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Disease Progression/Clinical Outcome Model for Castration-Resistant Prostate Cancer in Patients Treated With Eribulin.

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

Bottom Line: For clinical outcome, overall survival (OS) was used.The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%).The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.

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
Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development. Here we developed such a disease progression clinical outcome model for castration-resistant prostate cancer (CRPC) using historical phase II data of the anticancer agent eribulin. Disease progression was captured using the dynamics of prostate-specific antigen (PSA). For clinical outcome, overall survival (OS) was used. The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%). OS was modeled according to a Weibull distribution. Predictors for survival included model-predicted PSA time to nadir (TTN), PSA growth rate, Eastern Cooperative Oncology Group (ECOG) score, and baseline PSA. The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.

No MeSH data available.


Related in: MedlinePlus

Schematic diagram of the disease progression model for the dynamics of prostate-specific antigen (PSA). KG, growth rate; KD, inhibition rate; λ, drug resistance development; AUC, predicted area-under-the-concentration-time curve.
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fig01: Schematic diagram of the disease progression model for the dynamics of prostate-specific antigen (PSA). KG, growth rate; KD, inhibition rate; λ, drug resistance development; AUC, predicted area-under-the-concentration-time curve.

Mentions: The structure of the PSA DP model is depicted in Figure1. The final model parameter estimates for the DP model are provided in Table2. Structural model parameters could be estimated with adequate precision (RSE < 44.3%). The optimal value of the drug exposure parameter (KP) was selected based on evaluation of different fixed values in the model. We selected a large value of 6,000 to allow for a nearly instantaneous dosing event. Similar large values resulted in the same parameter estimates. The only relevance of this parameter was to include variability in predicted exposure. The baseline PSA (PSA0) was estimated at 23.2 ng/mL (RSE 16.5%), the growth rate parameter was estimated at 0.00879 day−1 (RSE 12.6%), the drug-induced inhibition parameter (KD) was estimated at 0.241 µg·h·l−1 day−1 (RSE 32.6%), and the drug resistance development parameter (λ) was estimated at 0.0113 day−1 (RSE 44.3%).


Disease Progression/Clinical Outcome Model for Castration-Resistant Prostate Cancer in Patients Treated With Eribulin.

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

Schematic diagram of the disease progression model for the dynamics of prostate-specific antigen (PSA). KG, growth rate; KD, inhibition rate; λ, drug resistance development; AUC, predicted area-under-the-concentration-time curve.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544052&req=5

fig01: Schematic diagram of the disease progression model for the dynamics of prostate-specific antigen (PSA). KG, growth rate; KD, inhibition rate; λ, drug resistance development; AUC, predicted area-under-the-concentration-time curve.
Mentions: The structure of the PSA DP model is depicted in Figure1. The final model parameter estimates for the DP model are provided in Table2. Structural model parameters could be estimated with adequate precision (RSE < 44.3%). The optimal value of the drug exposure parameter (KP) was selected based on evaluation of different fixed values in the model. We selected a large value of 6,000 to allow for a nearly instantaneous dosing event. Similar large values resulted in the same parameter estimates. The only relevance of this parameter was to include variability in predicted exposure. The baseline PSA (PSA0) was estimated at 23.2 ng/mL (RSE 16.5%), the growth rate parameter was estimated at 0.00879 day−1 (RSE 12.6%), the drug-induced inhibition parameter (KD) was estimated at 0.241 µg·h·l−1 day−1 (RSE 32.6%), and the drug resistance development parameter (λ) was estimated at 0.0113 day−1 (RSE 44.3%).

Bottom Line: For clinical outcome, overall survival (OS) was used.The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%).The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.

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
Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development. Here we developed such a disease progression clinical outcome model for castration-resistant prostate cancer (CRPC) using historical phase II data of the anticancer agent eribulin. Disease progression was captured using the dynamics of prostate-specific antigen (PSA). For clinical outcome, overall survival (OS) was used. The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%). OS was modeled according to a Weibull distribution. Predictors for survival included model-predicted PSA time to nadir (TTN), PSA growth rate, Eastern Cooperative Oncology Group (ECOG) score, and baseline PSA. The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.

No MeSH data available.


Related in: MedlinePlus