references.bib

@article{knuth84,
  author = {Knuth, Donald E.},
  title = {Literate Programming},
  year = {1984},
  issue_date = {May 1984},
  publisher = {Oxford University Press, Inc.},
  address = {USA},
  volume = {27},
  number = {2},
  issn = {0010-4620},
  url = {https://doi.org/10.1093/comjnl/27.2.97},
  doi = {10.1093/comjnl/27.2.97},
  journal = {Comput. J.},
  month = may,
  pages = {97–111},
  numpages = {15}
}

@book{derendorf_rowland_2019,
	title = {Rowland and {Tozer}'s {Clinical} {Pharmacokinetics} and {Pharmacodynamics}: {Concepts} and {Applications}},
	isbn = {978-1-4963-8504-8},
	publisher = {Wolters Kluwer},
	author = {Derendorf, H. and Schmidt, S.},
	year = {2019},
	lccn = {2019008147},
}

@article{Mould2012,
abstract = {Modeling is an important tool in drug development; population modeling is a complex process requiring robust underlying procedures for ensuring clean data, appropriate computing platforms, adequate resources, and effective communication. Although requiring an investment in resources, it can save time and money by providing a platform for integrating all information gathered on new therapeutic agents. This article provides a brief overview of aspects of modeling and simulation as applied to many areas in drug development.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e6; doi:10.1038/psp.2012.4; advance online publication 26 September 2012.},
author = {Mould, D R and Upton, R N},
doi = {10.1038/psp.2012.4},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mould, Upton - 2012 - Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development.pdf:pdf},
isbn = {0091270008315},
issn = {2163-8306},
journal = {CPT: Pharmacometrics & Systems Pharmacology},
number = {9},
pages = {e6},
pmid = {23835886},
title = {{Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development}},
volume = {1},
year = {2012}
}
@article{Mould2013,
abstract = {Population pharmacokinetic models are used to describe the time course of drug exposure in patients and to investigate sources of variability in patient exposure. They can be used to simulate alternative dose regimens, allowing for informed assessment of dose regimens before study conduct. This paper is the second in a three-part series, providing an introduction into methods for developing and evaluating population pharmacokinetic models. Example model files are available in the Supplementary Data online. {\textcopyright} 2013 ASCPT All rights reserved 2163-8306/12.},
author = {Mould, D. R. and Upton, R. N.},
doi = {10.1038/PSP.2013.14},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mould, Upton - 2013 - Basic concepts in population modeling, simulation, and model-based drug development - Part 2 Introduction to pharm.pdf:pdf},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
month = {apr},
number = {4},
title = {{Basic concepts in population modeling, simulation, and model-based drug development - Part 2: Introduction to pharmacokinetic modeling methods}},
volume = {2},
year = {2013}
}
@article{Upton2014,
abstract = {Population pharmacodynamic (PD) models describe the time course of drug effects, relating exposure to response, and providing a more robust understanding of drug action than single assessments. PD models can test alternative dose regimens through simulation, allowing for informed assessment of potential dose regimens and study designs. This is the third paper in a three-part series, providing an introduction into methods for developing and evaluating population PD models. Example files are available in the Supplementary Data.},
author = {Upton, R. N. and Mould, D. R.},
doi = {10.1038/PSP.2013.71},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Upton, Mould - 2014 - Basic concepts in population modeling, simulation, and model-based drug development Part 3-introduction to phar(2).pdf:pdf},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
month = {jan},
number = {1},
title = {{Basic concepts in population modeling, simulation, and model-based drug development: Part 3-introduction to pharmacodynamic modeling methods}},
volume = {3},
year = {2014}
}

@book{gabrielsson_pharmacokinetic_2007,
	title = {Pharmacokinetic and {Pharmacodynamic} {Data} {Analysis}: {Concepts} and {Applications}, {Fourth} {Edition}},
	isbn = {978-91-976510-0-4},
	publisher = {Taylor \& Francis},
	author = {Gabrielsson, J. and Weiner, D.},
	year = {2007},
}

@book{ette_pharmacometrics_2007,
	title = {Pharmacometrics: The Science of Quantitative Pharmacology},
	isbn = {978-0471677833},
	publisher = {Wiley},
	author = {Ette, Ene I. and Williams, Paul J.},
	year = {2007},
}

@article{Wang2016,
abstract = {This tutorial presents the application of an R package, RxODE, that facilitates quick, efficient simulations of ordinary differential equation models completely within R. Its application is illustrated through simulation of design decision effects on an adaptive dosing regimen. The package provides an efficient, versatile way to specify dosing scenarios and to perform simulation with variability with minimal custom coding. Models can be directly translated to Rshiny applications to facilitate interactive, real-time evaluation/iteration on simulation scenarios.},
author = {Wang, W. and Hallow, K. M. and James, D. A.},
doi = {10.1002/psp4.12052},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang, Hallow, James - 2016 - A tutorial on RxODE Simulating differential equation pharmacometric models in R.pdf:pdf},
issn = {21638306},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
number = {1},
pages = {3--10},
pmid = {26844010},
title = {{A tutorial on RxODE: Simulating differential equation pharmacometric models in R}},
volume = {5},
year = {2016}
}

@inproceedings{Hallow2015,
author = {Hallow, K. Melissa and James, David A. and Wang, Wenping},
booktitle = {PAGE 24},
file = {:C\:/Users/justin/Downloads/832-9965-PAGE 2015 - RxODE_final.pdf:pdf},
pages = {Abstr 3542},
title = {{Interactive evaluation of dosing regimens for a novel anti-diabetic agent: a case-study in the application of RxODE}},
url = {https://www.page-meeting.org/?abstract=3542},
year = {2015}
}

@Book{Pinheiro2000,
    title = {Mixed-Effects Models in S and S-PLUS},
    author = {José C. Pinheiro and Douglas M. Bates},
    year = {2000},
    publisher = {Springer},
    address = {New York},
    doi = {10.1007/b98882},
  }
  
@article{Delyon1999,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Delyon, B Y Bernard and Lavielle, Marc and Moulines, Eric},
doi = {doi:10.1214/aos/1018031103},
eprint = {arXiv:1011.1669v3},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Delyon, Lavielle, Moulines - 1999 - Convergence of a stochastic approximation version of the EM algorithm.pdf:pdf},
isbn = {9788578110796},
issn = {0090-5364},
journal = {Annals of Statistics},
keywords = {algorithm,carlo algorithm,convergence of the saem,em algorithm,incomplete data,maximum likelihood,missing data,monte-,optimization,short title,simulation,stochastic algorithm},
number = {1},
pages = {94--128},
pmid = {92},
title = {{Convergence of a stochastic approximation version of the EM algorithm}},
volume = {27},
year = {1999}
}

@article{Xiong2015,
doi = {10.1007/s10928-015-9432-2},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - 2015 - Abstracts Accepted for American Conference on Pharmacometrics 2015 (ACoP6).pdf:pdf},
issn = {1567-567X},
author = {Xiong, Yuan and James, David and He, Fei and Wang, Wenping},
journal = {Journal of Pharmacokinetics and Pharmacodynamics},
month = {oct},
number = {S1},
pages = {S11},
publisher = {Springer US},
title = {{PMXstan: An R Library to Facilitate PKPD Modeling with Stan (M-01)}},
volume = {42},
year = {2015}
}

@article{Almquist2015,
abstract = {The first order conditional estimation (FOCE) method is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. The advantages of this approach were demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that significant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization.},
author = {Almquist, Joachim and Leander, Jacob and Jirstrand, Mats},
doi = {10.1007/s10928-015-9409-1},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Almquist, Leander, Jirstrand - 2015 - Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the po.pdf:pdf},
issn = {15738744},
journal = {Journal of Pharmacokinetics and Pharmacodynamics},
keywords = {First order conditional estimation (FOCE),Nonlinear mixed effects modeling,Sensitivity equations},
number = {3},
pages = {191--209},
pmid = {25801663},
publisher = {Springer US},
title = {{Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood}},
volume = {42},
year = {2015}
}

@article{Fidler2019,
abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.},
author = {Fidler, Matthew and Wilkins, Justin J. and Hooijmaijers, Richard and Post, Teun M. and Schoemaker, Rik and Trame, Mirjam N. and Xiong, Yuan and Wang, Wenping},
doi = {10.1002/psp4.12445},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Fidler et al. - 2019 - Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages.pdf:pdf},
issn = {21638306},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
mendeley-groups = {nlmixr},
month = {sep},
number = {9},
pages = {621--633},
pmid = {31207186},
publisher = {John Wiley & Sons, Ltd},
title = {{Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages}},
volume = {8},
year = {2019}
}
@article{Schoemaker2019,
abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.},
author = {Schoemaker, Rik and Fidler, Matthew and Laveille, Christian and Wilkins, Justin J. and Hooijmaijers, Richard and Post, Teun M. and Trame, Mirjam N. and Xiong, Yuan and Wang, Wenping},
doi = {10.1002/psp4.12471},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Schoemaker et al. - 2019 - Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.pdf:pdf},
issn = {21638306},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
mendeley-groups = {nlmixr},
number = {12},
pages = {923--930},
pmid = {31654482},
title = {{Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr}},
volume = {8},
year = {2019}
}
@article{Fidler2021,
abstract = {In pharmacometrics, we try to understand how the dose of a drug and measurable attributes of patients influence their drug concentration-time profiles, and to relate drug concentration-time profiles and patient attributes to the time profile of drug effects. Using this information, we can use tools like "nlmixr" to try to identify the right dose for the right patient. Our discipline is a fusion of pharmacol-ogy, physiology, and quantitative sciences, such as statistics. Statistical methods commonly practiced in the analysis of clinical studies often utilize linear mixed models to investigate trial outcomes. The characterization of longitudinal concentration and drug effect in pharmacometrics extends this statistical paradigm to include nonlinear models, which are often based on ordinary differential equations (ODEs). These ODEs are required to describe complex systems affecting concentrations and effects of drugs that often cannot be described by simple linear models. Nonlinear models can be fitted using the statistical language R 1 with its included "nlme" package. The nlme package allows the expression of nonlinear functions in R and fitting these models in a nonlinear mixed effect (NLME) modeling statistical framework. Only nonlinear equations with closed-form solutions can be used, however, and if more than one dose is administered to a patient, these equations quickly become extremely complex. The nlmixr package 2 extends nlme by providing the ability to add flexible individual-based dosing regimens, and to describe the models using both ODEs and solved systems when these are available. The nlmixr package has made these ODEs easy to express by using Leibniz notation (see below). To run the model, one specifies the ODE/solved system and provides initial estimates for the model as described in the nlmixr tuto-rial. 2 The model then can be solved using the nlme algorithm, or, more optimally, using more advanced algorithms that have been shown to provide more accurate parameter estimates like first-order conditional estimation with interaction (FOCEI) 3 and stochastic approximation expectation maximization (SAEM). 3 Statisticians who are familiar with nlme and are becoming familiar with population pharmacokinetics (PKs) and pharmacodynamics (PDs) may thus find it relatively straightforward to transition from nlme to more robust methodologies and more complex models and data structures. An example of nlmixr applied to a simple PK system serves to illustrate the uncomplicated syntax and how a transition to more complex and robust NLME methodologies might be accomplished without leaving the familiar R environment. Theophylline is used to inhibit phosphodiesterase and is used as treatment in many respiratory diseases. The analysis of the theophylline dataset of Dr. Upton 4 is one of the most common introductory applications of NLME modeling, and is a common introductory dataset for learning nlme (it is included in the base R distribution as the Theoph and reformatted and supplied in nlmixr as theo_sd and extrapolated to multiple doses with theo_ md). If a single dose is given at time zero, the equations can be expressed as the closed-form solution to a one-compartment oral absorption system, and analyzed using nlme. In the syntax of nlmixr, fitting of the theophylline PK can be specified as: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.},
author = {Fidler, Matthew and Hooijmaijers, Richard and Schoemaker, Rik and Wilkins, Justin J. and Xiong, Yuan and Wang, Wenping},
doi = {10.1002/psp4.12618},
file = {:C\:/Users/justin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Fidler et al. - 2021 - R and nlmixr as a gateway between statistics and pharmacometrics(2).pdf:pdf},
issn = {21638306},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
mendeley-groups = {nlmixr},
month = {apr},
number = {4},
pages = {283--285},
pmid = {33951757},
publisher = {John Wiley & Sons, Ltd},
title = {{R and nlmixr as a gateway between statistics and pharmacometrics}},
volume = {10},
year = {2021}
}


@article{carpenterStanProbabilisticProgramming2017,
	title = {Stan: {A} {Probabilistic} {Programming} {Language}},
	volume = {76},
	url = {https://www.jstatsoft.org/index.php/jss/article/view/v076i01},
	doi = {10.18637/jss.v076.i01},
	abstract = {Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.},
	number = {1},
	journal = {Journal of Statistical Software},
	author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
	year = {2017},
	pages = {1--32},
}


@article{homanNoUturnSamplerAdaptively2014,
	title = {The {No}-{U}-turn sampler: adaptively setting path lengths in {Hamiltonian} {Monte} {Carlo}},
	volume = {15},
	issn = {1532-4435},
	shorttitle = {The {No}-{U}-turn sampler},
	abstract = {Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's performance is highly sensitive to two user-specified parameters: a step size ε and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS performs at least as efficiently as (and sometimes more effciently than) a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter ε on the fly based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all, making it suitable for applications such as BUGS-style automatic inference engines that require efficient "turnkey" samplers.},
	number = {1},
	journal = {The Journal of Machine Learning Research},
	author = {Hoffman, Matthew D. and Gelman, Andrew},
	month = jan,
	year = {2014},
	keywords = {adaptive Monte Carlo, Bayesian inference, dual averaging, Hamiltonian Monte Carlo, Markov chain Monte Carlo},
	pages = {1593--1623},
}

@incollection{ezzet_linear_2007,
	address = {New Jersey},
	title = {Linear, {Generalized} {Linear}, and {Nonlinear} {Mixed} {Effects} {Models}},
	isbn = {978-0-471-67783-3},
	language = {English},
	booktitle = {Pharmacometrics: {The} {Science} of {Quantitative} {Pharmacology}},
	publisher = {John Wiley \& Sons},
	author = {Ezzet, Farkad and Pinheiro, José C.},
	editor = {Ette, Ene I. and Williams, Paul J.},
	year = {2007},
	pages = {103--135},
}

@article{Bates2015,
	title = {Fitting linear mixed-effects models using lme4},
	volume = {67},
	number = {1},
	journal = {Journal of Statistical Software},
	author = {Bates, Douglas and Mächler, Martin and Bolker, Ben and Walker, Steve},
	year = {2015},
	pages = {1--48}
}


@article{belenky_patterns_2003,
	title = {Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study},
	volume = {12},
	issn = {1365-2869},
	shorttitle = {Patterns of performance degradation and restoration during sleep restriction and subsequent recovery},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1365-2869.2003.00337.x},
	doi = {10.1046/j.1365-2869.2003.00337.x},
	language = {en},
	number = {1},
	urldate = {2023-09-17},
	journal = {Journal of Sleep Research},
	author = {Belenky, Gregory and Wesensten, Nancy J. and Thorne, David R. and Thomas, Maria L. and Sing, Helen C. and Redmond, Daniel P. and Russo, Michael B. and Balkin, Thomas J.},
	year = {2003},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2869.2003.00337.x},
	keywords = {chronic sleep restriction, modeling, partial sleep deprivation, performance, recovery, sleep, sleep deprivation, sleep restriction},
	pages = {1--12},
}

@Manual{Plevyak,
  title = {dparser: Port of 'Dparser' Package},
  author = {Matthew Fidler and John Plevyak},
  year = {2023},
  note = {https://nlmixr2.github.io/dparser-R/,
https://github.com/nlmixr2/dparser-R/},
}

@article{Karlsson1993,
	title = {The importance of modeling interoccasion variability in population pharmacokinetic analyses},
	volume = {21},
	issn = {0090466X},
	doi = {10.1007/BF01113502},
	abstract = {Individual pharmacokinetic parameters may change randomly between study occasions. Analysis of simulated data with NONMEM shows that ignoring such interoccasion variability (IOV) may result in biased population parameter estimates. Particular parameters affected and the extent to which they are biased depend on study design and the magnitude of IOV and interindividual variability. Neglecting IOV also results in a high incidence of statistically significant spurious period effects. Perhaps most important, ignoring IOV can lead to a falsely optimistic impression of the potential value of therapeutic drug monitoring. A model incorporating IOV was developed and its performance in the presence and absence of IOV was evaluated. The IOV model performs well with respect to both model selection and population parameter estimation in all circumstances studied. Analysis of two real data examples using this model reveals significant IOV in all parameters for both drugs and supports the simulation findings for the case that IOV is ignored: predictable biases occur in parameter estimates and previously nonexistent period effects are found.},
	number = {6},
	journal = {Journal of Pharmacokinetics and Biopharmaceutics},
	author = {Karlsson, M. O. and Sheiner, L. B.},
	year = {1993},
	pmid = {8138894},
	note = {ISBN: 0090-466X (Print)},
	keywords = {pharmacokinetics, population analysis, NONMEM, interindividual variability, interoccasion variability, intraindividual variability},
	pages = {735--750},
}


@article{Wendling2016,
	title = {Reduction of a {Whole}-{Body} {Physiologically} {Based} {Pharmacokinetic} {Model} to {Stabilise} the {Bayesian} {Analysis} of {Clinical} {Data}},
	volume = {18},
	issn = {1550-7416},
	url = {http://link.springer.com/10.1208/s12248-015-9840-7},
	doi = {10.1208/s12248-015-9840-7},
	abstract = {Whole-body physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development for their ability to predict drug concentrations in clinically relevant tissues and to extrapolate across species, experimental conditions and sub-populations. A whole-body PBPK model can be fitted to clinical data using a Bayesian population approach. However, the analysis might be time consuming and numerically unstable if prior information on the model parameters is too vague given the complexity of the system. We suggest an approach where (i) a whole-body PBPK model is formally reduced using a Bayesian proper lumping method to retain the mechanistic interpretation of the system and account for parameter uncertainty, (ii) the simplified model is fitted to clinical data using Markov Chain Monte Carlo techniques and (iii) the optimised reduced PBPK model is used for extrapolation. A previously developed 16-compartment whole-body PBPK model for mavoglurant was reduced to 7 compartments while preserving plasma concentration-time profiles (median and variance) and giving emphasis to the brain (target site) and the liver (elimination site). The reduced model was numerically more stable than the whole-body model for the Bayesian analysis of mavoglurant pharmacokinetic data in healthy adult volunteers. Finally, the reduced yet mechanistic model could easily be scaled from adults to children and predict mavoglurant pharmacokinetics in children aged from 3 to 11 years with similar performance compared with the whole-body model. This study is a first example of the practicality of formal reduction of complex mechanistic models for Bayesian inference in drug development.Electronic supplementary material: The online version of this article (doi:10.1208/s12248-015-9840-7) contains supplementary material, which is available to authorized users.},
	number = {1},
	journal = {The AAPS Journal},
	author = {Wendling, Thierry and Tsamandouras, Nikolaos and Dumitras, Swati and Pigeolet, Etienne and Ogungbenro, Kayode and Aarons, Leon},
	year = {2016},
	pmid = {26538125},
	note = {ISBN: 1224801598},
	keywords = {bayesian population approach, mavoglurant, pbpk extrapolation, pharmacokinetic models, physiologically based, proper lumping},
	pages = {196--209},
	file = {Full Text:/Users/justin/Zotero/storage/MNNVFLCW/Wendling et al. - 2016 - Reduction of a Whole-Body Physiologically Based Ph.pdf:application/pdf;PDF:/Users/justin/Zotero/storage/6HGJ9RQ2/Wendling et al. - 2016 - Reduction of a Whole-Body Physiologically Based Pharmacokinetic Model to Stabilise the Bayesian Analysis of Cli.pdf:application/pdf},
}


@article{Wilkins2008,
	title = {Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption},
	volume = {52},
	issn = {00664804},
	doi = {10.1128/AAC.00461-07},
	abstract = {This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters·h-1, while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8\% for clearance and 43.4\% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5\%) and mean transit time during absorption (67.9\%). The use of single-drug formulations was found to increase both the mean transit time (by 104\%) and clearance (by 23.6\%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context. Copyright © 2008, American Society for Microbiology. All Rights Reserved.},
	number = {6},
	journal = {Antimicrobial Agents and Chemotherapy},
	author = {Wilkins, J.J. and Savic, R.M. and Karlsson, M.O. and Langdon, G. and McIlleron, H. and Pillai, G. and Smith, P.J. and Simonsson, U.S.H.},
	year = {2008},
	pages = {2138--2148},
	file = {PDF:/Users/justin/Zotero/storage/WHA6JIXY/Wilkins et al. - 2008 - Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to.pdf:application/pdf},
}

@article{Savic2007,
	title = {Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies},
	volume = {34},
	issn = {1567567X},
	doi = {10.1007/s10928-007-9066-0},
	abstract = {Purpose: To compare the performance of the standard lag time model (LAG model) with the performance of an analytical solution of the transit compartment model (TRANSIT model) in the evaluation of four pharmacokinetic studies with four different compounds. Methods: The population pharmacokinetic analyses were performed using NONMEM on concentration-time data of glibenclamide, furosemide, amiloride, and moxonidine. In the TRANSIT model, the optimal number of transit compartments was estimated from the data. This was based on an analytical solution for the change in drug concentration arising from a series of transit compartments with the same first-order transfer rate between each compartment. Goodness-of-fit was assessed by the decrease in objective function value (OFV) and by inspection of diagnostic graphs. Results: With the TRANSIT model, the OFV was significantly lower and the goodness-of-fit was markedly improved in the absorption phase compared with the LAG model for all drugs. The parameter estimates related to the absorption differed between the two models while the estimates of the pharmacokinetic disposition parameters were similar. Conclusion: Based on these results, the TRANSIT model is an attractive alternative for modeling drug absorption delay, especially when a LAG model poorly describes the drug absorption phase or is numerically unstable.},
	number = {5},
	journal = {Journal of Pharmacokinetics and Pharmacodynamics},
	author = {Savic, Radojka M. and Jonker, Daniël M. and Kerbusch, Thomas and Karlsson, Mats O.},
	year = {2007},
	keywords = {Pharmacokinetics, NONMEM, Absorption delay, LAG model, TRANSIT model},
	pages = {711--726},
	file = {PDF:/Users/justin/Zotero/storage/BTJX6BTW/Savic et al. - 2007 - Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies.pdf:application/pdf},
}