scholarly journals QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 6057
Author(s):  
Wonyoung Lee ◽  
So-Jung Park ◽  
Ji-Young Hwang ◽  
Kwang-Hyun Hur ◽  
Yong Sup Lee ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Cannabinoid Receptor ◽  
External Validation ◽  
Synthetic Cannabinoids ◽  
Qsar Model ◽  
Least Squares Regression ◽  
Cannabinoid Receptor 1 ◽  
Qsar Modeling ◽  
Activity Data

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure–activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

scholarly journals QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids

2020 ◽  
Author(s):  
Wonyoung Lee ◽  
So-Jung Park ◽  
Ji-Young Hwang ◽  
Kwang-Hyun Hur ◽  
Yong Sup Lee ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Cannabinoid Receptor ◽  
External Validation ◽  
Synthetic Cannabinoids ◽  
Qsar Model ◽  
Least Squares Regression ◽  
Cannabinoid Receptor 1 ◽  
Qsar Modeling ◽  
Activity Data

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure-activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared 15 SCs and their derivatives (tetrahydrocannabinol [THC], naphthoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

scholarly journals Insights into the molecular properties underlying antibacterial activity of prenylated (iso)flavonoids against MRSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sylvia Kalli ◽  
Carla Araya-Cloutier ◽  
Jos Hageman ◽  
Jean-Paul Vincken
Keyword(s):  
Prediction Models ◽  
External Validation ◽  
Qsar Model ◽  
Prediction Errors ◽  
Activity Data ◽  
Gram Positive Bacteria ◽  
Level Of Activity ◽  
Formal Charge ◽  
Validation Set

AbstractHigh resistance towards traditional antibiotics has urged the development of new, natural therapeutics against methicillin-resistant Staphylococcus aureus (MRSA). Prenylated (iso)flavonoids, present mainly in the Fabaceae, can serve as promising candidates. Herein, the anti-MRSA properties of 23 prenylated (iso)flavonoids were assessed in-vitro. The di-prenylated (iso)flavonoids, glabrol (flavanone) and 6,8-diprenyl genistein (isoflavone), together with the mono-prenylated, 4′-O-methyl glabridin (isoflavan), were the most active anti-MRSA compounds (Minimum Inhibitory Concentrations (MIC) ≤ 10 µg/mL, 30 µM). The in-house activity data was complemented with literature data to yield an extended, curated dataset of 67 molecules for the development of robust in-silico prediction models. A QSAR model having a good fit (R2adj 0.61), low average prediction errors and a good predictive power (Q2) for the training (4% and Q2LOO 0.57, respectively) and the test set (5% and Q2test 0.75, respectively) was obtained. Furthermore, the model predicted well the activity of an external validation set (on average 5% prediction errors), as well as the level of activity (low, moderate, high) of prenylated (iso)flavonoids against other Gram-positive bacteria. For the first time, the importance of formal charge, besides hydrophobic volume and hydrogen-bonding, in the anti-MRSA activity was highlighted, thereby suggesting potentially different modes of action of the different prenylated (iso)flavonoids.

scholarly journals Prediction of Micronucleus Assay Outcome using in Vivo Activity Data and Molecular Structure Features

2021 ◽  
Author(s):  
Priyanka Ramesh ◽  
Shanthi V
Keyword(s):  
High Precision ◽  
External Validation ◽  
Micronucleus Assay ◽  
Human Beings ◽  
Activity Data ◽  
In Vivo Experiments ◽  
Evaluation Strategies ◽  
Relationship Model ◽  
Structural Alerts

Abstract In vivo micronucleus assay is the widely used genotoxic test to determine the extent of chromosomal aberrations caused by the chemical compounds in human beings, which plays a significant role in the drug discovery paradigm. To reduce the uncertainties of the in vivo experiments and the expenses, we intended to develop novel machine learning-based tools to predict the toxicity of the compounds with high precision. A total of 472 compounds with known toxicity information were retrieved from the PubChem Bioassay database and literature. The fingerprints and descriptors of the compounds were generated using PaDEL and ChemSAR for the analysis. The performance of the models was assessed using three tires of evaluation strategies such as 5-fold, 10-fold, and external validation. The accuracy of the models during external validation lay between 0.57 and 0.86. Note that a combination of fingerprints and random forest showed reliable predictive capability. In essence, structural alerts causing genotoxicity of the compounds were identified using the structural activity relationship model of SARpy tool. This study highlights that the structural alerts such as chlorocyclohexane and trimethylamine are likely to be the leading cause of toxicity in humans, further validated using the Toxtree application. Indeed, the results from our study will assist in scrutinizing the genotoxicity of the compounds with high precision by replacing extensive sacrifice of animal models.

scholarly journals Structure-Based Identification of Potent Natural Product Chemotypes as Cannabinoid Receptor 1 Inverse Agonists

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2630 ◽  
Author(s):  
Pankaj Pandey ◽  
Kuldeep Roy ◽  
Haining Liu ◽  
Guoyi Ma ◽  
Sara Pettaway ◽  
...  
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Binding Affinity ◽  
Cannabinoid Receptor ◽  
New Drugs ◽  
In Vitro Screening ◽  
Cannabinoid Receptor 1 ◽  
Cardiometabolic Disorders ◽  
Cb1 Antagonists

Natural products are an abundant source of potential drugs, and their diversity makes them a rich and viable prospective source of bioactive cannabinoid ligands. Cannabinoid receptor 1 (CB1) antagonists are clinically established and well documented as potential therapeutics for treating obesity, obesity-related cardiometabolic disorders, pain, and drug/substance abuse, but their associated CNS-mediated adverse effects hinder the development of potential new drugs and no such drug is currently on the market. This limitation amplifies the need for new agents with reduced or no CNS-mediated side effects. We are interested in the discovery of new natural product chemotypes as CB1 antagonists, which may serve as good starting points for further optimization towards the development of CB1 therapeutics. In search of new chemotypes as CB1 antagonists, we screened the in silico purchasable natural products subset of the ZINC12 database against our reported CB1 receptor model using the structure-based virtual screening (SBVS) approach. A total of 18 out of 192 top-scoring virtual hits, selected based on structural diversity and key protein–ligand interactions, were purchased and subjected to in vitro screening in competitive radioligand binding assays. The in vitro screening yielded seven compounds exhibiting >50% displacement at 10 μM concentration, and further binding affinity (Ki and IC50) and functional data revealed compound 16 as a potent and selective CB1 inverse agonist (Ki = 121 nM and EC50 = 128 nM) while three other compounds—2, 12, and 18—were potent but nonselective CB1 ligands with low micromolar binding affinity (Ki). In order to explore the structure–activity relationship for compound 16, we further purchased compounds with >80% similarity to compound 16, screened them for CB1 and CB2 activities, and found two potent compounds with sub-micromolar activities. Most importantly, these bioactive compounds represent structurally new natural product chemotypes in the area of cannabinoid research and could be considered for further structural optimization as CB1 ligands.

Pharmacological comparison of traditional and non-traditional cannabinoid receptor 1 blockers in rodent models in vivo

2017 ◽  
Vol 159 ◽  
pp. 24-35 ◽  
Author(s):  
Balázs Varga ◽  
Ferenc Kassai ◽  
György Szabó ◽  
Péter Kovács ◽  
János Fischer ◽  
...  
Keyword(s):  
Cannabinoid Receptor ◽  
Rodent Models ◽  
Cannabinoid Receptor 1

Development and Validation of a Robust QSAR Model for Benzothiazole Hydrazone Derivatives as Bcl-XL Inhibitors

2018 ◽  
Vol 16 (1) ◽  
pp. 11-20
Author(s):  
Pawan Gupta ◽  
Aleksandrs Gutcaits
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
External Validation ◽  
B Cell Lymphoma ◽  
Qsar Model ◽  
Structural Features ◽  
Training Dataset ◽  
Qsar Modeling ◽  
Data Set ◽  
Test Set

Background: B-cell Lymphoma Extra Large (Bcl-XL) belongs to B-cell Lymphoma two (Bcl-2) family. Due to its over-expression and anti-apoptotic role in many cancers, it has been proven to be a more biologically relevant therapeutic target in anti-cancer therapy. In this study, a Quantitative Structure Activity Relationship (QSAR) modeling was performed to establish the link between structural properties and inhibitory potency of benzothiazole hydrazone derivatives against Bcl-XL. Methods: The 53 benzothiazole hydrazone derivatives have been used for model development using genetic algorithm and multiple linear regression methods. The data set is divided into training and test set using Kennard-Stone based algorithm. The best QSAR model has been selected with statistically significant r2 = 0.931, F-test =55.488 RMSE = 0.441 and Q2 0.900. Results: The model has been tested successfully for external validation (r2 pred = 0.752), as well as different criteria for acceptable model predictability. Furthermore, analysis of the applicability domain has been carried out to evaluate the prediction reliability of external set molecules. The developed QSAR model has revealed that nThiazoles, nROH, EEig13d, WA, BEHv6, HATS6m, RDF035u and IC4 descriptors are important physico-chemical properties for determining the inhibitory activity of these molecules. Conclusion: The developed QSAR model is stable for this chemical series, indicating that test set molecules represent the training dataset. The model is statistically reliable with good predictability. The obtained descriptors reflect important structural features required for activity against Bcl-XL. These properties are designated by topology, shape, size, geometry, substitution information of the molecules (nThiazoles and nROH) and electronic properties. In a nutshell, these characteristics can be successfully utilized for designing and screening of novel inhibitors.

Sign in / Sign up

Export Citation Format

Share Document