AI for healthcare and in silico clinical trials
AI-based methods and software to support In Silico Clinical Trials (ISCT) and decision making in clinical settings.
Our research focuses on the definition and the analysis of quantitative models for both treatment strategies (aka virtual doctors) and human physiology + PBPK (aka virtual patients) to perform in silico clinical trials (ISCT), in silico what-if analyses, in silico individualised treatment design and in silico optimisation of biomedical devices.
What are in silico clinical trials (ISCT)?
ISCT aim at performing, via computer simulations, the typical activities carried out to assess safety and efficacy of pharmacological treatments, biomedical devices, or other therapeutic procedures.
ISCT ask for highly cross- and inter-disciplinary knowledge and methods in: artificial intelligence, formal verification, model checking, computer engineering, modelling, simulation, high performance computing, biology, physiology, pharmacology, omics.

What are the advantages of ISCT?
Being entirely model-based, ISCT have the potential to:
- Reduce time and cost of traditional approaches
- Prioritise in vivo trials, via selection of most relevant patient phenotypes
- Avoid in vivo assessment of unsuccessful designs of drugs/treatments or device design (early pruning)
- Reduce number of animals and humans involved
- Enable precision medicine, in silico what-if analyses, individualised treatment design and biomedical device optimisations
- Tackle areas where human volunteer recruiting is hard or unethical (e.g., rare diseases, paediatric drugs, pregnancy).
From qualitative knowledge to quantitative computational models
ISCT leverage the available qualitative knowledge stemming from biology, omics, patho-physiology and clinical data to define quantitative models of the human physiology of interest and of the PKPD of medicinal compounds (drugs), clinical guidelines, treatment and decision strategies, medical devices.

Latest publications
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SBML2Modelica: Integrating Biochemical Models within Open-Standard Simulation Ecosystems
Filippo Maggioli, Toni Mancini, Enrico Tronci. Bioinformatics, 36(7), pages 2165-2172, 2020.
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Reconciling Interoperability with Efficient Verification and Validation within Open Source Simulation Environments
Stefano Sinisi, Vadim Alimguzhin, Toni Mancini, Enrico Tronci. Simulation Modelling Practice and Theory, 109, pages 102277, 2021.
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Optimal personalised treatment computation through in silico clinical trials on patient digital twins
Stefano Sinisi, Vadim Alimguzhin, Toni Mancini, Enrico Tronci, Federico Mari, Brigitte Leeners. Fundamenta Informaticae, 174(3-4), pages 283-310, 2020.
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Complete populations of virtual patients for in silico clinical trials
Stefano Sinisi, Vadim Alimguzhin, Toni Mancini, Enrico Tronci, Brigitte Leeners. Bioinformatics, 36(22-23), pages 5465–5472, 2020.