scholarly journals Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

2020 ◽  
Vol 21 (15) ◽  
pp. 5308
Author(s):  
Mikołaj Mizera ◽  
Dorota Latek ◽  
Judyta Cielecka-Piontek
Keyword(s):  
Virtual Screening ◽  
Cannabis Sativa ◽  
Cannabinoid Receptor ◽  
Data Bank ◽  
Qsar Model ◽  
Molecular Structures ◽  
Computer Assisted ◽  
Cannabinoid Receptor 2 ◽  
Selective Ligands ◽  
Qsar Models

The selective targeting of the cannabinoid receptor 2 (CB2) is crucial for the development of peripheral system-acting cannabinoid analgesics. This work aimed at computer-assisted identification of prospective CB2-selective compounds among the constituents of Cannabis Sativa. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of Cannabis Sativa constituents were collected from a phytochemical database. The collected records were curated and applied for the development of quantitative structure-activity relationship (QSAR) models with a machine learning approach. The validated models predicted the affinities of Cannabis Sativa constituents. Four structures of CB2 were acquired from the Protein Data Bank (PDB) and the discriminatory ability of CB2-selective ligands and two sets of decoys were tested. We succeeded in developing the QSAR model by achieving Q2 5-CV > 0.62. The QSAR models helped to identify three prospective CB2-selective molecules that are dissimilar to already tested compounds. In a complementary structure-based virtual screening study that used available PDB structures of CB2, the agonist-bound, Cryogenic Electron Microscopy structure of CB2 showed the best statistical performance in discriminating between CB2-active and non-active ligands. The same structure also performed best in discriminating between CB2-selective ligands from non-selective ligands.

scholarly journals Ligand-based virtual screening identifies a family of selective cannabinoid receptor 2 agonists

2015 ◽  
Vol 23 (1) ◽  
pp. 241-263 ◽  
Author(s):  
Matteo Gianella-Borradori ◽  
Ivy Christou ◽  
Carole J.R. Bataille ◽  
Rebecca L. Cross ◽  
Graham M. Wynne ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Cannabinoid Receptor ◽  
Cannabinoid Receptor 2

scholarly journals Pharmacophore-based Virtual Screening From Phytocannabinoids for Anti-obesity Activity

2021 ◽  
Author(s):  
Lenir C. Correia ◽  
Jaderson V. Ferreira ◽  
Henrique B. Lima ◽  
Guilherme M. Silva ◽  
Carlos H. T. P. da Silva ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Cannabis Sativa ◽  
Cannabinoid Receptor ◽  
Binding Free Energy ◽  
Endocannabinoid System ◽  
Mean Square ◽  
Cannabinoid Receptor 1 ◽  
Long Period ◽  
Cannabinoid Compounds ◽  
Pharmacological Target

Abstract Search for new pharmacological alternatives for obesity is based on the design and development of compounds that can aid in weight loss so that they can be used safely and effectively over a long period while maintaining their function. The endocannabinoid system is related to obesity by increasing orexigenic signals and reducing satiety signals. Cannabis sativa is a medicinal plant of polypharmaceutical potential that has been widely studied for various medicinal purposes. The in silico evaluation of their natural cannabinoids (also called phytocannabinoids) for anti-obesity purpose stems from the existence of synthetic cannabinoid compounds that have already presented this result, but which did not guarantee patient safety. In order to find new molecules from C. sativa phytocannabinoids, with the potential to interact with the pharmacological target cannabinoid receptor 1, a pharmacophore-based virtual screening was performed, including the evaluation of physicochemical, pharmacokinetic, toxicological predictions and molecular docking. The results obtained from the ZINC12 database pointed to Zinc 69 (ZINC33053402) and Zinc 70 (ZINC19084698) molecules as promising anti-obesity agents. Molecular Dynamics (MD) studies discloses that both complexes were stable by analyzing the RMSD (Root Mean Square Deviation) values, and the binding free energy values demonstrate that the selected structures can interact and inhibit their catalytic activity.

scholarly journals Absence of entourage: Terpenoids commonly found in Cannabis sativa do not modulate the functional activity of Δ9-THC at human CB1and CB2 receptors

2019 ◽  
Author(s):  
Marina Santiago ◽  
Shivani Sachdev ◽  
Jonathon C Arnold ◽  
Iain S McGregor ◽  
Mark Connor
Keyword(s):  
Membrane Potential ◽  
Potassium Channels ◽  
Cannabis Sativa ◽  
Cannabinoid Receptor ◽  
Somatostatin Receptors ◽  
Therapeutic Effects ◽  
Cannabinoid Receptor 1 ◽  
Cannabinoid Receptor 2 ◽  
Potassium Channel Activity

AbstractIntroductionCompounds present in Cannabis sativa such as phytocannabinoids and terpenoids, may act in concert to elicit therapeutic effects. Cannabinoids such as Δ9-tetrahydrocannabinol (Δ9-THC) directly activate cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2), however, it is not known if terpenoids present in Cannabis also affect cannabinoid receptor signalling. Therefore, we examined 6 common terpenoids alone, and in combination with cannabinoid receptor agonists, on CB1 and CB2 signalling in vitro.Materials and MethodsPotassium channel activity in AtT20 FlpIn cells transfected with human CB1 or CB2 receptors was measured in real-time using FLIPR® membrane potential dye in a FlexStation 3 plate reader. Terpenoids were tested individually and in combination for periods up to 30 minutes. Endogenous somatostatin receptors served as a control for direct effects of drugs on potassium channels.Resultsα-Pinene, β-pinene, β-caryophyllene, linalool, limonene and β-myrcene (up to 30-100 µM) did not change membrane potential in AtT20 cells expressing CB1 or CB2, or affect the response to a maximally effective concentration of the synthetic cannabinoid CP55,940. The presence of individual or a combination of terpenoids did not affect the hyperpolarization produced by Δ9-THC (10µM): (CB1: control, 59±7%; with terpenoids (10 µM each) 55±4%; CB2: Δ9-THC 16±5%, with terpenoids (10 µM each) 17±4%). To investigate possible effect on desensitization of CB1 responses, all six terpenoids were added together with Δ9-THC and signalling measured continuously over 30 min. Terpenoids did not affect desensitization, after 30 minutes the control hyperpolarization recovered by 63±6%, in the presence of the terpenoids recovery was 61±5%.DiscussionNone of the six of the most common terpenoids in Cannabis directly activated CB1 or CB2, or modulated the signalling of the phytocannabinoid agonist Δ9-THC. These results suggest that if a phytocannabinoid-terpenoid entourage effect exists, it is not at the CB1 or CB2 receptor level. It remains possible that terpenoids activate CB1 and CB2 signalling pathways that do not involve potassium channels, however, it seems more likely that they may act at different molecular target(s) in the neuronal circuits important for the behavioural effect of Cannabis.

scholarly journals Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy

Molecules ◽  
2021 ◽  
Vol 26 (21) ◽  
pp. 6679
Author(s):  
Mukuo Wang ◽  
Shujing Hou ◽  
Ye Liu ◽  
Dongmei Li ◽  
Jianping Lin
Keyword(s):  
Virtual Screening ◽  
Cannabinoid Receptor ◽  
Radioligand Binding ◽  
Endocannabinoid System ◽  
Screening Strategy ◽  
Antagonistic Activity ◽  
Cb2 Receptor ◽  
Binding Assays ◽  
Cannabinoid Receptor 2 ◽  
Functional Assays

The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a pKi of 5.15-6.66, among which five compounds showed antagonistic activities with pIC50 of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a pKi of 6.66 and pIC50 of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

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