Optimizing Chemotherapy Scheduling Using Local Search Heuristics

Abstract

We develop a method for computing efficient patient-specific drug protocols. Using this method, we identify two general categories of anticancer drug protocols, depending on the temporal cycle parameters of the host and cancer cells: a onetime intensive treatment, or a series of nonintensive treatments. Our method is based on a theoretical and experimental work showing that treatment efficacy can be improved by determining the dosing frequency on the drug-susceptible target and host cell-cycle parameters. Simulating the patient's pharmaco-dynamics in a simple model for cell population growth, we calculate the number of drug susceptible cells at every moment of therapy. Local search heuristics are then used to conduct a search for the desired solution, as defined by our criteria. These criteria include the patient's state at the end of a predetermined time period, the number of cancer and host cells at the end of treatment, and the time to the patient's cure. The process suggested here does not depend on the exact biological assumptions of the model, thus enabling its use in a more complex description of the system. We test three solution methods. Simulated annealing is compared to threshold acceptance and old bachelor acceptance, which are less known variants to this method. The conclusions concerning the three approximation methods are that good results can be achieved by choosing the proper parameters for each of the methods, but the computational effort required for achieving good results is much greater in simulated annealing than in the other methods. Also, a large number of iterations does not guarantee better solution quality, and resources would be better used in several short searches with different parameter values than in one long search.

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

“…The use of optimal control in the context of the VEPART method would allow a more general optimal protocol to be identified, where "optimal" could be defined in many ways; for instance, one could seek to minimize tumor volume at an endpoint, minimize tumor volume over a time horizon, minimize drug concentration, minimize toxicity, minimize some weighted average the above, etc. (37)(38)(39)(40)(41)(42)(43)(44)(45). Further, this optimization can be performed subject to various types of constraints; for instance, toxicity could be introduced as a constraint on the amount of drug that can be administered (37,46).…”

“…In future work, the use of approximate optimal control techniques or heuristic optimization algorithms (38,42) can extend the current implementation of VEPART. In particular, we can optimize over a wider range of protocols (for instance, considering a variable number of OV and DC doses, and/or allowing variable spacing between doses), and over a more complete range of dosing space.…”

Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-time Personalization

“…Mathematical models have facilitated rational design of cancer chemotherapy by suggesting optimal treatments that maximize drug efficacy and minimize toxicity (16)(17)(18). Model-based methods suggested for personalizing drug administration schedules (19) were prospectively validated in the clinic (20)(21). Cancer immunotherapy has been mathematically studied too (22)(23)(24)(25)(26)(27)(28)(29)(30) with some models retrospectively validated by preclinical and clinical population data (26,(31)(32).…”

Optimisation of Cancer Drug Treatments Using Cell Population Dynamics

“…In [1], Agur et al considered an age-structured cell cycle model with deterministic cycle phase lengths. The drug under consideration is toxic for cells in one of the phases only.…”