1,222 research outputs found

    The landscape of the methodology in drug repurposing using human genomic data:a systematic review

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    The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record (EHR) data, public availability of various databases containing biological and clinical information, and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1st May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies, and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies

    A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

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    Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

    Artificial intelligence for dementia drug discovery and trials optimization

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    Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation

    Aβ Vaccination in Combination with Behavioral Enrichment in Aged Beagles: Effects on Cognition, Aβ, and Microhemorrhages

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    Beta-amyloid (Aβ) immunotherapy is a promising intervention to slow Alzheimer’s disease (AD). Aging dogs naturally accumulate Aβ and show cognitive decline. An active vaccine against fibrillar Aβ 1–42 (VAC) in aged beagles resulted in maintenance but not improvement of cognition along with reduced brain Aβ. Behavioral enrichment (ENR) led to cognitive benefits but no reduction in Aβ. We hypothesized cognitive outcomes could be improved by combining VAC with ENR in aged dogs. Aged dogs (11–12 years) were placed into 4 groups: (1) control/control (C/C); (2) control/VAC (C/V); (3) ENR/control (E/C); (4) ENR and VAC (E/V) and treated for 20 months. VAC decreased brain Aβ, pyroglutamate Aβ, increased CSF Aβ42 and BDNF RNA levels but also increased microhemorrhages. ENR reduced brain Aβ and prevented microhemorrhages. The combination treatment resulted in a significant maintenance of learning over time, reduced Aβ and increased BDNF mRNA despite increased microhemorrhages, however there were no benefits to memory. These results suggest that the combination of immunotherapy with behavioral enrichment leads to cognitive maintenance associated with reduced neuropathology that may benefit people with AD

    Dissecting out the contribution of cognitive, social, and physical activities to environmental enrichment\u27s ability to protect Alzheimer\u27s mice against cognitive impairment

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    Retrospective studies suggest that lifestyle activities may provide protection against Alzheimer s Disease (AD). However, such studies can be inaccurate and prospective longitudinal studies investigating lifestyle protection against AD are both impractical and impossible to control for. Transgenic (Tg+) AD mice offer a model in a well controlled environment for testing the potential for environmental factors to impact AD development. In an initial study, Tg+ and non-transgenic (Tg-) mice were housed in either environmentally enriched (EE) or standard housing (SH) from 2-6 months of age, with a behavioral battery given during the last 5 weeks of housing. In the Morris maze, platform recognition, and radial arm water maze tasks, Tg+/EE mice were completely protected from cognitive impairment present in Tg+/SH mice and comparable to control Tg-/SH mice in cognitive performance. The current study utilized the same cognitive-based behavioral battery and multimetric statis statistical analysis to investigate the protective effects of complete environment enrichment (EE) versus several of its components (physical activity, social interactions) in AD transgenic mice. The AD transgenic mice utilized develop beta-amyloid (AB) deposition and cognitive impairment by 6-7 months of age. Similar to our initial study, results show that complete EE (physical, social, and cognitive activities) from 2 to 8 months of age completely protected AD transgenic mice from cognitive impairment in tasks representing different cognitive domains - working memory, reference learning, and search/recognition. In strong contrast, Tg+ mice reared in environments that included physical activity and social interaction, or only social interaction, were not protected from cognitive impairment in adulthood -- enhanced cognitive activity was required over and above that present in these other environments. Through use of discriminant function analysis, EE and/or NT mice were consistently discriminated from the poorer performing other housing groups. The cognitive benefits observed in EE-housed Tg+ mice occurred without significant changes in cortical AB levels, plasma cytokine levels, or plasma corticosterone levels, suggesting involvement of mechanisms independent of these endpoints. However, EE-housed Tg+ mice did have decreased dendritic length of neurons in the parietal cortex (but not hippocampus). Noteworthy is that plasma cytokine levels and hippocampal dendritic length consistently correlated with cognitive measures, suggesting their involvement in underlying mechanisms of cognitive performance. The present work provides the first evidence that complete EE (including enhanced cognitive activity) is needed to provide cognitive protection against AD in a Tg+ model of the disease, while the physical and social activity components of EE do not alone lead to protection. These results suggest that humans desiring to gain maximal environmental protection against AD should live a lifestyle high in cognitive, social, and physical activities together

    QUANTITATIVE PROTEOMIC ANALYSES OF HUMAN PLASMA: APPLICATION OF MASS SPECTROMETRY FOR THE DISCOVERY OF CLINICAL DELIRIUM BIOMARKERS

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    The biomarker discovery pipeline is a multi-step endeavor to identify potential diagnostic or prognostic markers of a disease. Although the advent of modern mass spectrometers has revolutionized the initial discovery phase, a significant bottleneck still exists when validating discovered biomarkers. In this doctoral research, I demonstrate that the discovery, verification and validation of biomarkers can all be performed using mass spectrometry and apply the biomarker pipeline to the context of clinical delirium. First, a systematic review of recent literature provided a birds-eye view of untargeted, discovery proteomic attempts for biomarkers of delirium in the geriatric population. Here, a comprehensive search from five databases yielded 1172 publications, from which eight peer-reviewed studies met our defined inclusion criteria. Despite the paucity of published studies that applied systems- biology approaches for biomarker discovery on the subject, lessons learned and insights from this review was instrumental in the study designing and proteomics analyses of plasma sample in our cohort. We then performed a targeted study on four biomarkers for their potential mediation role in the occurrence of delirium after high-dose intra-operative oxygen treatment. Although S100B calcium binding protein (S100B), gamma enolase (ENO2), chitinase-3-like protein 1 (CHI3L1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) have well-documented associations with delirium, we did not find any such associations in our cohort. Of note, this study demonstrates that the use of targeted approaches for the purposes of biomarker discovery, rather than an untargeted, systems-biology approach, is unavoidably biased and may lead to misleading conclusions. Lastly, we applied lessons learned and comprehensively profiled the plasma samples of delirium cases and non-delirium cases, at both pre- and post-surgical timepoints. We found 16 biomarkers as signatures of cardiopulmonary bypass, and 11 as potential diagnostic candidates of delirium (AuROC = 93%). We validated the discovered biomarkers on the same mass spectrometry platform without the use of traditional affinity-based validation methods. Our discovery of novel biomarkers with no know association with delirium such as serum amyloid A1 (SAA1) and A2 (SAA2), pepsinogen A3 (PEPA3) and cathepsin B (CATB) shed new lights on possible neuronal pathomechanisms

    Cognitive rehabilitation, self-management, psychotherapeutic and caregiver support interventions in progressive neurodegenerative conditions: a scoping review

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    BACKGROUND: Despite their potentially significant impact, cognitive disability may be overlooked in a number of progressive neurodegenerative conditions, as other difficulties dominate the clinical picture. OBJECTIVE: We examined the extent, nature and range of the research evidence relating to cognitive rehabilitation, self-management, psychotherapeutic and caregiver support interventions in Parkinsonian disorders, multiple sclerosis (MS), frontotemporal dementias (FTD), motor neuron disease and Huntington’s disease. METHODS: Scoping review based on searches of MEDLINE and CINAHL up to 15 March 2016. RESULTS: We included 140 eligible papers. Over half of the studies, and almost all the randomised controlled trials, related to MS, while a number of single case studies described interventions for people with FTD. CR interventions addressed functional ability, communication and interaction, behaviour or memory. The majority of psychotherapy interventions involved cognitive behavioural therapy for depression or anxiety. Self-management interventions were mainly available for people with MS. There were few reports of interventions specific to caregivers. Numerous methodological challenges were identified. CONCLUSIONS: The limited range of studies for all conditions except MS suggests a need firstly to synthesise systematically the available evidence across conditions and secondly to develop well-designed studies to provide evidence about the effectiveness of CR and other psychological interventions

    Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

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    Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer

    Identification of vascular endothelial growth factor receptor 3 (VEGFR3) as an in vitro and in vivo substrate of the Alzheimer's Disease linked protease BACE2

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    The protease ß-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key drug target in Alzheimer’s disease (AD). It catalyzes the first step in the generation of the pathogenic amyloid ß (Aß) peptide and its inhibition is therefore a promising approach to prevent or delay the onset of AD. To date however, most inhibitory compounds do not discriminate between BACE1 and its close non-amyloidogenic homologue BACE2 and therefore may lead to undesired off target effects, resulting from BACE2 biology. Therefore, future compounds require a higher selectivity for BACE1 and a biomarker is required to confirm unimpaired in vivo BACE2 activity. To replace a long lasting depigmentation assay, which is the current standard for in vivo BACE2 activity monitoring, the blood plasma of BACE2 knockout mice (B2KO) was screened and the tyrosine kinase receptor Vascular Endothelial Growth Factor 3 (VEGFR3) was identified as a putative BACE2 substrate. Subsequently, VEGFR3 was thoroughly validated as an in vitro and in vivo BACE2 substrate and the BACE2 cleavage site was determined. In direct comparison to the pigmentation readout, plasma VEGFR3 performed superior and displayed higher sensitivity and lower variance. Importantly, reduction of VEGFR3 was also detectable in the plasma of BACE inhibitor treated non-human primates (NHP) and clinical trial participants, highlighting potential for applicability in the clinical context. To test whether BACE2 cleavage may be a novel mechanism to control VEGFR3 function, downstream events of VEGFR3 signaling were monitored in primary lymphatic endothelial cells (LECs). Impairment of BACE2 dependent VEGFR3 processing was accompanied by increased activation of the VEGFR3 dependent pathways AKT and ERK and resulted into enhanced transcription of the VEGFR3 inducible genes (FOXC2) and Delta-like 4 (DLL4). As a consequence, alterations in the morphological structure and drainage efficiency of lymphatic vessels and cannot be excluded in the periphery and central nervous system (CNS). Future developments in the BACE inhibitor field need to consider these implications and plasma VEGFR3 levels may be used to control for possible of target effects from BACE2 inhibition
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