Literature DB >> 20176032

Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer.

Yoshito Hirata1, Nicholas Bruchovsky, Kazuyuki Aihara.   

Abstract

We propose a mathematical model that quantitatively reproduces the dynamics of the serum prostate-specific antigen (PSA) level under intermittent androgen suppression (IAS) for prostate cancer. Taking into account the biological knowledge that there are reversible and irreversible changes in a malignant cell, we constructed a piecewise-linear dynamical model where the testosterone dynamics are modelled with rapid shifts between two levels, namely the normal and castrate concentrations of the male hormone. The validity of the model was supported by patient data obtained from a clinical trial of IAS. It accurately reproduced the kinetics of the therapeutic reduction of PSA and predicted the future nadir level correctly. The coexistence of reversible and irreversible changes within the malignant cell provided the best explanation of early progression to androgen independence. Finally, since the model identified patients for whom IAS was effective, it potentially offers a novel approach to individualized therapy requiring the input of time sequence values of PSA only. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20176032     DOI: 10.1016/j.jtbi.2010.02.027

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  20 in total

1.  Personalizing Androgen Suppression for Prostate Cancer Using Mathematical Modeling.

Authors:  Yoshito Hirata; Kai Morino; Koichiro Akakura; Celestia S Higano; Kazuyuki Aihara
Journal:  Sci Rep       Date:  2018-02-08       Impact factor: 4.379

2.  Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome.

Authors:  Guillermo Lorenzo; Víctor M Pérez-García; Alfonso Mariño; Luis A Pérez-Romasanta; Alessandro Reali; Hector Gomez
Journal:  J R Soc Interface       Date:  2019-08-14       Impact factor: 4.118

3.  The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.

Authors:  David J Albers; Matthew E Levine; Lena Mamykina; George Hripcsak
Journal:  Math Biosci       Date:  2019-08-24       Impact factor: 2.144

Review 4.  Mathematically modelling and controlling prostate cancer under intermittent hormone therapy.

Authors:  Yoshito Hirata; Gouhei Tanaka; Nicholas Bruchovsky; Kazuyuki Aihara
Journal:  Asian J Androl       Date:  2012-01-09       Impact factor: 3.285

Review 5.  Intermittent androgen suppression for prostate cancer.

Authors:  Nicholas C Buchan; S Larry Goldenberg
Journal:  Nat Rev Urol       Date:  2010-09-14       Impact factor: 14.432

6.  A partial differential equation model and its reduction to an ordinary differential equation model for prostate tumor growth under intermittent hormone therapy.

Authors:  Youshan Tao; Qian Guo; Kazuyuki Aihara
Journal:  J Math Biol       Date:  2013-08-28       Impact factor: 2.259

7.  Quantitative mathematical modeling of PSA dynamics of prostate cancer patients treated with intermittent androgen suppression.

Authors:  Yoshito Hirata; Koichiro Akakura; Celestia S Higano; Nicholas Bruchovsky; Kazuyuki Aihara
Journal:  J Mol Cell Biol       Date:  2012-05-04       Impact factor: 6.216

8.  Modeling the synergistic properties of drugs in hormonal treatment for prostate cancer.

Authors:  Trevor Reckell; Kyle Nguyen; Tin Phan; Sharon Crook; Eric J Kostelich; Yang Kuang
Journal:  J Theor Biol       Date:  2021-01-07       Impact factor: 2.691

9.  Cancer dynamics for identical twin brothers.

Authors:  Ali Ghaffari; Mostafa Khazaee
Journal:  Theor Biol Med Model       Date:  2012-02-06       Impact factor: 2.432

10.  Identifying critical transitions and their leading biomolecular networks in complex diseases.

Authors:  Rui Liu; Meiyi Li; Zhi-Ping Liu; Jiarui Wu; Luonan Chen; Kazuyuki Aihara
Journal:  Sci Rep       Date:  2012-12-10       Impact factor: 4.379

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