scholarly journals Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through In Silico Ligand- and Structure-Based Approaches

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5262
Author(s):  
Deepti Mathpal ◽  
Tahani M. Almeleebia ◽  
Kholoud M. Alshahrani ◽  
Mohammad Y. Alshahrani ◽  
Irfan Ahmad ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
State Of The Art ◽  
Pharmacophore Model ◽  
Pharmacophore Modeling ◽  
Rational Approach ◽  
Binding Affinities ◽  
Reverse Transcriptase Inhibitors ◽  
Hydrogen Bond Acceptor ◽  
First Time ◽  
Screening Approaches

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.

scholarly journals Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Naresh Kandakatla ◽  
Geetha Ramakrishnan
Keyword(s):  
Virtual Screening ◽  
Histone Deacetylases ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Lipinski’S Rule ◽  
Discovery Studio ◽  
Pharmacophore Generation ◽  
Important Therapeutic Target

Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski’s rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors.

Pharmacophore modeling, 3D-QSAR and molecular docking studies of quinazolines and aminopyridines as selective inhibitors of inducible nitric oxide synthase

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Anshika Mittal ◽  
Ritu Arora ◽  
Rita Kakkar
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Inducible Nitric Oxide Synthase ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Acceptor ◽  
Oxide Synthase ◽  
Inducible Nitric Oxide

Pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies have been performed on a dataset of thirty-two quinazoline and aminopyridine derivatives to get an insight into the important structural features required for binding to inducible nitric oxide synthase (iNOS). A four-point CPH (Common Pharmacophore Hypothesis), AHPR.29, with a hydrogen bond acceptor, hydrophobic group, positively charged ionizable group and an aromatic ring, has been obtained as the best pharmacophore model. Satisfactory statistical parameters of correlation ([Formula: see text]) and cross-validated ([Formula: see text]) correlation coefficients, 0.9288 and 0.6353, respectively, show high robustness and good predictive ability of our selected model. The contour maps have been developed from this model and the analysis has provided an interpretable explanation of the effect that various features and substituents have on the potency and selectivity of inhibitors towards iNOS. Docking studies have also been performed in order to analyze the interactions between the enzyme and the inhibitors. Our proposed model can thus be further used for screening a large database of compounds and design new iNOS inhibitors.

scholarly journals Ligand Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Studies of Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) to Discover Novel Estrogen Receptors Alpha (ERα) Inhibitor

2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Hariyanti Hariyanti ◽  
Kusmadi Kurmardi ◽  
Arry Yanuar ◽  
Hayun Hayun
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Estrogen Receptor Alpha ◽  
Pharmacophore Model ◽  
Pharmacophore Modeling ◽  
Cytotoxic Agents ◽  
Breast Development ◽  
Hydrogen Bond Acceptor ◽  
Lipinski’S Rule ◽  
Pharmacophore Models

The estrogen receptor alpha (ERα) plays an important role in breast development and pro-proliferation signal activation in the normal and cancerous breast. The ERα inhibitors were potentially active as cytotoxic agents against breast cancer. This study was conducted in order to find Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) as hits of ERα inhibitor. A training set of 17 selected ERα inhibitors was used to create 10 pharmacophore models using LigandScout 4.2. The pharmacophore models were validated using 383 active compounds as positive data and 20674 decoys as negative data obtained from DUD.E. Model 2 was found as the best pharmacophore model and consisted of three types of pharmacophore features, viz. one hydrophobic, one hydrogen bond acceptor, and aromatic interactions. Model 2 was utilized for ligand-based virtual screening 186 of AIACs, AMACs, intermediates, and Mannich base derivative compounds. The hits obtained were further screened using molecular docking, analyzed using drug scan, and tested for its synthesis accessibility. Fourteen compounds were fulfilled as hits in pharmacophore modeling, in which 10 hits were selected by molecular docking, but only seven hits met Lipinski’s rule of five and had medium synthesis accessibility. In conclusion, seven compounds were suggested to be potentially active as ERα inhibitors and deserve to be synthesized and further investigated.

scholarly journals Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking

2018 ◽  
Vol 19 (10) ◽  
pp. 3204 ◽  
Author(s):  
Yoon Lee ◽  
Gwan-Su Yi
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Correlation Coefficient ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Test Set

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

A Combined approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and in silico ADME/Tox prediction of 4-Arylthio & 4- Aryloxy-3- Iodopyridine-2(1H)-one analogs to identify potential Reverse Transcriptase inhibitor: Anti-HIV agents

2020 ◽  
Vol 17 ◽  
Author(s):  
Debadash Panigrahi ◽  
Amiyakanta Mishra ◽  
Susanta Kumar Sahu ◽  
Mohd. Afzal Azam ◽  
C.M. Vyshaag
Keyword(s):  
Molecular Docking ◽  
Reverse Transcriptase ◽  
Active Site ◽  
3D Qsar ◽  
Qsar Model ◽  
Pharmacophore Modeling ◽  
Reverse Transcriptase Inhibitors ◽  
Rt Inhibitors ◽  
Anti Hiv ◽  
Exclusion Method

Background: Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, cost-effective and high throughput screening method to identify potential reverse transcriptase inhibitors. Methods: A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study. Results: The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square associated with the Sphere Exclusion method (PLS-SE) having r2 and q2 values 0.9509 and 0.8038 respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-cross-validated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties. Conclusion: The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

Molecular Docking Studies and Pharmacophore Modeling of Some Insulin Mimetic Agents from Herbal Sources: A Rational Approach towards Designing of Orally Active Insulin Mimetic Agents

2020 ◽  
Vol 6 (2) ◽  
pp. 121-133
Author(s):  
Joohee Pradhan ◽  
Sunita Panchawat
Keyword(s):  
Hydrogen Bond ◽  
Insulin Receptor ◽  
Pharmacophore Modeling ◽  
Rational Approach ◽  
Oral Insulin ◽  
Hydrogen Bond Acceptor ◽  
Insulin Receptor Tyrosine Kinase ◽  
Lipinski’S Rule ◽  
Insulin Mimetic ◽  
Orally Active

Background:: Many herbal drugs have been found to possess oral insulin mimetic property as evidenced from the literature. Although, to date there is no efficient, synthetic orally active insulin-mimetic drug available clinically. Computer-Aided Drug Design (CADD) may help in the development of such agents through Pharmacophore modeling. Objective:: The present work is aimed at the In-silico designing of Pharmacophore that defines the structural requirements of a molecule to possess oral insulin-mimetic properties. Methods:: A set of 16 orally active insulin-mimetic natural compounds available through literature was used to develop a structure-based pharmacophore in a “three-step filtration process” comprised of Lipinski’s rule of 5, Minimum binding energy with the receptor and Ghose filter to the Lipinski’s rule for oral bioavailability of the drugs. The selected ligands were docked with phosphorylated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog (PDB ID: 1IR3) using Autodock 4.2 and their interaction with the receptor was analyzed followed by the generation of shared and merged feature pharmacophore by Ligandscout 4.2.1. Results:: There are three important structural features that contribute to interaction with the active site of the insulin receptor: these are hydrogen bond donor groups, hydrogen bond acceptor groups and hydrophobic interactions. It is important to note that positive or negative ionizable groups or the presence of aromatic rings are not important for the activity. Conclusion:: Taking a clue from the developed pharmacophore, one may design new lead having necessary groups required for the insulin-mimetic activity that can be elaborated synthetically to get a series of compounds with possible oral insulin-mimetic activity.

scholarly journals In Silico Study Probes Potential Inhibitors of Human Dihydrofolate Reductase for Cancer Therapeutics

2019 ◽  
Vol 8 (2) ◽  
pp. 233 ◽  
Author(s):  
Rabia Mukhtar Rana ◽  
Shailima Rampogu ◽  
Amir Zeb ◽  
Minky Son ◽  
Chanin Park ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Root Mean Square ◽  
Dihydrofolate Reductase ◽  
Binding Free Energy ◽  
Pharmacophore Model ◽  
Cancer Therapeutics ◽  
Randomization Test ◽  
Pharmacophore Modeling ◽  
Mean Square ◽  
Hydrogen Bond Acceptor

Dihydrofolate reductase (DHFR) is an essential cellular enzyme and thereby catalyzes thereduction of dihydrofolate to tetrahydrofolate (THF). In cancer medication, inhibition of humanDHFR (hDHFR) remains a promising strategy, as it depletes THF and slows DNA synthesis and cellproliferation. In the current study, ligand-based pharmacophore modeling identified and evaluatedthe critical chemical features of hDHFR inhibitors. A pharmacophore model (Hypo1) was generatedfrom known inhibitors of DHFR with a correlation coefficient (0.94), root mean square (RMS)deviation (0.99), and total cost value (125.28). Hypo1 was comprised of four chemical features,including two hydrogen bond donors (HDB), one hydrogen bond acceptor (HBA), and onehydrophobic (HYP). Hypo1 was validated using Fischer's randomization, test set, and decoy setvalidations, employed as a 3D query in a virtual screening at Maybridge, Chembridge, Asinex,National Cancer Institute (NCI), and Zinc databases. Hypo1-retrieved compounds were filtered byan absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment test andLipinski's rule of five, where the drug-like hit compounds were identified. The hit compounds weredocked in the active site of hDHFR and compounds with Goldfitness score was greater than 44.67(docking score for the reference compound), clustering analysis, and hydrogen bond interactionswere identified. Furthermore, molecular dynamics (MD) simulation identified three compounds asthe best inhibitors of hDHFR with the lowest root mean square deviation (1.2 Å to 1.8 Å), hydrogenbond interactions with hDHFR, and low binding free energy (−127 kJ/mol to −178 kJ/mol). Finally,the toxicity prediction by computer (TOPKAT) affirmed the safety of the novel inhibitors of hDHFRin human body. Overall, we recommend novel hit compounds of hDHFR for cancer and rheumatoidarthritis chemotherapeutics.

scholarly journals Factors Associated with Virological Response to Etravirine in Nonnucleoside Reverse Transcriptase Inhibitor-Experienced HIV-1-Infected Patients

2009 ◽  
Vol 54 (1) ◽  
pp. 72-77 ◽  
Author(s):  
Anne-Genevieve Marcelin ◽  
Philippe Flandre ◽  
Diane Descamps ◽  
Laurence Morand-Joubert ◽  
Charlotte Charpentier ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Virological Response ◽  
New Drugs ◽  
Resistance Mutations ◽  
Reverse Transcriptase Inhibitors ◽  
Cd4 Cell Count ◽  
Factors Associated ◽  
The Impact ◽  
First Time ◽  
Hiv 1

ABSTRACT To identify factors associated with virological response (VR) to an etravirine (ETR)-based regimen, 243 patients previously treated with nonnucleoside reverse transcriptase inhibitors (NNRTIs) were studied. The impact of baseline HIV-1 RNA, CD4 cell count, past NNRTIs used, 57 NNRTI resistance mutations, genotypic sensitivity score (GSS) for nucleoside reverse transcriptase inhibitors (NRTIs) and protease inhibitors (PIs), and the number of new drugs used with ETR for the first time on the VR to an ETR regimen were investigated. Among the 243 patients, the median baseline HIV-1 RNA level was 4.4 log10 copies/ml (interquartile range [IQR], 3.7 to 4.9) and the median CD4 count was 175 cells/mm3 (IQR, 69 to 312). Patients had been previously exposed to a median of 6 NRTIs, 1, NNRTI, and 5 PIs. Overall, 82% of patients achieved a VR at month 2, as defined by a decrease of at least 1.5 log10 copies/ml and/or HIV-1 RNA level of <50 copies/ml. No difference in VR was observed between patients receiving or not a boosted PI in combination with ETR. Factors independently associated with a better VR to ETR were the number of drugs (among enfuvirtide, darunavir, or raltegravir) used for the first time in combination with ETR and the presence of the K103N mutation at baseline. Mutations Y181V and E138A were independently associated with poor VR, whereas no effect of the Y181C on VR was observed. In conclusion, ETR was associated with high response rates in NNRTI-experienced patients in combination with other active drugs regardless of the therapeutic class used.

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