In Silico Pharmacokinetic and Molecular Docking Studies of Natural Plants against Essential Protein KRAS for Treatment of Pancreatic Cancer

Jump To References Section

Authors

  • Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam - 686666, Kerala ,IN
  • Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam - 686666, Kerala ,IN

DOI:

https://doi.org/10.18311/jnr/2023/31947

Keywords:

ADME, KRAS, Machine Learning, Molecular Docking, Natural Compounds

Abstract

A kind of pancreatic cancer called Pancreatic Ductal Adenocarcinoma (PDAC) is anticipated to be one of the main causes of mortality during past years. Evidence from several researches supported the concept that the oncogenic KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene) mutation is the major cause of pancreatic cancer. KRAS acts as an on-off switch that promotes cell growth. But when the KRAS gene is mutated, it will be in one position, allowing the cell growth uncontrollably. This uncontrollable multiplication of cells causes cancer growth. Therefore, KRAS was selected as the target protein in the study. Fifty plant-derived compounds are selected for the study. To determine whether the examined drugs could bind to the KRAS complex’s binding pocket, molecular docking was performed. Computational analyses were used to assess the possible ability of tested substances to pass the Blood Brain Barrier (BBB). To predict the bioactivity of ligands a machine learning model was created. Five machine learning models were created and have chosen the best one among them for analyzing the bioactivity of each ligand. From the fifty plant-derived compounds the compounds with the least binding energies are selected. Then bioactivity of these six compounds is analyzed using Random Forest Regression model. Adsorption, Distribution, Metabolism, Excretion (ADME) properties of compounds are analyzed. The results showed that borneol has powerful effects and acts as a promising agent for the treatment of pancreatic cancer. This suggests that borneol found in plants like mint, ginger, rosemary, etc., is a successful compound for the treatment of pancreatic cancer.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2023-08-31

How to Cite

Kappan, M. M., & George, J. (2023). <i>In Silico</i> Pharmacokinetic and Molecular Docking Studies of Natural Plants against Essential Protein KRAS for Treatment of Pancreatic Cancer. Journal of Natural Remedies, 23(3), 1107–1123. https://doi.org/10.18311/jnr/2023/31947

Issue

Section

Short Communication
Received 2022-11-17
Accepted 2023-01-09
Published 2023-08-31

 

References

Buscail L, Bournet B and Cordelier P. Role of oncogenic KRAS in the diagnosis, prognosis, and treatment of pancreatic cancer. Nature Reviews, Gastroenterology, and Hepatology. 2020; 17(3): 153-168. https://doi.org/10.1038/s41575-019-0245-4 PMid: 32005945.

Treadwell JR, Zafar HM, Mitchell MD, Tipton K, Teitelbaum U. Imaging tests for the diagnosis and staging of pancreatic adenocarcinoma: A meta-analysis. Pancreas. 2016; 45(6):789-95. https://doi.org/10.1097/MPA.0000000000000524 PMid:26745859. DOI: https://doi.org/10.1097/MPA.0000000000000524

Dallongeville A, Corno L, Silvera S, Coletta IB, Zins M. Initial diagnosis and staging of pancreatic cancer including main differentials. Seminars in Ultrasound, CT, and MRI. 2019; 40(6):436-468. https://doi. org/10.1053/j.sult.2019.08.001 PMid:31806145. DOI: https://doi.org/10.1053/j.sult.2019.08.001

Schneewei S, Horger M, Grözinger A, Nikolaou K, Ketelsen D, Syha R, Grözinger G. CT-perfusion measurements in pancreatic carcinoma with different kinetic models: Is there a chance for tumour grading based on functional parameters? Cancer Imaging. 2016; 16(1):1-8. https://doi.org/10.1186/s40644-016- 0100-6 PMid:27978850 PMCid:PMC5159980. DOI: https://doi.org/10.1186/s40644-016-0100-6

Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2020; 70(1):7-30. https://doi. org/10.3322/caac.21590 PMid:31912902. DOI: https://doi.org/10.3322/caac.21590

Imamura T, Komatsu S, Ichikawa D, Kawaguchi T, Miyamae M, Okajima W. et al. Liquid biopsy in patients with pancreatic cancer: Circulating tumor cells and cell- free nucleic acids. World J. Gastroenterol. 2016; 22(25):5627-41. https:// doi.org/10.3748/wjg.v22.i25.5627 PMid:27433079 PMCid:PMC4932201. DOI: https://doi.org/10.3748/wjg.v22.i25.5627

Riva F, Dronov OI, Khomenko DI, Huguet F, Louvet C, Mariani P, et al. Clinical applications of circulating tumor DNA and circulating tumor cells in pancreatic cancer. Molecular Oncology. 2016; 10(3):481– 493. https://doi.org/10.1016/j.molonc.2016.01.006 PMid:2 6856794 PMCid:PMC5528974. DOI: https://doi.org/10.1016/j.molonc.2016.01.006

Tanne J. Paracetamol causes most liver failure in UK and US. BMJ. 2006; 332(7542): 628. https://doi.org/10.1136/bmj.332.7542.628-a PMCid: PMC1403265. DOI: https://doi.org/10.1136/bmj.332.7542.628-a

Efferth T, Kaina B. Toxicities by herbal medicines with emphasis to traditional Chinese medicine. Current drug metabolism. 2011; 12(10):989-96. https://doi. org/10.2174/138920011798062328 PMid:21892916. DOI: https://doi.org/10.2174/138920011798062328

Simeon S, Anuwongcharoen N, Shoombuatong W, Malik AA, Prachayasittikul V, Wikberg JES, et al. Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking. PeerJ. 2016; 9(4):e2322. https://doi.org/10.7717/ peerj.2322 PMid:27602288 PMCid:PMC4991866. DOI: https://doi.org/10.7717/peerj.2322

Buscail L, Bournet B, Cordelier P. Role of oncogenic KRAS in the diagnosis, prognosis and treatment of pancreatic cancer. Nature Reviews, Gastroenterology and Hepatology. 2020; 17(3):153-168. https://doi. org/10.1038/s41575-019-0245-4 PMid:32005945. DOI: https://doi.org/10.1038/s41575-019-0245-4

Sausen M, Phallen J, Adleff V, Jones S, Leary RJ, Barrett MT, et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat Commun. 2015; 6:7686. https:// doi.org/10.1038/ncomms8686 PMid:26154128 PMCid:PMC4634573. DOI: https://doi.org/10.1038/ncomms8686

Brychta N, Krahn T. Von Ahsen O. Detection of KRAS mutations in circulating tumor DNA by digital PCR in early stages of pancreatic cancer. Clinical Chemistry. 2016; 62(11):1482-1491. https://doi. org/10.1373/clinchem.2016.257469 PMid:27591291. DOI: https://doi.org/10.1373/clinchem.2016.257469

Lee J, Jang KT, Ki CS, Lim T, Park YS, Lim HY, et al. Impact of Epidermal Growth Factor Receptor (EGFR) kinase mutations, EGFR gene amplifications, and KRAS mutations on survival of pancreatic adenocarcinoma. Cancer. 2007; 109(8):1561-9. https://doi.org/10.1002/cncr.22559 PMid:17354229. DOI: https://doi.org/10.1002/cncr.22559

Kim ST, Lim DH, Jang KT, Lim T, Lee J, Choi YL, et al. Impact of KRAS mutations on clinical outcomes in pancreatic cancer patients treated with first- line gemcitabine- based chemotherapy. Molecular cancer therapeutics. 2011; 10(10):1993- 9. https://doi.org/10.1158/1535-7163.MCT-11-0269 PMid:21862683. DOI: https://doi.org/10.1158/1535-7163.MCT-11-0269

Schultz N, A, Roslind A, Christensen IJ, Horn T, Høgdall E, Pedersen LN, et al. Frequencies and prognostic role of KRAS and BRAF mutations in patients with localized pancreatic and ampullary adenocarcinomas. Pancreas. 2012; 41(5):759-66. https://doi.org/10.1097/MPA.0b013e31823cd9df PMid:22699145. DOI: https://doi.org/10.1097/MPA.0b013e31823cd9df

Ogura T, Yamao K, Hara K, Mizuno N, Hijioka S, Imaoka H, et al. Prognostic value of K- ras mutation status and subtypes in endoscopic ultrasound-guided fine-needle aspiration specimens from patients with unresectable pancreatic cancer. J. Gastroenterol. 2013; 48(5):640-6. https://doi.org/10.1007/s00535- 012-0664-2 PMid:22983505. DOI: https://doi.org/10.1007/s00535-012-0664-2

Sinn BV, Striefler JK, Rudl MA, Lehmann A, Bahra M, Denkert C, et al. KRAS mutations in codon 12 or13 are associated with worse prognosis in pancreatic ductal adenocarcinoma. Pancreas. 2014; 43(4):578- 83. https://doi.org/10.1097/MPA.0000000000000077 PMid:24681874. DOI: https://doi.org/10.1097/MPA.0000000000000077

Nakajima EC, Drezner N , Li X, Mishra-Kalyani PS , Liu Y , Zhao H, et al. FDA approval summary: Sotorasib for KRAS G12C-mutated metastatic NSCLC. Clinical Cancer Research. 2022; 28(8):1482- 6. https://doi.org/10.1158/1078-0432.CCR-21-3074 PMid:34903582 PMCid:PMC9012672. DOI: https://doi.org/10.1158/1078-0432.CCR-21-3074

Oveissi V, Ram M, Bahramsoltani R, Ebrahimi F, Rahimi R, Naseri R, et al. Medicinal plants and their is,olated phytochemicals for the management of chemotherapy-induced neuropathy: therapeutic targets and clinical perspective. DARU. 2019; 27(1):389–406. https://doi.org/10.1007/s40199-019- 00255-6 PMid:30852764 PMCid:PMC6593128. DOI: https://doi.org/10.1007/s40199-019-00255-6

Wang JH, Yang Y, Du J, Zhao M, Lin F, Zhang S, et al. Systems pharmacology dissection of multiscale mechanisms of action for herbal medicines in treating rheumatoid arthritis. Mol. Pharmacol. 2017; 14(9):3201-3217. https://doi.org/10.1021/acs. molpharmaceut.7b00505 PMid:28771010. DOI: https://doi.org/10.1021/acs.molpharmaceut.7b00505

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Revs. 2001; 46(1-3):3-26. https://doi.org/10.1016/S0169- 409X(00)00129-0 PMid:11259830. DOI: https://doi.org/10.1016/S0169-409X(00)00129-0

Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002; 45(12):2615-23. https://doi.org/10.1021/ jm020017n PMid:12036371. DOI: https://doi.org/10.1021/jm020017n

Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999; 1(1):55-68. https://doi.org/10.1021/ cc9800071 PMid:10746014. DOI: https://doi.org/10.1021/cc9800071

Egan WJ, Merz KM, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000; 43(21):3867-77. https://doi.org/10.1021/ jm000292e PMid:11052792. DOI: https://doi.org/10.1021/jm000292e

Muegge I, Heald SL, Brittelli D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 2001; 44(12):1841-6. https://doi.org/10.1021/ jm015507e PMid:11384230. DOI: https://doi.org/10.1021/jm015507e

Baell JB, Holloway GA. New substructure filters for removal of Pan Assay Interference Compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry. 2010; 53(7):2719-40. https://doi. org/10.1021/jm901137j PMid:20131845. DOI: https://doi.org/10.1021/jm901137j

Congreve M, Carr R, Murray C, Jhoti H. ‘Rule of three’ for fragment-based lead discovery? Drug Discovery Today. 2003; 8(19):876-7. https://doi.org/10.1016/ S1359-6446(03)02831-9 PMid:14554012. DOI: https://doi.org/10.1016/S1359-6446(03)02831-9