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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

A prototype software methodology for the rapid evaluation of biomanufacturing process options.

Sunil Chhatre¹, Richard Francis, Kieran O'Donovan

¹Advanced Centre for Biochemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK. chhatre@ucl.ac.uk

Biotechnology and Applied Biochemistry

|September 18, 2007

Summary

This study introduces a three-layered simulation method to quickly assess biomanufacturing process alternatives. It efficiently identifies optimal strategies for developing cost-effective bioprocesses, like rattlesnake antivenom production.

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Area of Science:

Biotechnology and Bioprocess Engineering
Pharmaceutical Manufacturing
Process Optimization

Background:

Current biomanufacturing process development is time-consuming and costly.
Evaluating multiple process alternatives requires extensive experimentation.
Efficient screening of bioprocess options is crucial for cost-effective development.

Purpose of the Study:

To present a novel three-layered simulation methodology for rapid evaluation of biomanufacturing process options.
To streamline the selection of promising candidates for laboratory studies.
To facilitate efficient and cost-effective bioprocess development.

Main Methods:

A three-layered simulation approach was developed, progressively refining evaluations.
Multi-attribute decision-making techniques combined production, cost, and time into a single value.
The methodology was applied to evaluate alternatives for rattlesnake antivenom production.

Main Results:

The simulation methodology effectively screened out inferior biomanufacturing process options.
Alternatives evaluated included increasing feed volume, microfiltration, and Protein G chromatography.
The optimal alternative identified involved a higher feed volume and a Protein G step.

Conclusions:

The described simulation methodology enables rapid assessment of biomanufacturing process alternatives.
This approach focuses experimental efforts on the most promising options.
It leads to more efficient and cost-effective bioprocess development and optimization.