As a discovery or early development scientist, you’re tasked with identifying the most promising biologics candidates. Your hard work during early development enables straightforward and efficient downstream process development and manufacturing phases.
With so many criteria to assess and multiple analytical characterization tools to choose from, it’s important to work with tools that give you results you can use to make decisions about your candidates.
Learn how biologics scientists use DLS to get more detailed and reliable insights into their candidates so they advance a strong candidate, and de-risk downstream development.
Dynamic Light Scattering (DLS) reveals size heterogeneity in your sample, detects poorly-folded candidates, and tells you if your candidate is likely to self-associate or form aggregates.
Early development teams use DLS in first-pass screens to remove poor candidates
If you start by removing candidates with no promise, you can focus on a smaller and more manageable number of candidates that you can characterize in more detail.
DLS gives you the full profile of particle sizes in your sample. When you see multiple populations of different sizes, this is a warning sign that your candidate is prone to self and non-specific interactions, or is contaminated with other materials.
Antibody engineering teams use DLS to rank candidates when other tools don’t detect differences
If you are comparing candidates with very small differences like single amino acid changes, they often have very similar attributes like thermostability profiles, hydrophobicity, or pI. So you don’t have any notable differences that can be used to rank them.
DLS shows how uniformly your candidate is folded. If you see a broad peak in your size distribution plot, you’ll see a high PDI. This is a warning that your candidate is poorly folded, which makes it a less than ideal candidate.
If you see multiple peaks or a peak at a high hydrodynamic radius (rH), this is indicative of aggregation. While DLS cannot identify subvisible particles, populations of larger sizes are a signal to check for them using alternative methods.
With a clear ranking of candidates, it’s easier to make decisions about next steps for your lead molecules — whether it’s to design or test another set of candidates, or to move on to the next step in developability assessment or pre-formulation.
Learn how DLS provided the missing information that scientists at Merck needed to rank their mAbs and select the most stable candidate: Stability optimization of engineered mAbs
Biologics researchers use DLS in their multiparameter screens to assess developability and advance the most promising candidate
Viable candidates are selected from assessments of self-interaction and aggregation, colloidal stability, and thermal stability. To get the full picture of your candidates, these parameters are measured in forced degradation and short-term accelerated stability studies, low pH exposure, and manufacturability assays.
When DLS is used in developability tests, it shows how your sample responds to the stresses imposed, and whether it remains stable – or not. DLS reveals the risks of self-association, aggregation, and viscosity.
The value of this multiparameter analysis goes beyond ranking and selecting candidates. Downstream teams use this data to make informed decisions for development process optimization.
When you combine DLS with other methods that rely on different measurement parameters, you get a more complete picture of your candidate — whether you screen with DLS first, include it as part of a multiparameter analysis, or use DLS to reveal differences in highly similar candidates that earlier analyses didn’t reveal.
To learn more about DLS, and how to add it to your biologics development toolbox, visit NanoTemper’s Biologics resource center
To learn how DLS gives you deeper insights into your biologics, watch this video: DLS easily explained: What it tells you about your protein