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Title: Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory
Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton–Jacobi–Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.  more » « less
Award ID(s):
1738010
NSF-PAR ID:
10198179
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the Royal Society B: Biological Sciences
Volume:
287
Issue:
1925
ISSN:
0962-8452
Page Range / eLocation ID:
20192454
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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