Protein Therapeutics Immunogenicity Stephen Lynn 11-16-11 CHEM645 0.

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Protein Therapeutics Immunogenicity Stephen Lynn 11-16-11 CHEM645 1
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Transcript of Protein Therapeutics Immunogenicity Stephen Lynn 11-16-11 CHEM645 0.

Protein TherapeuticsImmunogenicity

Stephen Lynn11-16-11CHEM645

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Anti-therapeutic Protein Antibodies

• Known as monoclonal antibodies (mAbs) or anti-drug antibodies (ADAs)

• Produced by the B lymphocytes of the immune system in response to foreign proteins

• Neutralize or compromise the clinical effect of therapeutic proteins

• May cause serious adverse events related to cross-reactivity with autologous proteins– ADAs that interact with a ligand–receptor may inhibit

the efficacy of all ligands in the same class.

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Immunogenicity

• Ability of a particular substance, such as an antigen or epitope, to provoke an immune response in the body of a human or animal

• Strategies for reducing immunogenicity:– Reduce the total number of doses– Minimize impurities (contaminants/aggregates)– Diminish T cell activation through sequence

modifications (must maintain biological function)– Avoid delivery vehicles that have altering effects on

other biological agents

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Immune ResponseT-cell Dependent

• B cell activation can be T cell-independent (Ti) or T cell-dependent (Td)

• Td activation of B cells results in a more robust antibody response, isotype switching, and the development of B cell memory

• The induction of IgG class ADAs (measurable in immunogenicity assays) through B cell secretion implies that the therapeutic protein is a Td antigen

• Td responses require T cell recognition of epitopes contained in the protein drug, binding of peptide epitopes [derived from internal processing by antigen-presenting cells (APCs)] to HLAs (human leukocyte antigens) MHC class II molecules, and recognition of the epitope–HLA complex by activated T helper cells

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Reducing risk, improving outcomes: Bioengineering less immunogenic protein therapeutics

Anne S. De Groot a,b , William Martin aa EpiVax, Inc., USAb University of Rhode Island Biotechnology Program, USA

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In vitro Assays

• Class I epitopes are short and fit tightly in the bounded MHC molecule; by contrast, Class II (T helper) epitopes lie within an open-ended groove in the MHC II complex

• Class II epitope can shift within the groove thereby accommodating MHC of various haplotypes. The only limit on the size of the peptide is its ability to remain in a linear conformation in the open-ended groove.

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In vitro Assays

• HLA binding assays can be used to assess whether peptides derived from protein sequences can bind to either MHC class I or class II.

• In vitro evaluation of HLA binding can be performed by quantifying the ability of exogenously added peptides to compete with a fluorescently-labeled MHC ligand and can be adapted for high throughput.

• ELISA, ELISpot, Fluorescent labeling (FACS), and intracellular cytokine staining (ICS) are also available methods for measuring and defining T cell response.

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In silico Methods• A number of in silico tools for predicting T-cell epitopes have been

developed involving computer algorithms that map the locations of MHC class I and class II-restricted T-cell epitopes within proteins of various origins. The in silico methods include frequency analysis, support-vector machines, hidden Markov models, and neural networks.

• MHC class II prediction methods are more directly related to Td immunogenicity assessment, as class II restricted T cells provide help to B cells, leading to the development of ADA.

• Some epitope-mapping algorithms allow researchers to measure the potential immunogenicity by epitope density of whole proteins such as bioengineered protein therapeutics, monoclonal antibodies (mAbs) and their sub-regions.

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Comparison of in silico Algorithms• The Immune Epitope Database (IEDB) developed a “gold standard

list” of T-cell epitopes. Using the list as a benchmark, a number of tools have been compared by the team at the IEDB. Available online epitope-mapping algorithms were compared for their ability to correctly predict a set of validated class I and class II HLA-restricted epitopes competitors.

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ClustiMer Analysis• The ClustiMer algorithm identifies polypeptides predicted to bind

to an unusually large number of HLA alleles; these sequences are then extended at the n- and c-terminal flanks until the predicted epitope density of the candidate epitope cluster falls below a given threshold value. In general, T-cell epitope clusters identified by the ClustiMer algorithm tend to be promiscuous MHC binders and are frequently T-cell epitopes

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EpiBar Analysis• An EpiBar is a single 9-mer frame that is predicted to be reactive to at least four different

HLA alleles (Fig. 4B) EpiBars may be a signature feature of highly immunogenic, promiscuous class II epitopes.

• Peptides scoring above 1.64 on the EpiMatrix “Z” scale (typically the top 5% of any given sample) are likely to be MHC ligands.

• CLIP (Class II invariant chain peptide) binds MHC II groove before receptor

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Role of regulatory T-cell epitopes

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• Natural regulatory T cells appear to serve as regulators or suppressors of autoimmune, auto-reactive immune responses

• They do not promote immune function, but act to decrease it instead. Despite their low numbers during an infection, these cells are believed to play an important role in the self-limitation of the immune system; they have been shown to prevent the development of various auto-immune diseases.

• T cells with high affinity for self antigens are deleted whereas those with moderate affinity for self antigens may escape deletion and may be re-programmed to function as “natural” regulatory T cells.

• Further studies must be done to fully understand role in immune response and immunogenicity.

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Pros and cons of assays for measuringimmunogenicity: antibody assays

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