A Predictive Model of Vaccine Reactogenicity Using Data from an In Vitro Human Innate Immunity Assay System

A primary concern in vaccine development is safety, particularly avoiding an excessive immune reaction in an otherwise healthy individual. An accurate prediction of vaccine reactogenicity using in vitro assays and computational models would facilitate screening and prioritization of novel candidates early in the vaccine development process. Using the modular in vitro immune construct model of human innate immunity, PBMCs from 40 healthy donors were treated with 10 different vaccines of varying reactogenicity profiles and then cell culture supernatants were analyzed via flow cytometry and a multichemokine/cytokine assay. Differential response profiles of innate activity and cell viability were observed in the system. In parallel, an extensive adverse event (AE) dataset for the vaccines was assembled from clinical trial data. A novel reactogenicity scoring framework accounting for the frequency and severity of local and systemic AEs was applied to the clinical data, and a machine learning approach was employed to predict the incidence of clinical AEs from the in vitro assay data. Biomarker analysis suggested that the relative levels of IL-1B, IL-6, IL-10, and CCL4 have higher predictive importance for AE risk. Predictive models were developed for local reactogenicity, systemic reactogenicity, and specific individual AEs. A forward-validation study was performed with a vaccine not used in model development, Trumenba (meningococcal group B vaccine). The clinically observed Trumenba local and systemic reactogenicity fell on the 26th and 93rd percentiles of the ranges predicted by the respective models. Models predicting specific AEs were less accurate. Our study presents a useful framework for the further development of vaccine reactogenicity predictive models.