Well-tempered MCMC simulations for population pharmacokinetic models

Bois, Frédéric Y.; Hsieh, Nan‐Hung; Gao, Wang; Chiu, Weihsueh A.; Reisfeld, Brad

doi:10.1007/s10928-020-09705-0

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…The PBPK model was therefore rewritten in GNU MCSim, a more suitable language for intensive computations. Calibration was conducted using the thermodynamic integration variant of MCMC ( Bois et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

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Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data

et al. 2021

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A physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the biokinetics in humans after single oral doses. The model was parameterized with in vitro and in silico derived parameters and uncertainty and sensitivity analysis was used during the model development process to assess structure, biological plausibility and behaviour prior to simulation and analysis of human biological monitoring data. To provide possible explanations for some of the counter-intuitive behaviour of the biological monitoring data the model included a simple lymphatic uptake process for DPHP and enterohepatic recirculation (EHR) for DPHP and the mono ester metabolite mono-(2-propylheptyl) phthalate (MPHP). The model was used to simultaneously simulate the concentration-time profiles of blood DPHP, MPHP and the urinary excretion of two metabolites, mono-(2-propyl-6-hydroxyheptyl) phthalate (OH-MPHP) and mono-(2-propyl-6-carboxyhexyl) phthalate (cx-MPHP). The availability of blood and urine measurements permitted a more robust qualitative and quantitative investigation of the importance of EHR and lymphatic uptake. Satisfactory prediction of blood DPHP and urinary metabolites was obtained whereas blood MPHP was less satisfactory. However, the delayed peak of DPHP concentration relative to MPHP in blood and second order metabolites in urine could be explained as a result of three processes: 1) DPHP entering the systemic circulation from the lymph, 2) rapid and very high protein binding and 3) the efficiency of the liver in removing DPHP absorbed via the hepatic route. The use of sensitivity analysis is considered important in the evaluation of uncertainty around in vitro and in silico derived parameters. By quantifying their impact on model output sufficient confidence in the use of a model should be afforded. This approach could expand the use of PBPK models since parameterization with in silico techniques allows for rapid model development. This in turn could assist in reducing the use of animals in toxicological evaluations by enhancing the utility of “read across” techniques.

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Section: Discussionmentioning

confidence: 99%

“…Inference for the model parameters was made using Markov chain Monte Carlo (MCMC) implemented in MCSim (see Software). Inference for model parameters in the final calibration model was made using thermo-dynamic integration (TI) as described in Bois et al (2020) . A single chain of 1,000,000 iterations was run with every 10th retained.…”

Section: Methodsmentioning

confidence: 99%

Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data

et al. 2021

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“…Inference for the model parameters was made using Markov chain Monte Carlo (MCMC) implemented in MCSim (see Software ). Inference for model parameters in the final calibration model was made using thermodynamic integration (TI) as described in ( Bois et al, 2020 ). A single chain of 150,000 iterations was run with every 10th retained.…”

Section: Discussionmentioning

confidence: 99%

Refinement and calibration of a human PBPK model for the plasticiser, Di-(2-propylheptyl) phthalate (DPHP) using in silico, in vitro and human biomonitoring data

McNally

Loizou²

2023

Front. Pharmacol.

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An existing physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was refined to improve the simulations of the venous blood concentrations of the primary monoester metabolite, mono-(2-propylheptyl) phthalate (MPHP). This was considered a significant deficiency that should be addressed because the primary metabolite of other high molecular weight phthalates has been associated with toxicity. The various processes that influence the concentration of DPHP and MPHP in blood were re-evaluated and modified. A few simplifications of the existing model were made, including the removal of enterohepatic recirculation (EHR) of MPHP. However, the primary development was describing the partial binding of MPHP to plasma proteins following uptake of DPHP and metabolism in the gut affording better simulation of the trends observed in the biological monitoring data. Secondly, the relationship between blood concentrations and the urinary excretion of secondary metabolites was explored further because the availability of two data streams provides a better understanding of the kinetics than reliance on just one. Most human studies are conducted with few volunteers and generally with the absence of blood metabolite measurements which would likely imply an incomplete understanding of the kinetics. This has important implications for the “read across” approach proposed as part of the development of New Approach Methods for the replacement of animals in chemical safety assessments. This is where the endpoint of a target chemical is predicted by using data for the same endpoint from another more “data rich” source chemical. Validation of a model parameterized entirely with in vitro and in silico derived parameters and calibrated against several data streams would constitute a data rich source chemical and afford more confidence for future evaluations of other similar chemicals using the read-across approach.

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“…Therefore, HMCMC performs better in high dimensional problems where the posterior has high curvature compared to other MCMC methods. Yet, per step, HMCMC sampling is much heavier computationally 31 …”

Section: Inference Methods and Algorithmsmentioning

confidence: 99%

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers

Wedagedera

Afuape

Chirumamilla

et al. 2022

CPT Pharmacom & Syst Pharma

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Physiologically-based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi-random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis-Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?No single software platform allows practitioners to compare parametric and nonparametric estimates of calibrated physiologically-based pharmacokinetic (PBPK) model parameters. WHAT QUESTION DID THIS STUDY ADDRESS?How do a maximum likelihood parametric (quasi-random parametric expectation maximization) and nonparametric (nonparametric adaptive grid estimation) algorithms, and two Bayesian numerical methods (Hamiltonian Markov Chain Monte Carlo and MH) compare in results and timing for calibration of a PBPK model?

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Well-tempered MCMC simulations for population pharmacokinetic models

Cited by 9 publications

References 38 publications

Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data

Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data

Refinement and calibration of a human PBPK model for the plasticiser, Di-(2-propylheptyl) phthalate (DPHP) using in silico, in vitro and human biomonitoring data

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers

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