In-silico modelling studies on some C14-urea-tetrandrine derivatives as potent anti-cancer agents against prostate (PC3) cell line

Abdullahi, Mustapha; Uzairu, Adamu; Shallangwa, Gideon Adamu; Mamza, Paul Andrew; Arthur, David Ebuka; Ibrahim, Muhammad

doi:10.1016/j.jksus.2019.01.008

Cited by 21 publications

(20 citation statements)

References 18 publications

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“…The activity concentrations was measured as the concentration that effectively inhibited the virus plaque formation by 50% (EC 50 ) in μM. The H5N1 inhibitory concentration values of these molecules were further converted to the negative logarithmic scale (pEC 50 = EC 50 /10 6 ) in order to reduce skewness in the activity values [4]. The chemical structures of the aryl sulfonamide derivatives along with their observed logarithmic effective concentration were shown in Table 1.…”

Section: Qsar Analysis 221 Collection Of Dataset and Optimizationmentioning

confidence: 99%

“…PaDEL descriptor output in MS Excel sheet was subjected to the variable reduction method so as to eliminate constant and highly inter-correlated descriptors based on user-specified variance and correlation coefficient cutoff values using Data Pretreatment GUI 1.2, downloaded from Drug Theoretics and Cheminformatics (DTC) Laboratory website. In order to establish a rational selection of training set and test set, the Dataset Division GUI 1.0 software was used by engaging Kennard-Stone's algorithm division technique [4].…”

Section: Data Pretreatment and Divisionmentioning

confidence: 99%

“…The development of more newly potent compounds is very expensive or high-price trial and it is timeconsuming. Computational chemistry techniques such as computer-aided drug design (CADD) might save time and reduce the cost of synthesis of the new potent drugs [4]. Ligand-based drug design used quantitative structureactivity relationship (QSAR) approach in designing new compounds from the best statistical model containing some structural features in numerical data (molecular descriptors) which predicts the properties of the compound such as activity, toxicity [5].…”

Section: Introductionmentioning

confidence: 99%

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In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Abdullahi

Shallangwa

Uzairu

2020

Beni-Suef Univ J Basic Appl Sci

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Background: This research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC 50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target. Results: In building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (R 2 train = 0.72963, R 2 adjusted = 0.67169, Q 2 cv = 0.598, R 2 pred ¼ 0.67295, R 2 test = 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus. Conclusion: The results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.

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Section: Qsar Analysis 221 Collection Of Dataset and Optimizationmentioning

confidence: 99%

Section: Data Pretreatment and Divisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Abdullahi

Shallangwa

Uzairu

2020

Beni-Suef Univ J Basic Appl Sci

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“…The calculated descriptors were pre-treated at a correlation coefficient cut-off of 0.8 using DTC Lab's Data Pre-treatment GUI 1.2 software to remove constant, non-informative and highly correlated descriptors (Abdullahi et al, 2019). Furthermore, the pre-treated descriptors were divided in to two independent sets (training and test set) using Kennard and Stone algorithm in the DTC Lab's Dataset Division GUI 1.29 software (Kennard and Stone, 1969;Arthur et al, 2018).…”

Section: Qsar Modelmentioning

confidence: 99%

In silico studies of piperazine derivatives as potent anti-proliferative agents against PC-3 prostate cancer cell lines

Ikwu

Shallangwa

Mamza

et al. 2020

Heliyon

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Quantitative Structure Activity Relationship studies were carried out on arylpiperazine derivatives to investigate their anti-proliferate activity against prostate PC-3 cancer cell lines. The built model with statistical parameters; R 2 ¼ 0.8483, R 2 adj ¼ 0.8078, Q 2 cv ¼ 0.7122 and external validation (R 2 test) 0.6682 revealed that the antiproliferate activities were strongly dependent on the descriptors: MATS7c, MATS3e, maxwHBa and WPSA-3. The Variance Inflation Factor of the descriptors were all greater than one but less than two and all descriptors were poorly correlated (r < 0.4). A graph of the experimental activities and predicted activities showed a high correlation and a William's plot showed the presence of only one outlier compound. These results are similar to those reported for stable and robust models with high predicting power. Molecular docking studies of compounds 5 (1-phenyl-4-(4-(2-(p-tolyloxy)ethyl)benzyl)piperazine) and 17 (4-(4-((4-phenylpiperazin-1-yl)methyl)phenethoxy)benzonitrile) with the androgen receptor gave binding affinities of À7.5 and À7.1 kcal/mol respectively. Compound 5 formed a more stable complex having hydrogen, electrostatic and hydrophobic bond interactions while compound 17 had hydrogen and hydrophobic bond interactions only. This study provides a roadmap to the design of more potent anti-prostate cancer compounds.

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“…However, Gyr A or Gry B can be blocked by these inhibitors for the termination of the DNA replication or to prevent their binding to the DNA [5]. The concept of computational chemistry like computer-aided drug design (CADD) might save the time of discovering or designing new compounds with better potency, and also reduce the cost of synthesis [6]. Virtual docking simulation is very important when carrying out a structure-based drug design (SBDD).…”

Section: Introductionmentioning

confidence: 99%

Virtual molecular docking study of some novel carboxamide series as new anti-tubercular agents

et al. 2020

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A virtual docking simulation study was performed on thirty-five newly discovered compounds of N-(2-phenoxy) ethyl imidazo[1,2-a] pyridine-3-carboxamide (IPA), to explore their theoretical binding energy and pose with the active sites of the Mycobacterium tuberculosis target (DNA gyrase). The chemical structures of the compounds were drawn correctly with ChemDraw Ultra software, and then geometrically optimized at DFT level of theory with Spartan 14 software package. Consequently, the docking analysis was carried out using Molegro Virtual Docker (MVD). Five complexes (Complex 5, 24, 25, 33 and 35) with high binding energy were selected to examine their binding pose with the active sites of the protein. The docking results suggested a good MolDock score (≥ -90 kcal/mol) and Protein-Ligand ANT System (PLANTS) score (≥ -60 kcal/mol) which depicted that the compounds can efficiently bind with the active sites of the target. However, compound 5 has the best binding pose with the MolDock score of -140.476 kcal/mol which formed three hydrogen bond interactions with the Gln 538, Ala 531, and Ala 533 amino acid residues. This research gives a firsthand theoretical knowledge to improve the binding efficiency of these compounds with the target.

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In-silico modelling studies on some C14-urea-tetrandrine derivatives as potent anti-cancer agents against prostate (PC3) cell line

Cited by 21 publications

References 18 publications

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

In silico studies of piperazine derivatives as potent anti-proliferative agents against PC-3 prostate cancer cell lines

Virtual molecular docking study of some novel carboxamide series as new anti-tubercular agents

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