Periportal steatosis in mice affects distinct parameters of pericentral drug metabolism

Bl6J_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003

* 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),
...

• 03. HE-Whole slide scans.zip

HE-Steatosis analysis: Steatosis Quantification

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

• report.html

HE steatosis pattern analysis: Generic Classification 128

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

• report.html

Bl6J_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005

* 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),
...

• 05. CYP3A4.zip

CYP3A4: Generic Classification 128

CYP3A4: Generic Classification 128

• report.html

Bl6J_CYP1A2-1 500_Run 08_LLL, RML, RSL, ICL_MAA_0002

* 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
...

Bl6J_CYP1A2-1 500_Run 08_LLL, RML, RSL, ICL_MAA_0002

* 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
...

• 02. CYP1A2.zip

CYP1A2: Generic Classification 128

CYP1A2: Generic Classification 128

• report.html

Mouse_Bl6J_J-20-0154_CYP2D6- 1 3000_Run 14_ML, RML, RSL, ICL_MAA_004

* 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
* MNT-025_Bl6J_J-20-0160_CYP2D6- 1/3000_Run
...

Mouse_Bl6J_J-20-0154_CYP2D6- 1 3000_Run 14_ML, RML, RSL, ICL_MAA_004

* 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
* MNT-025_Bl6J_J-20-0160_CYP2D6- 1/3000_Run
...

• 04. CYP2D.zip

Bl6J_CYP2E1- 1 400_Run 11_LLL, RML, RSL, ICL_MAA_0006

* MNT-021_Bl6J_J-20-0152_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-022_Bl6J_J-20-0154_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-023_Bl6J_J-20-0156_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-024_Bl6J_J-20-0158_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-025_Bl6J_J-20-0160_CYP2E1- 1/400_Run
...

Bl6J_CYP2E1- 1 400_Run 11_LLL, RML, RSL, ICL_MAA_0006

* MNT-021_Bl6J_J-20-0152_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-022_Bl6J_J-20-0154_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-023_Bl6J_J-20-0156_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-024_Bl6J_J-20-0158_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
* MNT-025_Bl6J_J-20-0160_CYP2E1- 1/400_Run
...

• 06. CYP2E1.zip

CYP2E1: Generic Classification 128

CYP2E1: Generic Classification 128

• report.html

2022-12-08_ Histology Quant., CYP activity, Protein, TG, AUC

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

• 2022-12-08_ Histology Quant., CYP activity, Protein, TG, AUC.xlsx

2022-07-18_ Experimental data- Drug injection and Pharmacokinetics_Stea pk - documentation

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

• 2022-07-16_ Experimental data- Drug injection and Pharmacokinetics_Stea pk - documentation.xlsx

Visualization of marginals, credibility intervals and ensemble output predictions

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.

• Visualization.zip

Bayesian UQ for experimental data: Code

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,
...

• Bayesian UQ for experimental data code.zip

Posterior distributions

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.

• Posterior.zip

AUC values and parameters of the corresponding curves for each replicate of each drug and condition

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.

• AUC values.zip

Exponential Decay PEtab Problem in yaml format

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.

• Exponential_decay_model.yaml

Compiled SBML models from PEtab yaml file

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.

• SBML models.zip