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Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim


The aim of this study is to benchmark two Bayesian software tools, namely Stan and GNU MCSim, that use different Markov chain Monte Carlo (MCMC) methods for the estimation of physiologically based pharmacokinetic (PBPK) model parameters. The software tools were applied and compared on the problem of updating the parameters of a Diazepam PBPK model, using time-concentration human data. Both tools produced very good fits at the individual and population levels, despite the fact that GNU MCSim is not able to consider multivariate distributions. Stan outperformed GNU MCSim in sampling efficiency, due to its almost uncorrelated sampling. However, GNU MCSim exhibited much faster convergence and performed better in terms of effective samples produced per unit of time.

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We would like to thank Gueorgieva I. for granting us access to the Diazepam data. H. Sarimveis and F. Bois acknowledge financial support by OpenRiskNet (Grant Agreement 731075), a project funded by the European Commission under the Horizon 2020 Programme. Periklis Tsiros acknowledges financial support by the NTUA internal reward Programme Numbered 95/0085.

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Correspondence to Haralambos Sarimveis.

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Appendix 1: Coefficients of organ weight polynomials for humans

See Tables 4 and 5.

Table 4 Coefficients of organ weight polynomials for male humans [26]
Table 5 Coefficients of organ weight polynomials for female humans [26]

Appendix 2: Posterior estimates

See Tables 6 and 7.

Table 6 Posterior estimates obtained with Stan
Table 7 Posterior estimates obtained with GNU MCSim

Appendix 3: Impact of LKJ prior on computational efficiency

See Table 8.

Table 8 Computational efficiency for different values of the shape parameter a of the LKJ prior

Appendix 4: Comparison of the univariate models using noninformative priors

See Figs. 13 and 14.

Fig. 13
figure 13

Comparison of individual parameter estimates between GNU MCSim and Stan univariate models using noninformative priors

Fig. 14
figure 14

Comparison of population estimates between GNU MCSim and Stan univariate models using noninformative priors. The red crosses represent the mean population estimates while the blue crosses represent the population variance estimates

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Tsiros, P., Bois, F.Y., Dokoumetzidis, A. et al. Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim. J Pharmacokinet Pharmacodyn 46, 173–192 (2019).

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  • Diazepam
  • Population pharmacokinetics
  • PBPK
  • Bayesian
  • GNU MCSim
  • Stan