(743c) Digital Twins As Means of Process Intensification: A Case Study on Monoclonal Antibody Production | AIChE

(743c) Digital Twins As Means of Process Intensification: A Case Study on Monoclonal Antibody Production


Papathanasiou, M. - Presenter, Imperial College London
Rigou, D., Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London
Kontoravdi, C., Imperial College London
Robust process design in biopharmaceuticals requires simultaneous optimisation of various parameters for the identification and maintenance of optimal operation. This is often translated into lengthy and costly experiments that aim to identify the interplay between process parameters and product performance. Following the principles of Quality by Design (QbD), as endorsed by regulatory authorities, an offline framework is applied whereby the Critical Process Parameters (CPPs) are identified and their impact on Critical Quality Attributes (CQAs) is monitored. CQAs should be maintained within pre-defined limits to ensure product safety and efficacy. Post process development, suitable and reliable Process Analytical Technologies (PATs) need to be in place to allow continuous process monitoring and minimise the risk of off-speck batches.

Digital analogues of the production setup can serve as lower-cost testing platforms to enhance understanding of the dominant process dynamics (1). These are commonly termed as “digital twins” and are used for process simulation, optimisation, control and monitoring. In this work we focus on the development of a digital twin to integrate upstream and downstream processing in monoclonal antibody production, aiming to investigate the impact of the former on the latter. In particular, we investigate how different upstream operating conditions impact the downstream load, based on previously presented process models (2–4). Working under QbD principles, we explore how upstream conditions can be optimised to improve downstream performance. The operating profiles are assessed based on their performance with respect to two Key Performance Indicators (KPIs); purity and yield.


  1. Papathanasiou MM, Kontoravdi C. Engineering challenges in therapeutic protein product and process design. Vol. 27, Current Opinion in Chemical Engineering. Elsevier Ltd; 2020. p. 81–8.
  2. Kotidis P, Jedrzejewski P, Sou SN, Sellick C, Polizzi K, del Val IJ, et al. Model‐based optimization of antibody galactosylation in CHO cell culture. Biotechnol Bioeng. 2019 Jul 21;116(7):1612–26. 10.1002/bit.26960
  3. Papathanasiou MM, Steinebach F, Morbidelli M, Mantalaris A, Pistikopoulos EN. Intelligent, model-based control towards the intensification of downstream processes. Comput Chem Eng. 2017;105:173–84.
  4. Müller-Späth T, Aumann L, Melter L, Stroehlein G, Morbidelli M. Chromatographic separation of three monoclonal antibody variants using multicolumn countercurrent solvent gradient purification (MCSGP). Biotechnol Bioeng. 2008;100(6):1166–77. 10.1002/bit.21843