Approaches to combat hypoxia in cancer therapy and the potential for in silico models in their evaluation. (August 2019)

• Record Type: Journal Article
• Authors: Forster, Jake C.; Marcu, Loredana G.; Bezak, Eva
• Is Part Of: Physica medica. Volume 64(2019)
• Page Start: 145
• Page End: 156
• Publication Date: 2019-08
• Subjects: Hypoxic radiosensitizers -- In silico oncology -- Predictive models -- Treatment optimization -- Radiotherapy -- Chemotherapy; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2019.07.006
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Despite advances in knowledge, tumour hypoxia remains a challenge in radiotherapy. Several hypoxia-targeting methods exists, all with their advantages and drawbacks. In silico models are complementary tools offering novel solutions to manage hypoxia. The paper reviews the current landscape of hypoxia models and response to therapy. Future models must consider connected vascular networks to simulate agent delivery. Abstract: Aim: The negative impact of tumour hypoxia on cancer treatment outcome has been long-known, yet there has been little success combating it. This paper investigates the potential role of in silico modelling to help test emerging hypoxia-targeting treatments in cancer therapy. Methods: A Medline search was undertaken on the current landscape of in silico models that simulate cancer therapy and evaluate their ability to test hypoxia-targeting treatments. Techniques and treatments to combat tumour hypoxia and their current challenges are also presented. Results: Hypoxia-targeting treatments include tumour reoxygenation, hypoxic cell radiosensitization with nitroimidazoles, hypoxia-activated prodrugs and molecular targeting. Their main challenges are toxicity and not achieving adequate delivery to hypoxic regions of the tumour. There is promising research toward combining two or more of these techniques. Different types of in silico therapy models have been developed ranging from temporal to spatial and from stochastic to deterministic models. Numerous models have compared the effectiveness of different radiotherapy fractionation schedules for controlling hypoxic tumours. Similarly, models could help identify and optimize new treatments for overcoming hypoxia that utilize novel hypoxia-targeting technology. Conclusion: Current therapy models should attempt to incorporate more sophisticated modelling of tumour angiogenesis/vasculature and vessel perfusion in order to become more useful for testing hypoxia-targeting treatments, which typically rely upon the tumour vasculature for delivery of additional oxygen, (pro)drugs and nanoparticles.