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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. [ABSTRACT FROM AUTHOR]

The growing interest in the composition and effects of microbiota raised the question how drug pharmacokinetics could be influenced by concomitant application of probiotics. The aim of this study was to find whether probiotic E. coli strain Nissle 1917 (EcN) influences the pharmacokinetics of concomitantly taken antiarrhythmic drug amiodarone (AMI). Live bacterial suspension of probiotic EcN (or non-probiotic E. coli strain ATCC 25922) was applied orally to male Wistar rats for seven days, while a control group of rats was treated with a saline solution. On the eighth day, the amiodarone hydrochloride was administered as one single oral dose (50 mg/kg) to all rats (N = 60). After 0, 1, 2, 3, 4, 5.5, 7, 9, 14, 22, and 30 hours, blood samples were taken from the rat abdominal aorta. The plasma level of AMI and its metabolite N-desethylamiodarone (DEA) was determined using the HPLC with UV detection. Administration of EcN led to a 43% increase of AMI AUC0-30 in comparison with control samples. However, this effect was not observed if EcN was replaced by a reference non-probiotic E. coli strain. Thus, EcN administration was most probably responsible for better drug absorption from the gastrointestinal tract. Plasma levels of DEA were also increased in plasma samples from animals treated with EcN. This change was again not found in the experiment with the reference non-probiotic strain. Higher DEA levels in samples from EcN-treated rats may be explained either by better absorption of AMI and/or by an increased activity of CYP2C forms, known to participate in metabolism of this drug, after EcN administration. In this paper, it is documented that concomitantly taken probiotic EcN may modulate pharmacokinetics of a drug; in this case, it led to an increased bioavailability of AMI. [ABSTRACT FROM AUTHOR]

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task. [ABSTRACT FROM AUTHOR]