How stability characterization parameters help you create biologics developability profiles

Developability profiles help you avoid risk by collecting many quality attributes associated with each candidate, enabling you to rank each candidate by the most optimal attributes and make a decision on which are worth further development.

There are many biophysical characteristics to evaluate when it comes to complex, protein-based therapeutics like biologics. Anyone working to characterize biologics will have different attributes they prioritize or different parameters they aim to optimize – there’s no singular approach.

However, there are commonalities in approaches to de-risking the downstream development of a biologic therapeutic. Broadly, early development researchers are concerned with the structure, stability, and functionality of their biologics candidates.

Biologics researchers characterize many critical quality attributes of their candidates

Structural studies investigate what post-translational modifications a biologic has, what its hydrophobicity and charge properties are, and fragmentation propensity.

Functionality studies aim to determine how active a therapeutic is, how likely it is to remain active once administered, and how specifically and efficiently it targets its intended cellular destination.

Stability studies tell researchers about the thermal stability, colloidal stability, and purity of their biologics candidates. They are also used to predict how long a biologic will remain stable in storage, or whether it will remain stable once it is scaled up for clinical administration. This is critical for long-term storage and administration, as an unstable drug will lose its function quickly.

There are many experiments required to create full developability profiles with all these attributes carefully ranked. Read on to learn more about stability studies specifically, and how to gain insight on multiple stability parameters on your biologics candidates all at once.

Biologics are complex, protein-based therapeutics with complex stability behaviors

Protein-based therapeutics like antibodies have a complicated balance between their folded, unfolded, and aggregated states, each of which is potentially reversible.

Heat acts as a denaturant, so as you apply a thermal gradient to your samples, they will start to unfold. But how quickly they unfold, the temperature at which they unfold, and whether they also aggregate varies from candidate to candidate, and is dependent on their chemical environment.

Researchers often do iterative optimization approaches to achieve a more stable candidate. Common approaches for making a candidate more stable are:

• Rational design and engineering
• Buffer formulation
• Stabilizing additives

These optimization techniques involve altering the chemical environment of the biologic to improve its stability. Thankfully, there are approaches available that enable you to measure many of these parameters all at once, so it’s possible to screen or rank the best candidates to determine whether the optimization process was successful with just a single experiment.

A developability profile is a holistic look at the quality attributes of a biologic candidate – and stability studies are crucial

Stability studies deliberately introduce thermal stress to your candidates to get information about how stable they are. They do not require high sample concentrations or volumes, and allow you to measure many parameters all at once.

Common stability parameters: colloidal stability

动态光散射技术DLS uses the movement of particles in solution to inform on their size and the distribution of particle sizes in your prep.

With DLS measurements, you find the r_H, or hydrodynamic radius of your particles, and you calculate a PDI, or polydispersity index. The PDI is indicative of how tightly folded a protein is, and how many species are present in the sample.

When your prep has a high PDI, it means that it is not very uniform in folding, or that there are multiple sized particles present in the overall population. High PDIs are to be avoided.

静态光散射技术SLS informs on the size and behavior of the particles in your prep more globally. It is used to find the average molecular weight of your particles, and if you follow the overall scattering from your prep, you can identify at what temperature a size changes is triggered due to unfolding, called the scattering onset temperature or T_scattering.

The key developability parameters you’ll get out of single-point light scattering experiments are PDI and T_scattering.

With multi-point dilution series it’s possible to measure self-association parameters. These parameters are used to predict how a candidate will behave at the high concentrations required for therapeutic administration.

Common stability parameters: thermal stability

One of the key parameters used to rank candidates is melting temperature, or T_m. This is the temperature at which half of your protein-based therapeutic is unfolded, and half of it remains folded.

There are several approaches to measure T_m, but nanoDSF, or nano-differential scanning fluorimetry is the highest resolution option available. It uses the intrinsic fluorescence of your protein biologic to monitor its unfolding in response to a thermal gradient.

When properly folded, most of a protein’s hydrophobic residues are buried in its core. However, as a thermal gradient is applied, the protein unfolds and those residues become exposed to solution. In particular, the hydrophobic residue Tryptophan has different light emission properties depending on whether it is buried in the protein fold or exposed to aqueous solution. Typtophan’s emission peak shifts from 330 to 350 nanometers as it becomes exposed to aqueous solution. By monitoring the emission at these two wavelengths, it is possible follow the shift in overall emission as it changes in response to a temperature gradient. The T_m is the inflection point of the graph.

Another important parameter from this thermal unfolding information is the onset of melting, or T_on. This is determined from the point where the protein starts to unfold, or a 2% deviation in the linearity at baseline.

The higher value you find for T_m and T_on, the more thermostable your candidate is. High thermal stability is indicative of an overall more stable, and thus more promising, protein-based therapeutic.

There are also instances where protein unfolding is a slow or gradual process, so the T_m may be relatively high, but the T_on is relatively low. When this happens, it generally indicates a less stable candidate. A protein that rapidly transitions from unfolded to folded at a relatively high temperature is more stable than one that starts gradually unfolding at a lower Ton.

This illustrates the importance of evaluating stability parameters as a matrix – just ranking one or two won’t give you the full picture of your biologic proteins’ stability.

Common stability parameters: Turbidity

Turbidity is the ‘cloudiness’ of a solution. Biologic solutions generally become turbid as a result of aggregated proteins. Large, amorphous aggregates are tangled proteins with no order, and therefore no function and the potential to cause off-target toxicity effects.

When proteins are well-folded and not aggregated, they allow most light to pass through – it reflects off a mirror and returns to the system. However, as those big chunks of amorphous aggregation form, they bounce the light off in all directions, and the light that returns back to the system is attenuated.

As large aggregates form in response to the temperature denaturation, less light returns to the system, and a detector helps determine at temperature that process begins.

Turbidity measurements offer a nice corollary to the T_scattering measurements from SLS. Light scattering detectors are very sensitive but get saturated at a certain level of aggregation. Turbidity helps pick up where the SLS optics leave off – 背反射技术(Backreflection) is less sensitive to low levels of aggregation or a small number of aggregates, but as the solution becomes more aggregated, it won’t oversaturated since it’s always measuring how much light is lost from the system.

A full matrix of parameters is required to make the best decisions about your candidates

One piece of information is never enough to draw a conclusion. There are many critical quality attributes to evaluate when it comes to your biologics candidates, but stability studies are a great place to start because of their low sample consumption and the fact that many parameters are collected in parallel.

Groups like the Protein Sciences Group at Merck & Co. use these profiles to make decisions about their candidates. With more in-depth, high-quality information, they have the power to narrow their pipeline more efficiently.

Set up your developability experiments to maximize the amount of useful information you get, so you have confidence you’re selecting the best candidates for further development.

Watch this video to learn more about how nanoDSF, backreflection, DLS, and SLS give you important information about your biologics.