In-Silico Perspectives on the Potential Therapeutic Aids of Hesperetin Derivatives for Lung Cancer

Abdulaziz Asiri

Abstract


Background: Lung carcinoma has become one of the most noteworthy and dangerous health problems discovered today. The disease is mainly caused by smoking and becomes among the leading causes of death spread by growing the cancerous cells into the lining of the lungs and nearby lobes. The FDA has given clearance to numerous drugs and chemotherapy agents; nevertheless, they can be exceedingly costly as well as frequently fall short of entirely addressing the ailment. In the medical management of lung cancer, new medicines or active leads with high efficacy and minimal toxicity are required in this era.

Methods: The study has been conducted with the intent to uncover a prospective approach to treating lung cancer through structural modification of Hesperetin by creating its analogs to enhance its efficacy compared to its parent compound with the computational drug design. The analysis has been conducted with various approaches followed by PASS prediction, ADME, toxicity profile, molecular docking, data filtration, and anticancer activity.

Results: All the compounds showed satisfactory criteria in each parameter that was assessed. The data mining was done carefully by pointing out the compounds that had the greatest value among all the compounds in each investigation, which resulted in a total of 3 compounds out of 50. 

Conclusions: Finally, the selected compounds were further analyzed for MD simulation studies. Afterward, PCA analysis was also conducted in order to get the lead compound with this additional investigation.

Keywords: Lung cancer; Anti-cancer lead; Hesperetin; Drug designing; Molecular docking; MD simulation; PCA 


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References


Cui W, Aouidate A, Wang S, Yu Q, Li Y, et al. Discovering Anti-Cancer Drugs via Computational Methods. Frontiers in Pharmacology, (2020); 11: 733.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, et al. Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, (2021); 71 (3): 209-249.

Schmitt FC, Bubendorf L, Canberk S, Chandra A, Cree IA, et al. The World Health Organization Reporting System for Lung Cytopathology. Acta Cytologica, (2023); 67 (1): 80-91.

Dela Cruz CS, Tanoue LT, Matthay RA. Lung Cancer: Epidemiology, Etiology, and Prevention. Clinics in Chest Medicine, (2011); 32 (4): 605-644.

Carrillo-Perez F, Morales JC, Castillo-Secilla D, Molina-Castro Y, Guillén A, et al. Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion. BMC Bioinformatics, (2021); 22(1):454.

Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, (2022); 72 (1): 7-33.

Singh N, Agrawal S, Jiwnani S, Khosla D, Malik PS, et al. Lung Cancer in India. Journal of Thoracic Oncology, (2021); 16 (8): 1250-1266.

Collin J. Tobacco control, global health policy and development: towards policy coherence in global governance. Tobacco Control, (2012); 21(2): 274-280.

Rudin CM, Avila-Tang E, Samet JM. Lung Cancer in Never Smokers: A Call to Action. Clinical Cancer Research (2009); 15 (8): 5622-5625.

Guo Q, Liu L, Chen Z, Fan Y, Zhou Y, et al. Current treatments for non-small cell lung cancer. Frontiers in Oncology, (2022); 12:945102.

Debela DT, Muzazu SG, Heraro KD, Ndalama MT, Mesele BW, et al. New approaches and procedures for cancer treatment: Current perspectives. SAGE Open Medicine, (2021); 9: 205031212110343.

Khan T, Date A, Chawda H, Patel K. Polysaccharides as potential anticancer agents-A review of their progress. Carbohydrate Polymers, (2019); 210: 412-428.

Ramteke, Prerna Yadav, Umesh CS. Hesperetin, a Citrus bioflavonoid, prevents IL-1β-induced inflammation and cell proliferation in lung epithelial A549 cells. NISCAIR-CSIR, India, (2019); 57: 7-14.

Muhammad T, Ikram M, Ullah R, Rehman S, Kim M. Hesperetin, a Citrus Flavonoid, Attenuates LPS-Induced Neuroinflammation, Apoptosis and Memory Impairments by Modulating TLR4/NF-κB Signaling. Nutrients, (2019); 11(3): 648.

Wang Y, Liu S, Dong W, Qu X, Huang C, Yan T, Du J. Combination of hesperetin and platinum enhances anticancer effect on lung adenocarcinoma. Biomedicine & Pharmacotherapy, (2019); 113: 108779.

Jiao Q, Xu L, Jiang L, Jiang Y, Zhang J, Liu B. Metabolism study of hesperetin and hesperidin in rats by UHPLC-LTQ-Orbitrap MS n . Xenobiotica, (2020); 50 (11): 1311-1322.

Ávila-Gálvez MÁ, Giménez-Bastida JA, González-Sarrías A, Espín JC. New Insights into the Metabolism of the Flavanones Eriocitrin and Hesperidin: A Comparative Human Pharmacokinetic Study Antioxidants, (2021); 10 (3): 435.

Alipour M, Sharifi S, Samiei M, Shahi S, Aghazadeh M, et al. Synthesis, characterization, and evaluation of Hesperetin nanocrystals for regenerative dentistry. Scientific Report, (2023); 13 (1): 2076.

Alipour M, Pouya B, Aghazadeh Z, SamadiKafil H, Ghorbani M, et al. The Antimicrobial, Antioxidative, and Anti-Inflammatory Effects of Polycaprolactone/Gelatin Scaffolds Containing Chrysin for Regenerative Endodontic Purposes. Stem Cells International, (2021); 1-11: :3828777.

Modee R, Mehta S, Laghuvarapu S, Priyakumar UD. MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning. Journal of Physical Chemistry B, (2023); 127 (48): 10295-10303.

Chen J, Swamidass SJ, Dou Y, Bruand J, Baldi P. ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics (2005); 21 (22): 4133-4139.

Sarkar M, Nath A, Kumer A, Mallik C, Akhter F, et al. Synthesis, molecular docking screening, ADMET and dynamics studies of synthesized 4-(4-Methoxyphenyl)-8-Methyl-3, 4, 5, 6, 7, 8-Hexahydroquinazolin-2 (1H)-one and quinazolinone derivatives. Journal of Molecular Structure, (2021); 1244.

Singh R, Bhardwaj VK, Sharma J, Das P, Rituraj R. Discovery and in silico evaluation of aminoarylbenzosuberene molecules as novel checkpoint kinase 1 inhibitor determinants. Genomics, (2021); 113: 707-715.

Al Azzam K. SwissADME and pkCSM Webservers Predictors: an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME/T Properties of Artemisinin and its Derivatives. Kompleksnoe Ispolʹzovanie Mineralʹnogo syrʹâ/Complex Use of Mineral Resources/Mineraldik Shikisattardy Keshendi Paidalanu, (2023); 325: 14-21.

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

Pires DE V, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. The Journal of Medicinal Chemistry, (2015); 58 (9): 4066-4072.

Mai C, NA, P.I. Thai Journal of Pharmaceutical Sciences. Thai Journal of Pharmaceutical Sciences, (2018); 93-97.

Nath A, Kumer A, Zaben F, Khan Md W. Investigating the binding affinity, molecular dynamics, and ADMET properties of 2,3-dihydrobenzofuran derivatives as an inhibitor of fungi, bacteria, and virus protein. Beni-Suef University Journal of Basic and Applied Sciences, (2021); 10: 36.

Schrodinger L, DeLano W. PyMol. (2020); http://www.pymol.org/pymol, version = {2.4.0}.

Morris GM, Huey R, Lindstrom W, Michel F, Belew RK, et al. Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry, (2009); 30 (16): 2785-2791.

Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Molecular Informatics, (2011); 30 (2-3): 241-250.

Ivanov SM, Lagunin AA, Rudik AV, Filimonov DA, Poroikov VV. ADVERPred-Web Service for Prediction of Adverse Effects of Drugs. Journal of Chemical Information and Modeling, (2018); 58 (1): 8-11.

Cadow J, Born J, Manica M, Oskooei A, Rodríguez Martínez M. PaccMann: a web service for interpretable anticancer compound sensitivity prediction. Nucleic Acids Research, (2020); 48 (W1): W502-W508.

Stenberg S, Stenqvist B. An Exact Ewald Summation Method in Theory and Practice. The Journal of Physical Chemistry A, (2020); 124 (19): 3943-6.

Fischer NM, Van Maaren PJ, Ditz JC, Yildirim A, Van Der Spoel D. Properties of Organic Liquids when Simulated with Long-Range Lennard-Jones Interactions. Journal of Chemical Theory and Computation, (2015); 11 (7): 2938-2944.

Grant BJ, Rodrigues APC, ElSawy KM, McCammon JA, Caves LSD. Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics, (2006); 22 (21): 2695-2696.

Khan MKA, Ahmad S, Rabbani G, Shahab U, Khan MS. Target‐based virtual screening, computational multiscoring docking and molecular dynamics simulation of small molecules as promising drug candidate affecting kinesin‐like protein KIFC1. Cell Biochemistry & Function, (2022); 40 (5): 451-472.

Islam S, Hosen MA, Ahmad S, ul Qamar MT, Dey S, et al. Synthesis, antimicrobial, anticancer activities, PASS prediction, molecular docking, molecular dynamics and pharmacokinetic studies of designed methyl α-D-glucopyranoside esters. Journal of Molecular Structure, (2022); 1260: 132761.

Vardhan S, Sahoo SK. In silico ADMET and molecular docking study on searching potential inhibitors from limonoids and triterpenoids for COVID-19. Computers in Biology and Medicine, (2020); 124: 103936.

Hanee U, Rahman MR, Matin and MM. Synthesis, PASS, In Silico ADMET and thermodynamic studies of some galactopyranoside esters. Physical Chemistry Research, (2021); 9(4): 591-603.

Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research, (2018); 46: W363-W367.

Sulimov VB, Gribkova IV, Kochugaeva MP, Katkova EV, et al. Application of Molecular Modeling to Development of New Factor Xa Inhibitors. Biomed Research International, (2015); 2015: 1-15.

GA, Adeniji SE. Binding profile of protein–ligand inhibitor complex and structure based design of new potent compounds via computer-aided virtual screening. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, (2021); 24: 100256.




DOI: http://dx.doi.org/10.62940/als.v11i4.3308

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