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

Beni-Suef University Journal of Basic and Applied Sciences - Tập 9 Số 1 - 2020
Muhammad Ibrahim1, Adamu Uzairu1, Gideon Adamu Shallangwa1
1Department of Chemistry, Ahmadu Bello University Zaria, Kaduna State, Nigeria

Tóm tắt

AbstractBackgroundLung 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).ResultsFive 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:R2of 0.8764,R2adjof 0.8370,Qcv2of 0.7655,R2testof 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.ConclusionQSAR 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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Tài liệu tham khảo

Song J, Jang S, Lee JW, Jung D, Lee S, Min KH (2019) Click chemistry for improvement in selectivity of quinazoline-based kinase inhibitors for mutant epidermal growth factor receptors. Bioorg Med Chem Lett 29:477–480

Hanan EJ et al (2016) 4-Aminoindazolyl-dihydrofuro [3, 4-d] pyrimidines as non-covalent inhibitors of mutant epidermal growth factor receptor tyrosine kinase. Bioorg Med Chem Lett 26:534–539

Kong L-L, Ma R, Yao M-Y, Yan X-E, Zhu S-J, Zhao P, Yun C-H (2017) Structural pharmacological studies on EGFR T790M/C797S. Biochem Biophys Res Commun 488:266–272

Cross DA et al (2014) AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Disc 4:1046–1061

Solca F et al (2012) Target binding properties and cellular activity of afatinib (BIBW 2992), an irreversible ErbB family blocker. J Pharmacol Exp Ther 343:342–350

Tsao M-S et al (2005) Erlotinib in lung cancer—molecular and clinical predictors of outcome. N Engl J Med 353:133–144

Ojha Lokendra K, Rachana S, Rani BM (2013) Modern drug design with advancement in QSAR: a review. Int J Res Biosci 2:1–12

Abdulfatai U, Uba S, Umar BA, Ibrahim MT (2019) Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents SN. Appl Sci 1:499

Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935

Khan MF, Verma G, Akhtar W, Shaquiquzzaman M, Akhter M, Rizvi MA, Alam MM (2016) Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1, 3, 4-thiadiazole amides and sulfonamides as antitubulin agents. Arab J Chem

Chen Y et al (2017) Discovery of N-(5-((5-chloro-4-((2-(isopropylsulfonyl) phenyl) amino) pyrimidin-2-yl) amino)-4-methoxy-2-(4-methyl-1, 4-diazepan-1-yl) phenyl) acrylamide (CHMFL-ALK/EGFR-050) as a potent ALK/EGFR dual kinase inhibitor capable of overcoming a variety of ALK/EGFR associated drug resistant mutants in NSCLC. Eur J Med Chem 139:674–697

Abdullahia, M., Shallangwaa, G. A., Ibrahima, M. T., Bello, A. U., Arthura, D. E., Uzairua, A., Mamzaa, P. (2018) QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer agents against leukemia cell line (K562) JKS-S

Mills, N. (2006). ChemDraw Ultra 10.0 CambridgeSoft, 100 CambridgePark Drive, Cambridge, MA 02140. www. cambridgesoft. com. Commercial Price: 1910fordownload, 2150 for CD-ROM; Academic Price: 710fordownload, 800 for CD-ROM: ACS Publications.

Ibrahim MT, Uzairu A, Shallangwa GA, Uba S (2019a) QSAR modelling and docking analysis of some thiazole analogues as⍺-glucosidase inhibitors. J Eng Exact Sci 5:0257–0270

Kohn W, Becke AD, Parr RG (1996) Density functional theory of electronic structure. J Phys Chem 100:12974–12980

Ibrahim MT, Uzairu A, Shallangwa GA, Uba S (2020a) Computer-aided molecular modeling studies of some 2, 3-dihydro-[1, 4] dioxino [2, 3-f] quinazoline derivatives as EGFR WT inhibitors. Beni-Suef Univ J Basic Appl Sci 9:1–10

Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474

Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

Ibrahim MT, Uzairu A, Uba S, Shallangwa GA (2020b) Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors. Heliyon 6:e03289

Adedirin O, Uzairu A, Shallangwa GA, Abechi SE (2018a) Computational studies on α-aminoacetamide derivatives with anticonvulsant activities. Beni-Suef Univ J Basic Appl Sci 7:709–718

Grisoni F, Ballabio D, Todeschini R, Consonni V (2018) Molecular descriptors for structure–activity applications: a hands-on approach. Comput Toxicol:3–53 Springer

Mustapha A, Shallangwa G, Ibrahim MT, Bello AU, Ebuka DA, Uzairu A, Mamza P (2018) QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against leukemia cell line (K562). J Turkish Chem Soc Section A Chem 5:1387–1398

Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK (2011) Validation of QSAR models-strategies and importance. Int J Drug Design Disc 3:511–519

Tropsha A, Bajorath JR (2015) Computational methods for drug discovery and design. ACS Publications

Beheshti A, Pourbasheer E, Nekoei M, Vahdani S (2016) QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. J Saudi Chem Soc 20:282–290

Adedirin O, Uzairu A, Shallangwa GA, Abechi SE (2018b) QSAR and molecular docking based design of some n-benzylacetamide as γ-aminobutyrate-aminotransferase inhibitors. J Eng Exact Sci 4:0065–0084

Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Mol Inform 22:69–77

Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

Capra JA, Laskowski RA, Thornton JM, Singh M, Funkhouser TA (2009) Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput Biol 5:e1000585

Rizvi SMD, Shakil S, Haneef M (2013) A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians. EXCLI J 12:831

Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717

Ismail SY, Uzairu A, Sagagi B, Sabiu M (2018) In silico molecular docking and pharmacokinetic study of selected phytochemicals with estrogen and progesterone receptors as anticancer agent for breast cancer. 5:1337–1350

Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references (Vol. 41). Wiley

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Beni-Suef University Journal of Basic and Applied Sciences

Tập 9 Số 1

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