Data governance is an integral part of a regulated company’s quality system. Having a chromatography data system can simplify system administration and ensure regulatory compliance (including 21 CFR Part 11) and adherence to data integrity guidelines.
White Papers
03 August 2017
08 June 2017
Audit trails are considered the key to the security of a system since they track changes to data and metadata. In this way, an incomplete or absent audit trail can impact data integrity or even product quality. The absence of an audit trail is considered to be, “highly significant when there are data discrepancies” according to the FDA.
08 June 2017
Today, laboratory-based organizations face a wide variety of unaddressed data management challenges, and yet ultimately the scientific data is the currency with which they trade. Proper data management may not pay shareholders but it fundamentally defines the integrity of the organization and it’s purpose for existing. Being the cheapest, the fastest or the most definitive is desirable but it is all meaningless if the data is untrustworthy.
08 June 2017
Externalization of R&D activities and the deluge of instrumental analytical data generated on a daily basis has resulted in increasing interest in analytical data standardization. Any standardization efforts, however, to either a single format or for data exchange between formats; should be weighed against the requirements of different users of that data, and hardware innovations.
19 May 2017
The benefit of design exploration and optimization is understood and accepted by engineers but the required intensive computational resources have been a challenge for their adoption into the design process. The HyperWorks Unlimited (HWUL) appliance provides an effective solution to these challenges as it seamlessly connects all the necessary tools together in the cloud. The aim of this study is to showcase the benefits of HWUL on an optimization driven design of a complex system. For this purpose, an automotive seat design for crash loadcases is selected as an example.
Pages
Latest issue
Drug development is facing change – both from technological pressures, such as the use of AI and machine learning, plus new regulations which are driving sweeping changes to the way electronic records are created and stored for clinical trials.
Robert Roe speaks with laboratory informatics software providers about the future of their software and the introduction of new technologies, such as AI and deep learning
Scientists are now beginning to use new technologies such as the internet of things (IoT), artificial intelligence (AI) and machine learning (ML) in their daily workflows.
In today’s world, where drug development integrates science and technology, consumer safety is paramount in the pharmaceutical industry.