At Chiesi Group’s Chippenham laboratory in the UK, Waters’ UPLC systems with mass detection and informatics keep the team at the forefront of inhalation drug delivery products.
For biopharmaceutical organizations working with contract labs, it’s important to consider how data and methods will be transferred and how generated data will be managed so that it meets data integrity and compliance requirements.
In Japan, Professor Masanori Kataoka finds that the ACQUITY QDa Mass Detector is suitable for the synthesis and analysis of oligonucleotides – and sees ways to expand its use.
Waters recently partnered with United States Pharmacopeia (USP) experts to host a webinar discussing the upcoming changes in the pending USP General Chapter on chromatography that will provide increased flexibility for gradient methods. Here’s an extended Q&A from the webinar.
Analytical methods transfer. Data integrity. A changing and more stringent regulatory landscape. All this, and more, impacts productivity in upstream and downstream biopharmaceutical processes. How is Waters looking to help address development challenges for biologics and biosimilars?
There are two primary multi-attribute monitoring (MAM) assay choices for biologic development and QC: subunit protein mass analysis and peptide mapping by LC-MS. Here we explore how peptide mapping LC-MS MAM workflows are being used.
Two MAM approaches for biotherapeutic analysis are being implemented today; one focused on the analysis of monoclonal antibody (mAb) subunits, and the other focused on the analysis of peptides from a protein digest (peptide mapping workflow). Both have their advantages and disadvantages. Here, we explore MAM for mAb subunit analysis.
“Assuming continuous improvement is a worthy goal, there is every reason to improve validated chromatographic methods if you’re working with the right instrument technology,” says Eric Grumbach of Waters. See what that means for developing and transferring methods for biologic drugs.
A thousand small delays and opportunities for error can snowball over the years of a complex biotherapeutic drug development program. They add up to lost time and increased risk in an endeavor that has little tolerance for either. It doesn’t have to be this way.
To get the most out of recent innovations in biopharma development and QC such as multi-attribute monitoring, it’s time to better connect the systems that manage biopharma data. This series explores the what, the why, and the how of better biopharma data.
