Models of schedule dependent haematological toxicity of 2'-deoxy-2'-methylidenecytidine (DMDC)

Friberg, Lena E.; Brindley, Charlie; Karlsson, Mats O.; Devlin, Anna-Claire

doi:10.1007/s002280000181

Cited by 36 publications

(25 citation statements)

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“…Th e model structure was the same as in the original publication [8] except that the half-life of circulating neutrophils was fi xed to the literature value of 7 hours [22] and the neutrophil data were Box-Cox transformed (ANC transformed = (ANC λ -1)/λ) with λ=0.2 prior to the analysis as this transformation resulted in residuals with a symmetrical distribution around zero [23,24].…”

Section: Pharmacodynamic Modeling Of Myelosuppressionmentioning

confidence: 99%

Limited inter-occasion variability in relation to inter-individual variability in chemotherapy-induced myelosuppression

Hansson

Wallin

Lindman

et al. 2009

Cancer Chemother Pharmacol

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Purpose: A previously developed semi-physiological model of chemotherapy-induced myelosuppression has shown consistent system-related parameter and inter-individual variability (IIV) estimates across drugs. A requirement for dose individualization to be useful is relatively low variability between treatment courses (IOV) in relation to IIV. Th e objective of this study was to evaluate and compare magnitudes of IOV and IIV in myelosuppression model parameters across six diff erent anti-cancer drug treatments. Methods: Neutrophil counts from several treatment courses following therapy with docetaxel, paclitaxel, epirubicin-docetaxel, 5-fl uorouracil-epirubicin-cyclophosphamide, topotecan and etoposide were included in the analysis. Th e myelosuppression model was fi tted to the data using NONMEM VI. IOV in the model parameters baseline neutrophil counts (ANC 0 ), mean transit time through the non-mitotic maturation chain (MTT) and the parameter describing the concentration-eff ect relationship (Slope) were evaluated for statistical signifi cance (P < 0.001). Results: IOV in MTT was signifi cant for all the investigated datasets, except for topotecan, and was of similar magnitude (8-16 CV %). IOV in Slope was signifi cant for docetaxel, topotecan and etoposide (19-39 CV %). For all six investigated datasets the IOV in myelosuppression parameters was lower than the IIV. Th ere was no indication of systematic shifts in the system-or drug sensitivity-related parameters over time across data sets. Conclusion: Th is study indicates that the semi-physiological model of chemotherapyinduced myelosuppression has potential to be used for prediction of the time-course of myelosuppression in future courses and is thereby a valuable step towards individually tailored anticancer drug therapy.

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Section: Pharmacodynamic Modeling Of Myelosuppressionmentioning

confidence: 99%

Limited inter-occasion variability in relation to inter-individual variability in chemotherapy-induced myelosuppression

Hansson

Wallin

Lindman

et al. 2009

Cancer Chemother Pharmacol

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“…In fact, transit compartments can be used as a general approach for the characterization of delays in response due to maturation processes. Furthermore, the assumption of physiological rates for maturation allows for parameterization of drug and system-specific terms, which facilitates the comparison of treatment effects across compounds (Dayneka et al 1993;Friberg et al 2000;Krzyzanski et al 2008).…”

Section: Modeling and Prediction Of Adverse Eventsmentioning

confidence: 99%

PKPD and Disease Modeling: Concepts and Applications to Oncology

Pasqua

2010

Clinical Trial Simulations

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Despite the transition from chemotherapy with cytotoxic and cytostatic drugs to targeted therapy with monoclonal antibodies, clinical decisions regarding the benefit of pharmacological interventions still rely on the concept of a maximum tolerated dose (MTD). In this chapter, it is shown that a model-based approach can be used in oncology to optimize dose selection and characterize drug effect on tumor growth, overall survival and safety. Furthermore, modeling and simulation also provides insight into the underlying mechanisms of action, enabling translation of the differences in pharmacology, safety and disease processes from preclinical experiments to clinical trials. A paradigm shift is proposed to bring the benefits of model-based drug development to cancer patients, in which biomarkers of safety and prognostic markers of overall survival are assessed to predict treatment outcome and disease progression. General Concepts and History of Model-Based Research in OncologyModels aim to represent and simplify complex systems. The accurate representation and simplification of the complexity and heterogeneity of biological systems have characterized oncology modeling efforts in clinical pharmacology and translational medicine research (Rew 2000a;Quaranta et al. 2005). Although clinical oncologists can appreciate the value and potential implications of experimental models, the justification for the use of mathematical models remains unclear to most of them. Mathematical modeling can be a powerful tool for analyzing

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“…For each patient, the three first daily doses were fixed a priori, and the two last doses were adjusted to reach a total area under the curve (AUC) within a targeted range (37,500-75,000 nM min). The PK data were analyzed by Friberg et al [11] on a subsample of N 1 = 31 women. Let us recall briefly their results.…”

Section: The Pharmacokinetic Modelmentioning

confidence: 99%