Together we can make the difference
Thanks to joint efforts and teamwork, some very important milestones have been reached since the last newsletter issue.
You missed all that? Read below to keep up with the news.
In this issue:
- Full article: interview with Dr M.A. Juarez - STriTuVaD modeling simulation platform extended to immune system-TBC-vaccines interactions - Tuberculosis Vaccine RUTI® to be tested in phase IIb clinical trial
- In Silico Trials workshop at the University of Catania
- Related News
- Upcoming Events
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. I will do this jointly with Ksenia N. Kyzyurova, that recently joined my team: she is a Research Associate in the School of Mathematics and Statistics at the University of Sheffield, UK, with a PhD in Statistical Science at Duke University, USA. Previously Ksenia received a MSc in Applied Mathematics and Computer Science from Saint-Petersburg State University of Information Technologies, Mechanics and Optics in Russia. 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.
The clinical study will be conducted by the All India Institute of Medical Science in New Delhi and will enroll a total of 140 patients with... Keep Reading
The project aims at developing new technologies that makes possible to test the efficacy of new therapies at least partly in a computer simulation, rather than through expensive pre-clinical studies on animals, or clinical studies on patients. Read more...
In Silico Trials workshop at the University of Catania
Last June, a two days workshop about In Silico Clinical Trials (ISCTs) was held at the University of Catania by Dr. Epifanio Fichera, (project coordinator), Prof. Francesco Pappalardo, (scientific coordinator) and Dr. Giulia Russo (WP2 leader).
Dr. Fichera gave an overview of STriTuVaD project focusing on the main objectives and its impact, while Prof. Pappalardo gave a lecture on ISCTs, their impact in helping the speed up of the pipeline within the drug discovery process, also accordingly to the recent regulatory authorities recommendations.
Dr. Giulia Russo presented a specific application case of an in silico methodology used in precision medicine that revealed a promising strategy to overcome drug resistance phenomena in cancer.
This event represented a great opportunity for undergraduate students of Chemical and Pharmaceutical Technologies to get a clue of several in silico methodologies and techniques, such as the ones employed within the STriTuVaD project.
Related News
Dassault Systèmes buys Medidata for $5.5 billions - the largest buyout in the emerging area of in silico medicine and in silico trials and "a significant milestone to address the complexity of developing personalized medicine and patient-centric experiences"
Dompé launches the supercomputer that accelerates drug response time - Exscalate is a platform that reduces the process of virtual selection on pharmacologically relevant objectives to reach new active compounds
FDA new policy framework - FDA takes steps to develop a framework for regulating artificial intelligence products used in medicine: white paper released
The Economist Intelligence Unit report on TB - In March 2018 Narendra Modi, the prime minister of India (the country facing by far the highest DR-TB burden), publicly committed to eradicate TB in the country by 2025
Avicenna Alliance invited by VPH institute director L. Geris - to discuss effective use of in silico methods for new drugs approval with Belgian & European regulatory authorities, leading academics & pharma companies
Treatment of highly drug-resistant forms of tuberculosis - FDA Advisory Committee votes favorably on the question of the effectiveness and safety of pretomanid in combination with bedaquiline and linezolid
SAVE THE DATE!
"EU funded collaborative research and innovation makes a difference for TB" 8th October 2019 in Bruxelles.
"An open dialogue between researchers, policy makers and funders to sustain and strengthen collaborative R&I to accelerate the development of vaccines for TB"
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