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Exploiting Legacy Safety Studies
Background
About Collaborative Projects
Current Collaborative Projects - Enrollment Open
Evaluating Preclinical Cardio-Toxicity Assessment Tools
Leveraging Biomarker & Translational Research in China
Assessing GLP Compliance and Quality Practices in China
Exploiting Legacy Safety Studies
Assessing the Asian Supply Chain of Non-Human Primates (NHPs)
Case Studies of Past CHA Projects
Background
Drug safety managers need and desire to gain reasonable
access
to their own safety history
: Most large organizations have invested hundreds of millions of dollars in the execution and evaluation of these safety studies, yet the study data/results remain largely inaccessible on a practical level because of the cumbersome processes for gaining access.
The need to shorten the
response time
required to answer questions from regulatory authorities
: Presently, Many CHA clients have stated it can take from weeks to months to do a full search of their study archives to answer new questions about a marketed compound’s safety history.
These companies have estimated that this could be shortened to hours and even minutes if a reasonable solution were correctly implemented. At least one company estimated that this time saving alone would justify the implementation costs for the project.
The need to m
igrate the LIMS safety data
from “old” systems that must be retired. Legacy systems are often significantly beyond their useful life
: Often the hardware and software vendors that originally supplied the systems are no longer supporting the product. In addition, many companies are pursuing an “integrated” LIMS strategy and thus want to dismantle their independent legacy systems.
The need to
share
the safety data
: Companies must be able to share data at the department level, the site level, and the global level. Currently, many companies cannot share their safety findings on an international scale because their systems do not support that broad an architectural solution.
Correlations Over Large Data Sets
: The (opportunity) need to
look across
very large data sets and look for (uncover/discover) correlations and outcomes that are not visible when only looking at one study at a time.
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