Towards Autonomous Operation of Biologics and Botanicals

Dear Colleagues,

Biologics are increasingly dominating the pharmaceutical market. The current pandemic conditions have enhanced the urgency of developing innovative manufacturing technologies to provide sufficient doses for all patients. Besides the recent rise in mAbs usage, a broader molecular variety of fragments, exosomes, and VLPs, and pDNA/mRNA, etc., as well as recombinant peptides and proteins, are in the early product development phase. In addition, various biologics, from anti-MRSA to glyphosate substitution, are under consideration for current urgent socio-economic tasks in agrochemistry. As no teachnological platform has been established for mAbs manufacturing, there is no solution for fast and efficient process development under the regulatory constraints of the QbD and PAT approaches.

Autonomous manufacturing operation would speed-up the supply and process robustness of biologics, as well as the product safety. Advanced process control (APC) techniques are well established in other branches of production, but in biotechnology, industrialization is prevented by the misleading prejudice related to the lack of suitable validated process models as digital twins and inline measurement technologies with regard to the regulatory demanded process analytical technology approach (PAT) within the regulatory QbD (quality by design) framework.

Model-based methods are increasingly being used in all areas of biotechnology. They can be applied along the entire workflow of product development, process development and design, piloting, engineering, and manufacturing operations, including life cycle management. Nevertheless, the molecular complexity of biologics challenges the accuracy and precision of model-based predictions. Under the strict regulation of QbD and PAT approaches, any digital twin of any manufacturing process must be defined and validated early on in process development when the first test amounts are supplied for approval, because the process, including its natural interactions with a digital twin, is fixed. Post-approval changes are organized through the use of guidelines, but the benefit of any modification must be clearly and quantitatively documented in order to evaluate the benefits and risks of data-driven decisions. The variety of modeling methods is broad, ranging from molecular dynamics in drug development to statistical and regression models as well as artificial intelligence tools like neuronal networks or data mining, machine learning algorithms for rigorous process modeling, and model-based advanced process control concepts.

This Special Issue on “Towards Autonomous Operation of Biologics and Botanicals Manufacturing” intends to curate novel advances in the development and application of model-based tools, process analytical technology, and advanced process control applications to address the ever-present challenges related to traditional pharmaceutical manufacturing practices. Topics of interest include, but are not limited to

• Advanced process control concepts and studies;
• Process analytical technology concepts and studies;
• Validation of digital twins with the development of new modeling concepts for biologics manufacturing at different steps of the workflow (phenomena, unit operation, and plant-wide);
• Design and optimization of biological processes through the derived models based on the QbD-approach;
• Process intensification, robustification, and flexibilization of multipurpose manufacturing as well as dedicated continuous bioprocessing CBP;
• Hybrid modeling combining classical first-principles models with (big) data-driven concepts;
• Process control, monitoring, and fault detection in the biologics industry.

Prof. Dr. Jochen Strube
Dr. Axel Schmidt
Guest Editors

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