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Mechanistic Models for Myelosuppression

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Publisher Springer
Specialty Oncology
Date 2003 Aug 2
PMID 12889739
Citations 39
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Abstract

As myelosuppression is the dose-limiting toxicity for most chemotherapeutic drugs, modelers attempt to find relationships between drug and toxicity to optimize treatment. Mechanistic models, i.e. models based on physiology and pharmacology, are preferable over empirical models, as prior information can be utilized and as they generally are more reliable for extrapolations. To account for different dosing-regimens and possible schedule-dependent effects, the whole concentration-time profile should be used as input into the pharmacokinetic-pharmacodynamic model. It is also of importance to model the whole time course of myelosuppression to be able to predict both the degree and duration of toxicity as well as consecutive courses of therapy. A handful of (semi)-mechanistic pharmacokinetic-pharmacodynamic models with the above properties have been developed and are reviewed. Ideally, a model of myelosuppression should separate drug-specific parameters from system related parameters to be applicable across drugs and useful under different clinical settings. Introduction of mechanistic models of myelosuppression in the design and evaluation of clinical trials can guide in the decision of optimal sampling times, contribute to knowledge of optimal doses and treatment regimens at an earlier time point and identify sub-groups of patients at a high risk of myelosuppression.

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References
1.
Haurie C, Dale D, Mackey M . Cyclical neutropenia and other periodic hematological disorders: a review of mechanisms and mathematical models. Blood. 1998; 92(8):2629-40. View

2.
Botnick L, Hannon E, Hellman S . Nature of the hemopoietic stem cell compartment and its proliferative potential. Blood Cells. 1979; 5(2):195-210. View

3.
Gupta P, Blazar B, Gupta K, Verfaillie C . Human CD34(+) bone marrow cells regulate stromal production of interleukin-6 and granulocyte colony-stimulating factor and increase the colony-stimulating activity of stroma. Blood. 1998; 91(10):3724-33. View

4.
Sun Y, Jusko W . Transit compartments versus gamma distribution function to model signal transduction processes in pharmacodynamics. J Pharm Sci. 1998; 87(6):732-7. DOI: 10.1021/js970414z. View

5.
Evans W, Relling M, Rodman J, Crom W, Boyett J, Pui C . Conventional compared with individualized chemotherapy for childhood acute lymphoblastic leukemia. N Engl J Med. 1998; 338(8):499-505. DOI: 10.1056/NEJM199802193380803. View