Mathematical models of androgen resistance in prostate cancer patients under intermittent androgen suppression therapy

Javier Baez, Yang Kuang

Research output: Contribution to journalArticle

10 Scopus citations

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.

Original languageEnglish (US)
Article number352
JournalApplied Sciences (Switzerland)
Volume6
Issue number11
DOIs
StatePublished - Nov 16 2016

Keywords

  • Androgen deprivation therapy
  • Data fitting
  • Mathematical modeling
  • Prostate cancer

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Mathematical models of androgen resistance in prostate cancer patients under intermittent androgen suppression therapy'. Together they form a unique fingerprint.

  • Cite this