Managing Regulatory Compliance in Laboratories
News Oct 07, 2014
The Matrix Gemini LIMS (Laboratory Information Management System) from Autoscribe meets the requirements of highly regulated laboratories by providing audit trails, time and date stamping of all actions and version control of all reference data, such as test definitions. This helps with compliance to any guidelines that require change control, validation and audit trailing of changes to stored data.
Examples include cGLPs and cGMPs and FDA 21 CFR Part 11 in pharmaceutical companies, NELAC and ISO 17025 in water and food testing laboratories or NAMAS / UKAS regulations. With Matrix Gemini in use in a wide range of industries and applications, experts from Autoscribe are available to discuss in detail how the software helps achieve compliance for individual guidelines.
Matrix Gemini, with its comprehensive security controls, audit trails and e-signature functionality, has been developed following FDS recommended software development practices and procedures in an ISO 9001:2008 certified environment. The system produces exact, complete and readable copies of all records (acquired data, audit trail, electronic signatures, etc.) in electronic format or paper format. Multiple security levels are available for each system function down to screen, field and button level.
Autoscribe’s internal procedures for controlling the distribution and release of documentation for system operation and maintenance also meet the requirements of guidelines such as FDA 21 CFR Part 11. A system development “life-cycle” process is used and the user requirements, functional specifications, design specifications, source code and test scripts are all available for review and audit for each release.
Computer bits are binary, with a value of 0 or 1. By contrast, neurons in the brain can have all kinds of different internal states, depending on the input that they received. This allows the brain to process information in a more energy-efficient manner than a computer. A new study hopes to bring the two closer together.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
5th International Congress on Epigenetics & Chromatin
Aug 22 - Aug 23, 2019