Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning. (July 2022)

• Record Type: Journal Article
• Authors: Mousavi, Milad; Manshadi, Mahsa Dehghan; Soltani, Madjid; Kashkooli, Farshad M.; Rahmim, Arman; Mosavi, Amir; Kvasnica, Michal; Atkinson, Peter M.; Kovács, Levente; Koltay, Andras; Kiss, Norbert; Adeli, Hojjat
• Is Part Of: Computers in biology and medicine. Volume 146(2022)
• Publication Date: 2022-07
• Subjects: Solid tumor -- Tumor growth -- Anti-angiogenic drugs -- Bevacizumab -- Ranibizumab -- Brolucizumab -- Artificial intelligence -- Cancer; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105511
• Languages: English
• ISSNs: 0010-4825

Abstract

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%. Highlights: The efficacy of three different anti-angiogenic drug treatments for tumor growth is studied. Machine learning is proposed to optimize the chemotherapy. Machine learning methods improved the prediction of the tumor growth.