10,104 research outputs found

    A comparative analysis of the innovator and generic package inserts for selected central nervous system medication in South Africa

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    Research Report in partial fulfillment of MSc (Med) Pharmaceutical Affairs Department of Pharmacy and Pharmacology University of Witwatersrand Faculty of Health SciencesThe evolution of generic medicines has provided greater access to healthcare for the general population. But this has also given rise to disparities with respect to information that needs to accompany generic medicines. Generic companies face a major problem when bringing a new product to the market place. Access to the originators’ clinical and post surveillance data is prohibited and not easily attainable. This data provides the basis of the labelling information and if copied, generic companies run the risk of copyright infringement which creates tension on how appropriate information is delivered. Consequently, generic manufacturers are forced to develop PIs. Previous studies indicate that this process yields a package insert of substandard quality with questionable quality, which leaves the impression that the pharmaceutical industry is more profit-driven than patient-driven. This is a comparative study of innovator and generic package inserts for molecules; fluoxetine, citalopram, sertraline, venlafaxine and risperidone. A textual analysis was used to compare package inserts. Key findings from this novel study demonstrate that there are considerable differences between the originator PI and the generic brands. Side effects experienced solely by women who are twice more likely than males to administer antidepressants were found to be omitted from two out of three generics. Limited access to correct information impedes the decision making process for the healthcare professional as well as the patient resulting in lack of confidence in the product itself. Other significant findings highlighted in this study revealed inconsistencies with respect to the use of words with opposing meanings, incorrect use of prefixes which ultimately shifts the meanings of words as well as the alteration of nouns which downplays the severity of side effects further emphasizing the variability of prescribing information currently available in South Africa. Ordering of risk within a package insert is an important factor in determining the risk profile conveyed by text. Deciphering the most important information from the less important information proves to be difficult with the package inserts analysed. The on-going trend depicts gaps in the South African registration system with respect to labelling of medicines and thus warrants urgent attention.MT201

    Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

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    Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation

    Examining Cultural Competence Among Osteopathic Medical Students

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    With the growing number of racial and ethnic minorities in the United States, there also has been a rise in health disparities. Ethnic minorities have been experiencing an overabundance of medical issues, particularly chronic diseases like cardiovascular disease, diabetes, and cancer. Although these chronic diseases are preventable, ethnic minorities are at greater risk for death from them. Possible contributing factors to the increase in health disparities include limited medical attention and follow-up appointments with physicians and the physician-patient relationship. The purpose of this study was twofold: to examine cultural competence among osteopathic medical students and to examine their attitudes toward cultural competence training while in medical school. The results do not suggest a significant difference between Group A, first and second year medical students, and Group B, third and fourth year medical students on the level of cultural awareness and sensitivity and cultural competence behaviors. Additionally, all 4 years of medical students endorsed the importance of learning cultural competence training while in medical school

    Current knowledge, challenges and innovations in developmental pharmacology: A combined conect4children Expert Group and European Society for Developmental, Perinatal and Paediatric Pharmacology White Paper

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    Developmental pharmacology describes the impact of maturation on drug disposition (pharmacokinetics, PK) and drug effects (pharmacodynamics, PD) throughout the paediatric age range. This paper, written by a multidisciplinary group of experts, summarizes current knowledge, and provides suggestions to pharmaceutical companies, regulatory agencies and academicians on how to incorporate the latest knowledge regarding developmental pharmacology and innovative techniques into neonatal and paediatric drug development. Biological aspects of drug absorption, distribution, metabolism and excretion (ADME) throughout development are summarized. Although this area made enormous progress during the last two decades, remaining knowledge gaps were identified. Minimal risk and burden designs allow for optimally informative but minimally invasive PK sampling, while concomitant profiling of drug metabolites may provide additional insight in the unique PK behavior in children. Furthermore, developmental PD needs to be considered during drug development, which is illustrated by disease- and/or target organ-specific examples. Identifying and testing PD targets and effects in special populations, and application of age- and/or population-specific assessment tools are discussed. Drug development plans also need to incorporate innovative techniques like preclinical models to study therapeutic strategies, and shift from sequential enrollment of subgroups, to more rational designs. To stimulate appropriate research plans, illustrations of specific PK/PD-related as well as drug safety-related challenges during drug development are provided. The suggestions made in this joint paper of the Innovative Medicines Initiative conect4children Expert group on Developmental Pharmacology and the European Society for Developmental, Perinatal and Paediatric Pharmacology, should facilitate all those involved in drug development

    Enabling late-stage translation of regenerative medicine based products

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    The primary aim of the thesis is to contribute to demonstrating how established and emerging science in the regenerative medicine (RM) domain can be translated into profitable commercial practice, and generate clinically- and cost-effective therapies. It achieves this by exploring and assessing underlying economics, including investment readiness and economic assessment, exploring regulatory and reimbursement frameworks, developing stem cell culture systems and assessing fit with clinical practice. The thesis is the first public domain wide-ranging analysis of business trends in the production, manufacturing and supply segments of the RM industry. It analyses the clinical potential of the domain as well as the translational and commercial challenges facing the industry. The industry is at a turning point as big pharmaceutical companies engage with RM in order to explore technologies as potential therapeutics and discovery tools. This unlocks the industry by confirming an exit path for RM based small- and medium-sized enterprises. Translation has come to be recognised as a core issue in the overall space and translation of regenerative therapies into the clinic is presently challenging, high-risk and expensive. This research addresses the question what are the mechanisms required to enable translation of emerging scientific knowledge into commercially viable clinical RM products? These mechanisms are particularly important as their creation involves and requires major investment decisions, which can determine the success or failure of RM developments and indeed of the companies concerned. The lack of well-established business models and the complexity of the domain suggested a conceptual approach drawing upon relevant literature from product and process development, applied business and revenue models, technological evolution and capital market ingenuity. The research was carried out in two phases. The first phase was concerned with identification of key challenges and mapping the overall industry emergence including emergence of related regulations to provide a context and framework for understanding the domain. Based on the emergence mapping a timeline of key parallel factors was identified, and their inherent connections explored to identify transforming events affecting and influencing multiple factors on the journey to clinical success within a business environment. This creates the reference model. The second phase was concerned with manufacturing a stem cell based therapeutic and applying health economic principles to determine available headroom for investment, cost of goods and return on investment, taking hearing disorders as a case exemplar, and exploring the behaviour of the net present value curve to identify key parameters affecting the economic positioning of this novel regime. A key output of the research is the investment readiness reference model. It integrates key RM business issues against reducing uncertainty and increasing value. The model argues that the complex nature of RM products means that the issues affecting industry emergence and development go well beyond the primarily scientific and technological concerns on which much current research focuses. The performance of RM firms ultimately hinges upon the successful clinical application of their developed products, the key step for creating and realising value, and their ability to deal with the fundamental business issues specific to the area. The framework deals with these business issues, which are investment & technology readiness, business models, organisational challenges, public policy and industry emergence. This thesis explores ideas that may bridge the chasm between the promise and reality of RM i.e. mechanisms to enable late stage translation of RM products. It links technological capability and business models for firms in the domain. Furthermore, it offers a unique perspective on the nature and characteristics of investment readiness and financial assessment, specifically identifying key parameters affecting economic positioning. The key contributions are therefore: New insights into the key challenges involved in realising the commercial potential of cell based therapeutics. Technology road mapping to link fundamental enabling technological capability for developing RM products with robust business plans integrating strategy, technology development and the regulatory and reimbursement framework. A generic investment readiness reference model generated from the enabling technology, value and supply chain structures to identify key indicators and characteristics of industry readiness. A novel experimental programme demonstrating expansion, maintenance and differentiation of human embryonic stem cells by manual and automated methods. New insights into economic positioning by mapping net present value, and economic analysis by estimating available headroom, cost of goods and return on investment for a putative hearing therapeutic

    Modelling and genetic correction of liver genetic diseases

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    The urea cycle is a set of biochemical reactions that converts highly toxic ammonia into urea for excretion. Deficiencies in any of the genes of the cycle can be life-threatening, with liver transplantation currently being the only definitive treatment. However, the scarcity of donor organs dictates the investigation of alternative treatments, which requires appropriate disease models, in vitro and in vivo, that faithfully recapitulate the disease pathology. Recent advancements in the field of genome engineering make interventions in the genetic code less challenging, thereby assisting in the generation of such tools, as well as raising the potential for genetic correction of these conditions. The research conducted in this thesis centres around two broad aims: the investigation of disease models and genetic correction of inherited liver disorders. Induced pluripotent stem cells (iPSC) hold great potential both for disease modelling and as a source of cells for cell therapy. However, their generation through cell reprogramming is sometimes challenging and inefficient. Therefore, in PAPER I we sought to optimize the reprogramming procedure by introducing modifications to the currently existing protocols, and managed to increase the reprogramming efficiency. IPSC could theoretically differentiate into any cell type, including hepatocytes. In order to assess the level of differentiation of the hepatocyte-like cells (HLC) generated from stem cell sources, comparisons with authentic primary liver tissues are necessary. To this end, in PAPER II we created gene expression profiles of fetal and mature (post-natal) liver tissues from a significant number of individuals. The dataset can serve as an accurate and simple assessment tool to evaluate and compare HLC, generated in different laboratories, to authentic human liver tissues. If HLC resemble the functions observed in mature primary hepatocytes, they could be used as in vitro disease models. In addition, programmable nucleases can be applied to either correct or introduce disease-causing of interest in the genome. In PAPER III, we generated iPSC from a patient with a pathogenic variant in the ornithine transcarbamylase (OTC) gene, the most common UCD, corrected the genetic defect and differentiated the cells into HLC. The correction was molecularly, as well as phenotypically confirmed by the restoration of urea cycle function. The thesis also focuses on the investigation of in vivo disease models of UCD. Specifically, in PAPERS IV and V we created liver-humanized mice with hepatocytes from patients with UCD, OTC deficiency (OTCD) or carbamoyl phosphate synthetase 1 deficiency (CPS1D). Highly repopulated animals faithfully recapitulated the clinical manifestations of the disease observed in patients, including hyperammonemia which is considered a hallmark of these UCD. Furthermore, in PAPER V, we investigated the efficacy and safety of ex vivo gene editing of primary OTCD hepatocytes. Ureagenesis was restored in vitro in edited cells, as well as in vivo as mice liver-repopulated with genetically engineered cells partially or completely reversed all markers of the disease investigated. Finally, extensive gene expression and deep sequencing analysis revealed no unspecific mutagenesis effected by the programmable nucleases, pointing out the safety of the application. In conclusion, the research work conducted in this thesis demonstrates the prospects that iPSC and humanized mice possess for the generation of models of liver genetic diseases, in vitro and in vivo. Furthermore, the emergence of genome editing technologies further enhances the aforementioned potentials, as well as raises possibilities for the treatment of liver genetic defects through genome manipulation

    Clinical Applications of Pharmacogenetics

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    The rapidly evolving field of Pharmacogenetics aims at identifying the genetic factors implicated in the inter-individual variation of drug response. These factors could enable patient sub-classification based on their treatment needs thus expediting drug development and promoting personalized, safer and more effective treatments. This book presents Pharmacogenetic examples from a broad spectrum of different drugs, for different diseases, which are representative of different stages of evaluation or application. It has been designed so as to serve both the unfamiliar reader through explanations of basic Pharmacogenetic concepts, the clinician with presentation of the latest developments and international guidelines, and the research scientist with examples of Pharmacogenetic applications, discussions on the limitations and an outlook on the new scientific trends in this field
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