The IDMP data standards provide structured medicinal, pharmaceutical product and substance data models, as well as mechanisms to uniquely identify products and their components. As well as facilitating a wide range of exciting regulatory and healthcare use cases, the standards can help organizations overcome traditional information silos and inconsistent data quality across different functional areas, to improve important business insights.

This has the potential to transform information flows. The ISO IDMP data models can be used as a tool to help organizations more easily understand and identify where their common and authoritative data sources reside in their operations. And, once teams can capture product and substance details in a structured way in databases, and transfer relevant data automatically to other departments, this can help to break down walls – enabling data to be linked and tracked from early development (clinical trials), across the registration process, right along the supply chain and during pharmacovigilance.

As well as improving information accuracy and reliability, and providing a clear line of sight from one end of the product lifecycle to the other, this end-to-end data flow and visibility can contribute to better products.

Inter-department data sharing

End-to-end information visibility could help firms identify opportunities to improve operational efficiency around managing regulatory submissions, by transforming communication and coordination between different functional groups.

Take, for example, the Medicinal Product IDMP data class, and the scope to link this with Clinical Particulars (important clinical information about indications/contraindications, side effects, target population, co-morbidity and other therapy specifics that are important for managing and monitoring drug safety risks and benefits).

Analysis of drug safety risks is performed throughout a product’s lifecycle yet, often, information about the therapeutic benefits and risks (which begin at the development phase) is not accessible or reconcilable between related systems (e.g., clinical trial {CT} and pharmacovigilance {PV}). The growing interest in learning systems, artificial intelligence, and analysis of real-world evidence (RWE) data, further highlights the need for a consistent construct – one that would help reconcile data sets (including subsets of related IDMP information, such as substance/product information and clinical particulars).

Information contained in RWE data sets is often misaligned or inconsistent in the use of terms, definitions and identifiers referred to in regulatory submissions, so more harmony here could have a significant impact. Certainly there is a dire need for consistent representation and coding of IDMP-related information between regulatory and healthcare stakeholders.

RWE findings are only beneficial if they help improve understanding; provide useful insights into how a company can improve a product (e.g., more effective dose form, route of administration or reduced side effects); identify uses for other conditions (e.g., off-label use); or inform regulatory decision-making.

Data standards are essential to enabling such improvements.

My name is Lise Stevens. I am currently serving as Associate Director for regulatory information management and pharmacovigilance at Iperion in the US. My personal philosophy is deeply rooted in the belief that good health and overall well-being is a basic human right and collectively healthcare providers, regulators, bio/pharmaceutical companies and health IT vendors equally share in the responsibility to protect and promote public health – especially in underserved or under-represented populations.  In addition to this, am I a Certified Ayurvedic Wellness Counselor.