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A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies
Andrew Burbanks1, Marianna Cerasuolo1, Roberto Ronca2
1School of Mathematics and Physics, University of Portsmouth, Lion Gate Building, Lion Terrace, Portsmouth, PO1 3HF, Hampshire, United Kingdom.
Abstract:
Using a hybrid cellular automaton with stochastic elements, we investigate the effectiveness of multiple drug therapies on prostate cancer (PCa) growth. The ability of Androgen Deprivation Therapy to reduce PCa growth represents a milestone in prostate cancer treatment, nonetheless most patients eventually become refractory and develop castration-resistant prostate cancer. In recent years, a "second generation" drug called enzalutamide has been used to treat advanced PCa, or patients already exposed to chemotherapy that stopped responding to it. However, tumour resistance to enzalutamide is not well understood, and in this context, preclinical models and in silico experiments (numerical simulations) are key to understanding the mechanisms of resistance and to assessing therapeutic settings that may delay or prevent the onset of resistance. In our mathematical system, we incorporate cell phenotype switching to model the development of increased drug resistance, and consider the effect of the micro-environment dynamics on necrosis and apoptosis of the tumour cells. The therapeutic strategies that we explore include using a single drug (enzalutamide), and drug combinations (enzalutamide and everolimus or cabazitaxel) with different treatment schedules. Our results highlight the effectiveness of alternating therapies, especially alternating enzalutamide and cabazitaxel over a year, and a comparison is made with data taken from TRAMP mice to verify our findings.
Insights
This study uses computer simulations to explore prostate cancer drug resistance. Alternating enzalutamide and cabazitaxel therapies show promise in delaying castration-resistant prostate cancer progression.
Area of Science:
- Computational Biology
- Oncology
- Mathematical Modeling
Background:
- Androgen Deprivation Therapy is a key prostate cancer (PCa) treatment, but resistance leads to castration-resistant PCa.
- Enzalutamide is a second-generation drug for advanced PCa, yet resistance mechanisms remain unclear.
- Preclinical models and in silico experiments are crucial for understanding resistance and optimizing therapies.
Purpose of the Study:
- Investigate the effectiveness of single and combination drug therapies for prostate cancer growth.
- Model the development of drug resistance, including phenotype switching and micro-environment effects.
- Assess therapeutic strategies to delay or prevent resistance to enzalutamide.
Main Methods:
- Utilized a hybrid cellular automaton with stochastic elements for in silico experiments.
- Incorporated cell phenotype switching to simulate increased drug resistance.
- Modeled micro-environment dynamics influencing tumor cell necrosis and apoptosis.
Main Results:
- Evaluated single-drug (enzalutamide) and combination therapies (enzalutamide with everolimus or cabazitaxel).
- Identified alternating therapies as effective, particularly enzalutamide and cabazitaxel over a year.
- Compared simulation findings with data from TRAMP mice to validate results.
Conclusions:
- Alternating drug schedules, specifically enzalutamide and cabazitaxel, demonstrate significant potential in managing prostate cancer.
- The hybrid cellular automaton model provides insights into resistance mechanisms and therapeutic optimization.
- Further research and validation are needed to translate these findings into clinical practice.
