De-risk stability before it becomes a problem
A candidate that clears binding screening is not necessarily a candidate that will survive development.
Aggregation, colloidal instability, and thermal sensitivity are among the most common reasons for biologics to fail downstream – and they often go undetected because the measurements capable of catching them are applied too late.
The window between hit confirmation and lead nomination is where stability risk is most manageable. At this stage, sample volumes are small, 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 several months further into the program. As a biologics program transitions from discovery and early development into the preclinical stage, hit validation gives way to lead and candidate optimization, coming with an increasing requirement for deeper, more extensive biophysical characterization. This is the point at which stability screening moves from a supporting role to a central one.
In this blog, we focus on the power of dynamic light scattering (DLS) and static light scattering (SLS) as the foundation of early stability screening, and how they work alongside nanoparticle tracking analysis (NTA) and differential scanning calorimetry (DSC) to build a complete picture of candidate developability.
Why stability risk is a hidden problem
A molecule that binds its target with high affinity can still aggregate under formulation conditions, denature during storage, or behave unpredictably at the concentrations required for subcutaneous delivery.
However, the potential for these issues to arise is largely invisible to the binding and activity assays that dominate early screening. A high-throughput binding screen can tell you that a molecule interacts with its target but tells you nothing about whether that molecule will remain monodisperse at high concentrations, survive three freeze/thaw cycles, or maintain its higher-order structure across the full pH range of a typical formulation buffer.
Early biophysical stability screening is designed to close this gap by adding the orthogonal readouts that binding data cannot provide.
What DLS and SLS reveal – and why it matters early
Dynamic light scattering on the Zetasizer Advance range measures the hydrodynamic radius of proteins in solution by exploiting the relationship between particle size and diffusion rate. For biotherapeutic candidates, it is one of the most information-dense early measurements available. It’s also fast to run, requires minimal sample, and is sensitive to diagnostically significant changes long before they become visible by other means.
The earliest signal of aggregate formation
The sixth-power dependence of light scattering intensity on particle size makes DLS disproportionately sensitive to larger species. A single aggregate in a predominantly monomeric population generates a scattering signal far out of proportion to its volume contribution. This means DLS can flag the earliest signs of aggregate formation at a point where it would be entirely invisible in a volume- or number-weighted distribution. Monitoring size and polydispersity across a temperature ramp gives you the aggregation temperature (Tagg), which is an early measurable point at which a formulation begins to fail under thermal stress.
Predicting behavior at formulation concentrations
Tagg tells you when a protein aggregates under thermal stress, but not why, or how the candidate will behave at the high concentrations required for formulation. For that, two interaction parameters derived from the same DLS and SLS measurement workflow provide the forward-predictive answer.
The diffusion interaction parameter kD, derived from DLS measurements across a concentration series, quantifies the net effect of protein-protein interactions in solution. A large positive kD indicates repulsive interactions: a candidate that resists self-association and is likely to remain stable at formulation concentrations. A negative kD signals attractive interactions, predicting a higher propensity for aggregation and elevated viscosity under formulation conditions.
The second virial coefficient B22, derived from SLS measurements, provides the thermodynamic component of the same picture. Positive B22 values indicate that the protein prefers solvent association over self-association, making it a favorable indicator for solubility and colloidal stability. Negative B22 values flag candidates at risk of aggregation or precipitation.
Critically, both parameters are derived from the same concentration-series experiments as kD is related to B22. Measuring B22 directly is preferable, because it is the thermodynamic, concentration-dependent measure of net protein-protein attraction/repulsion and thus more directly interpretable for aggregation risk, whereas kD is an indirect diffusion-based proxy that can be distorted by many factors as molecular size, friction and viscosity.
This means kD, B22, Tagg, PDI, and particle size distribution can all be obtained from a single Zetasizer Advance instrument across a coordinated measurement workflow. This is the practical value of early DLS and SLS screening: identifying the conditions under which aggregation is most likely to occur, before significant resources have been committed to a formulation.
Resolving complex populations with NTA
DLS measures an ensemble average of the bulk population. In heterogeneous samples where aggregated species span a wide size range, nanoparticle tracking analysis (NTA) on the NanoSight Pro provides particle-by-particle size and concentration data that ensemble DLS cannot resolve.
The two techniques are complementary: DLS for speed, sensitivity, and colloidal parameter extraction; NTA for resolving and quantifying the larger aggregated populations that require a higher-resolution view.
What DSC adds to the picture
DSC supports DLS and SLS by characterizing structural stability, determining how the protein’s folded architecture responds to thermal stress.
The MicroCal PEAQ-DSC measures the heat absorbed by a protein as it unfolds with increasing temperature. The melting temperature (Tm) is the temperature at which 50 % of the protein population has unfolded. Higher Tm generally correlates with better real-world stability under storage conditions, and a lower Tm is a well-established early indicator of formulation risk.
Combined DSC and DLS data provide the ability to distinguish between two mechanistically distinct failure modes: aggregation coupled to unfolding, and aggregation that occurs at sub-denaturing temperatures as a purely colloidal event.
These two scenarios have different implications for formulation strategy and very different impacts on downstream development. Identifying early which mode applies to a given candidate can prevent a formulation team from spending months optimizing conditions for a molecule with an intrinsic liability that cannot be fully resolved.
From screening to developability
Used together and applied early, the combination of DLS, SLS, NTA, and DSC gives development teams the data to make better candidate decisions at the point when failure is still preventable.
A candidate that shows excellent affinity but high PDI, negative kD, and a Tm significantly below the field norm has revealed something important: it is a developability liability. Catching this during lead candidate evaluation is fundamentally different from catching it during formulation development when a preferred candidate has already been nominated.
Looking ahead, the emergence of high-throughput platforms capable of running DLS, SLS, and intrinsic fluorescence-based structural stability measurements could extend this early-screening capability to a scale previously impractical with individual instruments. This would bring the depth of characterization described here to the earliest stages of the candidate funnel.
Find out more about Malvern Panalytical’s complete biologics characterization workflow.
Coming soon! Accelerate biologics characterization with a breakthrough new platform
Stability data is only valuable when you can generate it quickly, confidently, and at scale. That’s why, in September, we’re introducing a breakthrough new analytical platform designed to remove the traditional trade-off between speed and data depth in biologics characterization.
See the new platform in action
Join our upcoming webinar, Ready to Accelerate Your Biologics Pipeline? Stop Compromising in Biologics Characterization, where we’ll unveil this breakthrough platform and demonstrate how it can help deliver faster, more confident development decisions.
Register now to secure your place:
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