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Exploring Impact of Treatment Design on Ability to Leverage Intratumor Competition and Control Multiply Resistant Populations.

Monday, July 17 at 6:00pm

SMB2023 SMB2023 Follow Monday during the "PS01" time block.
Room assignment: in The Ohio Union.
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Afton Widdershins

Pennsylvania State University College of Medicine
"Exploring Impact of Treatment Design on Ability to Leverage Intratumor Competition and Control Multiply Resistant Populations."
BACKGROUND: Cancer is a disease with an incredible ability to adapt when exposed to clinical treatment. This evolution of resistance presents a real challenge to the long-term success of treatments like targeted therapies. Taking into account tumor evolutionary dynamics like inter-clonal competition could provide insight to how to design therapies to best utilize the drugs that are already available to clinicians. METHODS: To allow for a model that could be validated through laboratory work, we worked with an ordinary differential equation (ODE) system of an in vitro cell population approximating a heterogeneous tumor. The ODE system is composed of four individual cell populations that respond to two drugs, with one cell population being fully susceptible, one being fully resistant, and the other two populations being resistant to one or the other drug. Competition and growth are modeled through the logistic growth term, while cell death is based on each drug’s concentration. Three different regimen categories were simulated using MATLAB – alternation, combination, and sequential. In order to explore fully explore different regimen designs, drug concentration was varied in all of the regimens, while the ratio between the two drugs was varied in the combination regimen settings and the frequency of alternation was varied in the alternation settings. The population parameters of total initial cell burden and the ratios between susceptible and various resistant populations were also varied to explore patient impact on regimen effectiveness. A regimen’s ability to control a population was defined as how long it could maintain the population below a chosen threshold less than the carrying capacity of the system. The regimens were then analyzed for their ability to control both the fully resistant population and the total population and compared to the control achieved by minimal and maximal competition scenarios established by previous work. RESULTS: The most important parameter varied in regimen design was concentration, as alternation and combination regimens with the same total concentration achieved grossly similar population control. In terms of the impact of alternation frequency, daily and weekly alternation had similar control of the fully resistant population and total population. Longer frequency alternations, like monthly, achieved better fully resistant control but had worse total population control. DISCUSSION: Earlier work suggests that incorporating competition into regimen design could extend control of a tumor population, though the similarities between the performance of different regimens suggests that maintaining a certain level of competition is more important than the method used to manage the population. However, this resemblance may be attributable to the simplicity of the model and the lack of consideration of consequences like toxicity for each regimen or of tumor abilities like mutation. Future work would include analysis to see if inclusion of more complex pharmacokinetics or more complex cell behaviors significantly change these results.
Additional authors: Elsa Hansen, PhD, Pennsylvania State University, The Huck Institutes; Andrew Read, PhD, Pennsylvania State University, The Huck Institutes; Raymond Hohl, MD-PhD, Pennsylvania State University College of Medicine, Department of Pharmacology

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Annual Meeting for the Society for Mathematical Biology, 2023.