Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning.

Angiogenesis Inhibitors / adverse effects Computer Simulation Humans Machine Learning Neoplasms / pathology Neovascularization, Pathologic / drug therapy Ranibizumab / adverse effects Vascular Endothelial Growth Factor A
Anti-angiogenic drugs Artificial intelligence Bevacizumab Brolucizumab Cancer Ranibizumab Solid tumor Tumor growth

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
07 2022
Historique:
received: 22 12 2021
revised: 06 04 2022
accepted: 07 04 2022
pubmed: 2 5 2022
medline: 25 6 2022
entrez: 1 5 2022
Statut: ppublish

Résumé

Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m

Identifiants

DOI: 10.1016/j.compbiomed.2022.105511 PMID: 35490641
pubmed: 35490641
pii: S0010-4825(22)00303-1
doi: 10.1016/j.compbiomed.2022.105511
pii:
doi:

Substances chimiques

Angiogenesis Inhibitors 0
Vascular Endothelial Growth Factor A 0
Ranibizumab ZL1R02VT79

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

105511

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Milad Mousavi (M)

Cancer Institute of Iran, Tehran University of Medical Sciences (TUMS), Tehran, Iran.

Mahsa Dehghan Manshadi (MD)

Cancer Institute of Iran, Tehran University of Medical Sciences (TUMS), Tehran, Iran; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran.

Madjid Soltani (M)

Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, Canada; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran.

Farshad M Kashkooli (FM)

Department of Physics, Ryerson University, Toronto, ON, Canada; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran.

Arman Rahmim (A)

Department of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.

Amir Mosavi (A)

Institute of Software Design and Development, Obuda University, 1034, Budapest, Hungary; National University of Public Service, Budapest, Hungary; Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia. Electronic address: amir.mosavi@uni-obuda.hu.

Michal Kvasnica (M)

Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia.

Peter M Atkinson (PM)

Faculty of Science and Technology, Lancaster University, Lancaster, UK; Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Beijing 100101, China; Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster LA1 4YR, UK.

Levente Kovács (L)

Biomatics Institute, John von Neumann Faculty of Informatics, Obuda University, 1034, Budapest, Hungary; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034, Budapest, Hungary.

Andras Koltay (A)

National University of Public Service, Budapest, Hungary.

Norbert Kiss (N)

National University of Public Service, Budapest, Hungary.

Hojjat Adeli (H)

Department of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, OH, 43220, USA.

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Classifications MeSH

questionsmedicales.fr › Actions chimiques et utilisations › Actions pharmacologiques › Utilisations thérapeutiques › Antinéoplasiques › Inhibiteurs de l'angiogenèse › Modeling the efficacy of different...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Simulation numérique › Modeling the efficacy of different...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Modeling the efficacy of different...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Modeling the efficacy of different...
questionsmedicales.fr › Tumeurs › Modeling the efficacy of different...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Métaplasie › Néovascularisation pathologique › Modeling the efficacy of different...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Globulines › Sérum-globulines › Immunoglobulines › Anticorps › Anticorps monoclonaux › Anticorps monoclonaux humanisés › Ranibizumab › Modeling the efficacy of different...
questionsmedicales.fr › Facteurs biologiques › Protéines et peptides de signalisation intercellulaire › Protéines angiogéniques › Facteurs de croissance endothéliale vasculaire › Facteur de croissance endothéliale vasculaire de type A › Modeling the efficacy of different...