SteaPKMod

The experimental data of Midazolam, OH-Midazolam, Caffein, Codeine, Norcodeine, Codein-6Glucuronide, Morphine-3Glucuronide and Morphine was analyzed via a Bayesian uncertainty quantification. An underlying model describing the bolus injection, followed by the exponential decay was written in sbml and a PEtab problem was created. The sampling and ensemble creation was conducted with the python toolbox pyPESTO.

For further details, please take a look at the methods section of the paper.

Stained sections were digitalized using a whole slide scanner (L11600, Hamamatsu, Ja-pan) equipped with the NDP.view2Plus Image viewing software (Version U12388-02).

• MNT-021_J-20-0152_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-022_J-20-0154_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-023_J-20-0156_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-024_J-20-0158_HE_LLL(green), RML(red), RSL (black), ...

This code uses the PEtab problem (written in the yaml file) to perform MCMC sampling with pyPESTO. Afterwards an ensemble is created, that allows to compute the posterior predictive distribution and therefore the credibility intervals for the model output.

The sampling.py scipt of pyPESTO visulize was adjusted for an improved visualization. The used code was also uploaded.

A Conda environment file (.yml) containing the specific version of Python and installed packages with the according versions, ...

Complete posterior distributions for each drug and condition.

The files are in the hdf5 format and contain the complete information of the analysis for a FAIR data sharing.

The parameters A and B were estimated for the function f(x)=x⋅e^(B-Ax) and the AUC was calculated for the decay phase only. For OH-Midazolam the fourth replicate in the 2 weeks condition has an outlier in the measurement after six hours (more than 2SD above the mean of this condition) and was omitted for the AUC calculation as this could not be fitted to an exponential decay.

CYP2E1: Generic Classification 128

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• MNT-025_Bl6J_J-20-0160_CYP2E1- 1/400_Run ...

• MNT-021_Bl6J_J-20-0152_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
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• MNT-021_Bl6J_J-20-0152_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-022_Bl6J_J-20-0154_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-023_Bl6J_J-20-0156_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-024_Bl6J_J-20-0158_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
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• MNT-021_Bl6J_J-20-0152_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-022_Bl6J_J-20-0154_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-023_Bl6J_J-20-0156_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-024_Bl6J_J-20-0158_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
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CYP1A2: Generic Classification 128

• MNT-021_J-20-0152_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-022_J-20-0154_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-023_J-20-0156_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-024_J-20-0158_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-025_J-20-0160_Bl6J_CYP1A2-1/500_Run ...

• MNT-021_J-20-0152_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-022_J-20-0154_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-023_J-20-0156_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-024_J-20-0158_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
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CYP3A4: Generic Classification 128

• MNT-021_J-20-0152_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-022_J-20-0154_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-023_J-20-0156_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-024_J-20-0158_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-025_J-20-0160_CYP3A4 1/2000_Run 10_LLL(green), RML(red), ...

• MNT-021_J-20-0152_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-022_J-20-0154_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-023_J-20-0156_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-024_J-20-0158_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-025_J-20-0160_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-026_J-20-0162_HE_LLL(green), RML(red), ...

For each drug and condition, there is one:

• Visualization of the marginals and the sampling traces
• The credibility intervals for the parameters with the median
• Visualization of the individual ensemble output predictions with boxplots of the underlying data

Furthermore all conditions of the ensemble output predictions for one drug are visulized in one plot.

The exponential decay model with all parameters, observables and conditions was specified in a yaml file.

This yaml file is converted with yaml2sbml (2020 Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schaelte and Jan Hasenauer) to a PEtab problem, which contains also the SBML model.

The SOP creates a separate SBML model for each drug and condition, as the PEtab problem contains diffrent experimental data for them.

However, the SBML models only differ in their name as for all drugs and conditions, the same exponential decay model was assumed.

The SBMLs are automatically created by yaml2sbml, when the SOP is executed. Therefore, these files are for completeness only and are not necessary to replicate the analysis.

steatosis pattern analysis report using Histokat, a proprietary software based on a machine-learning algorithm created by Fraunhofer MEVIS

Steatosis Quantification (Lipid droplet analysis) report using Histokat, a proprietary software based on a machine-learning algorithm created by Fraunhofer MEVIS

Raw data used for Steatosis and CYP quantification and TG, PK-data, AUC, t-halflife and C-max

Excel file involves all experiment data including feeding plan, sampling plan, and the Pharmacokinetic data results