Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning

Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning Publisher Pubmed

Mousavi M¹ ; Manshadi MD^{1, 17} ; Soltani M^{2, 3, 17} ; Kashkooli FM^{4, 17} ; Rahmim A^{5, 6} ; Mosavi A^{7, 11, 13} ; Kvasnica M¹³ ; Atkinson PM^{8, 14, 15, 16} ; Kovacs L^{9, 10} ; Koltay A¹¹ ; Kiss N¹¹ ; Adeli H¹²

Show Affiliations

1. Cancer Institute of Iran, Tehran University of Medical Sciences (TUMS), Tehran, Iran
2. Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada
3. Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada
4. Department of Physics, Ryerson University, Toronto, ON, Canada
5. Department of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
6. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
7. Institute of Software Design and Development, Obuda University, Budapest, 1034, Hungary
8. Faculty of Science and Technology, Lancaster University, UK, Lancaster, United Kingdom
9. Biomatics Institute, John von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary
10. Physiological Controls Research Center, University Research and Innovation Center, Obuda University, Budapest, 1034, Hungary
11. National University of Public Service, Budapest, Hungary
12. Department of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, 43220, OH, United States
13. Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
14. Geography and Environmental Science, University of Southampton, Highfield, UK, Southampton, SO17 1BJ, United Kingdom
15. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Beijing, 100101, China
16. Lancaster Environment Centre, Lancaster University, Lancaster, UK, Bailrigg, LA1 4YR, United Kingdom
17. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 1999143344, Iran

Source: Computers in Biology and Medicine Published:2022

Abstract

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−3 with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%. © 2022 The Authors

Related Docs

View other Related Docs

1. Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification, International Journal of Neural Systems (2022)

2. A Multiscale Cell-Based Model of Tumor Growth for Chemotherapy Assessment and Tumor-Targeted Therapy Through a 3D Computational Approach, Cell Proliferation (2022)

3. Effect of Vascular Normalization on Drug Delivery to Different Stages of Tumor Progression: In-Silico Analysis, Journal of Drug Delivery Science and Technology (2020)

4. A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network, European Neurology (2020)

5. Estimation of Drug and Tumor Properties Using Novel Hybrid Meta-Heuristic Methods, Journal of Theoretical Biology (2020)

6. Image-Based Spatio-Temporal Model of Drug Delivery in a Heterogeneous Vasculature of a Solid Tumor — Computational Approach, Microvascular Research (2019)

7. Multiscale Modeling of Tumor Growth and Angiogenesis: Evaluation of Tumor-Targeted Therapy, PLoS Computational Biology (2021)

8. Targeted Nano-Sized Drug Delivery to Heterogeneous Solid Tumor Microvasculatures: Implications for Immunoliposomes Exhibiting Bystander Killing Effect, Physics of Fluids (2023)

Experts (# of related papers)

View all Related Experts

Saeid Amanpour (1)

Bahador Makkiabadi (1)

Style	Citing Format
MLA	Mousavi M, et al.. "Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning." Computers in Biology and Medicine, vol. 146, no. , 2022, pp. -.
APA	Mousavi M, Manshadi MD, Soltani M, Kashkooli FM, Rahmim A, Mosavi A, Kvasnica M, Atkinson PM, Kovacs L, Koltay A, Kiss N, Adeli H (2022). Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning. Computers in Biology and Medicine, 146(), -.
Chicago	Mousavi M, Manshadi MD, Soltani M, Kashkooli FM, Rahmim A, Mosavi A, Kvasnica M, et al.. "Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning." Computers in Biology and Medicine 146, no. (2022): -.
Harvard	Mousavi M et al. (2022) 'Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning', Computers in Biology and Medicine, 146(), pp. -.
Vancouver	Mousavi M, Manshadi MD, Soltani M, Kashkooli FM, Rahmim A, Mosavi A, et al.. Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning. Computers in Biology and Medicine. 2022;146():-.
BibTex	@article{ author = {Mousavi M and Manshadi MD and Soltani M and Kashkooli FM and Rahmim A and Mosavi A and Kvasnica M and Atkinson PM and Kovacs L and Koltay A and Kiss N and Adeli H}, title = {Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning}, journal = {Computers in Biology and Medicine}, volume = {146}, number = {}, pages = {-}, year = {2022} }
RIS	TY - JOUR AU - Mousavi M AU - Manshadi MD AU - Soltani M AU - Kashkooli FM AU - Rahmim A AU - Mosavi A AU - Kvasnica M AU - Atkinson PM AU - Kovacs L AU - Koltay A AU - Kiss N AU - Adeli H TI - Modeling the Efficacy of Different Anti-Angiogenic Drugs on Treatment of Solid Tumors Using 3D Computational Modeling and Machine Learning JO - Computers in Biology and Medicine VL - 146 IS - SP - EP - PY - 2022 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract