QSAR modelling and docking analysis of some thiazole analogues as alfa-glucosidase inhibitors

In-silico activity prediction was performed to predict new inhibitory activities of 2, 9-disubstituted 8-phenylthio/ phenylsulfinyl-9h-purine derivatives as anti-proliferative agents using QSAR technique. The anti-proliferative agents were optimized using Density Functional Theory (DFT) method utilizing the B3LYP/6-31G* level of theory. Genetic Function Algorithm (GFA) was used to build the QSAR models. Out of the models built, the best one was selected and reported because of its fitness statistically with the following assessment parameters: R 2 trng ¼ 0.919035, R 2 adj ¼ 0.893733, Q 2 cv ¼ 0.866475, R 2 test ¼ 0.636217, and LOF ¼ 0.215884. The selected model was further subjected to other assessment such as VIF, Y-scrambling test, applicability domain and found to be statistically significant. The binding mode of some selected 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9H-purine (ligands) in the active site of EGFR-tyrosine kinase (EGFR-TK) (receptor) was studied via Molecular docking. Molecule 22 was identified to have the highest binding energy (-10.4 kcal/mol) among the other selected ligands which it might be as a result of hydrogen interactions formed with MET793 (2.48599 Å, 2.04522 Å) & THR854 (3.76616 Å) amino acid residues and hydrophobic/other interactions with amino acid residues (LEU718, LEU844, MET766, VAL726, ALA743, LYS745 and MET790) in the active site of EGFR-tyrosine kinase (EGFR-TK). The druglikeness of these selected anti-proliferative agents were predicted via the pharmacokinetics profile of the molecules utilizing SWISS ADME. The anti-proliferative agents were found to be orally safe by not having more than 1 violation of the Lipinski's rule of five. This research proposed a way for designing potent anti-proliferative agents against their target enzyme.

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“…The new built QSAR models were anticipated to give low Q 2 and R 2 value. The validation parameter for this test is cR p (cR 2 p >0.5) [28].…”

Section: Validation Of the Selected Modelmentioning

confidence: 99%

In-silico activity prediction and docking studies of some 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9h-purine derivatives as Anti-proliferative agents

Ibrahim

Uzairu

Shallangwa

2020

Heliyon

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“…The Spartan 14 software was used to transform the 2D structures of the sketched NSCLC therapeutic agents to 3D structures before energy minimization (it is achieved by direct importation of the 2D structures to the interface of the software). Also, prior to stable structure generations, there is need to remove constrain from the generated 3D structures, and this was achieved via energy minimization [14]. Stable structure generation is a process of determining the optimum structure of a compound, and this was performed by utilizing the Spartan 14 software.…”

Section: Sourcing Of Datasetmentioning

confidence: 99%

“…1. The NSCLC therapeutic agents were prepared by saving the already determined optimum structures in 2.2 above saved in protein data bank file with the help of the Spartan'14 wave software [14]. The prepared structure of one NSCLC therapeutic agent among the dataset is shown in Fig.…”

Section: Egfr Tyrosine Kinase Enzyme and Ligand Preparation For The Dmentioning

confidence: 99%

Quantitative structure-activity relationship, molecular docking, drug-likeness, and pharmacokinetic studies of some non-small cell lung cancer therapeutic agents

Ibrahim

Uzairu

Shallangwa

2020

Beni-Suef Univ J Basic Appl Sci

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Background Lung cancer has been reported to be among the leading cancer cases in the world. It was also reported to have caused a lot of death every year and accounted for about one-third of the whole cancer deaths in the globe. The main subset of lung cancers that accounts for about 85% of the problems of lung cancer raised above was non-small cell lung cancer (NSCLC). The most common cause of NSCLCs that mostly affects women and cigarette smokers was recognized to be overexpression of epidermal growth factor receptor tyrosine kinase (EGFR TK). Results Five models on thirty five (35) NSCLC therapeutic agents were developed via quantitative structure-activity relationship (QSAR) technique. The best model among them was selected and reported due to its fitness statistically with the following validation parameters: R2 of 0.8764, R2adj of 0.8370, Qcv2 of 0.7655, R2test of 0.7024, and LOF of 0.3312. Molecular docking was used to elucidate the mode of binding interactions between the thirty five (35) NSCLC therapeutic agents and the binding pose of EGFR tyrosine kinase receptor (3IKA) in this research. Compound 29 was recognized to have the most excellent binding affinity of − 8.8 kcal/mol among others. The drug-likeness and pharmacokinetic properties of all the NSCLC therapeutic agents were predicted using SWISSADME, and none among the molecules under investigation violated more than the permissible limit of the conditions stated by Lipinski’s RO5 filters. Five hit compounds were identified using molecular docking virtual screening. The five (5) hit compounds were further screened and identified compound 16 and 27 as excellent among them using their pharmacokinetic profiles and drug-likeness properties. Conclusion QSAR technique was used to build five models on thirty five (35) NSCLC therapeutic agents. The best model among them was reported because it is statistically significant with good validation parameters. The molecular docking result has identified five (5) hit compounds. The most common amino acid residues to all hit compounds under investigation were Glu762, Leu718, Lys745, and Val726 which might be responsible for the higher inhibitory activities/binding affinities of the compounds under investigation. Furthermore, these five (5) hit compounds were further subjected to drug-likeness and pharmacokinetic properties prediction to determine which among them have the best pharmacokinetic profile. Compounds 16 and 27 among the hit compounds were observed to have high chance of passive absorption by the gastrointestinal tract while the other three have low tendency of passive absorption. More so, only compounds 16 and 27 have higher bioavailability scores, and none of the two have more than one violation of the RO5 criteria. The cause of efficiency of compounds 16 and 27 might be as a result of good pharmacokinetic profiles and drug-likeness properties possessed by the molecules when compared to other hit compounds.

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“…It is done by reshuffling the actual activities and keeping the descriptors unchanged to generate new QSAR models for several trials, the new built QSAR models were anticipated to give low Q 2 and R 2 value. The validation parameter for this test is cR p (cR 2 p > 0.5) [12].…”

Section: Validation Of the Selected Modelmentioning

confidence: 99%

Computer-aided molecular modeling studies of some 2, 3-dihydro-[1, 4] dioxino [2, 3-f] quinazoline derivatives as EGFRWT inhibitors

Ibrahim

Uzairu

Shallangwa

2020

Beni-Suef Univ J Basic Appl Sci

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Background: Quinazoline are known to possess different biological activities which among is anti-cancer most especially NSCLC. Epidermal growth factor receptor (EGFR) belongs to the receptor tyrosine kinases (RTKs) family, which is known to be one of the most important therapeutic targets for the treatment of cancer most especially NSCLC. Results: QSAR modeling was performed to develop a model with high predictive power on some non-small cell lung cancer agents (NSCLC) (EGFR WT inhibitors). The EGFR WT inhibitors were optimized using density functional theory (DFT) method utilizing B3LYP/6-31G* level of theory. Genetic function algorithm (GFA) was used to build five models. Out of these five models, the studied one was selected and reported because of its fitness statistically with the following validation parameters: R 2 trng = 0.9459, R 2 adj = 0.9311, Q 2 cv = 0.8947, R 2 test = 0.7008, and LOF = 0.1195. The selected model was further subjected to other validation test such as VIF and Y-scrambling test applicability domain and found to be statistically significant. The kind of interactions between five most active EGFR WT inhibitors and EGFR WT enzyme were explored via molecular docking. Molecule 4 was ranked top in comparison to other ligands because it has the highest docking score of − 8.3 kcal/mol. The pharmacokinetics studies indicated that these molecules have good absorption, low toxicity level, and permeability properties because none of them violate the Lipinski's rule of five. Conclusion:A model with a very high predictive power on some EGFR WT inhibitors was developed using QSAR model. The model was validated and found to have good internal and external assessment parameters: R 2 of 0.9459, R 2 adj of 0.9311, Q cv 2 of 0.8947, R 2 test of 0.7008, and LOF of 0.1195. The nature of interaction of these molecules with their target protein was explored via molecular docking and found molecule 4 to have the highest docking score of − 8.3 kcal/mol among co-ligands. Pharmacokinetics studies revealed that these molecules have good absorption, low toxicity level, and permeability properties. These findings proposed a way for designing potent EGFR WT inhibitors against their target enzyme.

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QSAR modelling and docking analysis of some thiazole analogues as alfa-glucosidase inhibitors

Cited by 11 publications

References 26 publications

In-silico activity prediction and docking studies of some 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9h-purine derivatives as Anti-proliferative agents

In-silico activity prediction and docking studies of some 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9h-purine derivatives as Anti-proliferative agents

Quantitative structure-activity relationship, molecular docking, drug-likeness, and pharmacokinetic studies of some non-small cell lung cancer therapeutic agents

Computer-aided molecular modeling studies of some 2, 3-dihydro-[1, 4] dioxino [2, 3-f] quinazoline derivatives as EGFRWT inhibitors

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