September 15, 2017 (Vol. 37, No. 16)
Michael F. Drenski Cofounder and Chief Technical Officer Fluence Analytics
Using a Novel Methodology to Generate Aggregation Rates
Static light scattering is widely used to measure the molar masses of molecules, particles, and their aggregates under various solution conditions and compositions. A light scattering–based instrument from Fluence Analytics can run 16 independent light-scattering experiments simultaneously under varying stressor conditions. With this instrument, an aggregation rate generator called Argen (Figure 1), each sample can be independently stressed by any combination of thermal condition (between 18–100 °C) and stirring condition (between 0 and 2,000 rpm).
The light-scattering signature is continuously monitored throughout the experiment, providing a direct indication of a stress-induced change to the sample. These stresses and resulting effects on the samples’ formulation stability can be directly monitored and quantified with the Argen system to reveal any change in both absolute molecular weight and normalized molecular weight throughout aggregation or degradation processes.
While this note exemplifies the use of Argen to characterize monoclonal antibodies (mAbs), the the monitoring and analysis methods described herein are easily translatable to nearly all soluble synthetic and natural products. These methods can be used to generate Arrhenius representations of aggregation rate, facilitating direct and vivid comparisons of stability for samples subjected to different stressors, such as formulation condition (pH and ionic strength) and composition (excipient and surfactant content). The Arrhenius plot also yields activation energy over one or more thermal regimes.
In this study, a monoclonal antibody was monitored in a set of 16 independent temperatures to determine the aggregation rate (AR) behavior. To eliminate as many variables as possible, the sample was prepared in a single buffer formulation at a fixed concentration of 1 mg/mL. The temperature, however, was isothermally varied from 50-80 °C to test the molecule for thermal stability and determine the AR value at each isothermal condition.
Figure 2 shows the time-dependent trend of normalized molecular weight (MWnorm) for the sample across a temperature spectrum of 50–80 °C. The MWnorm is calculated by dividing the raw light-scattering data by the initial molecular weight value (MW0). As illustrated, the sample held at 80 °C aggregated to 10× its initial mass within 45 seconds, whereas the sample held at 50 °C did not show any significant aggregation after 14 hours of continuous monitoring.
It is important to note that without the use of the simultaneous multiple sample light scattering (SMSLS) technique provided by Argen, each of these measurements would have to be carried out sequentially, equating to nearly four days of continuous sample monitoring. Additionally, since all Argen data is collected continuously and in parallel, there is no discontinuity or loss of signal resolution due to serial measurements or moving optical components.
The measurements obtained with Argen support clear and concise interpretations of how any sample’s aggregation behavior evolves over time. It is also clear that aggregation occurs over a wide range of temperatures, and hence there is no specific “aggregation temperature.”
Interpretation and Analysis
The increase in light scattering from the initial MW0 value is a direct representation of the sample’s aggregation profile. Selection of the linear regime allows the user to easily determine the linear aggregation rate of change for the sample at that temperature. When characterized over several temperatures, it is easy to determine the thermally rate limited aggregation characteristics of the sample as it undergoes partial unfolding or full denaturation.
A good means of visualizing the thermal behavior is through an Arrhenius plot (Figure 3). An Arrhenius plot is constructed by plotting the log(AR) (s−1) value for each experiment against the inverse of temperature, where the temperature must be represented in Kelvin; i.e., T(K) = T(°C) + 273. The data are plotted on a log scale since the Arrhenius plot is based on the notion of exponentially sensitive rates; i.e.,
where C is a constant with units of s−1, ΔEact is the process activation energy, and R is the gas constant.
Aggregation rates vary by an enormous factor of over a million across a broad range of temperatures (1 s−1 to 10x−7s−1), and the Arrhenius representation gives a vivid means of comparing stability at different temperatures. The slope of log(AR) vs. 1/T(K) directly yields ΔEact/R, and since R is known, ΔEact can be found.
This allows for a direct comparison of the relative propensity to aggregate under the various solution conditions. It is also known that as solution conditions change for a given biologic sample, the aggregation pathway also changes. These relative differences are captured in direct comparisons of aggregation rates. It is important to note that the aggregation rate alone provides a clear indication to the stability of the protein and formulation of each experiment. A general interpretation insinuates that a low AR value is more stable than a high AR value when comparing multiple sample formulations under similar stressor conditions.
Figure 3 displays three distinct aggregation rate regimes. Between 55–65 °C, there is an Arrhenius regime where the AR is exponentially dependent on the inverse of temperature as shown in blue. This equates to an activation energy of ΔEact = 207.6 kcal/mol. This value is typical of many proteins. At high temperatures, 65 °C and above, there is another Arrhenius regime with a much lower slope. This regime equates to an activation energy of ΔEact = 49.7 kcal/mol. At temperatures below 54 °C, Arrhenius behavior may be lost and stochastic factors beyond just temperature may have a strong influence on aggregation rates.
The changes that occur from regime to regime make it unreasonable to rely on extrapolation. The aggregation behavior determined from high-temperature measurements may be a poor indicator of thermal stability for a protein sample at storage conditions. At the low temperatures that are typical of storage conditions, it is beneficial to utilize the additional features of Argen to study other effects that may lead to aggregation such as stirring, thermal cycling, material interactions, or liquid/gas interface interactions.
Argen allows fast and efficient analysis of multiple samples using SMSLS as a noninvasive method for continuous monitoring of aggregation for different proteins and protein formulations. Aggregation rate determination within the Argen control software provides a powerful measurement for quantitatively and directly comparing aggregation behavior across experiments within Argen studies. Additionally, further analysis utilizing the data generated by Argen reveal clear distinction of thermal energy regimes where Arrhenius behavior is observed.
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