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

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.

Modeling the Relationship Between Exposure to Abiraterone and Prostate-Specific Antigen Dynamics in Patients with Metastatic Castration-Resistant Prostate Cancer

“…Many recent modeling studies offer thoughtful and detailed analyses of the relationship between PSA dynamics and survival in prostate cancer [25,26,32]; however, these analyses do not consider the exposure-response relationship, only use either simulated data or a previously developed pharmacokinetic model based on pooled data from unrelated, smaller or earlier-phase clinical trials, or are focused on earlier prostate cancer disease states. Our model builds on the previous literature by evaluating the PSA dynamics for an agent targeting androgen biosynthesis for CRPC treatment, a therapeutic strategy that has become increasingly important for this patient population and for which an exposure-survival relationship through PSA dynamics has not been fully described.…”

“…The model was evaluated using goodness-of-fit criteria. The primary diagnostic criterion was the match between individual predicted values and observed data, as used in similar studies [13], and was the main diagnostic criterion used in many recent publications for PSA dynamic models and other tumor dynamic models [25][26][27].…”

Disease Progression Modeling: Key Concepts and Recent Developments

“…The disease progression/clinical outcome model described the dynamics of prostate-specific antigen and its relationship to overall survival [56]. The models for eribulin-induced toxicity included dose-limiting neutropenia and other graded toxicities (nausea, fatigue, peripheral neuropathy, paresthesia, diarrhea, asthenia, and anemia), and the dose adaptation model incorporated definitions for when and how dose adjustments should be made following toxicities.…”

Assessing the suitability of capillary electrophoresis‐mass spectrometry for biomarker discovery in plasma‐based metabolomics

“…Supervised and unsupervised chemometric approaches are often used to get visualization of the relations between the metabolic profiles and to define borders between groups of samples. Global profiling of (endogenous) metabolites in organisms has been vastly explored for its potential application in research areas, such as diagnosis of diseases, guidance for personalized medicine, and evaluation of therapeutic treatments . Despite the efforts dedicated to metabolomics for biomarker discovery, its impact on recent clinical practice is still rather limited due to various challenges encountered during the analytical process, including study design, sample handling, data acquisition and data analysis, which may potentially lead to contradictory results in reported biomarkers.…”

“…Global profiling of (endogenous) metabolites in organisms has been vastly explored for its potential application in research areas, such as diagnosis of diseases, [1,3,6] guidance for personalized medicine, [11] and evaluation of therapeutic treatments. [12,13] Despite the efforts dedicated to metabolomics for biomarker discovery, its impact on recent clinical practice is still rather limited * These authors have contributed equally for this work.…”