Disparate data in the pharmaceutical R&D lifecycle creates many challenges, hindering efficiency, delaying regulatory reporting and obscuring many insights that could be gained throughout the R&D chain.
Creating knowledge graphs with TopBraid EDG can be an ideal way to solve many of these challenges and to make data more findable, accessible, interoperable and reusable (FAIR). This enables sciences professionals to easily and readily view, explore, and analyze the data they need.
Semantic approaches have many advantages versus traditional data management methods.
- Changing requirements due to evolving compliance rules and regulations, competitive pressures, and new strategies
- The need to integrate data from third parties – including scientific journals, patent databases, CROs, CMOs, EMRs, regulators, or public Web content, as well as life sciences vocabularies such as SNOMED CT, ICD10, and MedDRA
- Managing multiple data of formats – from structured databases to completely unstructured documents
Building blocks of controlled vocabularies enable your SMEs to enhance data with additional meaning in a governed and collaborative way, and then standardize it and publish it in RDF, a highly consumable, application independent format. These important sets of data can then be used across the organization, such as across pre-clinical and clinical phases.
All of the substances, processes and policies used in clinical trials can be modeled in knowledge graphs together, making the data easier to find, share and reuse to improve operations.
Building blocks can be combined with other data assets and tools, such as reference data or terminologies like MEDra, to bring automation to important — and yet common — procedures in R&D, such as adverse event reporting.
Other common uses for semantic data products is to extract codes from unstructured data, and then deploy them to other applications or processes.
Enterprise semantic layers can provide a holistic and detailed look at the entire R&D lifecycle, uncovering insights from previous trials, for example, and streamlining regulatory compliance and ultimately speeding product development.
Enterprise Semantic Layer also improve AI initiatives, creating efficiency in modeling, more meaningful and accurate data for training, while also providing transparency and explainability into the results of the algorithms.
These semantic layers can be built from the bottom up with an accumulation of semantic data products that work together, or from top-down initiatives such as a semantic data catalog or master data management initiatives.
Once in place, semantic systems can be used with to identify relationships that may be ordinarily hidden via logical inferencing, providing insights into the research and testing process that can translate to new drug and interaction discoveries.