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Join Steve Hamby (Vitality TechNet) and Eliud Polanco (Fluree) for a discussion on transforming siloed pharma data into rich semantic knowledge graphs for faster drug discovery and automated global regulatory compliance. We will cover how the IDMP-O can enhance the Regulatory Information Management function to reduce error in regulatory submissions, streamline the identification of potential risks, and enhance safety monitoring. We will also cover how pharmaceutical organizations can leverage the semantic alignment that IDMP provides to reduce the time and cost profiles associated with drug discovery and development. Specifically, this solution streamlines the aggregation and analysis of safety and efficacy data for medicinal products, leading to more efficient drug development processes as well as enables the identification of potential risks and safety issues earlier, thereby reducing the time and costs associated with late-stage failures. What we’ll cover: Challenges to data interoperability and electronic reporting within Pharma and Life Sciences Overview of IDMP-O (Identification of Medicinal Products Ontology) Emerging AI technologies for semantic alignment and integration Emerging digital trust technologies for sharing and compliance Why is this important? According to the Pistoia Alliance, The European Medicines Agency (EMA) is poised to become the inaugural health authority to enforce ISO IDMP compliance by Q4 of 2024, with the FDA following closely in its footsteps. Nevertheless, the divergent implementations of IDMP across different geographic regions and jurisdictional domains are already giving rise to disparities in interpretation among implementing entities. Since there is a lack of semantic alignment between regulatory bodies, there is a considerable risk that adhering to regulatory compliance will result in substantial integration and interoperability expenses, potentially impeding the realization of the benefits of IDMP in drug safety, innovation, and other critical domains. Instead of prioritizing the exploration of novel medications, organizations will be confronted with data challenges, such as the imperative to map product data throughout their entire operations during the product lifecycle.