On a Multi‐Scale and Multi‐Phase Model of Paracetamol‐induced Hepatotoxicity for Human Liver (original) (raw)
Related papers
Spatio-Temporal Simulation of First Pass Drug Perfusion in the Liver
PLoS Computational Biology, 2014
The liver is the central organ for detoxification of xenobiotics in the body. In pharmacokinetic modeling, hepatic metabolization capacity is typically quantified as hepatic clearance computed as degradation in well-stirred compartments. This is an accurate mechanistic description once a quasi-equilibrium between blood and surrounding tissue is established. However, this model structure cannot be used to simulate spatio-temporal distribution during the first instants after drug injection. In this paper, we introduce a new spatially resolved model to simulate first pass perfusion of compounds within the naive liver. The model is based on vascular structures obtained from computed tomography as well as physiologically based mass transfer descriptions obtained from pharmacokinetic modeling. The physiological architecture of hepatic tissue in our model is governed by both vascular geometry and the composition of the connecting hepatic tissue. In particular, we here consider locally distributed mass flow in liver tissue instead of considering well-stirred compartments. Experimentally, the model structure corresponds to an isolated perfused liver and provides an ideal platform to address first pass effects and questions of hepatic heterogeneity. The model was evaluated for three exemplary compounds covering key aspects of perfusion, distribution and metabolization within the liver. As pathophysiological states we considered the influence of steatosis and carbon tetrachloride-induced liver necrosis on total hepatic distribution and metabolic capacity. Notably, we found that our computational predictions are in qualitative agreement with previously published experimental data. The simulation results provide an unprecedented level of detail in compound concentration profiles during first pass perfusion, both spatio-temporally in liver tissue itself and temporally in the outflowing blood. We expect our model to be the foundation of further spatially resolved models of the liver in the future.
Theoretical Biology and Medical Modelling, 2013
We extend a physiologically-based lattice model for the transport and metabolism of drugs in the liver lobule (liver functional unit) to consider structural and spatial variability. We compare predicted drug concentration levels observed exiting the lobule with their detailed distribution inside the lobule, and indicate the role that structural variation has on these results. Liver zonation and its role on drug metabolism represent another aspect of structural inhomogeneity that we consider here. Since various liver diseases can be thought to produce such structural variations, our analysis gives insight into the role of disease on liver function and performance. These conclusions are based on the dominant role of convection in well-vascularized tissue with a given structure.
Journal of Pharmaceutical Sciences, 2010
Drug-drug interactions (DDIs) are a great concern to the selection of new drug candidates. While in vitro screening assays for DDI are a routine procedure in preclinical research, their interpretation and relevance for the in vivo situation still represent a major challenge. The objective of the present study was to develop a novel mechanistic modeling approach to quantitatively predict DDI solely based upon in vitro data. The overall strategy consisted of developing a model of the liver with physiological details on three subcompartments: the sinusoidal space, the space of Disse, and the cellular matrix. The substrate and inhibitor concentrations available to the metabolizing enzyme were modeled with respect to time and were used to relate the in vitro inhibition constant (K i ) to the in vivo situation. The development of the liver model was supported by experimental studies in a stepwise fashion: (i) characterizing the interactions between the three selected drugs (R-bufuralol (BUF), bunitrolol (BUN), and debrisoquine (DBQ)) in microsomal incubations, (ii) modeling DDI based on binary mixtures model for all the possible pairs of interactions (BUF-BUN, BUF-DBQ, BUN-DBQ) describing a mutual competitive inhibition between the compounds, (iii) incorporating in the binary mixtures model the related constants determined in vitro for the inhibition, metabolism, transport, and partition coefficients of each compound, and (iv) validating the overall liver model for the prediction of the perfusate kinetics of each drug determined in isolated perfused rat liver (IPRL) for the single and paired compounds. Results from microsomal coincubations showed that competitive inhibition was the mechanism of interactions between all three compounds, as expected since those compounds are all substrates of rat CYP2D2. For each drug, the K i values estimated were similar to their K m values for CYP2D2 indicative of a competition for the same substrate-binding site. Comparison of the performance between the novel liver physiologically based pharmacokinetic (PBPK) model and published empirical models in simulating the perfusate concentration-time profile was based on the area under the curve (AUC) and the shape of the curve of the perfusate time course. The present liver PBPK model was able to quantitatively predict the metabolic interactions determined during the perfusions of mixtures of BUF-DBQ and BUN-DBQ. However, a lower degree of accuracy was obtained for the mixtures of BUF-BUN, potentially due to some interindividual variability in the relative proportion of CYP2D1 and CYP2D2 isoenzymes, both involved in BUF metabolism. Overall, in this metabolic interaction prediction exercise, the PBPK model clearly showed to be the best y Consultant. Abbreviations: AUC, area under the curve; BUF, bufuralol; BUN, bunitrolol; C C , drug concentration in the cellular matrix; C Cu , free drug concentration in hepatocytes; C D , drug concentration in the Disse space; C Du , free concentration in the space of Disse; C i , intracellular drug concentration; C PS , drug sinusoid concentration in previous segment; C S , drug concentrations in sinusoids; C Su , free drug concentrations in sinusoids; C u , free drug concentration; DBQ, debrisoquine; DDI, drug-drug interaction; EF, rapid exchange factor; Fu C , unbound fraction in hepatocytes; Fu D , fraction unbound in space of Disse; Fu mic , fraction unbound in microsomal incubate; Fu p , fraction unbound in plasma; IPRL, isolated perfused rat liver; K i , inhibition constant; K m , affinity constant; K U , affinity constant for uptake; PA, permeability  area product; PA z , permeability  area product of a given segment; PBPK, physiologically based pharmacokinetic; P CDu , cell-to-unbound drug in Disse ratio; P C:Du , hepatocyte-to-buffer ratio; P C:W , cellular matrix to water (i.e., buffer) ratio; Q, blood flow rate; Q HA , blood flow in hepatic artery; Q L , liver blood flow; Q PV , portal venous blood flow; VF, volume fraction of the segment; predictor of perfusate kinetics compared to more empirical models. The present study demonstrated the potential of the mechanistic liver model to enable predictions of metabolic DDI under in vivo condition solely from in vitro information. ß Number of times the model is closest to experimental data compared to other models. The denominator is the total number of data points of a compound concentration in perfusate from the corresponding perfusion.
A Liver-Centric Multiscale Modeling Framework for Xenobiotics
PloS one, 2016
We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclu...
2011
We develop a physiologically-based lattice model for the transport and metabolism of drugs in the functional unit of the liver, called the lobule. In contrast to earlier studies, we have emphasized the dominant role of convection in well-vascularized tissue with a given structure. Estimates of convective, diffusive and reaction contributions are given. We have compared drug concentration levels observed exiting the lobule with their predicted detailed distribution inside the lobule, assuming that most often the former is accessible information while the latter is not.
Modeling of xenobiotic transport and metabolism in virtual hepatic lobule models
PLOS ONE
Computational models of normal liver function and xenobiotic induced liver damage are increasingly being used to interpret in vitro and in vivo data and as an approach to the de novo prediction of the liver's response to xenobiotics. The microdosimetry (dose at the level of individual cells) of xenobiotics vary spatially within the liver because of both compoundindependent and compound-dependent factors. In this paper, we build model liver lobules to investigate the interplay between vascular structure, blood flow and cellular transport that lead to regional variations in microdosimetry. We then compared simulation results obtained using this complex spatial model with a simpler linear pipe model of a sinusoid and a very simple single box model. We found that variations in diffusive transport, transportermediated transport and metabolism, coupled with complex liver sinusoid architecture and blood flow distribution, led to three essential patterns of xenobiotic exposure within the virtual liver lobule: (1) lobular-wise uniform, (2) radially varying and (3) both radially and azimuthally varying. We propose to use these essential patterns of exposure as a reference for selection of model representations when a computational study involves modeling detailed hepatic responses to xenobiotics.
Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches
Frontiers in physiology, 2012
We provide an overview of computational systems biology approaches as applied to the study of chemical-and drug-induced toxicity. The concept of "toxicity pathways" is described in the context of the 2007 US National Academies of Science report, "Toxicity testing in the 21st Century: A Vision and A Strategy." Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) -a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular "virtual tissue" model of the liver lobule that combines molecular circuits in individual hepatocytes with cell-cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.
Journal of Theoretical Biology, 2012
Isolated liver perfusion systems have been extensively used to characterize intrinsic metabolic changes in liver under various conditions, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were performed utilizing fasted animals prior to perfusion so that a simplified metabolic network could be used in order to determine intracellular fluxes. However, fasting induced metabolic alterations might interfere with disease related changes. Therefore, there is a need to develop a ''unified'' metabolic flux analysis approach that could be similarly applied to both fed and fasted states. In this study we explored a methodology based on elementary mode analysis in order to determine intracellular fluxes and active pathways simultaneously. In order to decrease the solution space, thermodynamic constraints, and enzymatic regulatory properties for the formation of futile cycles were further considered in the model, resulting in a mixed integer quadratic programming problem. Given the published experimental observations describing the perfused livers under fed and fasted states, the proposed approach successfully determined that gluconeogenesis, glycogenolysis and fatty acid oxidation were active in both states. However, fasting increased the fluxes in gluconeogenic reactions whereas it decreased fluxes associated with glycogenolysis, TCA cycle, fatty acid oxidation and electron transport reactions. This analysis further identified that more pathways were found to be active in fed state while their weight values were relatively lower compared to fasted state. Glucose, lactate, glutamine, glutamate and ketone bodies were also found to be important external metabolites whose extracellular fluxes should be used in the hepatic metabolic network analysis. In conclusion, the mathematical formulation explored in this study is an attractive tool to analyze the metabolic network of perfused livers under various disease conditions. This approach could be simultaneously applied to both fasted and fed data sets.
Frontiers in Bioengineering and Biotechnology
In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical (CL) homogeneous compartment pharmacodynamic (PD) models and a spatial-temporal (ST), multiscale digital twin model resolving liver microarchitecture at cellular resolution. The models integrate consensus detoxification reactions in each individual hepatocyte. We study the consequences of the two model types on the extrapolation and show in which cases these models perform better than the classical extrapolation strategy that is based either on the maximal drug concentration (Cmax) or the area under the pharmacokinetic curve (AUC) of the drug blood concentration. We find that an CL-model based on a well-mixed blood compartment is sufficient to correctly predict the in vivo toxicity from in vitro data. However, the ST-model that integrates more experimental information requires a ch...