A Mathematical Model of Intermittent Androgen Suppression for Prostate Cancer

Ideta, Aiko Miyamura; Tanaka, Gouhei; Takeuchi, Takumi; Aihara, Kazuyuki

doi:10.1007/s00332-008-9031-0

Cited by 136 publications

(174 citation statements)

References 26 publications

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“…26 This hypothesis differs from the Jackson et al and Ideta et al models which assume that androgen levels have either no effect or a negative effect on the net growth rate of the AI population. 13,14 Since our final model produced significantly more accurate results than the Ideta model, we believe that the hypothesis of a hypersensitive AI population is more likely to be true.…”

Section: Discussionmentioning

confidence: 87%

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A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy

2012

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Prostate cancer is commonly treated by a form of hormone therapy called androgen suppression. This form of treatment, while successful at reducing the cancer cell population, adversely affects quality of life and typically leads to a recurrence of the cancer in an androgen-independent form. Intermittent androgen suppression aims to alleviate some of these adverse affects by cycling the patient on and off treatment. Clinical studies have suggested that intermittent therapy is capable of maintaining androgen dependence over multiple treatment cycles while increasing quality of life during off-treatment periods. This paper presents a mathematical model of prostate cancer to study the dynamics of androgen suppression therapy and the production of prostate-specific antigen (PSA), a clinical marker for prostate cancer. Preliminary models were based on the assumption of an androgen-independent (AI) cell population with constant net growth rate. These models gave poor accuracy when fitting clinical data during simulation. The final model presented hypothesizes an AI population with increased sensitivity to low levels of androgen. It also hypothesizes that PSA production is heavily dependent on androgen. The high level of accuracy in fitting clinical data with this model appears to confirm these hypotheses, which are also consistent with biological evidence

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Section: Discussionmentioning

confidence: 87%

“…14 The Ideta model includes an androgen-dependent (AD) cell population and an androgen independent (AI) cell population with mutation from the AD population to the AI population at a rate based on the androgen concentration. We use the version of their model with a constant AI proliferation rate for comparison with our models.…”

Section: Model Developmentmentioning

confidence: 99%

A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy

2012

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“…The growth rate G x of AD cells, the growth rate G y of AI cells and the mutation rate M xy from AD phenotype to AI phenotype, are, respectively, modelled as follows (Ideta et al 2008):…”

Section: (B) Growth and Mutation Rates Of Cancer Cellsmentioning

confidence: 99%

Mathematical modelling of prostate cancer growth and its application to hormone therapy

Tanaka

Hirata

Goldenberg³

et al. 2010

Phil. Trans. R. Soc. A.

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Hormone therapy in the form of androgen deprivation is a major treatment for advanced prostate cancer. However, if such therapy is overly prolonged, tumour cells may become resistant to this treatment and result in recurrent fatal disease. Long-term hormone deprivation also is associated with side effects poorly tolerated by patients. In contrast, intermittent hormone therapy with alternating on-and off-treatment periods is a possible clinical strategy to delay progression to hormone-refractory disease with the advantage of reduced side effects during the off-treatment periods. In this paper, we first overview previous studies on mathematical modelling of prostate tumour growth under intermittent hormone therapy. The model is categorized into a hybrid dynamical system because switching between on-treatment and off-treatment intervals is treated in addition to continuous dynamics of tumour growth. Next, we present an extended model of stochastic differential equations and examine how well the model is able to capture the characteristics of authentic serum prostate-specific antigen (PSA) data. We also highlight recent advances in time-series analysis and prediction of changes in serum PSA concentrations. Finally, we discuss practical issues to be considered towards establishment of mathematical model-based tailor-made medicine, which defines how to realize personalized hormone therapy for individual patients based on monitored serum PSA levels.

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“…There are several mathematical models of serum dynamics in healthy prostate, CaP cell growth coupled to testosterone, DHT and AR dynamics [16] [63], and models which incorporate the interplay between cancer cells and mutated cancer cells [46] [47] [48].…”

Section: Prostate Cancermentioning

confidence: 99%

Cancer as Multifaceted Disease

Friedman

2012

Math. Model. Nat. Phenom.

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Abstract. Cancer has recently overtaken heart disease as the world's biggest killer. Cancer is initiated by gene mutations that result in local proliferation of abnormal cells and their migration to other parts of the human body, a process called metastasis. The metastasized cancer cells then interfere with the normal functions of the body, eventually leading to death. There are two hundred types of cancer, classified by their point of origin. Most of them share some common features, but they also have their specific character. In this article we review mathematical models of such common features and then proceed to describe models of specific cancer diseases.

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A Mathematical Model of Intermittent Androgen Suppression for Prostate Cancer

Cited by 136 publications

References 26 publications

A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy

A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy

Mathematical modelling of prostate cancer growth and its application to hormone therapy

Cancer as Multifaceted Disease

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