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This large German pharmaceutical company uses metaphactory to power an omics data management knowledge graph application which makes genomics data efficiently available throughout the entire enterprise. The application provides a one-stop knowledge hub for gene expression data, proteomics and transcriptomics, helping data stewards in bridging the gap between business and IT, while bioinformaticians benefit from intuitive exploration of gene sequencing data for specific diseases and time frames.
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To learn more about Boehringer Ingelheim's journey to building an enterprise knowledge graph serving multiple use cases and applications, have a look at this interview with Maksim Kolchin, Knowledge Graph Platform Lead, presented at the 2022 BioIT World Conference & Expo.
You can also read the interview in text format here »
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metaphactory is used by this German pharmaceutical company to power a Knowledge Graph-based R&D Explorer providing actionable insights for target and drug discovery and drug safety reporting. The R&D Exoplorer offers a unified information hub for proprietary and publicly available research data related to performed clinical studies, reported adverse events, drug and substance data, molecular data, and information on indications. It supports researchers in their drug discovery processes by allowing them to connect the dots between these disparate pieces of information, perform similarity matches between indications, studies and subjects, and exploratively investigate interesting targets.
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This technology consultancy used metaphactory to build a knowledge graph application that supports the exploration of data on diseases, drugs, trials, trial locations, inclusion and exclusion criteria, or adverse events, and helps optimize the design of clinical trial studies. The application integrates information from a number of proprietary but also publicly available data sources, such as ClinicalTrials.gov, numerous medical publications and medical coding dictionaries, and allows clinical trial managers to perform aggregated data analysis and leverage insights from previous studies, such as therapeutic risks or patient safety issues, when selecting trial sites and recruiting patients for new clinical studies.
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Preclinical studies are a requirement for obtaining approval for clinical studies from regulatory agencies. They help assess drug candidates and determine safe doses for studies in humans. This Swiss healthcare company used metaphactory to build a FAIR in-vivo data sharing platform that allows researchers, bio-informaticians and lab scientists to browse, search, access, and extract meaningful insights obtained during preclinical studies and leverage this information when preparing electronic regulatory submissions required by regulatory agencies for entering clinical trials. The data sharing platform does not only deliver insights into dosages used in pre-clinical studies and potential side effects, but it also provides a valuable basis for understanding how certain compounds could be reused, repurposed, or repositioned.
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Sanofi used metaphactory to develop a target discovery dashboard which provides a consolidated view on gene and target information, e.g., data about functions, expressions, interactions, localisation, sequence, etc., and allows systems biologists to discover interesting gene interactions. Information about compounds and drugs was also integrated into the dashboard. Using machine learning algorithms, chemists can perform drug simulations and discover interesting compounds, thus supporting drug safety and drug repurposing processes.