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Featured Figures
By Thomas Woolley
In this issue, we feature the work of Jana Gevertz, College of New Jersey, and Giulia Celora, University College London.
We asked Jana Gevertz to tell us a bit more about her work here:
Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes
Clinical trials are research studies where novel medical interventions are tested on people who volunteer to receive the treatment of interest. These studies are the primary way that researchers find out if a new treatment is safe and effective in humans. The predictions made by clinical trials are generally limited by small sample sizes and may be biased to certain demographic groups which are more inclined to enroll in these studies.
Virtual clinical trials (VCTs), grounded in data-informed mathematics models, are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. They hold the promise of complementing standard clinical trials by computationally permitting the analysis of a more diverse and representative patient population. In the context of a VCT, a “plausible patient” is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A challenging question in the design of VCTs is to determine which set of model parameterizations (that is, which “plausible patients”) should actually be included in the virtual population.
The aim of our work was to rigorously quantify the impact that VCT design choices have on the heterogeneity of the virtual population, and on the predictions of a virtual clinical trial. To isolate the impact of VCT design choices, we worked with simulated patient data and a simple, toy model of tumor growth that predicted response to the treatment. In this controlled setting, we studied the impact of the following VCT design choices (see Figure): the prior distribution of each parameter that varies across patients, and the method for selecting which parameterizations are considered virtual patients and thus included in the VCT. Our analysis revealed that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the virtual population. Yet, the predictions of the virtual clinical trial were more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.
We asked Giulia Celora to tell us a bit more about her work here:
Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling
In solid tumours, the presence of regions of abnormally low oxygen levels (i.e., hypoxia) is recognised as a major driver of tumour progression and therapeutic resistance. Even though in vitro models of hypoxia exist, they often fail to capture the complex and heterogeneous oxygenation dynamics of real tumours. While most experimental studies have focussed on characterising cell responses to constant hypoxic conditions, in vivo observations show that tumour oxygen levels can fluctuate on fast timescales and expose cancer cells to periodic cycles of hypoxia; a phenomenon known as cyclic hypoxia. These observations raise questions regarding the applicability of such experimental findings to the clinical understanding of hypoxia: do cyclic and constant hypoxia elicit different responses in cancer cells? If so, what features of fluctuating oxygen conditions are cancer cells sensitive to? Is the frequency of hypoxia cycles, their duration? Or both?
In our recent publication, we used mechanistic mathematical modelling to quantify the impact of prolonged exposure to various cyclic hypoxia conditions on the population growth and survival of tumour cell cultures. In particular, we developed a structured stochastic individual-based cell cycle model that accounts for hypoxia-driven dysregulation of both cell cycle and cell survival. In this framework, each cell is an agent that can either proliferate or die with probabilities that depend on its internal state. The cell internal state is described by a list of categorical and continuous structure variables, that also evolve probabilistically over time. Structure variables allow us to capture the multi-layered feedback between oxygen levels, intracellular processes (such as DNA replication and repair), and cell fate – namely, proliferation and death.
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