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Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...

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Related Experiment Video

Updated: Jun 28, 2025

Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood

Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood

Published on: February 6, 2018

Prediction of polyspecificity from antibody sequence data by machine learning.

Szabolcs Éliás¹, Clemens Wrzodek¹, Charlotte M Deane²

¹Roche Pharma Research and Early Development Informatics, Roche Innovation Center Munich, Penzberg, Germany.

Frontiers in Bioinformatics

|April 23, 2024

Summary

This summary is machine-generated.

Scientists developed a machine learning model to predict polyspecific antibodies, which can cause side effects. This tool aids in developing safer and more effective therapeutic antibodies by identifying polyspecificity early.

Keywords:

antibody deep learning immune repertoire immunoglobulin machine learning neural network polyspecificity therapeutic antibodies

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Published on: June 29, 2016

Related Experiment Videos

Last Updated: Jun 28, 2025

Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood

Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood

Published on: February 6, 2018

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

Antibody Binding Specificity for Kappa (Vκ) Light Chain-containing Human (IgM) Antibodies: Polysialic Acid (PSA) Attached to NCAM as a Case Study

Antibody Binding Specificity for Kappa (Vκ) Light Chain-containing Human (IgM) Antibodies: Polysialic Acid (PSA) Attached to NCAM as a Case Study

Published on: June 29, 2016

Area of Science:

Biotechnology
Immunology
Computational Biology

Background:

Antibodies are crucial for biologic drugs due to their specificity.
Therapeutic antibodies must be safe and efficient.
Polyspecific antibodies can bind unintended targets, causing adverse effects and reducing efficacy.

Purpose of the Study:

To develop a predictive model for antibody polyspecificity.
To identify features influencing polyspecificity.
To aid in the development of safer therapeutic antibodies.

Main Methods:

Created a neural-network-based model using antibody heavy chain variable region sequences.
Developed a strategy to enrich antibodies for specific or polyspecific binding.
Generated and utilized a large sequencing dataset for model training and validation.

Main Results:

Successfully developed a machine learning model to predict antibody polyspecificity.
Identified key physico-chemical features that influence polyspecificity.
Demonstrated a method for enriching antibodies with desired binding properties.

Conclusions:

The neural network model offers a novel approach to predict antibody polyspecificity.
Understanding polyspecificity can improve therapeutic antibody design.
This machine learning approach may enhance the safety and efficacy of antibody-based drugs.