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Open Access Research article

On systems and control approaches to therapeutic gain

Tomas Radivoyevitch1*, Kenneth A Loparo2, Robert C Jackson3 and W David Sedwick4

Author Affiliations

1 Department of Epidemiology and Biostatistics Case Western Reserve University, Cleveland, Ohio 44106, USA

2 Department of Electrical Engineering and Computer Science Case Western Reserve University, Cleveland, Ohio 44106, USA

3 Cyclacel Ltd., Dundee Technopole James Lindsay Place, Dundee, DD1 5JJ, UK

4 Department of Hematology and Oncology Case Western Reserve University, Cleveland, Ohio 44106, USA

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BMC Cancer 2006, 6:104  doi:10.1186/1471-2407-6-104

Published: 25 April 2006

Abstract

Background

Mathematical 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.

Methods

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

Results

Two 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.

Conclusion

Cancer 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.