Androgen deprivation therapy (ADT) is a widely used treatment for patients with hormone-sensitive prostate cancer (PCa). However, duration of treatment response varies, and most patients eventually experience disease progression despite treatment. Leuprorelin is a luteinizing hormone-releasing hormone (LHRH) agonist, a commonly used form of ADT. Prostate-specific antigen (PSA) is a biomarker for monitoring disease progression and predicting treatment response and survival in PCa. However, time-dependent profile of tumor regression and growth in patients with hormone-sensitive PCa on ADT has never been fully characterized. In this analysis, nationwide medical claims database provided by Humana from 2007 to 2011 was used to construct a population-based disease progression model for patients with hormone-sensitive PCa on leuprorelin. Data were analyzed by nonlinear mixed effects modeling utilizing Monte Carlo Parametric Expectation Maximization (MCPEM) method in NONMEM. Covariate selection was performed using a modified Wald’s approximation method with backward elimination (WAM-BE) proposed by our group. 1113 PSA observations from 264 subjects with malignant PCa were used for model development. PSA kinetics were well described by the final covariate model. Model parameters were well estimated, but large between-patient variability was observed. Hemoglobin significantly affected proportion of drug-resistant cells in the original tumor, while baseline PSA and antiandrogen use significantly affected treatment effect on drug-sensitive PCa cells (Ds). Population estimate of Ds was 3.78 x 10−2 day-1. Population estimates of growth rates for drug-sensitive (Gs) and drug-resistant PCa cells (GR) were 1.96 x 10−3 and 6.54 x 10−4 day-1, corresponding to a PSA doubling time of 354 and 1060 days, respectively. Proportion of the original PCa cells inherently resistant to treatment was estimated to be 1.94%. Application of population-based disease progression model to clinical data allowed characterization of tumor resistant patterns and growth/regression rates that enhances our understanding of how PCa responds to ADT.

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Published in PLOS ONE, v. 15, no. 3, p. 1-22.

© 2020 Zou et al.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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CN is supported in the form of a salary from NewGround Pharmaceutical Consulting LLC.

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An independent researcher who would like to request the data would need to contact Humana (Vinit Nair (vnair1@humana.com)). The data dictionary for the Humana database is included in the Supporting Information. A non-author institutional contact is Tammy Harper (Tamela.harper@uky.edu), the project manager for the University of Kentucky Center for Clinical and Translational Science (CCTS) Enterprise Data Trust.

pone.0230571.s001.tif (128 kB)
S1 Fig. Patient selection flow diagram. https://doi.org/10.1371/journal.pone.0230571.s001

pone.0230571.s002.tif (159 kB)
S2 Fig. Prediction-corrected visual predictive check of the final model. https://doi.org/10.1371/journal.pone.0230571.s002

pone.0230571.s003.xlsx (107 kB)
S1 File. Data dictionary for the Humana dataset. https://doi.org/10.1371/journal.pone.0230571.s003