End-to-end digital twin software for continuous mRNA manufacturing.

Area of Science:

• Biotechnology
• Pharmaceutical Manufacturing
• Computational Modeling

Background:

• Messenger RNA (mRNA) therapeutics offer rapid development and broad applications but face manufacturing scalability and consistency issues with traditional batch processes.
• Continuous manufacturing presents a promising alternative for enhancing mRNA production efficiency and reliability.

Purpose of the Study:

• To develop and present a novel digital twin software for end-to-end continuous manufacturing of mRNA therapeutics.
• To integrate key unit operations into a modular, user-friendly platform for process simulation and optimization.

Main Methods:

• The software integrates In Vitro Transcription (IVT), Tangential Flow Filtration (TFF), Continuous Chromatography (CCTC), Lipid Nanoparticle (LNP) formulation, and Freeze-Drying modules.
• A graphical interface allows engineers to manipulate modules without coding, supported by mechanistic models and a database for data storage.
• Morris-based global sensitivity analysis was employed for process optimization and model validation.

Main Results:

• The software successfully simulated all five unit operations in silico for a representative mRNA production scenario.
• Generated time series and 3D plots provided insights into process dynamics and product characteristics.
• Sensitivity analysis identified critical input factors for process optimization.

Conclusions:

• The digital twin software accelerates process optimization and promotes Quality-by-Design (QbD) principles for mRNA manufacturing.
• It provides a platform for closed-loop control and operator training, supporting data-driven continuous manufacturing.
• This technology enhances scalability, reliability, and responsiveness for mRNA therapeutics production.