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Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNF? Biologics: Implications for Immunogenicity of Pharmaceutical Proteins.

ABSTRACT: The purpose of this study was to evaluate the extent of overlapping immunogenic peptides between three pharmaceutical biologics and influenza viruses. Clinical studies have shown that subsets of patients with rheumatoid arthritis (RA) develop anti-drug antibodies towards anti-TNF? biologics. We postulate that common infectious pathogens, including influenza viruses, may sensitize RA patients toward recombinant proteins. We hypothesize that embedded within infliximab (IFX), adalimumab (ADA), and etanercept (ETN) are ligands of class II major histocompatibility complex (MHC-II) that mimic T cell epitopes derived from influenza hemagglutinin (HA). The rationale is that repeated administration of the biologics would reactivate HA-primed CD4 T cells, stimulating B cells to produce cross-reactive antibodies. Custom scripts were constructed using MATLAB to compare MHC-II ligands of HA and the biologics; all ligands were predicted using tools in Immune Epitope Database and Resources (IEDB). We analyzed three HLA-DR1 alleles (0101, 0401 and 1001) that are prominent in RA patients, and two alleles (0103 and 1502) that are not associated with RA. The results indicate that 0401 would present more analogues of HA ligands in the three anti-TNF? biologics compared to the other alleles. The approach led to identification of potential ligands in IFX and ADA that shares sequence homology with a known HA-specific CD4 T cell epitope. We also discovered a peptide in the complementarity-determining region 3 (CDR-3) of ADA that encompasses both a potential CD4 T cell epitope and a known B cell epitope in HA. The results may help generate new hypotheses for interrogating patient variability of immunogenicity of the anti-TNF? drugs. The approach would aid development of new recombinant biologics by identifying analogues of CD4 T cell epitopes of common pathogens at the preclinical stage.

SUBMITTER: Andrick BJ

PROVIDER: S-EPMC4536234 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNFα Biologics: Implications for Immunogenicity of Pharmaceutical Proteins.

Andrick Benjamin J BJ Schwab Alexandra I AI Cauley Brianna B O'Donnell Lauren A LA Meng Wilson S WS

PloS one 20150813 8

The purpose of this study was to evaluate the extent of overlapping immunogenic peptides between three pharmaceutical biologics and influenza viruses. Clinical studies have shown that subsets of patients with rheumatoid arthritis (RA) develop anti-drug antibodies towards anti-TNFα biologics. We postulate that common infectious pathogens, including influenza viruses, may sensitize RA patients toward recombinant proteins. We hypothesize that embedded within infliximab (IFX), adalimumab (ADA), and ...[more]

PMID: 26270649

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Project description:BackgroundPrediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.MethodsSets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an "immunogenicity score." We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.ResultsThe immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p?=?0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p?=?0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).ConclusionThe new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.

Dataset Information

Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNF? Biologics: Implications for Immunogenicity of Pharmaceutical Proteins.

Publications

Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNFα Biologics: Implications for Immunogenicity of Pharmaceutical Proteins.

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