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About one-sixth of the world's population is affected by a neglected tropical disease as defined by the World Health Organization and Center for Disease Control. Parasitic diseases comprise most of the neglected tropical disease list and they are causing enormous amounts of disability, morbidity, mortality, and healthcare costs worldwide. The burden of disease of the top five parasitic diseases has been estimated to amount to a total 23 million disability-adjusted life-years. Despite the massive health and economic impact, most drugs currently used for the treatment of parasitic diseases have been developed decades ago and insufficient novel drugs are being developed. The current review provides a compilation of the systemic and target-site pharmacokinetics of established antiparasitic drugs. Knowledge of the pharmacokinetic profile of drugs allows for the examination and possibly optimization of existing dosing schemes. Many symptoms of parasitic diseases are caused by parasites residing in different host tissues. Penetration of the antiparasitic drug into these tissues, the target site of infection, is a prerequisite for a successful treatment of the disease. Therefore, for the examination and improvement of established dosing regimens, not only the plasma but also the tissue pharmacokinetics of the drug have to be considered. For the current paper, almost 7000 scientific articles were identified and screened from which 429 were reviewed in detail and 100 were included in this paper. Systemic pharmacokinetics are available for most antiparasitic drugs but in many cases, not for all the relevant patient populations and only for single- or multiple-dose administration. Systemic pharmacokinetic data in patients with organ impairment and target-site pharmacokinetic data for relevant tissues and body fluids are mostly lacking. To improve the treatment of patients with parasitic diseases, research in these areas is urgently needed. [ABSTRACT FROM AUTHOR]

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]

Since their discovery at the end of the previous millennium,carbonnanotubes (CNTs) have been the object of thousands of papers describingtheir applications in fields ranging from physics to electronics,photonics, chemistry, biology, and medicine. The development of chemicalapproaches to modify their graphitic sidewalls enabled the generationof poly(ethylene glycol) (PEG)-modified CNTs and their explorationin multiple biomedical applications. Studies at the cellular and organismlevel revealed that PEG-modified CNTs have favorable pharmacokineticand toxicology profiles. Recently, PEG-modified CNTs have been successfullytested in preclinical studies in the fields of oncology, neurology,vaccination, and imaging, suggesting that they are well suited forthe generation of novel multifunctional nanodrugs. Here we will reviewpublished data about the application of PEG-modified CNTs as in vitroand in vivotherapeutic and imagingtools and describe what is known about the interaction between PEG-modifiedCNTs and biological systems. Although several pieces of the puzzleare still missing, we will also attempt to formulate a preliminarystructure–function model for PEG-modified CNT cellular trafficking,disposition, and side effects. [ABSTRACT FROM AUTHOR]

This paper focuses on the question whether different estrogens (E) have different qualitative pharmacodynamic effects when used by women for contraception, Hormone Replacement Therapy (HRT) or prevention of osteoporosis. In this context estrogens have been defined as the estrogen agonists estradiol (E2), estrone (E1), estriol (E3), conjugated equine estrogens (CEE), diethylstilbestrol (DES) and ethinylestradiol (EE). Selective Estrogen Receptor Modulator’s (SERM’s) have been excluded from this analysis primarily because of lack of comparative (clinical) data with estrogen agonists. A major problem when addressing the issue of comparability of estrogen agonists is the lack of data from head-to-head estrogen-only comparative studies. Comparative studies have been performed almost exclusively with estrogen agonists combined with a series of different progestogens (P), that have been added to protect the uterus from endometrial hyperplasia. Since progestogens are known to exhibit different intrinsic pharmacodynamic properties and interactions with estrogens, it is impossible to judge which role the estrogen plays when qualitative differences between different combined E/P preparations are observed. In summary, no convincing evidence has been found that the estrogens mentioned differ qualitatively. Obviously quantitative differences are present due to differences in e.g. receptor affinity, metabolism (half life) and route of administration (transdermal/vaginal). Since DES has been discarded for human use due to teratogenicity, EE used in all combined E/P oral contraceptives is the most potent estrogen agonist available at present. In HRT, E2 and CEE are equally effective for the treatment of hot flushes and urogenital atrophy and superior to any other treatment option. For long term treatment to prevent osteoporosis and even for short term HRT, estrogen agonists are heavily debated recently because of a small increased risk of breast cancer, that has been known for a long time already. Well informed and individualised choice of treatment seems the appropriate solution. [Copyright &y& Elsevier]