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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

Selected individual plots for log-transformed prostate-specific antigen (PSA) vs. time after start of treatment (days) for observed values (gray circles), individual model predictions (black solid line), population model predictions (solid gray line), and dose events (vertical lines).
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fig02: Selected individual plots for log-transformed prostate-specific antigen (PSA) vs. time after start of treatment (days) for observed values (gray circles), individual model predictions (black solid line), population model predictions (solid gray line), and dose events (vertical lines).

Mentions: Based on inspection of observed and predicted individual PSA timecourse plots (Figure2) and goodness-of-fit diagnostic plots (Figure S2), the model was able to adequately capture the dynamics of the PSA–time profiles (Figure2). The visual predicted check (Figure S3) suggested adequate description of the PSA–time profiles in the first part of the dataset, where most patients were still included in the trial. At later stages, there was a divergence observed for observed and simulated PSA profiles due to disease progression-related dropout. The incorporation of dropout mechanisms in the simulation based on either protocol criterion defined dropout, or based on parametric survival models for dropout (including either last observed PSA, individual predicted PSA at time of dropout, or individual predicted PSA growth rate), did not result in relevant improvements of the VPC.


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)

Selected individual plots for log-transformed prostate-specific antigen (PSA) vs. time after start of treatment (days) for observed values (gray circles), individual model predictions (black solid line), population model predictions (solid gray line), and dose events (vertical lines).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig02: Selected individual plots for log-transformed prostate-specific antigen (PSA) vs. time after start of treatment (days) for observed values (gray circles), individual model predictions (black solid line), population model predictions (solid gray line), and dose events (vertical lines).
Mentions: Based on inspection of observed and predicted individual PSA timecourse plots (Figure2) and goodness-of-fit diagnostic plots (Figure S2), the model was able to adequately capture the dynamics of the PSA–time profiles (Figure2). The visual predicted check (Figure S3) suggested adequate description of the PSA–time profiles in the first part of the dataset, where most patients were still included in the trial. At later stages, there was a divergence observed for observed and simulated PSA profiles due to disease progression-related dropout. The incorporation of dropout mechanisms in the simulation based on either protocol criterion defined dropout, or based on parametric survival models for dropout (including either last observed PSA, individual predicted PSA at time of dropout, or individual predicted PSA growth rate), did not result in relevant improvements of the VPC.

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