The COMPO team's scientific activity consists in inventing and developing new mathematical models and computational methodologies to integrate data from clinical (trials and routine care) and experimental oncology into explanatory and predictive models.

The focus is on longitudinal (dynamic) data, and the methods aim to combine mechanistic modeling (i.e. models that simulate pharmaco-patho-physiological processes) with statistical (machine) learning. The main objectives are as follows:

• Developing personalized oncology
• Assist in the design and predictive analysis of clinical trials

To achieve these objectives, the team brings together mathematicians, pharmacologists and medical oncologists. The team's research activities are focused on three main areas:

• Quantitative modeling for personalized clinical oncology
• Individualizing cancer drug regimens
• Optimizing combinatorial strategies with immune checkpoint inhibitors

Click here for the 2024 activity report