scholarly journals Discovery of novel potential selective HDAC8 inhibitors by combine ligand-based, structure-based virtual screening and in-vitro biological evaluation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sudhan Debnath ◽  
Tanusree Debnath ◽  
Samhita Bhaumik ◽  
Swapan Majumdar ◽  
Arunasree M. Kalle ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Acid Group ◽  
Biological Evaluation ◽  
In Silico Admet ◽  
Glide Docking ◽  
Hdac8 Inhibitors

AbstractNeuroblastoma is the most common extracranial solid tumor found in children and survival rate is extremely meager. HDAC8, a class I zinc-dependent enzyme, is a potential drug target for treatment of neuroblastoma and T cell lymphoma. Most of the HDAC8 inhibitors discovered till date contains a hydroxamic acid group which acts as a zinc binding group. The high binding affinity to the zinc and other ions results in adverse effects. Also, the non-selective inhibition of HDACs cause a variety of side effects. The objective of this is to identify structurally diverse, non-hydroxamate, novel, potential and selective HDAC8 inhibitors. A number of five featured pharmacophore hypotheses were generated using 32 known selective HDAC8 inhibitors. The hypotheses ADDRR.4 were selected for building 3D QSAR model. This model has an excellent correlation coefficient and good predictive ability, which was employed for virtual screening of Phase database containing 4.3 × 106 molecules. The resultant hits with fitness score >1.0 were optimized using in-silico ADMET (absorption, distribution, metabolism,  excretion, and toxicity) and XP glide docking studies. On the basis of pharmacophore matching, interacting amino acid residues, XP glide score, more affinity towards HDAC8 and less affinity towards other HDACs, and ADME results five hits- SD-01, SD-02, SD-03, SD-04 and SD-05 with new structural scaffolds,  non-hydroxamate were selected for in vitro activity study. SD-01 and SD-02 were found to be active in the nanomolar (nM) range. SD-01 had considerably good selectivity for HDAC8 over HDAC6 and SD-02 had marginal selectivity for HDAC6 over HDAC8. The compounds SD-01 and SD-02 were found to inhibit HDAC8 at concentrations (IC50) 9.0 nM and 2.7 nM, respectively.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

2020 ◽  
Vol 17 (3) ◽  
pp. 365-375
Author(s):  
Vasyl Kovalishyn ◽  
Diana Hodyna ◽  
Vitaliy O. Sinenko ◽  
Volodymyr Blagodatny ◽  
Ivan Semenyuta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Isonicotinic Acid ◽  
Acid Hydrazide ◽  
Predictive Ability ◽  
Antitubercular Activity ◽  
Biological Evaluation ◽  
Starting Point ◽  
Binary Classifiers ◽  
Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

Virtual Screening, Molecular docking and in-silico ADME-Tox analysis for identification of potential main protease (Mpro) enzyme inhibitors

2020 ◽  
Vol 18 ◽  
Author(s):  
Debadash Panigrahi ◽  
Ganesh Prasad Mishra
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
In Silico ◽  
Data Bank ◽  
Future Research ◽  
Reference Compound ◽  
Unexpected Death ◽  
Main Protease ◽  
In Silico Admet

Objective:: Recent pandemic caused by SARS-CoV-2 described in Wuhan China in December-2019 spread widely almost all the countries of the world. Corona virus (COVID-19) is causing the unexpected death of many peoples and severe economic loss in several countries. Virtual screening based on molecular docking, drug-likeness prediction, and in silico ADMET study has become an effective tool for the identification of small molecules as novel antiviral drugs to treat diseases. Methods:: In the current study, virtual screening was performed through molecular docking for identifying potent inhibitors against Mpro enzyme from the ZINC library for the possible treatment of COVID-19 pandemic. Interestingly, some compounds are identified as possible anti-covid-19 agents for future research. 350 compounds were screened based on their similarity score with reference compound X77 from ZINC data bank and were subjected to docking with crystal structure available of Mpro enzyme. These compounds were then filtered by their in silico ADME-Tox and drug-likeness prediction values. Result:: Out of these 350 screened compounds, 10 compounds were selected based on their docking score and best docked pose in comparison to the reference compound X77. In silico ADME-Tox and drug likeliness predictions of the top compounds were performed and found to be excellent results. All the 10 screened compounds showed significant binding pose with the target enzyme main protease (Mpro) enzyme and satisfactory pharmacokinetic and toxicological properties. Conclusion:: Based on results we can suggest that the identified compounds may be considered for therapeutic development against the COVID-19 virus and can be further evaluated for in vitro activity, preclinical, clinical studies and formulated in a suitable dosage form to maximize their bioavailability.

Molecular Modeling Studies on 2,4-Disubstituted Imidazopyridines as Anti-Malarials: Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, In-Silico ADMET and Theoretical Analysis

2021 ◽  
pp. 2150012
Author(s):  
Suraj N. Mali ◽  
Anima Pandey
Keyword(s):  
Molecular Docking ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
In Silico ◽  
Energy Gap ◽  
3D Qsar ◽  
Modeling Studies ◽  
In Vitro Investigation ◽  
In Silico Admet ◽  
Molecular Modeling Studies

Malarial parasites have been reported for moderate-high resistance towards classical antimalarial agents and henceforth development of newer novel chemical entities targeting multiple targets rather than targeting single target will be a highly promising strategy in antimalarial drug discovery. Herein, we carried out molecular modeling studies on 2,4-disubstituted imidazopyridines as anti-hemozoin formation inhibitors by using Schrödinger’s molecular modeling package (2020_4). We have developed statistically robust atom-based 3D-QSAR model (training set, [Formula: see text]; test set, [Formula: see text]; [Formula: see text] [Formula: see text]; root-mean-square error, [Formula: see text]; standard deviation, [Formula: see text]). Our molecular docking, in-silico ADMET analysis showed that dataset molecule 37, has highly promising results. Our ligand-based virtual screening resulted in top five ZINC hits, among them ZINC73737443 hit was observed with lesser energy gap, i.e. 7.85[Formula: see text]eV, higher softness value (0.127[Formula: see text]eV), and comparatively good docking score of [Formula: see text]10.2[Formula: see text]kcal/mol. Our in-silico analysis for a proposed hit, ZINC73737443 showed that this molecule has good ADMET, in-silico nonames toxic as well as noncarcinogenic profile. We believe that further experimental as well as the in-vitro investigation will throw more lights on the identification of ZINC73737443 as a potential antimalarial agent.

scholarly journals COMBINATORIAL PHARMACOPHORE MODELING AND ATOM BASED 3D QSAR STUDIES OF BENZOTHIADIAZINES AS HCV-NS5B INHIBITORS

2018 ◽  
Vol 10 (3) ◽  
pp. 43
Author(s):  
Prasanthi Polamreddy ◽  
Vinita Vishwakarma ◽  
Manoj Kumar Mahto
Keyword(s):  
Virtual Screening ◽  
Correlation Coefficient ◽  
Current Model ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Qsar Model ◽  
Pharmacophore Modeling ◽  
Structural Protein ◽  
Pearson's R ◽  
Pearson’S R

Objective: The objective of the current study was to elucidate the 3D pharmacophoric features of benzothiadiazine derivatives that are crucial for inhibiting Hepatitis C virus (HCV) Non-structural protein 5B (NS5B) and quantifying the features by building an atom based 3D quantitative structure-activity relationship (3D QSAR) model.Methods: Generation of QSAR model was carried out using PHASE 3.3.Results: A five-point pharmacophore model with two hydrogen bond acceptors, one negative ionization potential and two aromatic rings (AANRR) was found to be common among a maximum number of benzothiadiazine based NS5B inhibitors. A statistically significant 3D QSAR model was obtained from AANRR.6 which had correlation-coefficient (R2) value of 0.924, cross-validated correlation-coefficient (Q2) of 0.774, high Fisher ratio of 138 and low root mean square standard error (RMSE=0.29). There is another parameter, Pearson’s R, its value emphasizes correlation between predicted and observed activities of the test set. For the current model, Pearson’s R-value is 0.90, hence underlining the good quality of the model. The present study suggests that nitrogen atom of benzothiadiazine sulfamide ring, oxyacetamide group attached to C7 carbon of benzothiadiazine and sulfonamide oxygens are crucial for NS5B inhibitory activity. Prediction of activities of hit drugs generated in earlier research suggests that Aprepitant (Phase predicted activity: 6.9) could be a potential NS5B inhibitor.Conclusion: This 3D QSAR model developed was statistically good and can be used to predict the activities of newly designed NS5B inhibitors and virtual screening as well. Predict the activities of newly designed NS5B inhibitors and virtual screening as well.

scholarly journals Design, synthesis, and biological evaluation of new 6,N2-diaryl-1,3,5-triazine-2,4-diamines as anticancer agents selectively targeting triple negative breast cancer cells

RSC Advances ◽  
2020 ◽  
Vol 10 (43) ◽  
pp. 25517-25528
Author(s):  
Ahmad Junaid ◽  
Felicia Phei Lin Lim ◽  
Edward R. T. Tiekink ◽  
Anton V. Dolzhenko
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Anticancer Agents ◽  
Breast Cancer Cells ◽  
Triple Negative ◽  
3D Qsar ◽  
Qsar Model ◽  
Biological Evaluation ◽  
Design Synthesis ◽  
Synthesis And Biological Evaluation

New highly potent and selective 6,N2-diaryl-1,3,5-triazine-2,4-diamines were designed and prepared using the 3D-QSAR model developed earlier.

scholarly journals Discovery of New Selective Butyrylcholinesterase (BChE) Inhibitors with Anti-Aβ Aggregation Activity: Structure-Based Virtual Screening, Hit Optimization and Biological Evaluation

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2568 ◽  
Author(s):  
Cheng-Shi Jiang ◽  
Yong-Xi Ge ◽  
Zhi-Qiang Cheng ◽  
Yin-Yin Wang ◽  
Hong-Rui Tao ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Cell Model ◽  
Neuroblastoma Cells ◽  
Biological Evaluation ◽  
Dynamics Simulation ◽  
Dependent Manner ◽  
Binding Modes ◽  
Catalytic Active Site ◽  
Aggregation Activity

In this study, a series of selective butyrylcholinesterase (BChE) inhibitors was designed and synthesized from the structural optimization of hit 1, a 4-((3,4-dihydroisoquinolin-2(1H)-yl)methyl)benzoic acid derivative identified by virtual screening our compound library. The in vitro enzyme assay results showed that compounds 9 ((4-((3,4-dihydroisoquinolin-2(1H)-yl)methyl)phenyl)(pyrrolidin-1-yl)methanone) and 23 (N-(2-bromophenyl)-4-((3,4-dihydroisoquinolin-2(1H)-yl)methyl)benzamide) displayed improved BChE inhibitory activity and good selectivity towards BChE versus AChE. Their binding modes were probed by molecular docking and further validated by molecular dynamics simulation. Kinetic analysis together with molecular modeling studies suggested that these derivatives could target both the catalytic active site (CAS) and peripheral anionic site (PAS) of BChE. In addition, the selected compounds 9 and 23 displayed anti-Aβ1–42 aggregation activity in a dose-dependent manner, and they did not show obvious cytotoxicity towards SH-SY5Y neuroblastoma cells. Also, both compounds showed significantly protective activity against Aβ1-42-induced toxicity in a SH-SY5Y cell model. The present results provided a new valuable chemical template for the development of selective BChE inhibitors.

scholarly journals The Integration of Pharmacophore-Based 3D QSAR Modeling and Virtual Screening in Identification of Natural Product Inhibitors against SARS-CoV-2

2020 ◽  
Author(s):  
Samira Norouzi ◽  
Maryam Farahani ◽  
Samad Nejad Ebrahimi
Keyword(s):  
Virtual Screening ◽  
3D Structure ◽  
3D Qsar ◽  
Qsar Model ◽  
Docking Studies ◽  
Molecular Characteristics ◽  
Qsar Modeling ◽  
Data Set ◽  
Public Health Emergencies ◽  
Current Outbreak

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.

scholarly journals In Silico Identification and Biological Evaluation of Antioxidant Food Components Endowed with Human Carbonic Anhydrase IX and XII Inhibition

Antioxidants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 775 ◽  
Author(s):  
Giosuè Costa ◽  
Annalisa Maruca ◽  
Roberta Rocca ◽  
Francesca Alessandra Ambrosio ◽  
Emanuela Berrino ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Biological Evaluation ◽  
Invasion And Metastasis ◽  
Biological Assays ◽  
Food Components ◽  
Tumor Environment ◽  
Dual Inhibitors ◽  
In Silico Identification ◽  
Dehydrodiconiferyl Alcohol

The tumor-associated isoenzymes hCA IX and hCA XII catalyze the hydration of carbon dioxide to bicarbonate and protons. These isoforms are highly overexpressed in many types of cancer, where they contribute to the acidification of the tumor environment, promoting tumor cell invasion and metastasis. In this work, in order to identify novel dual hCA IX and XII inhibitors, virtual screening techniques and biological assays were combined. A structure-based virtual screening towards hCA IX and XII was performed using a database of approximately 26,000 natural compounds. The best shared hits were submitted to a thermodynamic analysis and three promising best hits were identified and evaluated in terms of their hCA IX and XII inhibitor activity. In vitro biological assays were in line with the theoretical studies and revealed that syringin, lithospermic acid, and (-)-dehydrodiconiferyl alcohol behave as good hCA IX and hCA XII dual inhibitors.

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