A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer

Zhang, Jingsong; Gallaher, Jill; Cunningham, Jessica; Choi, Jung Woo; Ionescu, Filip; Chatwal, Monica Sheila; Jain, Rohit; Kim, Young‐Chul; Wang, Liang; Brown, Joel S.; Anderson, Alexander R.A.; Gatenby, Robert A.

doi:10.3390/cancers14215225

Cited by 9 publications

(11 citation statements)

References 31 publications

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“…This trial prompted further studies and investigations to analyze and interpret the significant improvement in disease control and patient survival, with a focus on the application of Evolutionary Game Theory [14,17,18,[20][21][22][23][24][25]. Evolutionary Game Theory is a subfield of game theory that combines game theory and evolutionary biology and has been applied in cancer research.…”

Section: Introductionmentioning

confidence: 99%

Abiraterone-Docetaxel scheduling for metastatic castration-resistant prostate cancer based on evolutionary dynamics

Deris¹,

Sohrabi-Haghighat²

2023

PLoS ONE

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Patients with metastatic castration-resistant prostate cancer (mCRPC) are divided into three groups based on their response to Abiraterone treatment: best responder, responder, and non-responder. In the latter two groups, successful outcomes may not be achieved due to the development of drug-resistant cells in the tumor environment during treatment. To overcome this challenge, a secondary drug can be used to control the population of drug-resistant cells, potentially leading to a longer period of disease inhibition. This paper proposes using a combination of Docetaxel and Abiraterone in some polytherapy methods to control both the overall cancer cell population and the drug-resistant subpopulation. To investigate the competition and evolution of mCRPC cancer phenotypes, as in previous studies, the Evolutionary Game Theory (EGT) has been used as a mathematical modeling of evolutionary biology concepts.

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Section: Introductionmentioning

confidence: 99%

Abiraterone-Docetaxel scheduling for metastatic castration-resistant prostate cancer based on evolutionary dynamics

Deris¹,

Sohrabi-Haghighat²

2023

PLoS ONE

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“…Excitingly, in some arenas, such model-based predictions are empowering scientists to intervene in real time. One example is in the adaptive treatment of metastatic castration-sensitive prostate cancer, where personalized model-informed treatment strategies were adjusted based on a patient's past and current prostate-specific antigen levels (Zhang et al 2022). Another example emerged during the COVID-19 pandemic, wherein model-suggested strategies were implemented to mitigate disease spread, which in turn necessitated re-calibrating the model as new data emerged (Cassidy 2023;Buchwald et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Minimally Sufficient Experimental Design using Identifiability Analysis

Gevertz,

Kareva

2023

Preprint

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Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by applying it to a modified version of a classic site-of-action pharmacokinetic/pharmacodynamic model that describes distribution of a drug into the tumor microenvironment (TME), where its efficacy is dependent on the level of target occupancy in the TME. In this context, we identify a minimal set of time points when data needs to be collected that robustly ensures practical identifiability of model parameters. The proposed methodology can be applied broadly to any mathematical model, allowing for the identification of a minimally sufficient experimental design that collects the most informative data.

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“…Our simulation studies suggest a lower threshold is better ( 17 ), so here we have used a 10% change in tumor burden to trigger a change in dose. C. Intermittent adaptive therapy stops dosing altogether if the tumor burden falls below a threshold (e.g., 50% of its initial value), and restarts treatment if the tumor recovers (e.g., to 100% of its initial value) ( 6,9 ).…”

Section: Introductionmentioning

confidence: 99%

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer

Seyedi,

Teo,

Foster

et al. 2023

Preprint

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Highly effective cancer therapies often face limitations due to acquired resistance and toxicity. Adaptive therapy, an ecologically inspired approach, seeks to control therapeutic resistance and minimize toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life over maximum cell kill. In preparation for a clinical trial in breast cancer, we used large populations of MCF7 cells to rapidly generate endocrine-resistance breast cancer cell line. We then mimicked second line therapy in ER+ breast cancers by treating the endocrine-resistant MCF7 cells in a mouse xenograft model to test adaptive therapy with capecitabine, gemcitabine, or the combination of those two drugs. Dose-modulation adaptive therapy with capecitabine alone increased survival time relative to MTD, but not statistically significant (HR: 0.22, 95% CI 0.043- 1.1 P = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI: 0.024 - 0.55, P = 0.007) and intermittent adaptive therapies significantly increased survival time compared to high dose combination therapy (HR = 0.07, 95% CI: 0.013 - 0.42; P = 0.003). Overall, survival time increased with reduced dose for both single drugs (P < 0.01) and combined drugs (P < 0.001). Adaptive therapy protocols resulted in tumors with lower proportions of proliferating cells (P = 0.0026) and more apoptotic cells (P = 0.045). The results show that Adaptive therapy outperforms high-dose therapy in controlling endocrine-resistant breast cancer, favoring slower-growing tumors, and showing promise in two-drug alternating regimens.

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A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer

Cited by 9 publications

References 31 publications

Abiraterone-Docetaxel scheduling for metastatic castration-resistant prostate cancer based on evolutionary dynamics

Abiraterone-Docetaxel scheduling for metastatic castration-resistant prostate cancer based on evolutionary dynamics

Minimally Sufficient Experimental Design using Identifiability Analysis

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer

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