SIAT^® In Silico Immunogenicity Assessment Service

Background SIAT® In Silico Immunogenicity Assessment Published Data FAQ Resources

A variety of evidence indicates that many biotherapeutic drugs in clinical trials or the market are immunogenic to a variable extent. The immunogenicity should be assessed during drug development because it may compromise the safety and alter the pharmacokinetics of drugs in patients. Creative Biolabs has developed an efficient immunogenicity assessment platform, using our exclusive Sensitive Immunogenicity Assessment Technology® (SIAT®). SIAT® in silico immunogenicity assessment is based on modern bioinformatics techniques in combination with experimental approaches and can be applied to the prediction of the immunogenicity of biotherapeutic drug candidates including protein, enzyme, antibody, ADC, etc.

Background

Antigen-presenting cells (APCs), typically dendritic cells (DCs), can take up antigen in a non-specific manner and cleave the antigen into peptides with a length less than 34 amino acid residues in the endosome. These residues, also known as T cell epitopes, are then presented by human leukocyte antigen (HLA, or major histocompatibility complex, MHC) class II molecules to helper-T (Th) cells that are able to recognize them through specific T cell receptors (TCRs). This recognition leads to the proliferation and differentiation of Th cells. As a result, activated Th cells stimulate certain types of B cells to produce antibodies. This specific stimulation is mediated by the capture of antigenic peptides by B cell receptors (BCRs) expressed on B cells and the presentation of these peptides by MHC II molecules to Th cells. Similarly, the activation of CD8⁺ T cells depends on the peptides presented by HLA class I molecules (MHC I).

Therefore, the immunogenicity of biotherapeutic drugs is critically dependent on the presence of T cell epitopes. Both HLA class I and class II molecules show a high degree of polymorphism and are divided into different subtypes. The existence of different HLA allotypes enables HLA molecules to bind a broad range of peptides while preserving some specificity. The binding affinity of HLA molecules and peptides with different amino acid sequence is different, resulting in the correlation between the concentration of the presented peptide and intensity of T cell activation signal. Thus, estimation of the binding affinity of peptide sequences in a biotherapeutic drug candidate provides valuable information for its potential immunogenicity.

SIAT® In Silico Immunogenicity Assessment

SIAT® in silico immunogenicity assessment service uses a three-dimensional (3D) structure computational modeling method to predict the binding affinity of the MHC molecule/peptide complex. The results of this service provide valuable information for customers to make important decisions for strategic plans. Below is a brief description of our workflow of SIAT® in silico immunogenicity assessment service.

Acquire protein sequence of the interested antibody from customer.
Retrieve an experimentally determined 3D structure of a selected HLA allotype from the database. If the experimental data is unavailable, the structure of the HLA allotype is modeled by homology to the most similar structure.
Cut the protein sequence into short overlapping peptides, typically consisting of 8 to 20 amino acids.
Multiple conformations of the tested peptide are retrieved from a database or by computer modeling algorithm.
Computational modeling of a possible structure of HLA/peptide complex.
Molecular interaction between short peptides and HLA molecules is scored by calculating the potential energy and conformational entropy of the complex structure.
Each peptide is categorized by the calculated score.
A statistical profile is constructed based on these data.

The binding pocket of HLA (yellow) and peptide (red). (Giguère et al. 2013)

SIAT® in silico immunogenicity assessment service takes into consideration the polymorphism of HLA molecules in both individual and a population. Most HLA alleles in the targeted population can be covered. We are able to screen large numbers of potential T epitopes in the biotherapeutic drug candidates, and the accuracy of our method has been confirmed by comparing the predicted values with the experimentally measured results. Our service has been successfully used in the evaluation of therapeutic agents, especially therapeutic antibodies. SIAT® in silico immunogenicity assessment service is best suited for early discovery and exploring stage of biotherapeutic drugs, the results of which provide valuable information that leads you to go through the next stage of drug development with fewer blind spots.

More SIAT® Immunogenicity Related Services at Creative Biolabs

Published Data

Identification of MHC I-Restricted Epitope Signatures Using In Silico and In Vitro Methods

Fig. 2 Panoramic Evaluation of HLA-A*02:01 Reactivity for Peptides Derived from Arbovirus. (Ágata Lopes-Ribeiro, 2022)

The article explores the identification of MHC class I-restricted epitope signatures for arboviruses using in silico and in vitro methods, highlighting its importance for vaccine development against multiple arboviruses. The study's key results include the identification of a limited number of overlapping peptides across different arboviruses through in silico analysis, suggesting specific immune targets, and confirming their immunogenic potential in vitro by demonstrating their binding to the HLA-A*02:01 molecule in a dose-dependent manner. The in silico immunogenicity assessment was crucial for pre-selecting potential immunodominant peptides based on their binding affinity to common human MHC class I molecules, streamlining vaccine design by focusing experimental efforts on the most promising epitopes, thereby enhancing the efficiency of developing broad-spectrum arboviral vaccines.

References

Giguère, Sébastien, et al. "Learning a peptide-protein binding affinity predictor with kernel ridge regression." BMC bioinformatics 14.1 (2013): 1.
Lopes-Ribeiro, Ágata, et al. "In silico and in vitro arboviral MHC class I-restricted-epitope signatures reveal immunodominance and poor overlapping patterns." Frontiers in immunology 13 (2022): 1035515.

FAQ

What is in silico immunogenicity assessment and how is it used in biotherapeutic drug development?
In silico immunogenicity assessment involves using computer-based models to predict the potential immune response that a biotherapeutic drug might elicit in humans. This method leverages algorithms and databases containing information about known epitopes and MHC molecule binding affinities to forecast how the immune system might react to a drug. It's a critical early step in drug development, helping to identify and modify potentially immunogenic regions in therapeutic proteins before clinical trials.
Why is predicting immunogenicity important for the development of biotherapeutics?
Predicting immunogenicity is crucial because an immune response against a biotherapeutic drug can reduce its efficacy, increase the risk of adverse effects, and affect patient safety. By assessing immunogenicity early in the development process, researchers can redesign drugs to minimize these responses, thus enhancing the therapeutic profile and increasing the likelihood of regulatory approval.
What types of data are required for effective in silico immunogenicity prediction?
Effective in silico prediction of immunogenicity requires a diverse set of data, including detailed information about the amino acid sequence of the biotherapeutic, the structure of the protein, and any post-translational modifications. Additionally, comprehensive databases of known T-cell and B-cell epitopes, along with MHC binding data, are essential for accurate modeling and prediction.
How reliable are in silico predictions of immunogenicity, and how do they integrate into the broader drug development process?
The in silico predictions of immunogenicity serve as a preliminary screening tool to guide modifications and focus subsequent experimental efforts. As part of an integrated approach, in silico assessments help streamline the development process by reducing the number of potential immunogenic candidates before advancing to costly and time-consuming experimental stages.
How does in silico immunogenicity assessment differ from traditional experimental methods?
In silico immunogenicity assessment utilizes computational models to predict the immune response to biotherapeutics, offering a faster and less expensive alternative to traditional experimental methods, which involve in vitro assays and animal testing. Computational assessments can quickly analyze multiple sequences and modifications, helping researchers prioritize which candidates to move forward with for more detailed experimental evaluation.
Can in silico models predict the immunogenicity of all types of biotherapeutic drugs?
In silico models are versatile but have limitations depending on the type of biotherapeutic drug being assessed. They are most effective with protein-based therapeutics where the primary structure and potential epitopes can be modeled against known immune-reactive sequences. However, for newer modalities like gene therapies or cell therapies, the models may require significant adaptation to accurately predict immune responses.
What advances in technology have enhanced the accuracy of in silico immunogenicity predictions?
Recent advances include the integration of machine learning and artificial intelligence, which improve the predictive accuracy of in silico models. These technologies can process vast datasets of immunological information to refine predictions based on patterns that might not be evident through traditional methods. Additionally, improvements in molecular simulation techniques and the increasing availability of immunological data have also enhanced these models.