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Model-informed treatment optimization of liver cirrhosis patients
No full textLiver cirrhosis is a progressive disease that is caused by chronic damage to the liver. It is accompanied by a variety of severe complications that drastically reduce patients’ quality of life and life expectancy. The aim of this study is to improve therapeutical interventions for liver cirrhosis patients by identifying patients with a high need for treatment and by characterizing the pathophysiological changes in patients to facilitate drug development and pharmacotherapy for cirrhosis patients. A hallmark of disease progression in liver cirrhosis is the transition from a compensated to a decompensated state. While patients are mostly free of symptoms in the compensated state, the decompensated stage is characterized by the presence of variceal bleeding, encephalopathy, ascites, hepatorenal syndrome, and/or jaundice. Consequently, decompensation is associated with a severe drop in life expectancy. In this study, predictors of decompensation were identified, and a prognostic clinical score was developed. Real-world patient data from three cohorts were used for score development and validation, representing a total of 19,305 liver cirrhosis patients. The developed Early Prediction Of Decompensation (EPOD) score uses the common biomarkers platelet count, albumin concentration, and bilirubin concentration to identify liver cirrhosis patients at high risk of decompensation. This enables both targeted monitoring and targeted therapeutic interventions in high-risk patients to delay or even prevent decompensation and potentially extend life expectancy. In general, pharmacological treatment of liver cirrhosis patients is challenging. The pathophysiological changes associated with liver cirrhosis, especially in more severe stages, may impact the pharmacokinetics of drugs in such patients putting them at high risk for adverse drug reactions through altered drug exposure. In this work, pathophysiological changes for 30 physiological parameters that are relevant for drug pharmacokinetics were quantified dependent on the disease stage using 216,609 data points of cirrhosis patients. To functionally account for the complexity of the interplay of these parameters and estimate their combined impact on drug pharmacokinetics, these parameter quantifications were embedded into a physiologically based pharmacokinetic modeling framework. For this purpose, virtual populations were generated and disease-related modifications were applied to the organism physiology. The ensuing simulations of the pharmacokinetics of several drugs in cirrhosis patients revealed good predictive performance of the approach. The presented work provides two tools for treatment optimization of liver cirrhosis patients: (1) a clinical score for identifying patients at high risk for decompensation that are in need of intervention as well as (2) a modeling approach for predicting changes in pharmacokinetics in liver cirrhosis patients that may help for dosing decisions and support the approval of medications for liver cirrhosis patients. In summary, this work contributes to the improvement of treatment of liver cirrhosis patients, thus, potentially improving patient health and life expectancy
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Physiologically-based pharmacokinetic modelling for the prediction of adverse drug reactions
No full textAdverse drug reactions endanger patients’ health and pose a considerable challenge to drug development and medical care. Despite a variety of approaches ranging from in silico up to clinical studies, predicting drug toxicity still fails in many cases due to limited inter-assay or cross-species translatability and the idiosyncrasy of many drug effects. Thus, findings from diverse sources, such as in vitro assays or animal models, need to be jointly analysed and contextualised with individual patient conditions, e.g., diseases, specific genotypes, or co-medications. Thereby, a systemic understanding and reliable predictions of adverse reaction risks become possible. However, experiments mimicking realistic patient scenarios are frequently expensive, infeasible, and insufficient. Therefore, integrating data from different levels into mechanistic in silico models has emerged as a promising and cost-effective alternative to overcome the imbalance between the lack of viable and sound models and the necessity to predict adverse drug reactions effectively. In this work, computational modelling was applied to identify drugs with a high risk of inducing hepatic adverse drug reactions as well as patients prone to experience such. Predisposing patient factors associated with drug toxicity were considered throughout the studies to account for the idiosyncrasy of adverse drug reactions. A model of bile acid circulation was developed to investigate drug-induced cholestasis by coupling it to a drug-specific whole-body physiologically-based pharmacokinetic model. Through contextualisation of physiological knowledge, pharmacokinetic data, genotype, and in vitro inhibition data, the model allowed the simulation of bile acid levels in healthy individuals and confirmed cholestasis susceptibility for familial cholestasis genotypes during cyclosporine A treatment. The further integration of time-resolved expression data from a drug-treated in vitro assay into the model enabled a systematic categorisation of the cholestasis risk of several hepatotoxic drugs. By providing a framework to benchmark potentially cholestatic drugs against a reference dataset of ten drugs, this approach could support the identification of drug-induced cholestasis in drug development in the future. Finally, to assist patient safety in clinical care, computational modelling was utilised to guide a clinical test strategy striving for a personalised treatment decision by investigating the individual metabolic phenotype of a patient. The simulations of virtual populations permitted to differentiate between biometric and metabolic contributions to drug exposure. Subsequently, recommendations for the test strategy were derived to support optimal study design in terms of sampling time points or selection of compounds. The presented approaches support the early identification of adverse drug reactions during drug development as well as in routine health care. Thus, by elucidating the link between individual patient factors and adverse drug reactions, this work can be employed to increase patients’ safety and optimise drug development in the future
Physiologically-based modeling of bile acid metabolism in mice and human
No full textBile acid (BA) metabolism represents a multifaceted system involving a diverse array of primary and secondary, conjugated and unconjugated BAs that engage in constant enterohepatic circulation (EHC). Disruptions in both BA composition and dynamics have been correlated with various ailments. Nonetheless, a comprehensive understanding of the intricate interplay between altered BA metabolism and associated diseases remains elusive. A range of animal models have been harnessed to explore BA metabolism’s implications in human disease. Among these models, mice take a prominent role; however, disparities between this preclinical model and humans regarding BA composition, recycling mechanisms, gut physiology, and energy homeostasis exist. Given the inherent intricacy of BA metabolism and the necessity for robust cross-species extrapolation strategies, computational modeling serves as a tool to unravel the mechanistic foundations of the complex network governing BA metabolism. This work endeavors to construct physiologically-based models of BA metabolism in both murine and human contexts. These models recapitulate the synthesis, hepatic and microbial transformations, systemic distribution, excretion, and EHC of BAs at the whole-body level. The murine models were applied in assessments of sex-related and species-specific differences in BA metabolism, as well as the effects of pathophysiological scenarios like BA malabsorption and compromised intestinal barrier function. Conversely, the human models reproduced inter-individual variations in BA levels reported in literature, but also proposing conceivable mechanisms explaining elevated BA levels observed in patients with liver-related disorders. The models developed in this thesis constitute a robust framework for conducting model-assisted inquiries into BA metabolism within prospective studies. Their potential contributions align with the principles of the ’3Rs’ (Reduction, Refinement, and Replacement) of animal testing and hold potential to advance patient care by enhancing our understanding and prediction of BA-related dynamics in disease
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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