Mechanical Means: For an unfiltered BSA solution, three populations can be detected (using multiple narrow mode) located at 6, 18, and 80 nm (Figure 6). Applying a physical stress (stirring) to the solution induces the appearance of a fourth peak at ~500 nm, showing the formation of large protein particles. Although the fourth peak seems to “grow” with respect to the others, the intensity distribution shown overestimates the presence of large particles because of the relation I ∞ R⁶, illustrating the extremely high sensitivity of this method for large particles.

A freshly prepared antibody solution of 99.9% of monomers as determined by HP-SEC shows in PCS analysis one peak with a hydrodynamic diameter of 11 nm (Figure 7). Shaking the sample for 20 minutes induced the formation of large particles (0.7 µm), but other protein aggregates were not detected. Figure 7A shows the corresponding autocorrelation function. Changes were obvious by visual inspection of the “raw” data.

Interfering Ingredients: Protein aggregates can also be generated by the presence of certain excipients. Figure 8 shows an example of a HCLF formulation (50-mg/mL protein concentration) at pH 6.1, but with different buffer salts. Protein aggregates are formed in citrate buffer and grow rapidly over time. When such particles become much too large, they sediment and thus cannot be detected by PCS.

PCS can be used to investigate samples for protein concentrations up to 200 mg/mL. For these samples, it is crucial to measure real viscosity and use that for characterizing the size distribution. Figure 9 represents the intensity distribution of the monomer peak for an antibody at various protein concentrations (10–145 mg/mL). Clearly, the peak shifts to larger sizes as a function of protein concentration, indicating protein–protein interactions and associations. Particle–particle excluded volume effects usually cause the apparent hydrodynamic diameter to increase with concentration, even when protein molecules remain entirely monomeric (11).

That experiment shows the formation of antibody associates. PCS measurement of highly concentrated samples can be difficult because of multiple scattering. So for investigating HCLF solutions, the optical path must be sufficiently short to prevent it. The phenomenon can also be prevented simply by choosing a scattering volume that is very close to the inner wall of the cuvette. The ways for light to move through the solution are thus shortened, so most photons are scattered only once (12). Another approach presented by Lämmle uses photon cross-correlation to enable measurement of very highly concentrated protein solutions (37).

Dilution of the 145-mg/mL sample (Figure 9) back to 10 mg/mL led to the appearance of a single peak located at 10–11 nm, as was found for a protein sample at the same concentration before the concentration step. No particle population is observed in the size range ≤5 µm (data not shown).

Pros and Cons

Experimentally derived PCS data are represented by the intensity autocorrelation curve, which contains all information regarding the diffusion coefficient distribution of an ensemble collection of particles in a sample solution. Diffusion coefficients are obtained by a mathematical procedure called deconvolution, which is applied to the intensity autocorrelation function. This procedure is ill defined. Several deconvolution algorithms are available for different applications. From the diffusion coefficient, the hydrodynamic diameter/radius is calculated.

One drawback to PCS is that it allows only qualitative information, making absolute quantification infeasible. Furthermore, this is a nonspecific detection method (particles are detected without being identified). For reliable results, some sample-specific parameters (e.g., dynamic viscosity) have to be known. And results can be affected by different PCS algorithms, which must be well understood before the right one can be chosen. Because of PCS's high sensitivity for large particles, dust contaminants strongly affect measurement. So samples should be prepared under a laminar flow cabinet. When investigating protein samples, users must be aware of the presence of excipients (especially detergents) that may self-associate and thus form larger particles. Up to the critical micelle concentration of a detergent, micelles are formed. Absorption of incident laser light, which can occur when measuring colored samples, is another drawback. Even the choice of cuvette can strongly influence the quality of size determinations. Sample temperatures should also be controlled during measurement.

The main advantages of using PCS come from its noninvasive and nondestructive nature for measuring the size distribution of a broad range of particles (from 1–2 nm to ~5–7 µm) and allowing sample reuse. So it can analyze samples containing broad distributions of particle species. Short measurement times and high sensitivities to larger particles underline the usefulness of this technique for investigating protein samples. The sample volume can be as low as a few microliters. Unlike SEC, for example, PCS can even be used to measure HCLFs (up to 200 mg/mL), which is not feasible with most other techniques. So HCLF formulations can be characterized at their original concentration. But correct data interpretation requires strong knowledge about the data evaluation software chosen and the use of intelligent experimental design approaches.