Genetic Engineering & Biotechnology News

SEP15 2017

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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Genetic Engineering & Biotechnology News | | SEPTEMBER 15, 2017 | 25 change for the sample at that temperature. When characterized over several tempera- tures, 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 (Fig- ure 3). An Arrhenius plot is constructed by plotting the log(AR) (s −1 ) value for each ex- periment 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 , ΔE act 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 −7 s −1 ), and the Arrhenius representation gives a vivid means of comparing stability at different tempera- tures. The slope of log(AR) vs. 1/T(K) direct- ly yields ΔE act /R, and since R is known, ΔE act 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 differ- ences 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 un- der similar stressor conditions. Figure 3 displays three distinct aggrega- tion rate regimes. Between 55–65 °C, there is an Arrhenius regime where the AR is ex- ponentially dependent on the inverse of tem- perature as shown in blue. This equates to an activation energy of ΔE act = 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 ΔE act = 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 re- gime make it unreasonable to rely on extrapo- lation. 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 addi- tional features of Argen to study other effects that may lead to aggregation such as stirring, thermal cycling, material interactions, or liq- uid/gas interface interactions. Conclusion Argen allows fast and efficient analysis of multiple samples using SMSLS as a nonin- vasive method for continuous monitoring of aggregation for different proteins and protein formulations. Aggregation rate de- termination within the Argen control soft- ware provides a powerful measurement for quantitatively and directly comparing aggregation behavior across experiments within Argen studies. Additionally, further analysis utilizing the data generated by Ar- gen reveal clear distinction of thermal en- ergy regimes where Arrhenius behavior is observed. References M. Kunitani et al., "Classical Light Scattering Quantita- tion of Protein Aggregates: Off-Line Spectroscopy Versus HPLC Detection," J. Pharm. Biomed. Anal. 16(4), 573 –586 (1997). R.K. Brummitt, D.P. Nesta, and C.J. Roberts, "Predicting Accelerated Aggregation Rates for Monoclonal Antibody Formulations, and Challenges for Low-Temperature Predictions," J. Pharm. Sci. 100(10), 4234–4243 (October 2011). M.F. Drenski et al., "Monitoring Protein Aggregation Kinetics with Simultaneous Multiple Sample Light Scattering," Anal. Biochem. 437, 185–197 (2013). M.F. Drenski et al., "Simultaneous Multiple Sample Light Scattering (SMSLS) for Continuous Monitoring of Protein Aggregation," ACS Symposium Series, Vol. 1202, Ed. J.E. Schiel, American Chemical Society, Chapter 6, 159–188 (2015). ©2017 All rights reserved Mirus Bio LLC. Mirus Bio and TransIT-X2 are registered trademarks of Mirus Bio LLC. All trademarks are the property of their respective owners. You want it to work every time. Just like your transfections. You have a good head on your shoulders. So stand tall and move forward with experimental success using the TransIT-X2 ® Dynamic Delivery System . Achieve superior transfections with an advanced non-liposomal, polymeric system that efficiently delivers DNA and/or small RNAs. Supported by the versatility needed to conduct multiple experiments for multiple applications. The TransIT-X2 ® System gives researchers: The Answer – Experience superior transfection performance from experiment to experiment Multi-use – Delivery of plasmid DNA, siRNA/miRNA and CRISPR/Cas9 components in multiple cell types Flexibility – Independent or simultaneous delivery of plasmid DNA and small siRNA Transfection Confidence with TransIT-X2 ® X2 X2 X2 REQUEST FREE SAMPLE Bioprocessing Michael F. Drenski (michael.drenski@ is cofounder and chief technical officer of Fluence Analytics. ( References available online. Tutorial AR(s −1 ) = Ce (-ΔE act /RT)

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