6533b7d1fe1ef96bd125c2c2
RESEARCH PRODUCT
The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives
Victor Mangas-sanjuanVictor Mangas-sanjuanAymara Sancho-araizIñaki F. Trocónizsubject
0301 basic medicineOncologymedicine.medical_specialtyComputer scienceImmune checkpoint inhibitorsPharmaceutical ScienceCancer immunityReview03 medical and health sciences0302 clinical medicinePharmacy and materia medicaDosing schedulesInternal medicinemedicineTumor growthimmuno-oncologyPK/PD modelsPredictive biomarkertop-down approachMathematical modelPK/PDmathematical modelingRS1-441030104 developmental biologybottom-up approach030220 oncology & carcinogenesisSpitemiddle-out approachdescription
Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-07-01 | Pharmaceutics |