The development of medicines from any modality in pharmaceutical research is a data-driven activity. The activity involves effective representation of substances and the efficient capture and presentation of research data for key decision-making. Considering the rate at which new modality types are being developed, there is a need for developing an alternative architecture for pharmaceutical research.
The approach to developing a new discovery data architecture should embrace and support the development of standards for substance and experimental data representation and operate across a multi-vendor hosted environment.
When developing standards for the modalities in pharmaceutical research, the new standards must describe how experimental and substance data should be treated and cover the services that provide access to that data. Moreover, these standards should be based on the FAIR guiding principles and developed by an industry consortium.
FAIR data standards and capability and performance standards for data services will produce a truly effective multi-vendor environment. Establishing new standards will create a scientific data framework that will enable seamless data transfer between systems, reduce system redundancies, enable effective support of current research, and provide extensibility for the future.
This approach will free organizations to build their optimal informatics environment and choose the best tools for their research. In addition, sponsors will gain near real-time data access and they will no longer have to deal with different business rules governing how data is aggregated, different definitions of assays that require different treatment, and the resulting inconsistencies in data presentation.
Even though developing standards for the modalities— both old and new – in pharmaceutical research is a major undertaking, it has to be performed because consistency is vital for rational, scientific decision-making.
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