Biology subjects

Kuepfer, L.

Publications and source records attributed to Kuepfer, L..

7 recordsLinked to original sources

bioRxiv2025

High-throughput PBK modelling for dermal exposure: a pragmatic approach to predict systemic pharmacokinetics

High-throughput physiologically based kinetic (HT-PBK) modelling provides a mechanistic framework for predicting systemic pharmacokinetics (PK) from in vitro and in silico data, supporting non-animal chemical safety assessment within Next generation risk assessment (NGRA). Here, we applied HT-PBK modelling to dermal exposure, a key route of human contact with chemicals. Using the skin permeation model of the Open Systems Pharmacology Suite, we simulated systemic PK profiles of 52 compounds based solely on physicochemical properties predicted by quantitative structure-activity relationship (QSAR) models and without any compound-specific in vitro measurements. We systematically compared different QSAR tools for lipophilicity, solubility, and other parameters to identify optimal model parameterisation strategies. Across all compounds, the best-performing HT-PBK strategy predicted 75% of Cmax and AUC values within a tenfold range of observed human plasma data extracted from published clinical studies. A systematic tendency toward overprediction of systemic PK was observed, likely due to missing study metadata and the default assumption of fully hydrated skin. Prediction errors were larger for dermal than for oral exposure, reflecting the greater complexity and variability of dermal absorption processes. Nevertheless, key exposure metrics were reproduced within acceptable limits. These results demonstrate the feasibility of fully in silico, non-animal HT-PBK modelling for dermal absorption and support its use as a pragmatic tool for exposure-driven safety assessment within NGRA frameworks.

pharmacology and toxicology↗

bioRxiv2025

Integration of In Vitro and In Silico Approaches Enables Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug attrition and poses a significant threat to patient safety. However, current preclinical prediction methods, including heuristic screening rules, in vitro assays, machine learning models and animal testing, have serious limitations. Here, we demonstrate that combining in vitro toxicity data (cytotoxicity, mitochondrial toxicity, bile salt export pump (BSEP) inhibition) with pharmacokinetic information enables high DILI predictivity. In a retrospective analysis of 241 drugs, we show that the ratio of their in vivo maximum plasma concentration (Cmax) to their lowest in vitro toxicity strongly correlates with clinical DILI risks, with ROC AUC up to 96%. Then, we show that comparable predictivity (ROC AUC up to 91%) is achievable prospectively when Cmax values are predicted in silico by high-throughput physiologically based kinetic modelling. Dynamic simulations of bile acid perturbations further identify drugs potentially causing DILI specifically through BSEP inhibition, providing additional mechanistic insights. This integrative, mechanistic approach shows enhanced DILI predictivity and interpretability, offering an animal-free alternative for early drug development.

pharmacology and toxicology↗

bioRxiv2025

A physiologically-based model of bile acid metabolism in humans

Altered serum BA profiles have been reported for several chronic liver diseases. However, relevant modifications in bile acid disposition are difficult to investigate mechanistically due to the interference of the various physiological processes involved at various levels of bile acid metabolism. In this regard, validated computational models represent a promising tool to understand the functional effect of disease-related alterations on bile acid composition along the gut-liver-axis through mechanistic analyses of the underlying physiological processes. We applied a physiologically-based models of bile acid metabolism in humans comprising the five predominant bile acids. The model describes various physiological aspects of bile acid metabolism including synthesis, enterohepatic circulation, hepatic and microbial conversions, postprandial gall bladder emptying as well as excretion via feces and urine. Model development was based on 39 previously published studies in patients without any liver diseases. In the future, the established model may serve as reference for analysis of the role of bile acids in health and disease.

systems biology↗

bioRxiv2025

Potential Systemic Availability Classification of Chemicals for Safety Assessment

The assessment of chemical safety is essential for protecting human health, yet current approaches have severe limitations. They are unable to address the rapidly growing number of substances requiring evaluation and further rely on animal testing, which the public expects to be phased out for ethical reasons. To address this, the European Partnership for Alternative Approaches to Animal Testing (EPAA) proposed the use of a novel framework for the classification of systemic toxicity based on new approach methodologies. One dimension of this framework is the grouping of substances into different classes of Potential Systemic Availability (PSA) concern (low, medium, high). But so far it remained conceptually and practically unclear how this classification can be achieved. Here, we outline the theoretical considerations for a health-protective definition of PSA concern classes and present a method for the quantitative evaluation of this property. Using high-throughput physiologically based kinetic modelling, we are able to classify the PSA concerns of 139 out of 150 EPAA NAM Designathon compounds. Further, we manually annotate these compounds to evaluate the plausibility of predicted classifications against expert judgement. Our results outline under which circumstances it is appropriate to prioritise or deprioritise chemicals due to their toxicokinetic properties. However, we find that most compounds cannot be assessed on their PSA alone and need to be considered medium PSA concern so that classification into any overall systemic toxicity concern class remains possible. Future integration of bioactivity data will be necessary to fully judge the utility of our method and of the entire framework.

pharmacology and toxicology↗

bioRxiv2024

Systematic Evaluation of High-Throughput PBK Modelling Strategies for the Prediction of Intravenous and Oral Pharmacokinetics in Humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for Non-Animal Methods. To address this limitation, High-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000+ publicly available human in vivo concentration-time profiles of 200+ compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic High-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within 10-fold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for High-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of High-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

pharmacology and toxicology↗

bioRxiv2022

A physiologically based model of bile acid metabolism in mice

Bile acid (BA) metabolism is a complex system that includes a wide variety of primary and secondary, as well as conjugated and unconjugated BAs that undergo continuous enterohepatic circulation (EHC). Alterations in both composition and dynamics of BAs have been associated with various diseases. However, a mechanistic understanding of the relationship between altered BA metabolism and related diseases is lacking. Computational modeling may support functional analyses of the physiological processes involved in the EHC of BAs along the gut-liver axis. In this study, we developed a physiologically-based model of murine BA metabolism describing synthesis, conjugation, microbial transformations, systemic distribution, excretion and EHC of BAs at the whole-body level. For model development, BA metabolism of specific pathogen-free (SPF) mice was characterized in vivo by measuring BA levels and composition in various organs, expression of transporters along the gut and cecal microbiota composition. We found significantly different BA levels between male and female mice that could only be explained by adjusted expression of the hepatic enzymes and transporters in the model. Of note, this finding was in agreement with experimental observations. The model for SPF mice could also describe equivalent experimental data in germ-free mice by specifically switching of microbial activity in the intestine. The here presented model can therefore facilitate and guide functional analyses of BA metabolism in mice, e.g., the effect of pathophysiological alterations on BA metabolism and translation of results from mouse studies to a clinically relevant context through cross-species extrapolation.

systems biology↗

bioRxiv2018

A physiology-based model of bile acid metabolism shows altered tissue concentrations after drug administration and in specific genotype subgroups

Drug-induced liver injuries (DILI) are an important issue in drug development and patient safety and often lead to termination of drug-development programs or late withdrawals of drugs. Since DILI events are hard to diagnose in preclinical settings, a need for alternative prediction methods such as computational modeling emerges. Impairment of bile acid (BA) metabolism, known as cholestasis, is a frequent form of DILI. Being rather a systemic then a single organ related disease, whole-body physiology-based modeling is a predestined approach for cholestasis modeling. The objectives of the presented study were 1) the development of a physiology-based model for human bile acid metabolism, 2) model validation and characterization for a virtual population, and 3) prediction and quantification of the effects of genetic predispositions and drug interaction on bile acid metabolism. The developed physiology-based bile acid (PBBA) model is based on the standard PBPK model of PKSim(R) and describes the bile acid circulation in a healthy reference individual. Active processes such as the hepatic synthesis, gallbladder emptying upon meal intake, transition through the gastrointestinal tract, reabsorption into the liver, distribution within the body, and excretion are included. The kinetics of active processes for the surrogate BA glycochenodeoxycholic acid were fitted to time-concentration profiles of blood BA levels reported in literature. The robustness of our PBBA model is underlined by the comparison of simulated plasma BA concentrations in a virtual population of 1,000 healthy individuals with reported data. In addition to plasma concentrations, the PBBA model allows simulations of BA exposure in relevant tissues like the liver and can therefore enhance the mechanistic understanding of cholestasis. This feature was used to analyse the reported increased risk of cholestatic DILI in Benign Recurrent Intrahepatic Cholestasis type 2 (BRIC2) patients. Simulations of the PBBA model suggest a higher susceptibility of BRIC2 patients towards cholestatic DILI due to BA accumulation in hepatocytes. Apart from these intrinsic effects, drug-interactions and their effect on the systemic bile acid metabolism were simulated by combining the PBBA model with a drug PBPK model of cyclosporine A (CsA). The results of which confirmed the reported higher risk of developing DILI as a consequence of CsA intake. Altogether, the presented model enhances our mechanistic understanding of cholestasis, allows the identification of drug-interactions leading to altered BA levels in blood and organs, and could be used to prevent clinical cases of cholestasis and enhance patient safety.

systems biology↗