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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

(A) Model predictions (95% prediction intervals) and observed incidence of transitions for the Markov-transition models for the other adverse events model and (B) ECOG performance score model. (C) Dropout model simulated median (thick solid lines) and 95% confidence intervals (areas) and observed (thin solid lines), stratified by patients above (blue) and below (gray) the median estimate for the PSA growth rate (KG).
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fig02: (A) Model predictions (95% prediction intervals) and observed incidence of transitions for the Markov-transition models for the other adverse events model and (B) ECOG performance score model. (C) Dropout model simulated median (thick solid lines) and 95% confidence intervals (areas) and observed (thin solid lines), stratified by patients above (blue) and below (gray) the median estimate for the PSA growth rate (KG).

Mentions: The Markov-transition model adequately described the ECOG transitions between neighboring ECOG states (Figure2). Transition rates could be estimated with a relative standard error (RSE) of <27%. The baseline ECOG score distribution was 51, 48, and 1% for ECOG scores of 0, 1, and 2, respectively. Most transitions were occurring within the same state (0 to 0, 1 to 1). The number of events of an ECOG score of 3 or 4 was low. Therefore, we pooled ECOG scores of 2, 3, and 4 into one state. We also estimated an effect of PSA disease progression on transition rates towards higher ECOG states. When >50% inhibition of the PSA was observed, the transition rates to higher ECOG scores decreased by a factor of 0.704 (RSE 36%). The parameter estimates of the Markov-transition model are provided in Table S1. When >50% inhibition of the PSA was observed, the transition rates to higher ECOG scores decreased by a factor of 0.704 (RSE 36%). When comparing these groups with >50% inhibition vs. <50%, the overall proportions of ECOG 0 and 1 were 75% and 23% vs. 44% and 48%, and was also consistent with observed transition frequencies. Nonetheless, the amount of ECOG data available was relatively limited, so the certainty and magnitude of the identified effect should be interpreted with caution.


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)

(A) Model predictions (95% prediction intervals) and observed incidence of transitions for the Markov-transition models for the other adverse events model and (B) ECOG performance score model. (C) Dropout model simulated median (thick solid lines) and 95% confidence intervals (areas) and observed (thin solid lines), stratified by patients above (blue) and below (gray) the median estimate for the PSA growth rate (KG).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig02: (A) Model predictions (95% prediction intervals) and observed incidence of transitions for the Markov-transition models for the other adverse events model and (B) ECOG performance score model. (C) Dropout model simulated median (thick solid lines) and 95% confidence intervals (areas) and observed (thin solid lines), stratified by patients above (blue) and below (gray) the median estimate for the PSA growth rate (KG).
Mentions: The Markov-transition model adequately described the ECOG transitions between neighboring ECOG states (Figure2). Transition rates could be estimated with a relative standard error (RSE) of <27%. The baseline ECOG score distribution was 51, 48, and 1% for ECOG scores of 0, 1, and 2, respectively. Most transitions were occurring within the same state (0 to 0, 1 to 1). The number of events of an ECOG score of 3 or 4 was low. Therefore, we pooled ECOG scores of 2, 3, and 4 into one state. We also estimated an effect of PSA disease progression on transition rates towards higher ECOG states. When >50% inhibition of the PSA was observed, the transition rates to higher ECOG scores decreased by a factor of 0.704 (RSE 36%). The parameter estimates of the Markov-transition model are provided in Table S1. When >50% inhibition of the PSA was observed, the transition rates to higher ECOG scores decreased by a factor of 0.704 (RSE 36%). When comparing these groups with >50% inhibition vs. <50%, the overall proportions of ECOG 0 and 1 were 75% and 23% vs. 44% and 48%, and was also consistent with observed transition frequencies. Nonetheless, the amount of ECOG data available was relatively limited, so the certainty and magnitude of the identified effect should be interpreted with caution.

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