SMARTC, CRCM, Marseille
Date(s) : 26/01/2021 iCal
14 h 00 min - 15 h 00 min
Although the term mathematical oncology was coined by R. Gatenby and P. Maini in 2003 , historically the use of mathematical modeling in clinical oncology dates back at least to the 1980’s with population pharmacokinetic modeling being applied to clinical trial design and dose individualization. Complemented with pharmacodynamic modeling, these approaches form what is called pharmacometrics. They have a major focus on trying to understand, quantify and predict inter-individual variability of response to pharmacological intervention (either efficacy or toxicity). I will present a few concrete preclinical or clinical applications of this approach. These have been performed by members of a new unit entitled COMPO (COMPutational pharmacology and clinical Oncology, Inria-Inserm, Center for Cancer Research of Marseille, France) which uniquely gathers clinical oncologists, pharmacists and mathematicians. The three examples that will be presented consist of: 1) clinical dose adaptation of chemotherapy (e.g. cisplatin), or targeted therapy (e.g. sunitinib), 2) the design of one of the first model-driven clinical trials, including Bayesian adaptive treatment individualization [2,3] and 3) model-driven optimal combination of cytotoxics and anti-angiogenics . To conclude, I will present a few ongoing studies involving the incorporation of higher dimensional data in an approach comining mechanistic modeling and machine learning termed mechanistic learning [5, 6].
1. Gatenby R a, Maini PK. Mathematical oncology: cancer summed up. Nature. 2003;421:321–321.
2. Meille C, Barbolosi D, Ciccolini J, Freyer G, Iliadis A. Revisiting Dosing Regimen Using Pharmacokinetic/Pharmacodynamic Mathematical Modeling: Densification and Intensification of Combination Cancer Therapy. Clin Pharmacokinet. 2016;55:1015–25.
3. Hénin E, Meille C, Barbolosi D, You B, Guitton J, Iliadis A, et al. Revisiting dosing regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients. Breast Cancer Research and Treatment [Internet]. 2016;156:331–41.
4. Imbs D-C, Cheikh RE, Boyer A, Ciccolini J, Mascaux C, Lacarelle B, et al. Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma. CPT: Pharmacometrics & Systems Pharmacology. 2018;7:42–50.
5. Benzekry S. Artificial intelligence and mechanistic modeling for clinical decision making in oncology. Clinical pharmacology and therapeutics. 2020
6. Ciccolini J, Barbolosi D, André N, Barlesi F, Benzekry S. Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators? JCO Precision Oncology [Internet]. 2020;486–91.
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