Andrew Anderson shares his experiences helping customers identify and articulate gaps in their process, and how software can help fill those gaps.
Richard Lee reflects on how biotechnology organizations can use their historical data to address real chemistry world problems; especially during this time when more and more scientists are working remotely.
Defining Technology Buzzwords: What Machine Learning, Data Lakes and Artificial Intelligence Means for You
Richard Lee breaks down the current technology buzzwords and what you need to know about machine learning, data lakes, artificial intelligence, and more.
Andrew Anderson muses about our upcoming visit to our 23rd Pittcon conference as we celebrate our 25th anniversary with an exciting new product launch.
At ACD/Labs we have assembled an extraordinary amount of experience in helping scientists and their organizations get maximum value from their analytical data. Not only by helping extract answers from data efficiently, but also enabling customers to store the knowledge gained in a scientifically intuitive manner with the context of the original experiment to maximize its value in future review and re-use. How is analytical data viewed in your organization, or are you scared to ask?
Data integrity has become an industry buzzword, but do people really understand what it means? A recent survey we conducted with Chemistry & Engineering News (CE&N) showed that scientists think about data integrity differently. Read on to learn more about our survey and the results we found.
As the number of conversations we’re having with scientists in validated environments increases year over year, we have found that a number of common misconceptions exist. Many arise because previous deployments of software accompanied the installation of new hardware, or have involved informatics systems that are intimately integrated across the development workflow and are the source of data and reports submitted directly to regulatory authorities. Below we clear up some of the grey areas that seem to have become industry myths that we commonly find ourselves correcting.
In pharma, drug substances (and the resulting formulated drug products) must conform to a variety of quality specifications in order to be approved for use by healthcare practitioners and patients. While most of us who have worked in pharma know the various regulatory statutes and advisory guidance (and can quote them chapter and verse!), my belief is that there is a challenge in the practical and efficient implementation of quality practices that support conformance. When considering the increasing ‘fracturing’ of supply chains that support demand for drug substances in both clinical and healthcare systems worldwide, this challenge only continues to grow.
Analytical data plays a critical role in R&D by supporting critical decision-making on a daily basis. Whether a synthetic chemist is looking to see if their reaction yielded the product they expected, a group of scientists in development are building an impurity control strategy, or experts in manufacturing are collecting data for regulatory submissions, applications of analytical data are ubiquitous. At a time when the volume of insight-rich data one can gather is extraordinary, chemists working in academic research, industry, and non-profit organizations alike face regular challenges in managing and sharing their data.
Chemical R&D generates a deluge of instrumental analytical data on a daily basis. As critical R&D decisions and regulatory submissions are based on this data, the need for quality data management is more important than ever before. A lot has changed since the days when paper notebooks were the leading data management ‘platform’ among scientists. Advancements in research and instrument hardware continue to increase the amount of data we are able to produce and process. Sanji Bhal sits down with Graham McGibbon, director of strategic partnerships at ACD/Labs, to discuss his outlook on the industry and the pressing need for better management of analytical chemistry data in R&D.