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Senior Pharmacometrician – Senior Principal Scientist

Sylvie Retout

Infectious Disease, Neurosciences, Hemophilia A, Geographic atrophy

“To support drug development defining the right dose for every patient via modeling and simulation technics.”

Your role at Institut Roche and brief presentation of your professional background:

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..).

My Focus at Institut Roche:

Optimal design for both population and individual parameter estimation in nonlinear mixed effect models; Model selection and model averaging methods in dose finding trials analyzed by nonlinear mixed effect models; Covariate model selection and associated uncertainty for covariate inference in pharmacometric analyses; Application of those methods to the analysis of drugs under development.

  1. Liu, Y., et al. (2022). “Pharmacokinetics, safety, and simulated efficacy of an influenza treatment, baloxavir marboxil, in Chinese individuals.” Clin Transl Sci.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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).
  7. 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.
  8. 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.
  9. Ostrowitzki, S., et al. (2017). “A phase III randomized trial of gantenerumab in prodromal Alzheimer’s disease.” Alzheimers Res Ther 9(1): 95.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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
  15. 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.
  16. 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.
  17. Fantin, B., et al. (2009). “Ciprofloxacin dosage and emergence of resistance in human commensal bacteria.” J Infect Dis 200(3): 390-398.
  18. 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.
  19. 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.
  20. 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.
  21. Bellier, C., et al. (2008). “Risk factors for Enterobacteriaceae bacteremia after liver transplantation.” Transpl Int 21(8): 755-763.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. Retout, S. and F. Mentre (2003). “Optimization of individual and population designs using Splus.” J Pharmacokinet Pharmacodyn 30(6): 417-443.
  27. 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.
  28. 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.
  29. Duffull, S.B., et al. (2002). “The use of simulated annealing for finding optimal population designs”. Comput Methods Programs Biomed 69(1): 25-35.
  30. 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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