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BMC Cancer

, 6:104

First Online: 25 April 2006Received: 17 June 2005Accepted: 25 April 2006DOI: 10.1186-1471-2407-6-104

Cite this article as: Radivoyevitch, T., Loparo, K.A., Jackson, R.C. et al. BMC Cancer 2006 6: 104. doi:10.1186-1471-2407-6-104

Abstract

BackgroundMathematical models of cancer relevant processes are being developed at an increasing rate. Conceptual frameworks are needed to support new treatment designs based on such models.

MethodsA modern control perspective is used to formulate two therapeutic gain strategies.

ResultsTwo conceptually distinct therapeutic gain strategies are provided. The first is direct in that its goal is to kill cancer cells more so than normal cells, the second is indirect in that its goal is to achieve implicit therapeutic gains by transferring states of cancer cells of non-curable cases to a target state defined by the cancer cells of curable cases. The direct strategy requires models that connect anti-cancer agents to an endpoint that is modulated by the cause of the cancer and that correlates with cell death. It is an abstraction of a strategy for treating mismatch repair MMR deficient cancers with iodinated uridine IUdR; IU-DNA correlates with radiation induced cell killing and MMR modulates the relationship between IUdR and IU-DNA because loss of MMR decreases the removal of IU from the DNA. The second strategy is indirect. It assumes that non-curable patient outcomes will improve if the states of their malignant cells are first transferred toward a state that is similar to that of a curable patient. This strategy is difficult to employ because it requires a model that relates drugs to determinants of differences in patient survival times. It is an abstraction of a strategy for treating BCR-ABL pro-B cell childhood leukemia patients using curable cases as the guides.

ConclusionCancer therapeutic gain problem formulations define the purpose, and thus the scope, of cancer process modeling. Their abstractions facilitate considerations of alternative treatment strategies and support syntheses of learning experiences across different cancers.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2407-6-104 contains supplementary material, which is available to authorized users.

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Autor: Tomas Radivoyevitch - Kenneth A Loparo - Robert C Jackson - W David Sedwick

Fuente: https://link.springer.com/







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