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Hybridoma Technology01:31

Hybridoma Technology

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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: Mar 13, 2026

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells
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Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

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A practical framework for clone selection, media-feed screening, and upstream process parameter optimization for a

Shreya Shah1, Dhananajay Kumar1, Aadesh Dhamdhere1

  • 1Upstream Process Development, R&D, Intas Pharmaceuticals Limited, Biopharma Division, Ahmedabad, Gujarat, India.

Biotechnology Progress
|March 12, 2026
PubMed
Summary

This study introduces a data-driven workflow for biologics manufacturing, using machine learning and statistical tools to enhance upstream process development for better consistency and efficiency. The integrated approach improves clone selection, media screening, and parameter optimization, aligning with Bioprocessing 4.0 principles.

Keywords:
Bayesian optimizationbioprocessing 4.0design of experimentsglycosylationmultivariate data analysis (MVDA)upstream process development

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Data Science

Background:

  • Upstream process development in biologics manufacturing traditionally relies on subjective interpretation.
  • There is a need for more consistent, efficient, and data-driven approaches to biologics process development.

Purpose of the Study:

  • To present a data-driven workflow for upstream process development in biologics manufacturing.
  • To improve consistency, efficiency, and decision-making using statistical and machine learning tools.
  • To integrate multiple analytical methods for structured support of key process development decisions.

Main Methods:

  • K-means clustering for clone selection, benchmarked with hierarchical clustering.
  • Principal Component Analysis (PCA) for media and feed screening to analyze glycosylation patterns.
  • Multi-objective Bayesian Optimization (MOBO) for adaptive upstream process parameter refinement.

Main Results:

  • Improved consistency and reduced subjectivity in clone selection by incorporating multiple product quality attributes.
  • Rapid identification of promising media and feed conditions for glycosylation optimization, reducing experimental burden.
  • More efficient identification of improved operating conditions through adaptive exploration of trade-offs among quality attributes and titer, minimizing experimental runs.

Conclusions:

  • The structured integration of established statistical and machine learning methods provides practical utility for industrial upstream bioprocess development.
  • The workflow supports improved process understanding and informed decision-making, aligning with Bioprocessing 4.0 principles.
  • This data-driven approach enhances biologics manufacturing by optimizing key development stages.