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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

Observed and predicted survival vs. time (days). (a) Observed (Kaplan-Meier), median predicted (blue line) with associated 95% confidence interval (blue area). (b) Observed (Kaplan-Meier) and model predictions (median and 95% prediction interval) stratified below and above the 50th percentile for covariates (ECOG, time to PSA nadir [days], KG [days−1], PSA0 [ng/ml]) in the final covariate survival, or stratified for different ECOG scores. C: Model predictions for different values of the covariates in the final survival model.
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fig03: Observed and predicted survival vs. time (days). (a) Observed (Kaplan-Meier), median predicted (blue line) with associated 95% confidence interval (blue area). (b) Observed (Kaplan-Meier) and model predictions (median and 95% prediction interval) stratified below and above the 50th percentile for covariates (ECOG, time to PSA nadir [days], KG [days−1], PSA0 [ng/ml]) in the final covariate survival, or stratified for different ECOG scores. C: Model predictions for different values of the covariates in the final survival model.

Mentions: A Weibull function best described the survival curve (Figure3A). The parameter estimates of the base survival model are provided in Table3, and could be estimated with good precision (RSE <23.1%). Subsequently, in the univariate covariate survival analysis we identified the following covariates as significant (P < 0.05): ECOG score, individual predicted values for time to PSA nadir (Tnadir), baseline (PSA0), relative maximum change from PSA baseline (CFB), areas under the PSA-time curve (PSAAUC), and PSA growth rate (KG).


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)

Observed and predicted survival vs. time (days). (a) Observed (Kaplan-Meier), median predicted (blue line) with associated 95% confidence interval (blue area). (b) Observed (Kaplan-Meier) and model predictions (median and 95% prediction interval) stratified below and above the 50th percentile for covariates (ECOG, time to PSA nadir [days], KG [days−1], PSA0 [ng/ml]) in the final covariate survival, or stratified for different ECOG scores. C: Model predictions for different values of the covariates in the final survival model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig03: Observed and predicted survival vs. time (days). (a) Observed (Kaplan-Meier), median predicted (blue line) with associated 95% confidence interval (blue area). (b) Observed (Kaplan-Meier) and model predictions (median and 95% prediction interval) stratified below and above the 50th percentile for covariates (ECOG, time to PSA nadir [days], KG [days−1], PSA0 [ng/ml]) in the final covariate survival, or stratified for different ECOG scores. C: Model predictions for different values of the covariates in the final survival model.
Mentions: A Weibull function best described the survival curve (Figure3A). The parameter estimates of the base survival model are provided in Table3, and could be estimated with good precision (RSE <23.1%). Subsequently, in the univariate covariate survival analysis we identified the following covariates as significant (P < 0.05): ECOG score, individual predicted values for time to PSA nadir (Tnadir), baseline (PSA0), relative maximum change from PSA baseline (CFB), areas under the PSA-time curve (PSAAUC), and PSA growth rate (KG).

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