Published Oct. 1, 2019 | Topics of Companion Animal Medicine
When conducting translational research, the ability to share data generated by researchers and clinicians working with for-profit companies is essential, particularly in cases that involve "one health" data (i.e., data that could come from human, animal, or environmental sources). The 1DATA Project, a collaboration between Kansas State University and the University of Missouri, has examined and overcome some of the barriers to sharing this information for “big data” projects. This article discusses some of the obstacles we encountered, and the ways those obstacles can be surmounted via a novel form of Master Sharing Agreement. Developed in collaboration with industry partners, it is presented here as a template for expediting future one health work.
Published Oct. 1, 2019 | Topics in Companion Animal Medicine
Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes including patient death. This constitutes a major public health concern. The US food and Drug Administration (FDA) requires drug labeling to include potential adverse effects for each newly developed drug product. With the advancement in incidence of adverse drug events (ADEs) and potential adverse drug events, published studies have mainly concluded potential ADEs from labeling documents obtained from the FDA's pre-approval clinical trials, and very few analyzed their research work based on reported ADEs after widespread use of a drug to animal subjects. The aforesaid procedure of deriving practice based on information from pre-approval labeling may misrepresent or deprecate the incidence and prevalence of specific ADEs. In this study, we make the most of the recently disseminated ADE data by the FDA for animal drugs and devices used in animals to address this public and welfare concern. For this purpose, we implemented five different methods (Pearson distance, Spearman distance, cosine distance, Yule distance, and Euclidean distance) to determine the most efficient and robust approach to properly discover highly associated ADEs from the reported data and accurately exclude noise-induced reported events, while maintaining a high level of correlation precision. Our comparative analysis of ADEs based on an artificial intelligence (AI) approach for the five robust similarity methods revealed high ADE associations for two drugs used in dogs and cats. In addition, the described distance methods systematically analyzed and compared ADEs from the drug labeling sections with a specific emphasis on analyzing serious ADEs. Our finding showed that the cosine method significantly outperformed all the other methods by correctly detecting and validating ADEs based on the comparative similarity association analysis compared with ADEs reported by pre-approval clinical trials, pre-market testing, or post-approval complication experience of FDA-approved animal drugs.