Sylvie Retout

Maladies infectieuses, neurosciences, hémophilie A et ophtalmologie

“Soutenir le développement de médicaments en définissant la bonne dose pour chaque patient grâce à des techniques de modélisation et de simulation.”

Rôle à l’Institut Roche et parcours professionnel :

I have been working for Roche since 2009, based within the Institut Roche since 2011. During those years, I have been supporting pRED (Roche Pharma Research and Early Development) project developments as a pharmacometrician within the Pharmaceutical Sciences Department. My role is to develop mathematical models to describe pharmacokinetic (PK), PK-pharmacodynamic (PD), and PK-Efficacy or Safety relationships using data from clinical studies (phase 1 to phase 3). Using those models, I have been supporting, dose and dosing regimen selection for drug development projects in various diseases, including Alzheimer’s Disease (Gantenerumab), Hemophilia A (Hemlibra) and Flu (Xofluza). I have also been supporting drug registrations and labellings in front of Health Authorities (FDA, EMA, PMDA, NMPA, etc..).

Objectif à l’Institut Roche :

Conception optimale pour l’estimation des paramètres individuels et de population dans les modèles non linéaires à effets mixtes. Sélection de modèles et méthodes de calcul de moyenne des modèles dans les essais de recherche de dose analysés par des modèles non linéaires à effets mixtes. Sélection de modèles de covariables et d’incertitudes associées pour l’inférence de covariables dans les analyses pharmacométriques. Application de ces méthodes à l’analyse des médicaments en cours de développement.

  1. Yoneyama, K., et al. (2023). « Clinical pharmacology of emicizumab for the treatment of hemophilia A. » Expert Rev Clin Pharmacol 16(9): 775-790.
  2. Liu, Y., et al. (2022). “Pharmacokinetics, safety, and simulated efficacy of an influenza treatment, baloxavir marboxil, in Chinese individuals.” Clin Transl Sci.
  3. Retout, S., et al. (2022). “Disease Modeling and Model-Based Meta-Analyses to Define a New Direction for a Phase III Program of Gantenerumab in Alzheimer’s Disease.” Clin Pharmacol Ther 111(4): 857-866.
  4. Retout, S., et al. (2022). “A Pharmacokinetics-Time to Alleviation of Symptoms Model to Support Extrapolation of Baloxavir Marboxil Clinical Efficacy in Different Ethnic Groups with Influenza A or B.” Clin Pharmacol Ther May 18. doi: 10.1002/cpt.2648. Online ahead of print.
  5. Buatois, S., et al. (2021). “cLRT-Mod: An efficient methodology for pharmacometric model-based analysis of longitudinal phase II dose finding studies under model uncertainty.” Stat Med 40(10): 2435-2451.
  6. Jonsson, F., et al. (2021). “Exposure-Bleeding Count Modeling of Emicizumab for the Prophylaxis of Bleeding in Persons with Hemophilia A with/Without Inhibitors Against Factor VIII.” Clin Pharmacokinet 60(7): 931-941.
  7. Koshimichi, H., et al. (2020). “Population Pharmacokinetics and Exposure-Response Relationships of Baloxavir Marboxil in Influenza Patients at High Risk of Complications.” Antimicrob Agents Chemother 64(7).
  8. Retout, S., et al. (2020). “Population Pharmacokinetic Analysis and Exploratory Exposure-Bleeding Rate Relationship of Emicizumab in Adult and Pediatric Persons with Hemophilia A.” Clin Pharmacokinet 59(12): 1611-1625.
  9. Buatois, S., et al. (2018). “Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models.” AAPS J 20(3): 56.
  10. Ostrowitzki, S., et al. (2017). “A phase III randomized trial of gantenerumab in prodromal Alzheimer’s disease.” Alzheimers Res Ther 9(1): 95.
  11. Buatois, S., et al. (2017). “Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson’s Disease Patients.” Pharm Res 34(10): 2109-2118.
  12. Combes, F.P. et al. (2014). “Powers of the likelihood ratio test and the correlation test using empirical bayes estimates for various shrinkages in population pharmacokinetics”. CPT Pharmacometrics Syst Pharmacol.  Apr 9;3:e109.
  13. Combes, F. P., et al. (2013). “Prediction of shrinkage of individual parameters using the bayesian information matrix in non-linear mixed effect models with evaluation in pharmacokinetics.” Pharm Res 30(9): 2355-2367.
  14. Delor, I. et al. (2013). “Modeling Alzheimer’s Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI.” CPT Pharmacometrics Syst Pharmacol.  Oct 2;2:e78.
  15. Bazzoli, C., et al (2011). “COPHAR2-ANRS 111 Study Group.Joint population pharmacokinetic analysis of zidovudine, lamivudine, and their active intracellular metabolites in HIV patients.” Antimicrob Agents Chemother 55(7):3423-3431
  16. Bazzoli, C., et al. (2010). “Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0.” Comput Methods Programs Biomed 98(1): 55-65.
  17. Retout, S., et al. (2009) “Design optimisation in nonlinear mixed effects models using cost functions: application to a joint model of infliximab and methotrexate pharmacokinetics.” Commun. Stat. – Theory Methods 38:3351–3368.
  18. Fantin, B., et al. (2009). “Ciprofloxacin dosage and emergence of resistance in human commensal bacteria.” J Infect Dis 200(3): 390-398.
  19. Mongardon, N., et al. (2009). “Predicted propofol effect-site concentration for induction and emergence of anesthesia during early pregnancy.” Anesth Analg 109(1): 90-95.
  20. Clairotte, C., et al. (2009). “Automated ankle-brachial pressure index measurement by clinical staff for peripheral arterial disease diagnosis in nondiabetic and diabetic patients.” Diabetes Care 32(7): 1231-1236.
  21. Bazzoli, C., et al. (2009). “Fisher information matrix for nonlinear mixed effects multiple response models: evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model.” Stat Med 28(14): 1940-1956.
  22. Bellier, C., et al. (2008). “Risk factors for Enterobacteriaceae bacteremia after liver transplantation.” Transpl Int 21(8): 755-763.
  23. Collin, F., et al. (2007). “Indinavir trough concentration as a determinant of early nephrolithiasis in HIV-1-infected adults.” Ther Drug Monit 29(2): 164-170.
  24. Gayat, E., et al. (2007). “Assessment of cough reflex sensitivity after planned caesareansection under spinal anaesthesia and after vaginal delivery.” Br J Anaesth. 99(5):694-698.
  25. Retout, S., et al. (2007). “Design in nonlinear mixed effects models: optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates.” Stat Med 26(28): 5162-5179.
  26. Bruno, R., et al. (2003). “Population pharmacokinetics and pharmacodynamics of enoxaparin in unstable angina and non-ST-segment elevation myocardial infarction.” Br J Clin Pharmacol 56(4): 407-414.
  27. Retout, S. and F. Mentre (2003). “Optimization of individual and population designs using Splus.” J Pharmacokinet Pharmacodyn 30(6): 417-443.
  28. Retout, S. and F. Mentre (2003). “Further developments of the Fisher information matrix in nonlinear mixed effects models with evaluation in population pharmacokinetics.” J Biopharm Stat 13(2): 209-227.
  29. Retout, S., et al. (2002). “Fisher information matrix for non-linear mixed-effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics.” Stat Med 21(18): 2623-2639.
  30. Duffull, S.B., et al. (2002). “The use of simulated annealing for finding optimal population designs”. Comput Methods Programs Biomed 69(1): 25-35.
  31. Retout, S., et al. (2001). “Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs.” Comput Methods Programs Biomed 65(2): 141-151.
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