Genetics of Autoimmune Diseases for Vaccine Safety Signal Detection is a collaborative project between scientists at the Georgetown University Innovation Center for Biomedical Informatics and the Center for Biologics Evaluation and Research (CBER) of the Food and Drug Administration (FDA).
The project goal is to review the FDA’s Vaccine Adverse Event Reporting System (VAERS) for potential links between vaccination and autoimmune disorders.
VAERS is a passive surveillance system that captures information voluntarily provided by vaccine manufacturers, clinicians, state and local public health authorities, and patients or parents and guardians of patients. In contrast, an active surveillance system would follow all individuals who receive vaccinations to determine their responses. To encourage reporting of any possible vaccine-induced adverse event, VAERS does not restrict the reporting criteria and will accept any report submitted; therefore causation of the adverse reaction has not been confirmed.
Such surveillance systems are essential to the discovery of potential rare adverse effects of medical products that may not manifest until millions of people are exposed. To aid these discoveries, this research project will use informatics tools to develop automated means to generate early warnings of safety problems.
The project will: 1) Create a high-quality, curated data set of select autoimmune diseases, including genetic loci, proteins, functions and pathways, and other molecular processes involved. 2) Curate a separate data set to link symptoms as described in VAERS to a coded, standard set of terms (created by MeDRA, the Medical Dictionary for Regulatory Activities) to molecular pathways and mechanisms. These data sets will enable Georgetown University and the FDA to support various molecular and informatics analyses. The data sets will also provide a valuable resource to apply machine learning approaches or other data classification algorithms, which could automatically flag events reported in VAERS as related to autoimmune mechanisms.