D. Mould and R. Upton, Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development, CPT: Pharmacometrics & Systems Pharmacology, vol.32, issue.9, p.6, 2012.
DOI : 10.1007/s10928-005-0062-y

D. Mould and R. Upton, Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development???Part 2: Introduction to Pharmacokinetic Modeling Methods, CPT: Pharmacometrics & Systems Pharmacology, vol.14, issue.4, p.38, 2013.
DOI : 10.1177/0091270010376965

D. Mould and R. Upton, Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development???Part 2: Introduction to Pharmacokinetic Modeling Methods, CPT: Pharmacometrics & Systems Pharmacology, vol.14, issue.4, p.88, 2014.
DOI : 10.1177/0091270010376965

C. Dumont, F. Mentré, C. Gaynor, K. Brendel, C. Gesson et al., Optimal Sampling Times for a Drug and its Metabolite using SIMCYP?? Simulations as Prior Information, Clinical Pharmacokinetics, vol.39, issue.1, pp.43-57, 2013.
DOI : 10.1007/s10928-012-9241-9

URL : https://hal.archives-ouvertes.fr/inserm-00769804

T. Nguyen, H. Bénech, A. Pruvost, and N. Lenuzza, A limited sampling strategy based on maximum a posteriori Bayesian estimation for a five-probe phenotyping cocktail, European Journal of Clinical Pharmacology, vol.2, issue.1, p.39, 2016.
DOI : 10.1038/psp.2013.19

V. Madelain, L. Oestereich, F. Graw, T. Nguyen, X. De-lamballerie et al., Ebola virus dynamics in mice treated with favipiravir, Antiviral Research, vol.123, pp.70-77, 2015.
DOI : 10.1016/j.antiviral.2015.08.015

URL : https://hal.archives-ouvertes.fr/inserm-01196078

T. Nguyen, J. Guedj, E. Chachaty, J. De-gunzburg, A. Andremont et al., Mathematical Modeling of Bacterial Kinetics to Predict the Impact of Antibiotic Colonic Exposure and Treatment Duration on the Amount of Resistant Enterobacteria Excreted, PLoS Computational Biology, vol.25, issue.5, pp.1003-840, 2014.
DOI : 10.1371/journal.pcbi.1003840.s007

J. Pinheiro, B. Bornkamp, E. Glimm, and F. Bretz, Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, vol.44, issue.7, pp.1646-1661, 2014.
DOI : 10.1002/1521-4036(200201)44:1<101::AID-BIMJ101>3.0.CO;2-H

URL : http://arxiv.org/pdf/1305.0889

P. Van-der-graaf, CPT: Pharmacometrics and Systems Pharmacology. CPT: Pharmacometrics & Systems Pharmacology, p.8, 2012.

G. Pillai, F. Mentré, and J. Steimer, Non-Linear Mixed Effects Modeling ??? From Methodology and Software Development to Driving Implementation in Drug Development Science, Journal of Pharmacokinetics and Pharmacodynamics, vol.72, issue.6, pp.161-183, 2005.
DOI : 10.1038/clpt.1994.142

L. Pronzato and A. Pázman, Design of Experiments in Nonlinear Models
DOI : 10.1007/978-1-4614-6363-4

URL : https://hal.archives-ouvertes.fr/hal-00879984

V. Fedorov and S. Leonov, Optimal Design for Nonlinear Response Models, 2014.

F. Mentré, A. Mallet, and D. Baccar, Optimal design in random-effects regression models, Biometrika, vol.84, issue.2, pp.429-442, 1997.
DOI : 10.1093/biomet/84.2.429

A. Hooker and P. Vicini, Simultaneous population optimal design for pharmacokineticpharmacodynamic experiments, American Association of Pharmaceutical Scientists Journal, vol.7, pp.759-785, 2005.
DOI : 10.1208/aapsj070476

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2750948/pdf

C. Bazzoli, S. Retout, and F. Mentré, Fisher information matrix for nonlinear mixed effects multiple response models: Evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model, Statistics in Medicine, vol.60, issue.14, pp.1940-1956, 2009.
DOI : 10.1111/j.0006-341X.2004.00148.x

URL : https://hal.archives-ouvertes.fr/inserm-00371363

T. Mielke and R. Schwabe, Some Considerations on the Fisher Information in Nonlinear Mixed Effects Models, Proceedings of the 9th International Workshop in Model-Oriented Design and Analysis, 2010.
DOI : 10.1007/978-3-7908-2410-0_17

J. Nyberg, C. Bazzoli, K. Ogungbenro, A. Aliev, S. Leonov et al., Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies, British Journal of Clinical Pharmacology, vol.282, issue.1, pp.6-17, 2014.
DOI : 10.1126/science.282.5386.103

URL : https://hal.archives-ouvertes.fr/inserm-00978789

T. Nguyen, C. Bazzoli, and F. Mentré, Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models, Statistics in Medicine, vol.30, issue.11-12, pp.11-121043, 2012.
DOI : 10.1002/sim.4191

URL : https://hal.archives-ouvertes.fr/inserm-00629594

Y. Merlé and F. Mentré, Bayesian design criteria: Computation, comparison, and application to a pharmacokinetic and a pharmacodynamic model, Journal of Pharmacokinetics and Biopharmaceutics, vol.19, issue.1, pp.101-125, 1995.
DOI : 10.1007/BF03036255

F. Combes, S. Retout, N. Frey, and F. Mentré, Prediction of Shrinkage of Individual Parameters Using the Bayesian Information Matrix in Non-Linear Mixed Effect Models with Evaluation in Pharmacokinetics, Pharmaceutical Research, vol.78, issue.1???2, pp.2355-2367, 2013.
DOI : 10.1016/S0378-3758(98)00221-3

URL : https://hal.archives-ouvertes.fr/inserm-00849820

R. Savi´csavi´c and M. Karlsson, Importance of shrinkage in empirical Bayes estimates for diagnostics: problems and solution, American Association of Pharmaceutical Scientists Journal, vol.11, pp.558-569, 2009.

F. Mentré, M. Chenel, E. Comets, J. Grevel, A. Hooker et al., Current Use and Developments Needed for Optimal Design in Pharmacometrics: A Study Performed Among DDMoRe???s European Federation of Pharmaceutical Industries and Associations Members, CPT: Pharmacometrics & Systems Pharmacology, vol.2, issue.6, p.46, 2013.
DOI : 10.1007/s11095-011-0659-3

M. Dodds, A. Hooker, and P. Vicini, Robust Population Pharmacokinetic Experiment Design, Journal of Pharmacokinetics and Pharmacodynamics, vol.99, issue.1, pp.33-64
DOI : 10.3181/00379727-168-41240

L. Foo, J. Mcgree, J. Eccleston, and S. Duffull, Comparison of Robust Criteria for D-Optimal Designs, Journal of Biopharmaceutical Statistics, vol.253, issue.1, pp.1193-1205, 2012.
DOI : 10.1023/A:1025701327672

M. Chang, Adaptive Design Theory and Implementation Using SAS and R. Boca Raton: Chapman and Hall/CRC, 2007.

L. Foo and S. Duffull, Adaptive Optimal Design for Bridging Studies with an Application to Population Pharmacokinetic Studies, Pharmaceutical Research, vol.34, issue.6, pp.1530-1543, 2012.
DOI : 10.1007/s10928-007-9066-0

G. Lestini, C. Dumont, and F. Mentré, Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology, Pharmaceutical Research, vol.11, issue.6, pp.3159-3169, 2015.
DOI : 10.1002/pst.1542

URL : https://hal.archives-ouvertes.fr/inserm-01179444

C. Dumont, M. Chenel, and F. Mentré, Two-stage Adaptive Designs in Nonlinear Mixed Effects Models: Application to Pharmacokinetics in??Children, Communications in Statistics - Simulation and Computation, vol.2, issue.5, pp.1511-1525, 2016.
DOI : 10.1038/clpt.2010.9

URL : https://hal.archives-ouvertes.fr/inserm-01076940

S. Retout, S. Duffull, and F. Mentré, Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs, Computer Methods and Programs in Biomedicine, vol.65, issue.2, pp.141-151, 2001.
DOI : 10.1016/S0169-2607(00)00117-6

C. Bazzoli, S. Retout, and F. Mentré, Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0, Computer Methods and Programs in Biomedicine, vol.98, issue.1, pp.55-65, 2010.
DOI : 10.1016/j.cmpb.2009.09.012

URL : https://hal.archives-ouvertes.fr/inserm-00431457

C. Bazzoli, T. Nguyen, E. Comets, A. Dubois, L. Nagard et al., New features for population design evaluation and optimisation with R functions

S. Retout, E. Comets, A. Samson, and F. Mentré, Design in nonlinear mixed effects models: Optimization using the Fedorov???Wynn algorithm and power of the Wald test for binary covariates, Statistics in Medicine, vol.39, issue.28, pp.5162-5179, 2007.
DOI : 10.1007/978-1-4419-0318-1

URL : https://hal.archives-ouvertes.fr/hal-00263513

J. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

V. Fedorov, Theory of Optimal Experiments, 1972.

H. Wynn, Results in the theory and construction of D-optimum designs, Journal of the Royal Statistical Society B, vol.34, pp.133-147, 1972.

J. Magnus and H. Neudecker, Matrix Differential Calculus with Applications in Statistics and Econometrics., Biometrics, vol.44, issue.4, 1988.
DOI : 10.2307/2531754

S. Ueckert and F. Mentré, A new method for evaluation of the Fisher information matrix for discrete mixed effect models using Monte Carlo sampling and adaptive Gaussian quadrature, Computational Statistics & Data Analysis, vol.111, pp.203-219, 2016.
DOI : 10.1016/j.csda.2016.10.011

URL : https://hal.archives-ouvertes.fr/inserm-01397584

M. Riviere, S. Ueckert, and F. Mentré, An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models, Biostatistics, vol.42, issue.4, pp.737-750, 2016.
DOI : 10.1081/BIP-120019267

URL : https://hal.archives-ouvertes.fr/hal-01323747

X. Panhard, A. Taburet, C. Piketti, and F. Mentré, Impact of modelling intra-subject variability on tests based on non-linear mixed-effects models in cross-over pharmacokinetic trials with application to the interaction of tenofovir on atazanavir in HIV patients, Statistics in Medicine, vol.13, issue.6, pp.1268-1284, 2007.
DOI : 10.1097/00126334-200008010-00008

URL : https://hal.archives-ouvertes.fr/inserm-00157143

D. Schuirmann, A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability, Journal of Pharmacokinetics and Biopharmaceutics, vol.12, issue.6, pp.657-680, 1987.
DOI : 10.1007/BF01059558

J. Pilz, Bayesian Estimation and Experimental Design in Linear Regression Models, 1991.

K. Chaloner and I. Verdinelli, Bayesian Experimental Design: A Review, Statistical Science, vol.10, issue.3, pp.273-304, 1995.
DOI : 10.1214/ss/1177009939

W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes: The Art of Scientific Computing, 2007.

J. Bertrand and F. Mentré, Mathematical expressions of the pharmacokinetic and pharmacodynamic models implemented in the MONOLIX. MONOLIX software, 2008.

E. Plan, A. Maloney, F. Mentré, M. Karlsson, and J. Bertrand, Performance Comparison of Various Maximum Likelihood Nonlinear Mixed-Effects Estimation Methods for Dose???Response Models, The AAPS Journal, vol.14, issue.3
DOI : 10.1208/s12248-012-9349-2

URL : https://hal.archives-ouvertes.fr/inserm-00709829

T. Nguyen and F. Mentré, Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature, Computational Statistics & Data Analysis, vol.80, pp.57-69, 2014.
DOI : 10.1016/j.csda.2014.06.011

URL : https://hal.archives-ouvertes.fr/inserm-01077176

G. Lestini, S. Ueckert, and F. Mentré, Robust design in model based analysis of longitudinal clinical data. Presented at PODE meeting, 2016.

F. Loingeville, T. Nguyen, M. Riviere, and F. Mentré, Using Hamiltonian Monte Carlo to design longitudinal studies with discrete data. 154th ICB Seminar -Eleventh International Seminar on Statistics and Clinical Practice, 2017.

C. Dumont, M. Chenel, and F. Mentré, Influence of Covariance Between Random Effects in Design for Nonlinear Mixed-Effect Models with an Illustration in Pediatric Pharmacokinetics, Journal of Biopharmaceutical Statistics, vol.7, issue.4, pp.471-492, 2013.
DOI : 10.2165/00003088-200847040-00002

URL : https://hal.archives-ouvertes.fr/inserm-00769812

S. Retout, E. Comets, C. Bazzoli, and F. Mentré, Design Optimization in Nonlinear Mixed Effects Models Using Cost Functions: Application to a Joint Model of Infliximab and Methotrexate Pharmacokinetics, Communications in Statistics - Theory and Methods, vol.34, issue.18, pp.3351-3368, 2009.
DOI : 10.1177/0962280206079018

URL : https://hal.archives-ouvertes.fr/inserm-00424247

T. Nguyen, T. Nguyen, and F. Mentré, Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification, Pharmaceutical Research, vol.15, issue.10, p.2119, 2017.
DOI : 10.1002/pst.1731

URL : https://hal.archives-ouvertes.fr/inserm-01549693

Y. Yu, Monotonic convergence of a general algorithm for computing optimal designs, The Annals of Statistics, vol.38, issue.3, pp.1593-1606, 2010.
DOI : 10.1214/09-AOS761

R. Chen, S. Chang, W. Wang, H. Tung, and W. Wong, Minimax optimal designs via particle swarm optimization methods, Statistics and Computing, vol.20, issue.4, pp.975-988, 2015.
DOI : 10.1002/1097-0258(20010115)20:1<123::AID-SIM643>3.0.CO;2-5

URL : https://cloudfront.escholarship.org/dist/prd/content/qt9vw0p4pn/qt9vw0p4pn.pdf

M. Smith, S. Moodie, R. Bizzotto, E. Blaudez, E. Borella et al., Model Description Language (MDL): A Standard for Modeling and Simulation, CPT: Pharmacometrics & Systems Pharmacology, vol.1, issue.6, pp.647-650, 2017.
DOI : 10.1049/sb:20045008