Analytical data can be so much more than a historical point in time documented in a single report. In fact, biopharmaceutical organizations can use LC-MS data to build a continuum of compliance.
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.
We listened and learned how scientists separate mAbs and ADCs; then we designed a novel column for LC-MS bioseparations A critical step toward the prolific and successful use of monoclonal antibodies (mAb) as biotherapeutics occurred… Read more >
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 new approach to stationary phase chemistry for reversed-phase LC analysis of proteins, including monoclonal antibodies and antibody drug conjugates, delivers higher fidelity data especially for MS-based peak identification.
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.
