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Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin.

van Hasselt JG, Gupta A, Hussein Z, Beijnen JH, Schellens JH, Huitema AD - CPT Pharmacometrics Syst Pharmacol (2015)

Bottom Line: In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important.Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin.Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Department of Pharmacy & Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University Leiden, The Netherlands.

ABSTRACT
Quantitative model-based analyses are helpful to support decision-making in drug development. In oncology, disease progression/clinical outcome (DPCO) models have been used for early predictions of clinical outcome, but most of such approaches did not include adverse events or dose intensity. In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important. Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin. The framework included (i) a DPCO model relating prostate-specific antigen (PSA) dynamics to survival; (ii) models for adverse events including dose-limiting neutropenia and other graded toxicities; (iii) a model for Eastern Cooperative Oncology Group (ECOG) performance score; (iv) a model for dropout; (v) the consideration of cost effectiveness. The model allowed simulation of realistic treatment courses. Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.

No MeSH data available.


Related in: MedlinePlus

Distribution of difference in cost (CU) vs. effect (overall survival, days) for the different simulation scenarios vs. the base scenario. The color intensity represents the relative density of cost-effectiveness pairs across individuals. The gray lines represent 2D density smoothers.
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fig04: Distribution of difference in cost (CU) vs. effect (overall survival, days) for the different simulation scenarios vs. the base scenario. The color intensity represents the relative density of cost-effectiveness pairs across individuals. The gray lines represent 2D density smoothers.

Mentions: Finally, the individual differences in costs and effects for the different simulation scenarios vs. placebo treatment are represented in Figure4. This figure depicts for each simulated individual the expected costs and expected efficacy, taking into consideration simulated values derived from toxicity and efficacy models and associated dose reductions, in comparison with the base scenario. These types of differential cost-effectiveness plots are commonly used in the field of CEA to evaluate different CEA scenarios, and, the effect of parameter uncertainty. In this case, however, the interindividual variability is thus depicted.


Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin.

van Hasselt JG, Gupta A, Hussein Z, Beijnen JH, Schellens JH, Huitema AD - CPT Pharmacometrics Syst Pharmacol (2015)

Distribution of difference in cost (CU) vs. effect (overall survival, days) for the different simulation scenarios vs. the base scenario. The color intensity represents the relative density of cost-effectiveness pairs across individuals. The gray lines represent 2D density smoothers.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544051&req=5

fig04: Distribution of difference in cost (CU) vs. effect (overall survival, days) for the different simulation scenarios vs. the base scenario. The color intensity represents the relative density of cost-effectiveness pairs across individuals. The gray lines represent 2D density smoothers.
Mentions: Finally, the individual differences in costs and effects for the different simulation scenarios vs. placebo treatment are represented in Figure4. This figure depicts for each simulated individual the expected costs and expected efficacy, taking into consideration simulated values derived from toxicity and efficacy models and associated dose reductions, in comparison with the base scenario. These types of differential cost-effectiveness plots are commonly used in the field of CEA to evaluate different CEA scenarios, and, the effect of parameter uncertainty. In this case, however, the interindividual variability is thus depicted.

Bottom Line: In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important.Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin.Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Department of Pharmacy & Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University Leiden, The Netherlands.

ABSTRACT
Quantitative model-based analyses are helpful to support decision-making in drug development. In oncology, disease progression/clinical outcome (DPCO) models have been used for early predictions of clinical outcome, but most of such approaches did not include adverse events or dose intensity. In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important. Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin. The framework included (i) a DPCO model relating prostate-specific antigen (PSA) dynamics to survival; (ii) models for adverse events including dose-limiting neutropenia and other graded toxicities; (iii) a model for Eastern Cooperative Oncology Group (ECOG) performance score; (iv) a model for dropout; (v) the consideration of cost effectiveness. The model allowed simulation of realistic treatment courses. Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.

No MeSH data available.


Related in: MedlinePlus