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The Insigneo Institute for in silico Medicine’s Annual Showcase took place on Friday 17 May in Sheffield, UK, and the event has been an opportunity for funding agencies, regulatory agencies, industrial colleagues and academics to meet and see the latest innovations in personalised, predictive medicine. Among the invited speakers, Sheffield lecturer Miguel A. Juarez talked about innovative mathematical approaches in in silico medicine applications, as for example in the STriTuVaD project. We asked him to tell us more about him and his role in the project.
M.: I hold an MSc in Economics (CIDE, Mexico) and a PhD in Mathematics (Valencia, Spain). After finishing my PhD, I worked as a research fellow in the University of Warwick. Since 2008 I am a lecturer in the School of Mathematics and Statistics at the University of Sheffield. Here I teach statistics to maths undergraduates and MSc students. I also carry out research mainly on the use of Bayesian statistics on longitudinal and spatial data.
In STriTuVaD we are investigating how to combine traditional clinical trials with computer-based trials. Both experiments have different sources of variability which must be understood and measured if we want the intervention being trialled to be safe and effective. I am in charge of the statistical content of the project, and my main task is to devise mathematical models and methods that acknowledge and quantify these variabilities. This implies liaising with the modellers and the clinicians to harmonise the information from the physical and synthetic trials in order to design an adaptive trial combining both.
What is your expertise and past experiences with In Silico Trials?
M.: I am new to the In Silico Trials (IST) community and am thrilled to be involved in a project at the cutting edge of the field! I have extensive experience researching Bayesian models for longitudinal data, both in medical and social sciences and it is really exciting to bring this expertise into this challenging field.
In the STriTuVaD project, In Silico Trials technologies are being tested on Tuberculosis. What do you think are the opportunities and challenges of working with this disease?
M.: One of the main challenges working with Tuberculosis (TB) is the complexity of the disease itself. We know TB is the main infectious cause of death and that it is endemic in some regions, so the dynamics of infection are quite complex. On top of this, various strains of TB are Multi Drug Resistant, hindering the development of effective interventions and thus making their development even more expensive. IST are ideally positioned to help bringing down the cost of treatment by shortening the duration of the clinical trial process.
What are, in your opinion, the grand challenges associated with the use of predictive models in the regulatory evaluation of new medical products?
M.: Credibility and reproducibility. Ours is an engineering approach to clinical trials and we must bear in mind biology is much more complex than physics. When designing an experiment in the lab, the engineer normally has great control over almost any aspect of it, while this is definitively not the case in clinical trials. In STriTuVaD, it is our task to identify, acknowledge and measure every source of variability in order to provide reliable and reproducible experiments for the regulators to assess.
What is the greatest benefit you expect IST can offer in the future?
M.: It is actually twofold. If successful, IST will indeed reduce the cost and duration of clinical trials, hence the cost of treatment. As importantly, its success would enable smaller clinical trials, thus reducing the risk associated with the testing of any new drug.
What do you believe could accelerate the adoption of in silico trials technologies?
M.: I would say that a clear communication strategy is paramount for the adoption of any new technology. In our case, we must device innovative ways of informing clinicians and regulators of the way in silico trials can be combined with physical trials, which can dispel any reasonable doubts on the validity and reproducibility of the approach. If we can do that, the ethical and economic gains will become apparent and accelerate their adoption.
