Discovery of potential epidermal growth factor receptor inhibitors from black pepper for the treatment of lung cancer: an in-silico approach
Olusola Olalekan Elekofehinti
1, 2
,
Ifeoluwa Rachael Adetoyi
1, 2
,
Hannah Oluwaseun Popoola
3
,
Folasade Oluwatobiloba Ayodeji
1, 2
,
Foluso Adeola Taiwo
1, 2
,
Moses Orimoloye Akinjiyan
1, 2
,
Omowunmi Funmilayo Koledoye
3
,
Opeyemi Iwaloye
1, 2
,
Abayomi Emmanuel Adegboyega
4, 5
2
Teady Bioscience Research Laboratory, Akure, Nigeria
|
5
Bioinformatics unit, Jaris Computational Biology Centre, Jos, Nigeria
|
Тип публикации: Journal Article
Дата публикации: 2024-04-08
PubMed ID:
38601803
General Medicine
Краткое описание
A tyrosine kinase receptor known as epidermal growth factor receptor (EGFR) is one of the main tumour markers in many cancer types and also plays a crucial role in cell proliferation, differentiation, angiogenesis, and apoptosis, which is a result of the auto-phosphorylations (kinase activity enhancement) that trigger signals involved in different cellular processes. Due to the discovery that non-small cell lung cancer (NSCLC) is a cause of this kinase activity enhancement, so far, several inhibitors have been tested against EGFR, but the side effects of these inhibitors necessitate an urgent measure to come up with an inhibitor that will be more specific to the cancer cells and not affect self-cells. This study was conducted to evaluate the efficacy of 37 compounds derived from Piper nigrum against EGFR using computer-aided drug design. Based on molecular docking, induced-fit docking, calculation of free binding energy, pharmacokinetics, QSAR prediction, and MD simulation. We propose five (5) lead compounds (clarkinol A, isodihydrofutoquinol B, Burchellin, kadsurin B, and lancifolin C) as a novel inhibitor, with clarkinol A demonstrating the highest binding affinity (−7.304 kcal/mol) with EGFR when compared with the standard drug (erlotinib). They also showed significant moderation for parameters investigated for a good pharmacokinetic profile, with a reliable R2 coefficient value predicted using QSAR models. The MD simulation of clarkinol A was found to be stable within the EGFR binding pocket throughout the 75 ns simulation run time. The findings showed that clarkinol A derived from Piper nigrum is worth further investigation and consideration as a possible EGFR inhibitor for the treatment of lung cancer.
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Всего цитирований:
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Цитирований c 2024:
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(100%)
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Elekofehinti O. O. et al. Discovery of potential epidermal growth factor receptor inhibitors from black pepper for the treatment of lung cancer: an in-silico approach // In Silico Pharmacology. 2024. Vol. 12. No. 1. 28
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Elekofehinti O. O., Adetoyi I. R., Popoola H. O., Ayodeji F. O., Taiwo F. A., Akinjiyan M. O., Koledoye O. F., Iwaloye O., Adegboyega A. E. Discovery of potential epidermal growth factor receptor inhibitors from black pepper for the treatment of lung cancer: an in-silico approach // In Silico Pharmacology. 2024. Vol. 12. No. 1. 28
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TY - JOUR
DO - 10.1007/s40203-024-00197-1
UR - https://link.springer.com/10.1007/s40203-024-00197-1
TI - Discovery of potential epidermal growth factor receptor inhibitors from black pepper for the treatment of lung cancer: an in-silico approach
T2 - In Silico Pharmacology
AU - Elekofehinti, Olusola Olalekan
AU - Adetoyi, Ifeoluwa Rachael
AU - Popoola, Hannah Oluwaseun
AU - Ayodeji, Folasade Oluwatobiloba
AU - Taiwo, Foluso Adeola
AU - Akinjiyan, Moses Orimoloye
AU - Koledoye, Omowunmi Funmilayo
AU - Iwaloye, Opeyemi
AU - Adegboyega, Abayomi Emmanuel
PY - 2024
DA - 2024/04/08
PB - Springer Nature
IS - 1
VL - 12
PMID - 38601803
SN - 2193-9616
ER -
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@article{2024_Elekofehinti,
author = {Olusola Olalekan Elekofehinti and Ifeoluwa Rachael Adetoyi and Hannah Oluwaseun Popoola and Folasade Oluwatobiloba Ayodeji and Foluso Adeola Taiwo and Moses Orimoloye Akinjiyan and Omowunmi Funmilayo Koledoye and Opeyemi Iwaloye and Abayomi Emmanuel Adegboyega},
title = {Discovery of potential epidermal growth factor receptor inhibitors from black pepper for the treatment of lung cancer: an in-silico approach},
journal = {In Silico Pharmacology},
year = {2024},
volume = {12},
publisher = {Springer Nature},
month = {apr},
url = {https://link.springer.com/10.1007/s40203-024-00197-1},
number = {1},
pages = {28},
doi = {10.1007/s40203-024-00197-1}
}