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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

Distribution of observed baseline PSA (PSA0) and PSA growth rates (KG) for the model building (dashed line) and external dataset (solid line) (top). Model-predicted (areas and bold solid lines) and observed (normal solid lines) in survival in external dataset, stratified for PSA0 and KG.
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fig04: Distribution of observed baseline PSA (PSA0) and PSA growth rates (KG) for the model building (dashed line) and external dataset (solid line) (top). Model-predicted (areas and bold solid lines) and observed (normal solid lines) in survival in external dataset, stratified for PSA0 and KG.

Mentions: An external evaluation of the DP-survival model was performed by predicting the reported overall survival of previously reported15 individual values of PSA0, KG, and Tnadir for two external clinical studies in patients with CRPC investigating (i) docetaxel with and without thalidomide and (ii) ketoconazole plus hydrocortisone with and without alendronate (Figure4). The predicted survival without stratification is depicted in Figure S4. A high level of uncertainty in the predictions was present, with some systematic overprediction of overall survival. The largest deviation was seen for the most favorable covariate values (e.g., patients with low growth rates and low PSA0 values).


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)

Distribution of observed baseline PSA (PSA0) and PSA growth rates (KG) for the model building (dashed line) and external dataset (solid line) (top). Model-predicted (areas and bold solid lines) and observed (normal solid lines) in survival in external dataset, stratified for PSA0 and KG.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig04: Distribution of observed baseline PSA (PSA0) and PSA growth rates (KG) for the model building (dashed line) and external dataset (solid line) (top). Model-predicted (areas and bold solid lines) and observed (normal solid lines) in survival in external dataset, stratified for PSA0 and KG.
Mentions: An external evaluation of the DP-survival model was performed by predicting the reported overall survival of previously reported15 individual values of PSA0, KG, and Tnadir for two external clinical studies in patients with CRPC investigating (i) docetaxel with and without thalidomide and (ii) ketoconazole plus hydrocortisone with and without alendronate (Figure4). The predicted survival without stratification is depicted in Figure S4. A high level of uncertainty in the predictions was present, with some systematic overprediction of overall survival. The largest deviation was seen for the most favorable covariate values (e.g., patients with low growth rates and low PSA0 values).

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