(560e) Control Engineering in (Bio)Pharmaceutical Production: Practical Workflow and Case Studies

The pharmaceutical industry faces multiple challenges in ensuring consistent product quality and optimizing production efficiency. These aspects are necessary to meet strict regulatory and market standards while achieving operational excellence. The ongoing transition to continuous manufacturing introduces new complexities in maintaining process stability and ensuring product quality. This leads to increasing demands for real-time measurement tools of critical quality attributes (CQAs) and process insight for critical process parameters (CPPs). Both information (QA and PP) is relevant for the successful implementation of robust process control to meet the challenges in respect to product quality and resource efficiency. Resource awareness involves quantifying the consumption of materials (e.g. water, measured in terms of process mass intensity) and energy of each unit operation.

Case studies and implementation frameworks for active process control (APC) in pharmaceutical manufacturing, especially for solid-dosage manufacturing, were presented in recent years[1],[2]. Within biopharmaceutical production advances were presented in APC of (mostly fed-batch operated) bioreactors[3],[4]. APC in downstream processing of biopharmaceutical manufacturing, however, has received comparatively less attention, and systematic APC approaches applicable to variety of processes appear to be limited.

The quality by design (QbD) and quality by control (QbC) methodologies focus on the process understanding as well as identifying and controlling CPPs and CQAs. While these frameworks provide a necessary foundation, they do not, by themselves, constitute a systematic approach for the development and application of APC. Hence, here we propose a typical control engineering framework, presented as a step-by-step application-independent workflow. This workflow includes the following tasks:

• Gaining process understanding by scientific approaches
• Developing a process monitoring strategy for real-time CQA monitoring by employing process analytical technology (PAT), soft sensors and considering their data management aspects
• Obtaining process models from thorough process understanding and available data to link CPPs with CQAs and relevant sustainability metrics through application of appropriate algorithms (e.g., data-driven, hybrid, mechanistic)
• Developing control algorithms that consider practical objectives, constraints, and plant architecture
• Benchmarking the developed control algorithms by artificial (worst-case) and realistic test scenarios and using key performance indicators to realistically evaluate achievements in comparison to state-of-the-art systems

The introduced workflow will be discussed from the perspective of selected case studies representative for the diverse challenges across the (bio)pharmaceutical manufacturing spectrum. The discussion will start with an overview of an integrated continuous biopharmaceutical production line. This production line includes the synthesis of active pharmaceutical ingredients (APIs) by means of a fed-batch bioreactor and a multistep purification process leading to a final API product for filling. Based on the integrated line, the link between the unit operations and the impacts of measurement positions on the APC will be discussed, leading to variations and limitations (e.g., closed-loop, open-loop) of underlying control concepts. The desire to achieve specified quality and sustainability conditions requires focus on a real-time monitoring strategy comprising hard and soft sensor approaches. The second case study focuses on a continuous upstream production line for small molecules. Here, the derivation of a control structure for a modular setup will be discussed on plant level. One unit to be highlighted in this case study is a liquid-liquid extraction. It aims to achieve an outlet flow with impurities (measured as concentrations) below threshold limits, decisive for in-spec and out-of-spec material. The benchmarking of this purification unit is highlighted while evaluating different options for APC lasting from feed-forward without real-time measurements up to model predictive control.[5]

The case studies highlight practical implementation aspects, demonstrating how the proposed framework supports the transition from process understanding to automated and controlled productions systems. The talk will be concluded by summing up common implementation challenges and proposing standardized workflows for developing control systems in (bio)pharmaceutical environment. By combining a systematic framework alongside practical case studies, we aim to provide both a solid theoretical foundation and practical guidance for implementing robust control strategies across the (bio)pharmaceutical manufacturing landscape.

[1] Sacher, S., Poms, J., Rehrl, J., Khinast, J.G., 2022. PAT implementation for advanced process control in solid dosage manufacturing - A practical guide. International journal of pharmaceutics 613, 121408. 10.1016/j.ijpharm.2021.121408.

[2]Celikovic, S., Poms, J., Khinast, J., Horn, M., Rehrl, J., 2024b. Development and application of control concepts for twin-screw wet granulation in the ConsiGmaTM-25: Part 2 granule size. International journal of pharmaceutics, 124125. 10.1016/j.ijpharm.2024.124125.

[3] Markana et al., 2018. Multi-criterion control of a bioprocess in fed-batch reactor using EKF based economic model predictive Control. Chemical Engineering Research and Design 136, Pages 282-294. 10.1016/j.cherd.2018.05.032.

[4] Lin et al. 2025. Advances in modeling analysis for multi-parameter bioreactor process control. Biotechnol Bioproc E. 10.1007/s12257-024-00174-7

[5] Boehm, J., Moser, D., Neugebauer, P., Rehrl, J., Poechlauer, P., Kirschneck, D., Horn, M., Steinberger, M., Sacher, S., 2024. A modeling and control framework for extraction processes. Chemical Engineering Science 298, 120384. 10.1016/j.ces.2024.120384.