In Silico Modeling for the Prediction of Dose and Pathway-Related Adverse Effects in Humans From In Vitro Repeated-Dose Studies.

Long-term repeated-dose toxicity is mainly assessed in animals despite poor concordance of animal data with human toxicity. Nowadays advanced human in vitro systems, eg, metabolically competent HepaRG cells, are used for toxicity screening. Extrapolation of in vitro toxicity to in vivo effects is possible by reverse dosimetry using pharmacokinetic modeling. We assessed long-term repeated-dose toxicity of bosentan and valproic acid (VPA) in HepaRG cells under serum-free conditions. Upon 28-day exposure, the EC50 values for bosentan and VPA decreased by 21- and 33-fold, respectively. Using EC(10) as lowest threshold of toxicity in vitro, we estimated the oral equivalent doses for both test compounds using a simplified pharmacokinetic model for the extrapolation of in vitro toxicity to in vivo effect. The model predicts that bosentan is safe at the considered dose under the assumed conditions upon 4 weeks exposure. For VPA, hepatotoxicity is predicted for 4% and 47% of the virtual population at the maximum recommended daily dose after 3 and 4 weeks of exposure, respectively. We also investigated the changes in the central carbon metabolism of HepaRG cells exposed to orally bioavailable concentrations of both drugs. These concentrations are below the 28-day EC(10) and induce significant changes especially in glucose metabolism and urea production. These metabolic changes may have a pronounced impact in susceptible patients such as those with compromised liver function and urea cycle deficiency leading to idiosyncratic toxicity. We show that the combination of modeling based on in vitro repeated-dose data and metabolic changes allows the prediction of human relevant in vivo toxicity with mechanistic insights.

Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models.

Mathematical modelling is increasingly becoming an indispensable tool for the study of cellular processes, allowing their analysis in a systematic and comprehensive manner. In the vast majority of the cases, models focus on specific subsystems, and in particular describe either metabolism, gene expression or signal transduction. Integrated models that are able to span and interconnect these layers are, by contrast, rare as their construction and analysis face multiple challenges. Such methods, however, would represent extremely useful tools to understand cell behaviour, with application in distinct fields of biological and medical research. In particular, they could be useful tools to study genotype-phenotype mappings, and the way they are affected by specific conditions or perturbations. Here, we review existing computational approaches that integrate signalling, gene regulation and/or metabolism. We describe existing challenges, available methods and point at potentially useful strategies.

Quantitative Evaluation and Prediction of Drug Effects and Toxicological Risk Using Mechanistic Multiscale Models.

Integrating in vitro and in silico approaches has great potential for reducing experimental effort and delivering know-how and intellectual property in drug development. Here, we focus on a possible framework for multiscale modeling in pharmaceutical drug development. Looking at the modeling frameworks at different scales, it is obvious that choosing the proper level of complexity and abstraction is not a trivial task. At cellular level, we consider that the application of validated kinetic models of cellular toxicity mechanisms of drugs is particularly important for deriving valid predictions. These kinetic models can be applied for integrating inter-individual differences, e.g. obtained from data measured in surgical liver samples, into predictions of drug effects. Challenges identified include (i) the development of sufficiently detailed, structured organ models, (ii) definition of multiscale models that can be efficiently handled by available super-computing facilities, and (iii) availability of validated cell-type and organ-specific kinetic metabolic models. Multiscale models can streamline drug development by facilitating the design of experiments and trials, by providing and testing hypotheses, and by reducing time and costs due to less experiments and improved decision-making. In this review, we discuss the required pieces, possibilities, and challenges in multiscale modeling for the prediction of drug effects.

Central energy metabolism remains robust in acute steatotic hepatocytes challenged by a high free fatty acid load.

Overnutrition is one of the major causes of non-alcoholic fatty liver disease (NAFLD). NAFLD is characterized by an accumulation of lipids (triglycerides) in hepatocytes and is often accompanied by high plasma levels of free fatty acids (FFA). In this study, we compared the energy metabolism in acute steatotic and non-steatotic primary mouse hepatocytes. Acute steatosis was induced by pre-incubation with high concentrations of oleate and palmitate. Labeling experiments were conducted using [U-(13)C(5),U-(15)N(2)] glutamine. Metabolite concentrations and mass isotopomer distributions of intracellular metabolites were measured and applied for metabolic flux estimation using transient 13C metabolic flux analysis. FFAs were efficiently taken up and almost completely incorporated into triglycerides (TAGs). In spite of high FFA uptake rates and the high synthesis rate of TAGs, central energy metabolism was not significantly changed in acute steatotic cells. Fatty acid ß-oxidation does not significantly contribute to the detoxification of FFAs under the applied conditions.

A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk.

In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes.

BACKGROUND: The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models. RESULTS: In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics. CONCLUSIONS: A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.