Updated: Feb 2, 2022
In her latest paper "Verification of an agent-based disease model of human Mycobacterium tuberculosis infection" co-authored with other partners from the University of Catania and University of Sheffield, biomedical engineer Cristina Curreli discusses the issue of UISS-TB model credibility assessment. In particular, she describes a new verification methodology that can be used to identify, quantify and reduce the numerical errors associated with the model.
We took the opportunity to talk with her about her work and role in the STriTuVaD project.
"Before my current appointment at the University of Bologna, I received my PhD in Biomechanics at the University of Pisa, Italy. My main interest is computer modeling and simulation for clinical applications and in the STriTuVaD project, together with the partners University of Catania and University of Sheffield, I work on the in silico trial definition and UISS-TB model credibility assessment.
We recently developed a new verification strategy that can be used to identify and remove possible numerical implementation errors in our agent-based models."
What is your expertise with in silico trials?
My research activities mainly focus on the definition of verification and validation methods that can be used to assess the credibility of computation models. I always liked spotting mistakes and working towards the prevention of errors! I also worked on the regulatory activities for other in silico trials applications. For example, in silico trials for new joint replacements solutions and in silico trials used to evaluate the efficacy of new treatments against osteoporosis.
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?
Tuberculosis is considered today the second leading cause of death worldwide from a single infectious agent, after SARS-COV-2. Even if in Europe the pathology is almost eradicated or under control, we can’t ignore that in some undeveloped countries the incidence rate is still high, about 26% in India. Working on the development of simulation tools that can be used to address this public health world challenge is not only a great opportunity but also a big responsibility.
Since you mentioned your involvement in regulatory activities, what are, in your opinion, the challenges associated with the use of predictive models in the regulatory evaluation of new medical products?
The greatest challenge is to show that the dream of predicting human health can be reality. I think that we need to work hard to define rigorous methods that can be used to test our models and demonstrate their credibility. This should be always done with a good dose of humility: our models can’t be perfect, they have to be useful!
What is the greatest benefit you expect in silico trials can offer in the future?
The acceleration of healthcare research. I really believe that thanks to the use of computer modeling and simulation, we will reduce the number of subjects involved in clinical trials, and the duration and cost of studies. This will allow us to “learn more and more in depth”. In silico trials technologies will allow us to fight deadly diseases, prevent new pathologies, reduce health inequalities and develop personalized therapies.
What do you believe could accelerate the adoption of in silico trials technologies?
Using only four words I would say education, community, data and rigor. First, we have to learn the basics of this promising revolution in medicine and establish networks to share knowledge among industries, regulators and academic researchers. Then, we should improve methods for data collection and sharing: main ingredients for the development and assessment of digital technologies. Last but not least, we have to be extremely thorough and careful. Important achievements are usually reached step by step or, as one of my colleagues would say, “one model at a time”!