QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and train-test data selection methods

Design, Synthesis, In vitro and In vivo Evaluation of New Imidazo[1,2-a]pyridine Derivatives as Cyclooxygenase-2 Inhibitors

BACKGROUND

Cyclooxygenase-2 (COX-2), the key enzyme in the arachidonic acid conversion to prostaglandins, is one of the enzymes associated with different pathophysiological conditions, such as inflammation, cancers, Alzheimer's, and Parkinson's disease. Therefore, COX-2 inhibitors have emerged as potential therapeutic agents in these diseases.

OBJECTIVE

The objective of this study was to design and synthesize novel imidazo[1,2-a]pyridine derivatives utilizing rational design methods with the specific aim of developing new potent COX-2 inhibitors. Additionally, we sought to investigate the biological activities of these compounds, focusing on their COX-2 inhibitory effects, analgesic activity, and antiplatelet potential. We aimed to contribute to the development of selective COX-2 inhibitors with enhanced therapeutic benefits.

METHODS

Docking investigations were carried out using AutoDock Vina software to analyze the interaction of designed compounds. A total of 15 synthesized derivatives were obtained through a series of five reaction steps. The COX-2 inhibitory activities were assessed using the fluorescent Cayman kit, while analgesic effects were determined through writing tests, and Born's method was employed to evaluate antiplatelet activities.

RESULTS

The findings indicated that the majority of the tested compounds exhibited significant and specific inhibitory effects on COX-2, with a selectivity index ranging from 51.3 to 897.1 and IC50 values of 0.13 to 0.05 μM. Among the studied compounds, derivatives 5e, 5f, and 5j demonstrated the highest potency with IC50 value of 0.05 μM, while compound 5i exhibited the highest selectivity with a selectivity index of 897.19. In vivo analgesic activity of the most potent COX-2 inhibitors revealed that 3-(4-chlorophenoxy)-2-[4-(methylsulfonyl) phenyl] imidazo[1,2-a]pyridine (5j) possessed the most notable analgesic activity with ED50 value of 12.38 mg/kg. Moreover, evaluating the antiplatelet activity showed compound 5a as the most potent for inhibiting arachidonic acidinduced platelet aggregation. In molecular modeling studies, methylsulfonyl pharmacophore was found to be inserted in the secondary pocket of the COX-2 active site, where it formed hydrogen bonds with Arg-513 and His-90.

CONCLUSION

The majority of the compounds examined demonstrated selectivity and potency as inhibitors of COX-2. Furthermore, the analgesic effects observed of potent compounds can be attributed to the inhibition of the cyclooxygenase enzyme.

Design and Development of COX-II Inhibitors: Current Scenario and Future Perspective

Innate inflammation beyond a threshold is a significant problem involved in cardiovascular diseases, cancer, and many other chronic conditions. Cyclooxygenase (COX) enzymes are key inflammatory markers as they catalyze prostaglandins production and are crucial for inflammation processes. While COX-I is constitutively expressed and is generally involved in "housekeeping" roles, the expression of the COX-II isoform is induced by the stimulation of different inflammatory cytokines and also promotes the further generation of pro-inflammatory cytokines and chemokines, which affect the prognosis of various diseases. Hence, COX-II is considered an important therapeutic target for drug development against inflammation-related illnesses. Several selective COX-II inhibitors with safe gastric safety profiles features that do not cause gastrointestinal complications associated with classic anti-inflammatory drugs have been developed. Nevertheless, there is mounting evidence of cardiovascular side effects from COX-II inhibitors that resulted in the withdrawal of market-approved anti-COX-II drugs. This necessitates the development of COX-II inhibitors that not only exhibit inhibit potency but also are free of side effects. Probing the scaffold diversity of known inhibitors is vital to achieving this goal. A systematic review and discussion on the scaffold diversity of COX inhibitors are still limited. To address this gap, herein we present an overview of chemical structures and inhibitory activity of different scaffolds of known COX-II inhibitors. The insights from this article could be helpful in seeding the development of next-generation COX-II inhibitors.

Design, synthesis, and biological evaluation of new 2-(4-(methylsulfonyl)phenyl)-N-phenylimidazo[1,2-a]pyridin-3-amine as selective COX-2 inhibitors

Cyclooxygenase (COX), which plays a role in converting arachidonic acid to inflammatory mediators, could be inhibited by non-steroidal anti-inflammatory drugs (NSAIDs). Although potent NSAIDs are available for the treatment of pain, fever, and inflammation, some side effects, such as gastrointestinal ulcers, limit the use of these medications. In recent years, selective COX-2 inhibitors with a lower incidence of adverse effects attained an important position in medicinal chemistry. In order to introduce some new potent COX-2 inhibitors, a new series of 2-(4-(methylsulfonyl)phenyl)-N-phenylimidazo[1,2-a]pyridin-3-amines was designed, synthesized, and evaluated. The docking studies performed by AutoDock Vina demonstrated that docked molecules were positioned as well as a crystallographic ligand in the COX-2 active site, and SO₂Me pharmacophore was inserted into the secondary pocket of COX-2 and formed hydrogen bonds with the active site. The designed compounds were synthesized through two-step reactions. In the first step, different 1-(4-(methylsulfonyl)phenyl)-2-(phenylamino)ethan-1-one derivatives were obtained by the reaction of aniline derivatives and α-bromo-4-(methylsulfonyl)acetophenone. Then, condensation of intermediates with different 2-aminopyridines gave final compounds. Enzyme inhibition assay and formalin test were performed to evaluate the activity of these compounds. Among these compounds, 8-methyl-2-(4-(methylsulfonyl)phenyl)-N-(p-tolyl)imidazo[1,2-a]pyridin-3-amine (5n) exhibited the highest potency (IC₅₀ = 0.07 µM) and selectivity (selectivity index = 508.6) against COX-2 enzyme (selectivity index: COX-1 IC₅₀/COX-2 IC₅₀). The antinociceptive activity assessment via the formalin test showed that nine derivatives (5a, 5d, 5h, 5i, 5k, 5q, 5r, 5s, and 5t) possessed significant activity compared with the control group with a p value less than 0.05.

The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method

Perhaps there is some similarity between the coronavirus of 2017 and the COVID-19. Consequently, a predictive model for the antiviral activity for the Middle East respiratory syndrome coronavirus (MERS-CoV, 2017) could be useful for designing the strategy and tactics in the struggle with coronaviruses in general and with COVID 19 in particular. Quantitative structure-activity relationships (QSARs) of inhibitory activity to MERS-CoV were developed. The index of ideality of correlation was applied to build up these models for the antiviral activity. The statistical quality of the best model is quite good (r² = 0.84). A mechanistic interpretation of these models based on the molecular features with strong positive (i.e. promoters for endpoint increase) and strong negative (i.e. promoters for endpoint decrease) influence on the inhibitory activity is suggested. A collection of possible biologically active compounds, constructed using data on the above molecular features which are statistically reliable promoters of increase or decrease of the activity, is presented.

In Silico Screening for Anti-inflammatory Bioactive Molecules from Ayurvedic Decoction, Balaguluchyadi kashayam

BACKGROUND

Balaguluchyadi kashayam, a polyherbal Ayurvedic decoction prepared from Sidacordifolia L., Tinospora cordifolia (Willd.) Miers, and Cedrusdeodara (Roxb. ex D.Don) G.Don, is used in Ayurveda for the treatment of chronic inflammatory conditions. Although this herbal decoction has been used for a long period for treating chronic inflammatory conditions, the mechanism of action of the decoction in reducing inflammatory conditions associated with chronic inflammation has not been clearly understood. Mass spectroscopy-based identification of bioactive molecules present in the decoction and its interaction with enzymes/proteins involved in the pathogenesis of chronic inflammation has been carried and reported in this study.

INTRODUCTION

Polyherbalism is one of the major principles of Ayurveda. Various phytoconstituents with different activities in the polyherbal decoction act on multi targets of a wide range of diseases. Balaguluchyadi kashayam is a polyherbal decoction prescribed for chronic inflammatory etiologies and the present study aims to evaluate the binding potential of the compounds, identified from Balaguluchyadi kashayam to enzymes/proteins involved in the development and progression of chronic inflammation.

METHODS

The bioactive compounds present in the Balaguluchyadi Kashayam fractions were extracted by preparative HPLC and identified using UPLC MS Q-TOF. The physicochemical characteristics and ADMET properties of the compounds were calculated using Mol soft, Swiss ADME and OSIRIS data warrior software. Then the binding interactions between the molecules and the proinflammatory mediators such as 5 Lipoxygenase, Cyclooxygenase 2, Tumor necrosis factoralpha convertase enzyme (TACE) and Caspase 1 were determined using molecular docking software Auto Dock 4.0 (http://autodock.scripps.edu/downloads).

RESULTS

The identified bioactive molecules in the decoction showed a good binding affinity towards the enzymes/proteins involved in the development and progression of chronic inflammation compared to the binding affinity of known inhibitors/drugs to the respective enzymes/proteins.

CONCLUSION

The bioactive molecules identified in Balaguluchyadi Kashayam could be developed as potential therapeutic molecules against enzymes/proteins involved in the development and progression of chronic inflammation.

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods

This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a data set of 1630 molecules by removing intermediately active inhibitors, and 12 new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.

Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives

BACKGROUND

Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance.

OBJECTIVE

In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives.

METHODS

The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation.

RESULTS

Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models.

CONCLUSION

According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.

Phosphorhydrazide inhibitors: toxicological profile and antimicrobial evaluation assay, molecular modeling and QSAR study

A series of 23 novel phosphorhydrazide derivatives were synthesized and characterized by spectral techniques, and their anti-ChE, antibacterial and insecticide activities were investigated.

2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods

Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models.

Synthesis and crystal structure of new temephos analogues as cholinesterase inhibitor: molecular docking, QSAR study, and hydrogen bonding analysis of solid state

A series of temephos (Tem) derivatives were synthesized and characterized by 31P, 13C, and 1H NMR and FT-IR spectral techniques. Also, the crystal structure of compound 9 was investigated. The hydrogen bonding energies (E2) were calculated by NBO analysis of the crystal cluster. The activities and the mixed-type mechanism of Tem derivatives were evaluated using the modified Ellman's and Lineweaver-Burk's methods on cholinesterase (ChE) enzymes. The inhibitory activities of Tem derivatives with a P═S moiety were higher than those with a P═O moiety. Docking analysis disclosed that the hydrogen bonds occurred between the OR (R=CH3 and C2H5) oxygen and N-H nitrogen atoms of the selected compounds and the receptor site (GLN and GLU) of ChEs. PCA-QSAR indicated that the correlation coefficients of the electronic variables were dominant compared to the structural descriptors. MLR-QSAR models clarified that the net charges of nitrogen and phosphorus atoms contribute important electronic function in the inhibition of ChEs. The validity of the QSAR model was confirmed by a LOO cross-validation method with q2=0.965 between the training and testing sets.

QSBR study of bitter taste of peptides: application of GA-PLS in combination with MLR, SVM, and ANN approaches

Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR) studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network) to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature). The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values), whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.

Molecular docking and QSAR studies: noncovalent interaction between acephate analogous and the receptor site of human acetylcholinesterase

Twelve new compounds of acephate (Ace) analogues were synthesized and characterized by (31)P, (13)C, and (1)H NMR and IR spectroscopy. The probable insecticide potential of these compounds as well as 23 previously prepared molecules with a general skeleton of RC(O)-NH-P(O)X1X2 was predicted by PASS software. Docking analysis showed that hydrophobic interaction and hydrogen bonding were created between the functional groups of Ace derivatives and the receptor sites of acetylcholinesterase. PCA-QSAR indicated that the electronic descriptors are dominated in comparison with the structural descriptors. The experimental-QSAR (R(2) = 0.903 and VIF < 2.997) and DFT-QSAR (R(2) = 0.990 and VIF ≤ 10) models clarified that the net charge of functional groups contributes an important function in an inhibition mechanism. Validity and integrity of this model were confirmed by the LOO cross-validation method with q(2) = 0.940 and low residuals between the training and testing sets. The correlation matrix of DFT-QSAR model confirmed the molecular docking results.