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A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

Published on: September 22, 2011

Quality by Digital Design for Developing Platform RNA Vaccine and Therapeutic Manufacturing Processes.

Adithya Nair¹, Kate A Loveday¹, Charlotte Kenyon¹

¹Department of Biological and Chemical Engineering, University of Sheffield, Sheffield, UK.

Methods in Molecular Biology (Clifton, N.J.)

|May 30, 2024

Summary

Quality by Digital Design (QbDD) uses computational models to optimize pharmaceutical manufacturing, improving upon Quality by Design (QbD). This data-driven approach enhances understanding of critical quality attributes and process parameters for efficient production.

Keywords:

Affinity chromatography Digital twins In vitro transcription Lipid nanoparticle Quality by digital design Tangential flow filtration mRNA vaccines

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Area of Science:

Pharmaceutical Manufacturing
Process Engineering
Computational Modeling

Background:

Quality by Design (QbD) is a widely adopted pharmaceutical approach.
Quality by Digital Design (QbDD) offers an advanced, data-driven methodology.
QbDD integrates computational models for enhanced process understanding.

Purpose of the Study:

To discuss the application of QbDD in optimizing unit operations for mRNA vaccine manufacturing.
To highlight QbDD's role in defining and optimizing manufacturing design spaces.
To showcase QbDD as a framework for disease-agnostic product development.

Main Methods:

Utilizing data-driven, mechanistic, or hybrid computational models.
Quantifying relationships between critical quality attributes (CQAs) and critical process parameters (CPPs).
Applying QbDD to unit operations including in vitro transcription, filtration, chromatography, and LNP formulation.

Main Results:

QbDD enables precise definition and optimization of manufacturing design spaces.
Identifies and quantifies critical process parameters impacting product quality.
Facilitates efficient development of complex manufacturing processes.

Conclusions:

QbDD provides an efficient framework for mRNA vaccine production.
It is particularly effective for disease-agnostic products requiring early-stage process-product characterization.
QbDD enhances the optimization of key unit operations in pharmaceutical manufacturing.