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Related Concept Videos

Hybridoma Technology01:31

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, polyethylene glycol...

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Related Experiment Video

Updated: Jul 14, 2026

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules
04:24

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Published on: June 16, 2023

Automation and Active Learning for the Multi-Objective Optimization of Antibody Formulations.

D Christopher Radford1, Matthew Tamasi1, Elena Di Mare1

  • 1Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|July 13, 2026
PubMed
Summary

Machine learning accelerates antibody bioformulation by predicting excipient effects. This high-throughput pipeline optimizes therapeutic protein formulations, improving stability and performance.

Keywords:
GRAS excipientsbayesian optimizationbiologicsmachine learningprotein formulation

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A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
08:17

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood

Published on: February 8, 2016

Related Experiment Videos

Last Updated: Jul 14, 2026

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules
04:24

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Published on: June 16, 2023

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
08:17

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood

Published on: February 8, 2016

Area of Science:

  • Biopharmaceutical Engineering
  • Protein Chemistry
  • Computational Biology

Background:

  • Monoclonal antibodies are crucial therapeutics, typically manufactured as preformulated solutions.
  • Bioformulation of complex proteins like antibodies presents significant engineering challenges due to intricate excipient interactions.
  • Traditional formulation development is time- and material-intensive, struggling with multidimensional design spaces and non-linear relationships.

Purpose of the Study:

  • To develop and validate a machine learning-driven pipeline for high-throughput antibody bioformulation.
  • To model and predict the impact of Generally Recognized As Safe (GRAS) excipients on antibody formulation behavior.
  • To integrate automated Design-Build-Test-Learn (DBTL) cycles for efficient formulation optimization.

Main Methods:

  • Utilized a high-throughput discovery pipeline incorporating machine learning algorithms.
  • Employed automation-assisted "on-demand" formulation to generate diverse antibody solutions.
  • Integrated an iterative DBTL cycle for model-guided experimental design and refinement.
  • Conducted biophysical characterization of formulated antibody solutions.

Main Results:

  • Achieved accurate predictive models for antibody formulation behavior across multiple objectives.
  • Successfully identified improved antibody formulations balancing competing design criteria.
  • Demonstrated optimization of key formulation parameters including melting temperature, diffusivity, and high-concentration viscosity.
  • Mapped the property-function landscape of excipient effects on antibody formulations.

Conclusions:

  • The machine learning pipeline effectively guides antibody formulation development.
  • This approach accelerates the optimization of biotherapeutics by managing complex design requirements.
  • The validated technique enables efficient exploration of the formulation design space for improved therapeutic agents.