Predicting inhibitor development using a random peptide phage-display library approach in the SIPPET Cohort.

Inhibitor development is the most severe complication of hemophilia A care, and is associated with increased morbidity and mortality. The aim of this study was to use a novel IgG epitope mapping method to explore the factor VIII (FVIII)-specific epitope profile in the SIPPET cohort population and to develop an epitope-mapping based inhibitor prediction model. The population consisted of 122 previously untreated patients with severe hemophilia A that were followed-up for 50 days of exposure to FVIII or 3 years, whichever occurred first. Sampling was performed before FVIII treatment and at the end of the follow-up. The outcome was inhibitor development. The FVIII epitope repertoire was assessed by means of a novel random peptide phage-display assay. A LASSO regression model and a random forest model were fitted on post-treatment sample data and validated in pre-treatment sample data. The predictive performance of these models was assessed by the C-statistic and a calibration plot. We identified 27,775 peptides putatively directed against FVIII, which were used as input for the statistical models. The C-statistic of the LASSO and random forest models were good at 0.78 (95%CI: 0.69-0.86) and 0.80 (95%CI: 0.72-0.89). Model calibration of both models was moderately good. Two statistical models, developed on data from a novel random peptide phage display assay, were used to predict inhibitor development before exposure to exogenous FVIII. These models can be used to set up diagnostic tests that predict the risk of inhibitor development before starting treatment with FVIII.

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