686 research outputs found

    Evolution and Impact of High Content Imaging

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    Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar

    Dictyostelium discoideum as a model for the evaluation of teratogenic compounds

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    Before new chemicals can be put on the market, they must be evaluated for toxicological safety. Evaluating the safety of new chemicals, for either medical, cosmetic or environmental application, is tightly regulated by worldwide legislation. A critical aspect of toxicity evaluation is developmental and reproductive toxicity (DART) testing. Traditionally, DART testing has been conducted in vivo in mammalian model systems. In fact, current EU DART testing guidelines accounts for the majority of animals used and the financial costs of new compound compliance testing. Therefore, because of the need to reduce the financial and animal costs associated with DART testing, there is a growing demand for new alternative model systems for toxicity evaluation. Dictyostelium discoideum is a eukaryotic amoeba which due to its unique developmental cycle has the potential to serve as a non-animal alternative model in DART testing. However, for a new alternative model to be proven effective it must allow for high-throughput screening, whilst maintaining biological complexity; allowing developmental toxicity results to be predictive of mammalian systems. To address these concerns, we developed new high-throughput D. discoideum growth and developmental toxicity assays. We use the assays to characterise toxicity across a broad range of test compounds, thereby revealing a significant relationship between D. discoideum and mammalian toxicity values. Our data demonstrates that D. discoideum has the biological complexity necessary to be predictive of mammalian toxicity. We further assess whether D. discoideum could be used to genetically characterise developmentally toxic compounds. Using next generation functional genomic screens, we show how the developmentally toxicity compounds, lithium and VPA can be globally genetically phenotyped. Using this genetic phenotyping approach, we were also able to identify the biological targets and processes that mediate lithium and VPA toxicity. Together, these studies illustrate the potential of D. discoideum to be developed as a new alternative model in DART testing

    High-Throughput Screening for Drug Discovery

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    The book focuses on various aspects and properties of high-throughput screening (HTS), which is of great importance in the development of novel drugs to treat communicable and non-communicable diseases. Chapters in this volume discuss HTS methodologies, resources, and technologies and highlight the significance of HTS in personalized and precision medicine

    Alternative methods for regulatory toxicology – a state-of-the-art review

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    This state-of-the art review is based on the final report of a project carried out by the European Commission’s Joint Research Centre (JRC) for the European Chemicals Agency (ECHA). The aim of the project was to review the state of the science of non-standard methods that are available for assessing the toxicological and ecotoxicological properties of chemicals. Non-standard methods refer to alternatives to animal experiments, such as in vitro tests and computational models, as well as animal methods that are not covered by current regulatory guidelines. This report therefore reviews the current scientific status of non-standard methods for a range of human health and ecotoxicological endpoints, and provides a commentary on the mechanistic basis and regulatory applicability of these methods. For completeness, and to provide context, currently accepted (standard) methods are also summarised. In particular, the following human health endpoints are covered: a) skin irritation and corrosion; b) serious eye damage and eye irritation; c) skin sensitisation; d) acute systemic toxicity; e) repeat dose toxicity; f) genotoxicity and mutagenicity; g) carcinogenicity; h) reproductive toxicity (including effects on development and fertility); i) endocrine disruption relevant to human health; and j) toxicokinetics. In relation to ecotoxicological endpoints, the report focuses on non-standard methods for acute and chronic fish toxicity. While specific reference is made to the information needs of REACH, the Biocidal Products Regulation and the Classification, Labelling and Packaging Regulation, this review is also expected to be informative in relation to the possible use of alternative and non-standard methods in other sectors, such as cosmetics and plant protection products.JRC.I.5-Systems Toxicolog

    Systems microscopy to unravel cellular stress response signalling in drug induced liver injury

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    Toxicological insults are met by cellular adaptive stress response pathway activation. We find that activation of adaptive stress responses occur well before the typical ultimate outcome of chemical cell injury. To increase our understanding of chemically-induced adaptive stress response pathway activation and its contribution to safety assessment we believe that a time-resolved, sensitive and multiplex readout of chemical-induced toxicological relevant cellular stress responses will be essential. For that purpose, we developed a platform containing a panel of distinct adaptive stress response reporter cell lines. These are used for automated high content live cell imaging and quantitative multi-parameter image analysis to elucidate critical adaptive stress response pathway activation that can contribute to human chemical safety assessment. To conserve the endogenous gene regulatory programs, we tag selected reporter target genes with GFP using BAC-transgenomics approaches. In this thesis we demonstrate the functionality of individual BAC-GFP pathway in toxicity reporter cell lines. The application of these reporters in chemical safety assessment in relation to drug-induced liver injury is discussed in detail. We anticipate that ultimately a phenotypic adaptive stress response profiling platform will allow a high throughput and time-resolved classification of chemical-induced stress responses assisting in the safety assessment of chemicals.UBL - phd migration 201

    Microarray Data Mining and Gene Regulatory Network Analysis

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    The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer from low accuracy or computational complexity. To overcome such limitations, the following strategies were proposed to integrate bioinformatics data mining techniques with existing GRN inference algorithms, which enables the discovery of novel biological knowledge. An integrated statistical and machine learning (ISML) pipeline was developed for feature selection and classifier genes identification to solve the challenges of the curse of dimensionality problem as well as the huge search space. Using the selected classifier genes as seeds, a scale-up technique is applied to search through major databases of genetic interaction networks, metabolic pathways, etc. By curating relevant genes and blasting genomic sequences of non-model organisms against well-studied genetic model organisms, a reference gene regulatory network for less-studied organisms was built and used both as prior knowledge and model validation for GRN reconstructions. Networks of gene interactions were inferred using a Dynamic Bayesian Network (DBN) approach and were analyzed for elucidating the dynamics caused by perturbations. Our proposed pipelines were applied to investigate molecular mechanisms for chemical-induced reversible neurotoxicity

    Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

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    This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

    Vincristine-Induced Peripheral Neuropathy: Assessing Preventable Strategies in Paediatric Acute Lymphoblastic Leukaemia

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    Background: Acute Lymphoblastic Leukaemia is the most common cancer experienced by children with overall survival rates now exceeding 90%. However, most children will experience vincristine-induced peripheral neuropathy (VIPN) during treatment resulting in sensory-motor abnormalities. To date, there are no approved preventative therapeutics or mitigation strategies for VIPN. This body of work set out to: (1) establish a high-throughput and high-content assay with the capacity to identify neuroprotective compounds, (2) test the feasibility of repurposing olesoxime as a neuroprotectant, and (3) compare traditional statistical methods with machine learning models to identify patients at risk of VIPN. Methods: (1) In vitro neuronal cultures were exposed to vincristine to recapitulate the VIPN phenotype and olesoxime assessed as a positive control. The neurotoxicity assay was miniaturised in 384-well microplates with automation steps to reduce manual handling. (2) Olesoxime and vincristine were applied to proliferating malignant cell lines to ensure the efficacy of vincristine was maintained. (3) Machine learning algorithms were developed using data from a local retrospective cohort to predict VIPN. Results: (1) Neurite length was reduced in a dose-responsive manner with vincristine. Assay miniaturisation and automation steps helped facilitate a high-throughput workflow. An optimised multiplexed dye solution enabled image acquisition and neurite quantification. Further, olesoxime was found to protect neurites and deemed suitable as a positive control (2) Cell viability assays confirmed olesoxime did not interfere with vincristine efficacy in leukemia cells. (3) Machine learning algorithms showed equivalency to traditional univariate analysis. The observation of severe class imbalance meant that patients who were least susceptible to VIPN could be identified. Conclusions: This body of work demonstrates the successful development of a neurotoxicity assay suitable for neuroprotectant drug discovery. Olesoxime was found suitable as a positive control in the assay. Further, viability studies indicated that vincristine retains it efficacy with olesoxime, opening the possibility of its use as an adjunctive therapy. Finally, this work developed machine learning models with the capacity to identify patients with VIPN-free survival. The utility of this model may mean that it can be used to stratify patients prospectively in the clinic based on favourable clinical features
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