John Stenson from Malvern Panalytical explains why integrating multiple stability measurements onto a single platform is the route to success in early-stage biologics testing.
Monoclonal antibodies and other proteins are highly sensitive to factors including pH, concentration, temperature, viscosity, buffer chemistry and more – making it essential to reliably assess both conformational and colloidal stability through real-world tests.
Clearly, the earlier this information is available, the better. But most of these interactions aren’t caught by the binding and activity assays that often dominate early screening. That creates a risk of time and money being expended on a promising candidate, only for it to fail later on when more rigorous stability tests are carried out.
Therefore, to reduce this risk, stability testing is now being introduced earlier in the pipeline, in the window between hit confirmation and lead nomination. At this stage, the candidate pool is still broad enough to make meaningful selections, and the cost of eliminating a problematic molecule is a fraction of what it will be later down the line.
However, to make this approach effective, it’s necessary to study a large number of molecules at low concentrations in numerous buffers and under a wide range of environmental conditions. And that in turn means using stability tests that focus not just on high throughput but also data quality, in order to avoid the risk of a fluke result or human error leading to a good candidate being discarded.
To understand what’s involved in this challenge, let’s look at three familiar stability-testing methods: DLS, SLS, and DSF.
Multiple methods, deep insights
Dynamic light scattering (DLS) exploits the relationship between particle size and diffusion rate and is ideal for early-stage screening because it is fast to run, requires minimal sample, and (because of the sixth-power dependence of light scattering intensity on particle size) is highly sensitive to diagnostically significant changes. DLS is also a multi-purpose method, because it quantifies three key parameters: the diffusion interaction parameter kD, which quantifies the net effect of protein–protein interactions in solution; the particle size distribution; and the aggregation temperature Tagg.
However, to determine with confidence how a candidate will behave at the high concentrations required for many biotherapeutics, we also need to determine the second virial coefficient B22, which provides a thermodynamic measure of protein interactions in solution. This variable is determined using static light scattering (SLS) experiments, but these are often carried out using DLS optics because that happens to be convenient. However, this has the major downside of providing unreliable values because it doesn’t take into account factors including molecular size, friction, and viscosity, and because it requires extensive calibration. Far more reliable values of B22 are determined using dedicated SLS optics, and so this is non-negotiable in any workable early-stage stability workflow.
And for another valuable angle on the stability picture, we turn to differential scanning fluorimetry (DSF) for determining protein unfolding by monitoring changes in fluorescence as a function of temperature. This has become an appealing alternative to differential scanning calorimetry over recent years, because of its speed, ease of use, and data quality, aided by the development of intrinsic DSF, which monitors the signal from aromatic residues rather than from added protein-binding hydrophobic fluorescent dyes.
Single-platform analyses for streamlined stability assessment
On the face of it, we might think that so long as methods such as DLS, SLS, and DSF run quickly and can be set up on high-throughput equipment, then that would be enough to ensure success in early-stage workflows.
But in practice, when multiple methods are being run, the disjoint between individual experiments becomes just as important. If custom sample holders are being used – as is currently commonplace in DSF – then that increases the expense and manual effort needed to manipulate samples. And if the specifications of the instrumentation mean that the experimental conditions vary between the methods, then that makes drawing conclusions more challenging and time-consuming. Either way, a lot of the potential advantage of running multiple methods is lost.
The solution to this is to bring DLS, SLS, and DSF onto a single platform that is able to handle industry-standard plates – as we’ve done on the new Zetasizer Core. The single optical head (Figure 1) contains a laser source for the light scattering analyses, and a separate DLS collector and linear SLS photodiode, greatly improving the quality of the B22 data acquired and greatly streamlining calibration. Also on the optical head are a UV LED and a fluorescence collector for the DSF acquisition, positioned to avoid crosstalk and allowing all three measurements to run simultaneously. In the Zetasizer Core, 192 samples can be completed per run when using a 1 °C/min thermal ramp.
Figure 1: The optical head on the Zetasizer Core features dedicated DLS, SLS, and DSF optics to enhance throughput and data quality.
The resulting time efficiencies have the potential to dramatically reduce the cost-per-sample, easily leading to savings in excess of tens or hundreds of thousands of dollars per year. An additional saving is provided by the use of industry-standard 96-, 384-, and 1536-well plates, which eliminate the expense of proprietary formats for DSF, and which slot into existing workflows for plate-handling infrastructure, robotics, and LIMS used in biologics laboratories.
But there’s also a data-quality advantage, because running three measurements at once on multiple samples using the same analytical conditions reduces the post-acquisition data-processing burden. As a result, there is a lower risk of erroneous conclusions being reached, giving teams greater confidence when ranking candidates (Figure 2).
Figure 2: The value of simultaneously acquiring kD and B22 on a single sample for avoiding erroneous conclusions: the kD data suggests that only one sample has a repulsive protein–protein interaction, whereas the B22 data correctly shows that in fact two samples have a repulsive interaction.
Future-proofing stability testing
The need for high throughput and better data quality is a trend that’s only going to accelerate, as pharma moves towards more advanced biologics such as fusion proteins and antibody–drug conjugates, which are likely to have even more tightly-defined stability windows than conventional proteins. Separately, automation through robotics and AI has already made a massive difference to discovery workflows in small-molecule pharma, and it’s now starting to have an impact on biologics too.
It’s important that we don’t allow those advances to be held up by limitations in assessing stability. Accordingly, this is where instruments that maximize both scalability and data quality high can play a major part in helping pharma companies get ahead in the race to develop effective biologics.
Find out more about the Zetasizer Core system on the Malvern Panalytical website.
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