(W1030-09-53) Prediction of Frozen Virus Stability Based on Protein Degradation Mechanisms, Real-Time Data of Representative Viruses, and Data Modeling

Purpose: Viruses are a unique critical reagent of clinical vaccine bioanalytical assays. As such, their storage stability is subject to regulatory oversight and formal expiry/recertification has to be established. While there are many publications regarding virus stability as drug products, critical reagent needs are different, and there is no comprehensive long-term stability study of viruses as critical reagents.

Methods: We built upon published knowledge of virus stability, degradation mechanisms of biological materials, glass transition temperatures (Tg) of relevant solutions, and modeling based on a collection of 271 lots of decades-long vaccine virus stability data of seven viruses stored at -70°C, to predict practical long-term stability of viruses as assay reagents.

Results: Literature review suggested virus storage stability is temperature-dependent. Virus was stable at -70°C or colder for several years in some cases. Literature also suggested that storage temperature below the glass transition temperature of the solution is the key to ensure long-term storage stability. Our own real-time stability data of seven virus solutions showed viruses were stable in the -70°C storage conditions for up to 22 years. Modeling these data indicates the probability that a virus maintains stability within 0.5 log₁₀ of its original lot potency is >99%, 90%, or 73% after 10, 20, or 30 years, respectively. 

Conclusion: The study supports the practice of virus storage at -70°C or colder and a 20 to 30-year recertification date. The choice of the recertification date will depend on assay requirements of acceptable loss and variability. Stability can be enhanced further by storage in liquid nitrogen. In all cases, freeze-thaw conditions should be evaluated and optimized to avoid loss upon freeze-thaw. This approach may allow focusing stability studies on the freeze-thaw process and limit investment into long-term studies at low and ultra-low temperature storage.

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Acknowledgments: The authors would like to acknowledge Don D. Monkovic for management support in data requisition, Maria A. Cueto for providing valuable paper searches, and Seth Robey for review of data analysis.
Financial & competing interest disclosure: The research was supported by Merck & Co., Inc. The authors are employees of Merck & Co., Inc. and may be eligible for stock options or have stock ownership. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Ethical conduct of research: The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Figure 1: Virus potency over time, averaged in 0.5-year time windows and represented as change from each lot’s baseline. 11 lots (2 measles, 1 mumps, 4 rotavirus, 2 rubella, 2 varicella) have only one timepoint and are excluded from this figure; three outlier points are excluded from the figure (see Methods). Note that individual measurements (not the binned averages depicted here) are used in the model described in Methods.



Figure 2: Distribution of trends in potency over time. The plotted normal curve predicts the distribution of trends anticipated across a range of viruses, with shading showing the interval in which trends for 95% of viruses are expected to reside (± 0.017 log10/yr). The vertical dashed lines show the interval in which trends for 95% of individual lots are expected to reside (± 0.030 log10/yr). Solid points and error bars show the separate estimates for each virus, as in Supplementary Table 1.



Figure 3: Estimated duration of control for a single lot of a single virus, defined as the time during which it remains within a specified range from initial potency. Three control bounds are shown: lower curve in green, ±0.3 log10 (~2-fold); middle curve in orange, ±0.5 log10 (~3-fold), upper curve in purple, ±0.7 log10 (~5-fold). Ribbon shows 95% uncertainty in the estimate. Labeled points are provided for 5, 10, 20, and 30 years of storage.