Accuracy of an automated knowledge base for identifying drug adverse reactions

Introduction Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research. Methods In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration. Results Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ⩾0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event. Conclusions Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research

Analytical performance of a standardized single-platform MHC tetramer assay for the identification and enumeration of CMV-specific CD8+ T lymphocytes

Major histocompatibility complex (MHC) multimers that identify antigen-specific T cells, coupled with flow cytometry, have made a major impact on immunological research. HLA Class I multimers detect T cells directed against viral, tumor, and transplantation antigens with exquisite sensitivity. This technique has become an important standard for the quantification of a T cell immune response. The utility of this method in multicenter studies, however, is dependant on reproducibility between laboratories. As part of a clinical study using a standardized two-tube three-color single-platform method, we monitored and characterized performance across multiple sites using tetramers against the T cell receptors (TCR) specific for MHC Class I, A*0101 - VTEHDTLLY, A*0201 - NLVPMVATV and B*0702 - TPRVTGGGAM CMV peptides. We studied the analytical performance of this method, focusing on reducing background, maximizing signal intensity, and ensuring that sufficient cells are enumerated to provide meaningful statistics. Inter and intra-assay performance were assessed, which included inherent variability introduced by shipping, type of flow cytometer used, protocol adherence, and analytical interpretation across a range of multiple sample levels and specificities under routine laboratory testing conditions. Using the described protocol, it is possible to obtain intra- and interlab CV's of <20%, with a functional sensitivity for absolute tetramer counts of 1 cell/μL and 0.2% tetramer+ percent for A*0101, A*0201, and B*0702 alleles. The standardized single-platform MHC tetramer assay is simple, rapid, reproducible, and useful for assessing CMV-specific T cells, and will allow for reasonable comparisons of clinical evaluations across multiple centers at clinically relevant thresholds (2.0-10.0 cells/μL)