Quantitative Systems Pharmacology Group
The Quantitative Systems Pharmacology (QSP) Group is a research group within COSBI, the non-profit Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology.
We develop mechanistic computational models and tools to understand complex disease biology, investigate variability in treatment response and support the development of new therapeutic strategies.
Our research spans tuberculosis, neurodegenerative and rare diseases, virtual populations and mechanistic digital twins, together with methodological research on the development, evaluation and credible use of QSP models.
The group conducts both investigator-led and collaborative research, combining methodological development with applications in translational biomedicine.
We work at the interface between mathematical modelling, pharmacology, systems biology, AI and biomedical research, in collaboration with academic and clinical groups, non-profit research organizations, and pharmaceutical and biotechnology partners.
Group Leader: Federico Reali, PhD
Group Leader – Quantitative Systems Pharmacology
Federico Reali holds a PhD in Mathematics from the University of Trento and leads the Quantitative Systems Pharmacology Group at COSBI.
His research focuses on the development and application of mechanistic mathematical models, QSP, virtual populations and digital twins for translational biomedical research and drug development.
He coordinates multidisciplinary research involving academic groups, clinical collaborators, non-profit organizations and pharmaceutical partners across therapeutic areas including infectious diseases, neurodegeneration and rare diseases.
His current methodological interests include virtual population generation, patient-specific mechanistic modelling, integration of artificial intelligence with QSP, and the assessment of model credibility for translational and regulatory applications.
He is also involved in university teaching at the University of Trento, with courses in statistics, mathematical modelling and systems biology, and supervises MSc and PhD research projects in quantitative and computational biomedicine.
Federico contributes to international QSP initiatives within the International Society of Pharmacometrics (ISoP) and serves as Vice President of the Scientific Committee of ALISEI, the Italian National Life Sciences Cluster.
Research
Our research combines systems biology, pharmacokinetics and pharmacodynamics, PBPK modelling, mathematical modelling and data-driven approaches to connect mechanisms across biological scales, from molecular and cellular processes to tissue exposure, disease progression and clinical outcomes.
Across our research areas, we work closely with clinicians, experimental scientists and disease experts so that model development remains anchored to relevant biological and clinical questions.
Tuberculosis and Global Health
A major research line of the group focuses on the quantitative pharmacology of tuberculosis.
We develop translational models integrating systemic and tissue pharmacokinetics with pulmonary and lesion-level processes, including minimal-PBPK models, spatial and machine-learning models of drug penetration, and multiscale representations of granuloma biology.
Our collaboration with the Gates Medical Research Institute led to the development of stormTB, an openly accessible simulator for anti-tuberculosis pharmacokinetics, and to subsequent work on lesion-level drug exposure, target attainment and translational dose prediction.
Current research combines PBPK, machine learning, partial differential equations and agent-based modelling to investigate how spatial drug distribution and the heterogeneous microenvironment of tuberculosis lesions may influence treatment response.
Neurodegenerative Diseases
We develop mechanistic models to investigate molecular and cellular processes involved in Parkinson’s disease and other neurodegenerative disorders.
Our work includes mathematical modelling of alpha-synuclein aggregation, with particular interest in the interaction between protein aggregation, cellular mechanisms and the lipid environment.
A further research line focuses on GBA1-associated Parkinson’s disease. Here, we integrate models of sphingolipid metabolism with biomarker and clinical information to investigate disease heterogeneity and develop patient-specific mechanistic representations.
This work provides a basis for exploring mechanistic digital twins as tools for patient stratification, hypothesis generation and the quantitative investigation of therapeutic interventions.
Rare and Complex Diseases
The group has longstanding experience in mechanistic modelling of lysosomal storage disorders, including Gaucher disease and related sphingolipidoses.
These models integrate biochemical pathways, pharmacological mechanisms and patient variability to investigate disease mechanisms, treatment response and sources of clinical heterogeneity.
This research also provides an important methodological foundation for our broader work on patient-specific modelling and mechanistic digital twins.
Virtual Populations and Mechanistic Digital Twins
We develop computational approaches to represent biological and clinical variability through plausible patients and virtual populations, and investigate how model structure, available data and context of use should guide their construction.
We extend these approaches towards mechanistic digital twins, integrating mechanistic models with patient-level biomarkers and clinical data to support patient stratification and individualized simulations.
AI, Model Credibility and Next-Generation QSP
We investigate how artificial intelligence and machine learning can complement mechanistic modelling, including applications in knowledge extraction, parameter-space exploration, surrogate modelling and hybrid mechanistic/data-driven approaches.
At the same time, increasingly complex models require rigorous approaches to credibility, transparency, reproducibility and context of use.
The group therefore conducts methodological research on model evaluation and contributes to international discussions on QSP best practices, credibility assessment and the translational and regulatory use of mechanistic models.
Translational QSP and Model-Informed Drug Development
Our disease-focused and methodological research is complemented by translational projects in which QSP and pharmacological modelling are used to address questions arising during drug development.
Depending on the scientific question, we integrate mechanistic disease models with PK/PD and physiologically based pharmacokinetic models to investigate drug exposure, target engagement, pharmacological response and variability across species, tissues and patient populations.
Research applications include preclinical-to-clinical translation, tissue and site-of-action pharmacokinetics, dose prediction, treatment-response modelling and virtual population analyses.
These projects span different therapeutic modalities and are carried out in collaboration with pharmaceutical and biotechnology companies as well as academic and non-profit partners.
Team
The QSP Group brings together researchers with backgrounds in mathematics, computational biology, pharmacology, bioinformatics and biomedical sciences, together with PhD and MSc students working on quantitative and computational biomedicine.
Software and Research Tools
stormTB
stormTB is an openly accessible simulator for anti-tuberculosis pharmacokinetics developed by COSBI in collaboration with the Gates Medical Research Institute.
Explore stormTB: https://apps.cosbi.eu/stormTB/
Publications
COSBI maintains an institutional publication database covering scientific papers, conference contributions and other research outputs.
The COSBI publication page also provides a direct search of COSBI-affiliated publications indexed in PubMed.
Opportunities and Collaborations
We welcome enquiries from students and researchers interested in quantitative systems pharmacology, mechanistic modelling, virtual populations and computational biomedicine.
We are also interested in scientific collaborations with academic and clinical research groups, non-profit organizations, international research initiatives and pharmaceutical and biotechnology partners where mechanistic modelling can contribute to fundamental or translational research questions.
Contact: info@cosbi.eu