Data files

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

• 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 ...

• 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 ...

• 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 ...

• 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 ...

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
• MNT-025_J-20-0160_Bl6J_CYP1A2-1/500_Run ...

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.