Modeling prostate cancer response to continuous versus intermittent androgen ablation therapy

Harsh Vardhan Jain; Avner Friedman

doi:10.3934/dcdsb.2013.18.945

Article Contents

2013, Volume 18, Issue 4: 945-967. Doi: 10.3934/dcdsb.2013.18.945 open access

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Modeling prostate cancer response to continuous versus intermittent androgen ablation therapy

Harsh Vardhan Jain^1, and
Avner Friedman^2,

1.
Department of Mathematics, Florida State University, Tallahassee, FL 32306
2.
The Ohio State University, Department of Mathematics, Columbus, OH 43210

Received: February 29, 2012

Revised: March 31, 2012

Published: May 2013

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Abstract

Due to its dependence on androgens, metastatic prostate cancer is typically treated with continuous androgen ablation. However, such therapy eventually fails due to the emergence of castration-resistance cells. It has been hypothesized that intermittent androgen ablation can delay the onset of this resistance. In this paper, we present a biochemically-motivated ordinary differential equation model of prostate cancer response to anti-androgen therapy, with the aim of predicting optimal treatment protocols based on individual patient characteristics. Conditions under which intermittent scheduling is preferable over continuous therapy are derived analytically for a variety of castration-resistant cell phenotypes. The model predicts that while a cure is not possible for androgen-independent castration-resistant cells, continuous therapy results in longer disease-free survival periods. However, for androgen-repressed castration-resistant cells, intermittent therapy can significantly delay the emergence of resistance, and in some cases induce tumor regression. Numerical simulations of the model lead to two interesting cases, where even though continuous therapy may be non-viable, an optimally chosen intermittent schedule leads to tumor regression, and where a sub-optimally chosen intermittent schedule can initially appear to result in a cure, it eventually leads to resistance emergence. These results demonstrate the model's potential impact in a clinical setting.

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Mathematics Subject Classification: Primary: 92C40, 92C50; Secondary: 37N25.

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