On systems and control approaches to therapeutic gain

Tomas Radivoyevitch, Kenneth A Loparo, Robert C Jackson, W David Sedwick
2006 BMC Cancer  
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
more » ... 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 noncurable 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. Background Recent scientific research trends toward systems biology have brought new life to theoretical cancer research [1]. Mathematical models of cancer relevant processes, rare in the past [2, 3] , have now become common [4] [5] [6] [7] , and some appear to have predictive capabilities [8, 9] . The ulti-
doi:10.1186/1471-2407-6-104 pmid:16638124 pmcid:PMC1484487 fatcat:ovizl65btzfw5lie3njus4bjm4