(327f) Physical and Data-Driven Modelling of in Vitro Transcription Process | AIChE

(327f) Physical and Data-Driven Modelling of in Vitro Transcription Process

Authors 

Kalospyrou, I. - Presenter, University of Sheffield
Tao, M., University of Manchester
Kis, Z., Imperial College London
Cordiner, J., University of Sheffeld
Brown, S. F., University of Sheffield
Milton, R., University of Sheffield
Ahmed, M., University Of Sheffield
Abstract

In Vitro transcription (IVT) process is the key part of the RNA production process [1]. Accurately modelling the general IVT process can significantly contribute to developing soft sensors and digital twins for automatically manufacturing RNA products [1, 2]. In this work, we utilised the up-to-date process knowledge and experimental data to build more detailed mechanistic models for predicting the RNA yield and its critical quality attributes (CQA) [1, 2, 3]. Meanwhile, the novel Gaussian process (GP) and global sensitivity analysis (GSA) [4] based machine learning approach was employed to construct fast-solving data-driven models, based on the high-quality data from the built physical models and pure experimentation. In future, the constructed mechanistic and data-driven models will be used to guide process automation and quality by design (QbD) [5] for the RNA manufacturing.

Keywords: Gaussian Process, RNA manufacturing, In Vitro transcription, Global Sensitivity Analysis, Soft Sensor, Digital Twin

[1] Berg D van de, Kis Z, et al. (2021) Quality by Design modelling to support rapid RNA vaccine production against emerging infectious diseases. NPJ Vaccines. 6, 1–10.

[2] Kis Z, Kontoravdi C, et al. (2020) Rapid development and deployment of high volume vaccines for pandemic response. J. Adv. Manuf. Process. 2, e10060.

[3] Kis Z, Kontoravdi C, et al. (2021) Resources, Production Scales and Time Required for Producing RNA Vaccines for the Global Pandemic Demand. Vaccines. 9, 1–14.

[4] Yeardley AS, Bellinghausen S, et al. (2021) Efficient global sensitivity-based model calibration of a high-shear wet granulation process. Chem. Eng. Sci. 238, 116569.

[5] Daniel, S., Kis, Z., Kontoravdi, C., et al. (2022). Quality by Design for enabling RNA platform production processes. Trends in Biotechnology.

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