Learn how secure, collaborative data environments enable pharma organisations to streamline research, integrate diverse datasets, and drive faster scientific breakthroughs.
Pharmaceuticals as an industry was slow to migrate from paper to electronic records and now realizes the necessity of maintaining current and informative data environments creating a challenging data landscape for the Pharma Industry.
Data type and structure of company-generated data vary based on the stage of drug development and combined across phase to yield a diverse array of data types including small, well structured data (text, ASCII, SAS data sets, etc), databases (e.g., Oracle, Access, etc.) and large, unstructured data (e.g., electronic medical records and natural history studies).
To improve the sequential, experimental process and reduce expenses, life science companies often collaborate with healthcare transformation firms to leverage extended real-world data (RWD) and real-world evidence (RWE), which have the potential for better understanding of the populations using their drugs, and their associated outcomes. And still, more data doesn’t guarantee better data and decisions; pharmaceutical companies must be aware of data redundancy and quality, especially given the highly scrutinized environment and regulatory review process in which they operate. In addition, integration of these diverse data types becomes a challenge in addition to the assessment of data quality of the individual and combined analysis data sets.
The Aridhia DRE has been used successfully to assess data quality, organize and track various degrees of curation and manage the creation of analysis data sets linked to model and tool development. Our TRE is actively used within the pharmaceutical industry and has contributed to FDA approval in drug development.
As both R&D and commercial development organizations of medium and large pharmaceutical companies is composed of highly specialized, multidisciplinary functional groups that must both complete specialized tasks and communicate and interact with the larger organization, they are reliant on a multitude of data and communication solutions that assist in advancing promising compound leads towards regulatory registration or promoting their approved medicines to the public.
The Aridhia DRE provides an excellent complement to existing PhRMA investments in compute and data solution infrastructure. Legacy solutions, particularly if they were purchased 5 or more years ago, can be siloed and not easily integrated with more modern platforms.
Such solutions are ubiquitous within the industry and support every aspect of R&D and commercial activities from data generation (e.g., LIMS and database systems), data manipulation (e.g., SAS, r, many others), report /document management (e.g., MasterControl Document Management, OpenText Documentum, TrackWise Digital, and DocuWare), clinical operations (e.g., Health Cloud, RealTime-CTMS, PatienTrial, etc.), data storage and communications (e.g., SharePoint) and regulatory submission (e.g., Veeva Vault).
As more organizations rely on acquiring real-world data (RWD) to support their regulatory submissions, the task of data integration and application of data standards becomes a “need-to-have” and not a “nice-to-have”. The Aridhia DRE’s backbone is the marriage of FAIR data and workspace services and is highly customized to the unique environment and practices of the customer. The customizability takes the form of branding, construction of APIs to facilitate data integration and connectivity to legacy systems and the construction of specific tools to enable efficient workflows.
A recent and prominent example of the Aridhia DRE’s successful use for drug development was in 2023 with the FDA approving the first medication, called Omaveloxolone, to treat Friedreich Ataxia (FA) in individuals 16 years of age or older. A critical component of the submission was based on data shared within a platform relying on the Aridhia DRE. This product was also approved in Europe in February 2024.
The source data for this use case was diverse and of disparate data types reliant on curation and integration, all managed within the DRE along with the creation of analysis datasets and a disease progression model.
