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UID:6565@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20210126T140000
DTEND;TZID=Europe/Paris:20210126T150000
DTSTAMP:20241120T201741Z
URL:https://www.i2m.univ-amu.fr/evenements/sebastien-benzekri-computationa
 l-pharmacology-and-clinical-oncology/
SUMMARY:Sébastien Benzekry (SMARTC\, CRCM\, Marseille): Sébastien Benzekr
 y - COMPO - COMPutational pharmacology and clinical Oncology: Optimization
  of therapeutic strategies by mechanistic modeling and statistical learnin
 g
DESCRIPTION:Sébastien Benzekry: Although the term mathematical oncology wa
 s coined by R. Gatenby and P. Maini in 2003 [1]\, 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 tria
 l design and dose individualization. Complemented with pharmacodynamic mod
 eling\, these approaches form what is called pharmacometrics. They have a 
 major focus on trying to understand\, quantify and predict inter-individua
 l variability of response to pharmacological intervention (either efficacy
  or toxicity). I will present a few concrete preclinical or clinical appli
 cations of this approach. These have been performed by members of a new un
 it entitled COMPO (COMPutational pharmacology and clinical Oncology\, Inri
 a-Inserm\, Center for Cancer Research of Marseille\, France) which uniquel
 y gathers clinical oncologists\, pharmacists and mathematicians. The three
  examples that will be presented consist of: 1) clinical dose adaptation o
 f 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 o
 ptimal combination of cytotoxics and anti-angiogenics [4]. 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].\n1. Gatenby R a\, Maini PK. 
 Mathematical oncology: cancer summed up. Nature. 2003\;421:321–321.\n2. 
 Meille C\, Barbolosi D\, Ciccolini J\, Freyer G\, Iliadis A. Revisiting Do
 sing Regimen Using Pharmacokinetic/Pharmacodynamic Mathematical Modeling: 
 Densification and Intensification of Combination Cancer Therapy. Clin Phar
 macokinet. 2016\;55:1015–25.\n3. 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 meta
 static breast cancer patients. Breast Cancer Research and Treatment [Inter
 net]. 2016\;156:331–41.\n4. Imbs D-C\, Cheikh RE\, Boyer A\, Ciccolini J
 \, Mascaux C\, Lacarelle B\, et al. Revisiting Bevacizumab + Cytotoxic
 s Scheduling Using Mathematical Modeling: Proof of Concept Study in Experi
 mental Non-Small Cell Lung Carcinoma. CPT: Pharmacometrics &amp\; Systems 
 Pharmacology. 2018\;7:42–50.\n5. Benzekry S. Artificial intelligence and
  mechanistic modeling for clinical decision making in oncology. Clinical p
 harmacology and therapeutics. 2020\n6. Ciccolini J\, Barbolosi D\, André 
 N\, Barlesi F\, Benzekry S. Mechanistic Learning for Combinatorial Strateg
 ies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigat
 ors? JCO Precision Oncology [Internet]. 2020\;486–91.\nParticiper à la 
 réunion Zoom\nhttps://univ-amu-fr.zoom.us/j/97207620584?pwd=dFo1K3IwVnBRN
 UY1emxYU1Z0cXhZQT09\n\nID de réunion : 972 0762 0584\n[su_spacer]
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 020/01/Sebastien_Benzekry.jpg
CATEGORIES:Groupe de travail,Maths Bio,Virtual event
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