A computational platform to predict the effects of vaccines: interview with Prof Pappalardo
Updated: Jan 13, 2022
During the last meeting in Bologna, partners from the University of Catania and University of Bologna worked together on the necessary steps to progress towards the Uncertainty Quantification and Technical Validation of UISS-TB based on ASME VV-40 standards. In this occasion, we asked Prof Francesco Pappalardo to talk about the UISS-TB platform.
Pappalardo is Associate Professor of Computer Science and Vice Director of the Dept. of Drug and Health Science at the University of Catania. He holds a MS (2000) and a PhD (2004) degrees in Computer Science and he is visiting Professor at Boston University.
How did the idea of a Universal Immune System Simulator (UISS) come about? Over the last fifteen years, I have worked closely with life scientists and medical doctors to develop and implement computational models for applications in biological knowledge discovery and simulation of biomedical experiments. These interactions were fruitful and they resulted in improved understanding of the modeled systems. The models and simulators I worked on have helped in increasing the efficiency of experimentation and resulted in saving time, effort, and cost.
What is your expertise and past experiences with In Silico Trials?
I worked on the Universal Immune System Simulator, a complex and large modelling and simulation framework that is able to reproduce the human immune system behaviour at cellular and tissue scale. The computational framework has been successfully applied in the optimization of immune system stimulating therapies against a large variety of human pathological conditions, ranging from cancers, infectious diseases and autoimmune diseases. It has been validated in preclinical conditions and, more recently, in clinical environments. The computational framework is now extensively being used to predict the outcome of clinical trials regarding vaccines.
What is your role and the role of the UISS-TB platform in the STriTuVaD project?
I am the scientific coordinator of the project and I coordinate the activities for work packages 2 and 5, that deal with the development of the main modelling and simulation framework, and with the realization of the in silico clinical trials. We will also have the great and challenging opportunity to validate the UISS predictions in a real clinical trial of phase II/b for vaccines against pulmonary tuberculosis.
Talking about Tuberculosis, what is in your opinion the priority in the fight against this disease?
The priority could be the achievement of an effective therapy that is able to completely eradicate all the tuberculosis strains, even the multi-drug resistant ones. As a matter of fact, I am strongly convinced that the added value and power of vaccination strategies could overcome also the MDR strains.
What are, in your opinion, the grand challenges associated with the use of predictive models in the regulatory evaluation of new medical products?
In my opinion, one of the grand challenges for in silico clinical trials is to win the trust of practitioners and, above all, regulators. This is usually related to the specific aspects of verification, validation and uncertainty quantification as formulated for models used in other industrial contexts. But when dealing with in silico trials, one faces with cultural resistance from the specialist workforce that still remains skeptical. One of the STriTuVaD ambition is to set a milestone toward the use of in silico trials in drugs approvals. We believe that it is the right time to move an important step in this direction.
And what could accelerate the adoption of in silico trials technologies in this sector?
The answer to this question is at the same time simple and complex: clinical validation and regulatory approval for the usage in the specific context of use of in silico trials technologies.
In conclusion, what is the greatest benefit you expect in silico trials can offer in the future? .
I imagine in silico trials to eliminate the possibilities to reach clinical trials in which the drug fails. I imagine in silico trials to avoid issues related to phase IV clinical trials (post-market evaluation). Because in silico trials can deal with scenarios almost impossible to take into account during the phase III clinical trials. Think about the possibility to simulate virtual patients that belong to the tails of the gaussian curve of the population: real clinical trials could fail in having these data in hand. And this issue could later determine the retirement of the approved drug simply because it wasn't possible to test certain uncommon scenarios.
COMBINE group at University of Catania. From the left: Dr Giuseppe Alessandro Parasiliti Palumbo, Dr Giuseppe Sgroi, Dr Valentina Di Salvatore, Prof Francesco Pappalardo, Dr Giulia Russo