Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.

ABSTRACT: Background: Neutralizing anti-drug antibodies (ADA) can greatly reduce the efficacy of biopharmaceuticals used to treat patients with multiple sclerosis (MS). However, the biological factors pre-disposing an individual to develop ADA are poorly characterized. Thus, there is an unmet clinical need for biomarkers to predict the development of immunogenicity, and subsequent treatment failure. Up to 35% of MS patients treated with beta interferons (IFNβ) develop ADA. Here we use machine learning to predict immunogenicity against IFNβ utilizing serum metabolomics data. Methods: Serum samples were collected from 89 MS patients as part of the ABIRISK consortium-a multi-center prospective study of ADA development. Metabolites and ADA were quantified prior to and after IFNβ treatment.

SUBMITTER: Waddington KE 

PROVIDER: S-EPMC7380268 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature