Stability of the two enveloped viruses NDV LaSota and YF-ZIKprM/E to support process development

by Sven Göbel, Lennart Jacobtorweihe, Max-Leopold Rössig, Frauke Braatz, Fabien Perugi, Yvonne Genzel, Udo Reichl

Building on the established use of enveloped viral vectors, like lentivirus and vesicular stomatitis virus, we investigated the stability of the oncolytic Newcastle disease virus LaSota strain and the chimeric construct of a Zika vaccine candidate YF ZIKprM/E. These vectors are currently being developed for the treatment of solid tumors, such as melanoma and glioblastoma, and for vaccine initiatives, respectively. Virus stability is a critical attribute during cell culture-based virus production and also relevant for downstream processing, storage of the produced material, final vaccine storage and shelf life. Therefore, temperature and pH stability were tested as important parameters during upstream processing and freeze-thaw cycles were tested in context of laboratory-analytics. In this study, both viruses exhibited strong stability of the infectious virus titer when subjected to repeated freeze-thaw cycles. However, exposure to temperatures above 22°C substantially reduced the infectious titers, indicating sensitivity to elevated temperatures. To improve viral stability during storage, we investigated the use of sucrose as a stabilizing excipient. While this did not result in significant improvements for YF-ZIKV, an extended half-life for NDV at room temperature was observed. The observed half-life values of upstream material from NDV of 2.6 h and 2.8 h for YF-ZIKV at 37°C demand consideration of changes to the process design, such as the implementation of a perfusion process to enable continuous, cooled virus harvesting.

Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network

by Tatiana Petukhova, Maria Spinato, Tanya Rossi, Michele T. Guerin, Cathy A. Bauman, Pauline Nelson-Smikle, Davor Ojkic, Zvonimir Poljak

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is endemic in many pig-producing countries and poses significant health and economic challenges. Enhanced surveillance strategies are essential for effective disease management. This study aimed to evaluate and compare the performance of different time-series modeling techniques to predict weekly PRRSV-positive laboratory submissions in Ontario, Canada. Ten years of PRRSV diagnostic data were obtained from the Animal Health Laboratory at the University of Guelph and were processed into a weekly time series. The dataset was analyzed with autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), random forest (RF), and recurrent neural network (RNN) models. Two validation strategies were employed: a traditional train-test split and a simulated prospective rolling forecast. Model accuracy was evaluated using common predictive error metrics. Descriptive analysis indicated a gradual increase in PRRSV positive submissions over time, with no consistent seasonal pattern. ARIMA and ETS models generally overpredict case counts, while RF and RNN tended to underpredict them. Among the evaluated models, the RF regression model most accurately captured the underlying time-series dynamics and produced the lowest prediction errors across both validation approaches. Despite outperforming other models, the RF model’s high relative prediction errors limit its suitability for accurate forecasting of PRRSV-positive submissions in Ontario’s routine surveillance system. Further data refinement and algorithm improvements are warranted.