D360 for Clinical and Translational Science

Data Access, Analysis, and Collaboration

D360 describes data in terms of entities of interest to researchers and the logical connections between those entities. Translational approaches to science revolve around multi-disciplinary approaches, and at heart are based on improved use of information. The ability of D360 to connect not only sources of data but to provide queryable data networks throughout the drug discovery and development phases means that knowledge from previously disparate disciplines and sources can be brought together to support translational science initiatives whether evolutionary or revolutionary in nature.

Seeing the Big Picture

In recent years pharmaceutical organizations have reorganized R&D groups along therapeutic lines. This change has led to a greater emphasis on accessing and analyzing combined data from clinical, preclinical, and discovery stages of drug development. This is a significantly complex task as the data is stored in a variety of different systems and formats. Understanding how the data is logically connected across these silos has required both IT and scientific expertise, and requires significant effort in order to answer single questions. D360 makes use of queryable data networks to bring together data from these disparate sources in a scientifically meaningful way. In so doing, scientists are provided with a more complete picture of the system they are working on. In effect, D360 provides the logical data linkages and user interface with which to exploit them, allowing scientists to readily explore data for relationships which otherwise would have remained obscured. By connecting data throughout discovery, preclinical, and clinical development, and by allowing that data to be viewed from the appropriate perspective, D360 allows translational science efforts to be more quickly implemented and proved:

• Clinical studies can be better designed by taking into account more of the preclinical data
• Biomarkers for disease states can be more readily discovered and validated
• Drug failure can be better understood, enhancing the entire research process
• Projects can be better prioritized to move forward those with the greatest chance of success

By linking together data and information from different disciplines and phases of the traditional drug discovery and development process D360 provides data exploration and informatics support for translational approaches to science.

Supporting Translational Science

Translational science workflows frequently attempt to solve problems from multiple angles. For example, if a drug is demonstrating toxicity in a clinical study, the following list of questions may be asked:

• From a clinical angle: is this toxicity related to increased exposure?
• From a preclinical angle: are there any biomarkers that could have predicted this toxicity?
• From a discovery angle: are there any structurally similar compounds that demonstrate similar toxicity?

D360 enables researchers to access and analyze the necessary data to answer these types of questions and then share those answers with their colleagues. With a simple and straightforward interface, D360 empowers scientists and managers with the appropriate tools for supporting translational science.