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Appl. Sci. 2016, 6(11), 352; doi:10.3390/app6110352

Mathematical Models of Androgen Resistance in Prostate Cancer Patients under Intermittent Androgen Suppression Therapy

School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
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Academic Editor: Serafim Kalliadasis
Received: 15 August 2016 / Revised: 14 October 2016 / Accepted: 5 November 2016 / Published: 16 November 2016
(This article belongs to the Special Issue Dynamical Models of Biology and Medicine)
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Abstract

Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction. View Full-Text
Keywords: mathematical modeling; prostate cancer; androgen deprivation therapy; data fitting mathematical modeling; prostate cancer; androgen deprivation therapy; data fitting
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Baez, J.; Kuang, Y. Mathematical Models of Androgen Resistance in Prostate Cancer Patients under Intermittent Androgen Suppression Therapy. Appl. Sci. 2016, 6, 352.

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