Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model-informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) CYP1A2 phenotype of 48 healthy volunteers participating in a single-dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual.In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, gender), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared to the base model. The percentage of predictions within 0.8-to 1.25fold of the observed values increased from 45.8 % (base model) to 57.8% (step 1). However, setting physiological parameters (liver blood flow determined by MRI, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in 1.25-fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved simulation results. The percentage of data within the 1.25-fold range was 66.15%. This case study shows that individual pharmacokinetic profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model informed precision dosing approaches in the future.

Background & Aims: Decompensation is a hallmark of disease progression in cirrhotic patients. Early detection of a phase transition from compensated cirrhosis to decompensation would enable targeted therapeutic interventions potentially extending life expectancy. This study aims to (a) identify the predictors of decompensation in a large, multicentric cohort of patients with compensated cirrhosis, (b) to build a reliable prognostic score for decompensation and (c) to evaluate the score in independent cohorts. Methods: Decompensation was identified in electronic health records data from 6049 cirrhosis patients in the IBM Explorys database training cohort by diagnostic codes for variceal bleeding, encephalopathy, ascites, hepato-renal syndrome and/ | 641 SCHNEIDER et al.

We present a generic workflow combining physiology-based computational modeling and in vitro data to assess the clinical cholestatic risk of different drugs systematically. Changes in expression levels of genes involved in the enterohepatic circulation of bile acids were obtained from an in vitro assay mimicking 14 days of repeated drug administration for ten marketed drugs. These changes in gene expression over time were contextualized in a physiology-based bile acid model of glycochenodeoxycholic acid. The simulated drug-induced response in bile acid concentrations was then scaled with the applied drug doses to calculate the cholestatic potential for each compound. A ranking of the cholestatic potential correlated very well with the clinical cholestasis risk obtained from medical literature. The proposed workflow allows benchmarking the cholestatic risk of novel drug candidates. We expect the application of our workflow to significantly contribute to the stratification of the cholestatic potential of new drugs and to support animal-free testing in future drug development.