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FDA Engages Collaborators to Address Nonclinical Data Challenges
Timothy J Kropp1, Lilliam A Rosario1, Susan DeHaven2
11 Center for Drug Evaluation and Research, US Food & Drug Administration, Silver Spring, MD, USA.
The FDA and PhUSE Computational Science Symposium (CSS) fostered collaboration between industry and government to address nonclinical assessment challenges. A workgroup developed a framework to optimize computational science for regulatory science.
Area of Science:
- Computational science
- Regulatory science
- Nonclinical informatics
Background:
- Stakeholders from the pharmaceutical industry and government convened at the 2012 FDA and PhUSE Computational Science Symposium (CSS).
- The symposium aimed to foster collaboration for addressing common needs and challenges in computational science.
- A dedicated nonclinical informatics workgroup was established to improve nonclinical assessments.
Purpose of the Study:
- To discuss the process and outcomes of the nonclinical informatics workgroup during the CSS.
- To describe an innovative framework for addressing key needs and challenges in nonclinical informatics.
- To optimize computational science for nonclinical assessment by crossing organizational barriers.
Main Methods:
- Formation of a nonclinical informatics workgroup.
- Identification, collection, and prioritization of key needs and challenges in nonclinical informatics.
- Development of a collaborative framework to address identified issues.
Main Results:
- Successful collaboration between pharmaceutical industry and government stakeholders.
- Establishment of a prioritized list of needs and challenges in nonclinical informatics.
- An innovative framework was created to guide collaborative efforts.
Conclusions:
- The nonclinical informatics workgroup effectively addressed challenges through collaboration.
- The developed framework offers an approach to optimize computational science for nonclinical assessment.
- Crossing organizational barriers is crucial for advancing regulatory science through computational methods.
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