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UID:6506@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20210315T150000
DTEND;TZID=Europe/Paris:20210315T160000
DTSTAMP:20241120T201725Z
URL:https://www.i2m.univ-amu.fr/evenements/compo-computational-pharmacolog
 y-and-clinical-oncology-optimization-of-therapeutic-strategies-by-mechanis
 tic-and-statistical-modeling/
SUMMARY:Sébastien Benzekry (INRIA Bordeaux): COMPO - COMPutational pharmac
 ology and clinical Oncology: Optimization of therapeutic strategies by mec
 hanistic and statistical modeling
DESCRIPTION:Sébastien Benzekry: \nIn this talk\, I will first introduce a 
 new Inria-Inserm unit entitled COMPO (COMPutational pharmacology and clini
 cal Oncology\, Inria-Inserm\, Center for Cancer Research of Marseille\, Fr
 ance) which uniquely gathers clinical oncologists\, pharmacists and mathem
 aticians. The objective of the team is to develop novel mathematical const
 ructs to model data arising from experimental and clinical oncology. Ultim
 ately\, the models are translated into numerical software of direct use in
  the clinic\, either for the design of dosing regimen in clinical trials\,
  or personalized medicine.I will first give an example using mixed-effects
  modeling to describe and understand experimental tumor growth kinetics. W
 e observed that the experimental and logistic growth models were unable to
  describe the data\, in contrast with the Gompertz model.  The population
  approach was then further leveraged to define a new model\, the reduced G
 ompertz model\, with only one individual-specific parameter (instead of tw
 o for the Gompertz) but still similarly good descriptive power. Such reduc
 tion in the number of parameters substantially improved the performance of
  the model when trying to predict the initiation time (inoculation) from l
 ate measurements. Improvements were particularly drastic when combining th
 e population approach with Bayesian estimation.If time allows\, I will the
 n present a concrete example of routine use of Bayesian estimation in the 
 Marseille university hospital for adaptive and personalized dosing of cisp
 latin in head and neck cancer patients. I will then conclude by presenting
  a few starting projects and the associated methodological challenges from
  a statistical learning point of view.Reference:Population modeling of tum
 or growth curves and the reduced Gompertz model improve prediction of the 
 age of experimental tumorsC. Vaghi\, A. Rodallec\, R. Fanciullino\, J. Cic
 colini\, J. Mochel\, M. Mastri\, C. Poignard\, J. ML Ebos\, S. BenzekryPLo
 S Computational Biology\, Volume 16\, Issue 2\, e1007178\, bioRxiv\, 2020\
 n
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 020/01/Sebastien_Benzekry.jpg
CATEGORIES:Séminaire,Statistique
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