Rikard Nordgren1, Andrew C. Hooker1, Stella Belin1, Xiaomei Chen1, Zhe Huang1, Mats O. Karlsson1
1Department of Pharmacy, Uppsala University
The Uppsala University pharmacometrics group develops and maintains several different open-source software packages.
ncappc – Performs traditional non-compartmental analysis and simulation based posterior predictive checks for PK and PKPD models [1].
https://github.com/UUPharmacometrics/ncappc
Pharmpy – A library for pharmacometric modelling. Functionality includes parsing and transforming models and datasets, estimating models, and automatic development of models [2]. NONMEM and nlmixr2/rxode2 are supported. Pharmpy also has a graphical user interface (GUI) for model building. See separate PAGE abstract and poster for more information about Pharmpy.
https://pharmpy.github.io, https://github.com/pharmpy/pharmpy
pharmr – R interface to Pharmpy.
https://pharmpy.github.io, https://github.com/pharmpy/pharmr
piraid – An aid for development and diagnostics of pharmacometric IRT and composite score models.
https://github.com/UUPharmacometrics/piraid
PopED – Computes optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix (FIM) [3], [4].
https://andrewhooker.github.io/PopED
xpose4/xpose – xpose4 is a collection of functions to be used as a model building aid for nonlinear mixed-effects (population) analysis using NONMEM. It facilitates data set checkout, exploration and visualization, model diagnostics, candidate covariate identification, and model comparison. xpose was designed as a ggplot2-based alternative to xpose4. xpose aims to reduce the post processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models [5].
https://uupharmacometrics.github.io/xpose4
https://uupharmacometrics.github.io/xpose/
bemod – Model-integrated bioequivalence analysis [6], [7].
MBAOD – Can be used to simulate experiments (often clinical or pre-clinical trials) using predefined adaptation and optimization rules. The package can be used to plan and evaluate the predicted effectiveness of an upcoming trial. In addition, the package can be used to optimize any specific cohort of an actual study [8], [9].
PsN – Open-source toolbox for population PK/PD model building using NONMEM. It has broad functionality ranging from results extraction to advanced computer-intensive statistical methods. PsN simplifies the organization of NONMEM output files, helps with starting jobs on different types of clusters (i.e. slurm, torque, sge and lsf) and can perform a cornucopia of different statistical, computational, and other methods, including:
* benchmark – Combinatoric benchmarking of different NONMEM control stream settings.
* bootstrap – Assessing uncertainty of parameter estimates.
* cdd – Case deletion diagnostic to look for influential individuals.
* crossval – Model cross validation.
* frem – Full random effects modelling.
* linearize – Generation of model approximation using linarization and second order approximation for likelihood models.
* llp – Log likelihood profiling.
* mcmp – Monte-Carlo mappend power for power compuations.
* parallel_retries – Estimate the same model multiple times with different initial parameter estimates.
* qa – Fast and automatic assumption assessment and quality assurance of models.
* resmod – Residual modelling for quickly assessing appropriateness of structural and residual error models.
* scm – Stepwise covariate model.
* simeval – Simulation evaluation diagnostics of outliers.
* sir – Sampling importance resampling for parameter uncertainty assessment.
* sse – Stochastic simulation and estimation.
* vpc – Visual predictive check [10], [11], [12], [13].
https://uupharmacometrics.github.io/PsN
[1] C. Acharya, A. C. Hooker, G. Y. Türkyilmaz, S. Jönsson, and M. O. Karlsson, “A diagnostic
tool for population models using non-compartmental analysis: The ncappc package for R,”
Comput. Methods Programs Biomed., vol. 127, pp. 83–93, Apr. 2016, doi:
10.1016/j.cmpb.2016.01.013.
[2] X. Chen et al., “A fully automatic tool for development of population pharmacokinetic models,”
CPT Pharmacomet. Syst. Pharmacol., vol. 13, no. 10, p. 1784, Aug. 2024, doi:
10.1002/psp4.13222.
[3] M. Foracchia, A. Hooker, P. Vicini, and A. Ruggeri, “poped, a software for optimal experiment
design in population kinetics,” Comput. Methods Programs Biomed., vol. 74, no. 1, pp. 29–46,
Apr. 2004, doi: 10.1016/S0169-2607(03)00073-7.
[4] J. Nyberg, S. Ueckert, E. A. Strömberg, S. Hennig, M. O. Karlsson, and A. C. Hooker, “PopED:
An extended, parallelized, nonlinear mixed effects models optimal design tool,” Comput.
Methods Programs Biomed., vol. 108, no. 2, pp. 789–805, Nov. 2012, doi:
10.1016/j.cmpb.2012.05.005.
[5] N. E. Jonsson and M. O. Karlsson, “Xpose—an S-PLUS based population
pharmacokinetic/pharmacodynamic model building aid for NONMEM,” Comput. Methods
Programs Biomed., vol. 58, no. 1, pp. 51–64, Jan. 1998, doi: 10.1016/S0169-2607(98)00067-4.[6] X. Chen et al., “Development and comparison of model-integrated evidence approaches for
bioequivalence studies with pharmacokinetic end points,” CPT Pharmacomet. Syst.
Pharmacol., vol. 13, no. 10, pp. 1734–1747, 2024, doi: 10.1002/psp4.13216.
[7] H. Bjugård Nyberg et al., “Evaluation of model-integrated evidence approaches for
pharmacokinetic bioequivalence studies using model averaging methods,” CPT Pharmacomet.
Syst. Pharmacol., vol. 13, no. 10, pp. 1748–1761, 2024, doi: 10.1002/psp4.13217.
[8] E. A. Strömberg and A. C. Hooker, “The effect of using a robust optimality criterion in model
based adaptive optimization,” J. Pharmacokinet. Pharmacodyn., vol. 44, no. 4, pp. 317–324,
Aug. 2017, doi: 10.1007/s10928-017-9521-5.
[9] P. B. Pierrillas, S. Fouliard, M. Chenel, A. C. Hooker, L. F. Friberg, and M. O. Karlsson,
“Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding
Designs: an Example in Oncology,” AAPS J., vol. 20, no. 2, p. 39, Mar. 2018, doi:
10.1208/s12248-018-0206-9.
[10] L. Lindbom, J. Ribbing, and N. E. Jonsson, “Perl-speaks-NONMEM (PsN)—a Perl module
for NONMEM related programming,” Comput. Methods Programs Biomed., vol. 75, no. 2, pp.
85–94, Aug. 2004, doi: 10.1016/j.cmpb.2003.11.003.
[11] M. O. Karlsson, R. Nordgren, and et al., “PsN: An open source toolkit for non-linear mixed
effects modelling.” Accessed: Feb. 21, 2025. [Online]. Available:
https://uupharmacometrics.github.io/PsN/
[12] L. Lindbom, P. Pihlgren, and N. E. Jonsson, “PsN-Toolkit—A collection of computer
intensive statistical methods for non-linear mixed effect modeling using NONMEM,” Comput.
Methods Programs Biomed., vol. 79, no. 3, pp. 241–257, Sep. 2005, doi:
10.1016/j.cmpb.2005.04.005.
[13] R. J. Keizer, M. O. Karlsson, and A. C. Hooker, “Modeling and Simulation Workbench for
NONMEM: Tutorial on Pirana, PsN, and Xpose,” CPT Pharmacomet. Syst. Pharmacol., vol. 2,
no. 6, p. e50, Jun. 2013, doi: 10.1038/psp.2013.24.
Reference: PAGE 33 (2025) Abstr 11342 [www.page-meeting.org/?abstract=11342]
Poster: Software Demonstration