Antibody-Drug Conjugates (ADCs) are an exciting and emerging class of biopharmaceuticals with applications in cancer. GEN talks to Yunus Saricay, PhD, an R&D specialist at Byondis B.V., about the challenges of characterizing ADC products.
Why is ADC product characterization challenging?
ADC molecules have a complex structure with a number of different species [of molecule], each with different physical properties. Since ADCs are highly diverse molecules, you need many types of analytics to detect small changes in physical properties that can affect product quality and safety.
How is Byondis helping to overcome these challenges?
We use a multi-level analytical approach where we analyze the product from different angles to gain a comprehensive overview. We’re also creative as a company, which is something I love because it generates a good environment to solve problems.
What was new about your talk at Bioprocessing Summit Europe?
There were two important novelties. The first was I highlighted how computational tools can assist with product development and analytics. I also gave several examples of how to use a structural mass spectrometry (MS) technique, such as limited-proteolysis-based MS, to get more in-depth structural data than with conventional (low-resolution) spectroscopic methods.
What’s the role of structural analysis in helping characterize these new pharmaceuticals?
In my talk at Bioprocess Summit Europe in March, I showed that, with fluorescence and circular dichroism spectroscopy, a linker-drug can contribute analytical signal. Thus, you can’t be sure whether the analytical signal is from the antibody, the linker-drug, or a combination of the two. Also, linker-drug conjugation can affect the sensitivity of the detection method.
So, what do you do? To confirm the structural data, we use a combination of two or three spectroscopic (low-resolution) techniques, one of these providing a mid-resolution mass spectrometry. With these methods, we can analyze structural change at the peptide level, thereby improving the quality of our structural data.