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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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Updated: Jun 5, 2025

Purification and Analytics of a Monoclonal Antibody from Chinese Hamster Ovary Cells Using an Automated Microbioreactor System

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Rationalizing mAb Candidate Screening Using a Single Holistic Developability Parameter.

Leon F Willis^1,2, Isabelle Trayton³, Janet C Saunders³

¹School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, U.K.

Molecular Pharmaceutics

|December 16, 2024

Summary

This summary is machine-generated.

Selecting minimal developability assays (DAs) aids drug development. This study identifies five key DAs to predict mAb stability, reducing costs and improving sustainability.

Keywords:

antibody developability assessment formulation kinetic stability protein aggregation

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Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

Area of Science:

Biopharmaceutical development
Protein chemistry
Analytical chemistry

Background:

Developing stable antibody formulations requires robust predictive assays.
Current methods for selecting developability assays (DAs) are not standardized, leading to inefficiency.
Assessing candidate drug stability often involves numerous assays, increasing resource utilization.

Purpose of the Study:

To establish a framework for selecting a minimal set of developability assays.
To identify key assays that predict antibody (mAb) developability and storage stability.
To optimize resource allocation and reduce development costs in biopharmaceutical research.

Main Methods:

Subjected nine formulation:mAbs to 12 distinct developability assays.
Measured accelerated and long-term storage stability of the mAbs.
Employed orthogonal statistical methods to identify key predictive variables from assay data.

Main Results:

A reduced set of five key developability assays was identified.
These five assays can predict mAb degradation rate at 25°C within one day.
The selected assays cover a range of critical physicochemical features relevant to stability.

Conclusions:

A rational framework for selecting minimal developability assays is now available.
This approach enhances resource efficiency, sustainability, and cost-effectiveness in drug development.
Predictive modeling using a minimal DA suite accelerates the identification of stable antibody candidates.