R&D Knowledge Discovery Enhanced for Global Pharma Company Using metaphacts and Ontotext

Ontotext joined forces with metaphacts to build a knowledge graph based solution, providing highly interlinked information across various data sources and offering a modular approach to R&D data discovery and knowledge consumption

  • Significantly decreased time from ideation to a production-ready solution
  • Improved efficiency by using a data model that adapts to evolving business needs
  • Minimized reputational risks by leveraging proprietary knowledge with global data and keeping it up-to-date
  • Improved user experience by providing access to deep and broad diverse data for meaningful insights
  • Driving digital transformation for better business outcomes

The Goal

A leading global Pharmaceutical company needed to improve their current process of developing new drugs and repurposing existing ones. They wanted to create a smart knowledge discovery solution that would help their researchers leverage all available proprietary and public data to:

  • better use and repurpose existing drugs
  • find relevant targets for new drug formulations
  • perform targeted searches to find biomolecular information for specific indications or groups of indications

The Challenge

The main challenge was how to extract knowledge from data residing in multiple sources, in heterogeneous formats, and across various business units.  In the existing drug development process, researchers looking to leverage preclinical or clinical data for particular compounds first had to find relevant data sources. Then they had to search in each system independently, often requiring assistance from IT departments to perform their queries.

After collecting all required information sources, they needed to integrate all different parts into a consistent report/record. This process was very time-consuming and prone to errors, and even with this effort, a lot of information still remained locked in unstructured data.

Additionally, as each of these reports/records related to one-off research, the resulting data, just like the data from previous drug testing (whether successful or not) was not reusable for other projects. Even when a new research had similar parameters to a previous project, researchers couldn’t build on existing results and had to start from scratch, creating inefficiencies and a poor user experience.

The Solution: A Preclinical Knowledge Discovery Platform

The preclinical knowledge discovery platform jointly developed by metaphacts and Ontotext enabled the Pharma company to transform and accelerate their drug development process. The solution covered the following steps:

  • Modeling the domain with metaphactory, based on the Pharma company’s specific information needs
  • Integrating all proprietary data with GraphDB and mapping the model to diverse data sources to build a customized knowledge graph
  • Providing access to relevant datasets from Ontotext’s inventory of more than 200 preloaded public datasets and ontologies in RDF format, covering various knowledge domains (such as genomics, proteomics, metabolomics, molecular interactions, and biological processes, pharmacology, clinical, medical, and scientific publications)
  • Building an intuitive user experience with metaphactory allowing end users to search and filter results, discover relevant information, bookmark and share results with their colleagues, add and edit data, build customized dashboards, etc.

The resulting application was rich and intuitive. It was easy to adjust, extend and reuse to meet the Pharma company’s changing business needs and cater to new use cases or end-user groups. With its ability to represent data as a network of relationships, the new knowledge graph not only provided access to diverse data sources but also revealed previously unknown relationships in the data.

The solution employed a standardized data model, ontologies, and vocabularies. Metadata was used to encode the meaning of the data and unique identifiers ensured that all meta-levels in the data were searchable, accessible, shareable, and traceable. The resulting data made finding, reproducing, and reusing research results a lot easier. It also had clear provenance for addressing any data consistency issues coming from the highly dynamic environment of drug development.

Business Benefits

  • 5-6 weeks to go from idea to a production-ready solution
  • Adaptive data model to accommodate changing business needs
  • Leveraging proprietary knowledge with global data and keeping it up-to-date
  • Empowering end users to work with large, diverse data and get meaningful insights
  • Driving digital transformation for better business outcomes

Why Choose metaphacts and Ontotext

With the new preclinical knowledge discovery platform developed by metaphacts and Ontotext, the Pharma company has fast and easy access to information via a live knowledge graph where the data for all integrated public datasets is constantly updated.

Combining a highly scalable and robust RDF database like GraphDB with a big inventory of ready-to-use biomedical datasets as well as Ontotext’s proven methodology for semantic data integration enabled the Pharma company to quickly create a large-scale customized knowledge graph.

Metaphactory’s intuitive search and data exploration capabilities also enabled the Pharma company’s researchers to interact with huge volumes of data consumed from the knowledge graph and use and reuse the knowledge locked in this data meaningfully.

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