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Modeling Pharmacokinetics in Steatotic Livers (SteaPKMod)

Subject Area Pharmacology
Bioinformatics and Theoretical Biology
Term from 2019 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 410848700
 
Final Report Year 2024

Final Report Abstract

Liver diseases may lead to an impairment of drug metabolism, making appropriate drug dosing difficult. Therefore, we wanted to develop a mathematical PK model for individualized dosage optimization and diagnosis support in zonated and heterogeneously distributed liver diseases such as steatosis. On the experimental side, we performed a drug metabolism study in normal and steatotic mice with non-alcoholic fatty liver disease (NAFLD). Periportal steatosis was induced using a dietary model resulting in the expected heterogenous fat distribution throughout the liver. However, in contrast to previous experiments in rats, prolonging feeding duration from 2w to 4w did not increase total fat content substantially, but affected the steatosis pattern from a predominantly microvesicular to predominant macrovesicular pattern, thereby increasing the complexity of the system. This affected distinct parameters of pericentrally located drug metabolism: caffeine elimination was accelerated after 2w of diet, midazolam elimination was delayed after 4w, codeine elimination was unaffected. Based on the highly discrepant effects of the diet in rats and mice, we began to explore clinical translatability by investigating cross-species variability in hepatic lobular geometry and cytochrome P450 zonation in mouse, rat, pig and human samples, revealing highest similarity between murine and human expression pattern. Currently we are exploring the impact of steatosis on lobular geometry and selected drug metabolism parameters. On the modeling side, we faced two challenges: the steatosis induction protocol resulted (1) in a difference of steatosis patterns rather than severity and (2) in high inter-individual variability and low inter-group differences in the PK elimination. The observed inter-individual differences could not be attributed to steatosis severity or type alone, but must be due to other factors. Therefore, the data were not suitable for the planned modeling approach. Instead, we pursued a Bayesian uncertainty analysis. We implemented a novel image analysis pipeline allowing joint quantification of multiple parameters from consecutive sections by employing automatic image registration. Moreover, we explored improvements of building blocks of the pipeline: further automating previously manual annotation steps and increasing the robustness of steatosis quantification with a potential clinical translation by employing modern Deep Learning techniques. This pipeline is not limited to mice and thus applicable beyond SteaPKMod. Conclusion: Individualized dosage optimization based on steatosis severity and diagnosis support were not feasible due to the unexpected response of the mice to the dietary induction protocol and the unexpected high inter-individual variability and low inter-group differences. Instead, we focused on uncertainty analysis, image analysis tool development, and cross-species analysis to prepare for clinical translation.

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