Use of Protein Pegylation to Prolong the Antiviral Effect of IFN Against FMDV.

Interferons (IFNs) are considered the first line of defense against viral diseases. Due to their ability to modulate immune responses, they have become an attractive therapeutic option to control virus infections. In fact, like many other viruses, foot-and-mouth disease virus (FMDV), the most contagious pathogen of cloven-hoofed animals, is highly sensitive to the action of IFNs. Previous studies demonstrated that type I, II, and III IFNs, expressed using a replication defective human adenovirus 5 (Ad5) vector, can effectively block FMDV replication in vitro and can protect animals when challenged 1 day after Ad5-IFN treatment, in some cases providing sterile immunity. Rapidly spreading foot-and-mouth disease (FMD) is currently controlled with vaccination, although development of a protective adaptive immune response takes 5-7 days. Therefore, an optimal strategy to control FMD outbreaks is to block virus replication and spread through sustained IFN activity while the vaccine-stimulated adaptive immune response is developed. Challenges with methods of delivery and/or with the relative short IFN protein half-life in vivo, have halted the development of such approach to effectively control FMD in the animal host. One strategy to chemically improve drug pharmacodynamics is the use of pegylation. In this proof-of-concept study, we demonstrate that pegylated recombinant porcine (po)IFNα displays strong and long-lasting antiviral activity against FMDV in vitro and in vivo, completely protecting swine against FMD for at least five days after a single dose. These results highlight the potential of this biotherapeutics to use in combination with vaccines to fully control FMD in the field.

Evaluation of signal detection algorithms within the Elanco Animal Health Pharmacovigilance database.

Statistical algorithms for detecting safety signals are beginning to be applied to Animal Health Pharmacovigilance (PV) databases. How these signal detection algorithms (SDAs) perform in an animal health PV database is the subject of this report. Statistical methods and SDAs were assessed against a set of known signals in order to identify which SDAs were most appropriate for signal detection using the Elanco Animal Health PV database. A reference set of adverse events that should signal was created for 31 products across four species. Nine SDAs based on five disproportionality statistical methods were evaluated against the reference set. The performance metrics were sensitivity, precision, specificity, accuracy, and F score. For bovine and porcine products, the Observed-to-Expected (O/E) SDA was the closest in terms of geometric distance to 100% sensitivity and 100% precision. For canine and feline products, the Information Component (IC) SDA was geometrically closest to 100% sensitivity and 100% precision. Principal Component Analysis confirmed that the O/E and IC SDAs were unique performers with respect to one another and other SDAs. The performance of the SDAs was dependent on the choice of the statistical method with differences seen between animal species.