Modeling the synergistic properties of drugs in hormonal treatment for prostate cancer. (7th April 2021)

• Record Type: Journal Article
• Authors: Reckell, Trevor; Nguyen, Kyle; Phan, Tin; Crook, Sharon; Kostelich, Eric J.; Kuang, Yang
• Is Part Of: Journal of theoretical biology. Volume 514(2021)
• Publication Date: 2021-04-07
• Subjects: Prostate cancer modeling -- Hormonal therapy -- Intermittent androgen deprivation therapy -- Adaptive therapy -- Optimal treatment schedule -- Drug effects -- Androgen dynamics -- Pharmacokinetics; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2020.110570
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: Formulation of dynamical model that explicitly incorporates the synergistic properties of drugs commonly used in hormonal therapy. Model is better at capturing the androgen dynamics under treatment than previously published models. By improving the ability of the model to describe androgen dynamics, the model also shows improvement in fitting and forecasting the progression of prostate cancer. Abstract: Prostate cancer is one of the most prevalent cancers in men, with increasing incidence worldwide. This public health concern has inspired considerable effort to study various aspects of prostate cancer treatment using dynamical models, especially in clinical settings. The standard of care for metastatic prostate cancer is hormonal therapy, which reduces the production of androgen that fuels the growth of prostate tumor cells prior to treatment resistance. Existing population models often use patients' prostate-specific antigen levels as a biomarker for model validation and for finding optimal treatment schedules; however, the synergistic effects of drugs used in hormonal therapy have not been well-examined. This paper describes the first mathematical model that explicitly incorporates the synergistic effects of two drugs used to inhibit androgen production in hormonal therapy. The drugs are cyproterone acetate, representing the drug family of anti-androgens that affect luteinizing hormones, and leuprolide acetate, representing the drug family of gonadotropin-releasing hormone analogs. By fitting the model to clinical data, we show that the proposed model can capture the dynamics of serum androgen levels during intermittent hormonal therapy better than previously published models. Our results highlight the importance of considering the synergistic effects of drugs in cancer treatment, thus suggesting that the dynamics of the drugs should be taken into account in optimal treatment studies, particularly for adaptive therapy. Otherwise, an unrealistic treatment schedule may be prescribed and render the treatment less effective. Furthermore, the drug dynamics allow our model to explain the delay in the relapse of androgen the moment a patient is taken off treatment, which supports that this delay is due to the residual effects of the drugs.