bond acceptor and two ring aromatic features, whereas PDE4D pharmacophore hypothesis (Hypo1_PDE4D) consist of one hydrogen-bond acceptor, one hydrophobic aliphatic, and two ring aromatic features. The validated pharmacophore hypotheses are used in virtual screening to identify selective PDE4B inhibitors. The hits were screened for their estimated activity, FitValue, and quantitative estimation of drug likeness. After molecular docking analysis, ten hits were purchased for in vitro analysis. Out of these, six hits have shown potent and selective inhibitory activity against PDE4B with IC50 values ranging from 2 to 378nM.

Insight into the interactions between novel isoquinolin-1,3-dione derivatives and cyclin-dependent kinase 4 combining QSAR and molecular docking

Several small-molecule CDK inhibitors have been identified, but none have been approved for clinical use in the past few years. A new series of 4-[(3-hydroxybenzylamino)-methylene]-4H-isoquinoline-1,3-diones were reported as highly potent and selective CDK4 inhibitors. In order to find more potent CDK4 inhibitors, the interactions between these novel isoquinoline-1,3-diones and cyclin-dependent kinase 4 was explored via in silico methodologies such as 3D-QSAR and docking on eighty-one compounds displaying potent selective activities against cyclin-dependent kinase 4. Internal and external cross-validation techniques were investigated as well as region focusing, bootstraping and leave-group-out. A training set of 66 compounds gave the satisfactory CoMFA model (q² = 0.695, r² = 0.947) and CoMSIA model (q² = 0.641, r² = 0.933). The remaining 15 compounds as a test set also gave good external predictive abilities with r²_pred values of 0.875 and 0.769 for CoMFA and CoMSIA, respectively. The 3D-QSAR models generated here predicted that all five parameters are important for activity toward CDK4. Surflex-dock results, coincident with CoMFA/CoMSIA contour maps, gave the path for binding mode exploration between the inhibitors and CDK4 protein. Based on the QSAR and docking models, twenty new potent molecules have been designed and predicted better than the most active compound 12 in the literatures. The QSAR, docking and interactions analysis expand the structure-activity relationships of constrained isoquinoline-1,3-diones and contribute towards the development of more active CDK4 subtype-selective inhibitors.

QSAR methods for the discovery of new inflammatory bowel disease drugs

INTRODUCTION

Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion.

AREAS COVERED

This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed.

EXPERT OPINION

IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.