Digitization: The Path to Biopharmaceuticals 4.0

Industry 4.0, also known as the Fourth Industrial Revolution (4IR), will lead to the digital transformation of the manufacturing industry. It is driven by transformative trends such as the surge in data and connectivity, advances in analytics, human-machine collaboration, and enhanced robotics technology. 

Biopharmaceutical manufacturers also attach great importance to automation and data analysis to embrace Industry 4.0. The ultimate goal is to create an automated smart factory where process operations and control decisions are executed without human supervision. 

This setup has multiple advantages, such as the ability to perform computer optimization as a means of gaining a deeper understanding of underlying physical and chemical processes, reducing the number of experiments required, ensuring process operation in the design space, real-time monitoring, process diagnosis, and prediction. 

The implementation of Industry 4.0 is expected to save time and cost in designing optimal processes, prevent batch losses, and maintain high consistency in product quality.

The biopharmaceutical industry is still in the process of transitioning from manual operation to automated operation, providing opportunities to integrate into Industry 4.0. Early work has shown that the implementation of digital solutions has encountered various challenges (Table 1). 

Obviously, there is an urgent need to incorporate digital solutions in the initial stages of process development, manufacturing, and quality assurance (QA).

Digitization in PART/1 process development has two key attributes for any production process, namely scientific knowledge and technical know-how. Scientific knowledge includes process development and validation, while technical knowledge involves process definition and commercial production scaling up. 

Multiple organizations need to collaborate to understand economic and process constraints, and then provide digital solutions focused on new model development as well as existing model validation and updates. 

This exercise involves integrating information flow, manufacturing, and automation to provide the best business processes.

Similar studies have also been conducted in the operation of downstream process units. Data driven models have been used to predict product recycling and optimization. Recently, a hybrid modeling approach that combines mechanical modeling and data-driven modeling has been proven to provide multiple benefits. 

For chromatography, correlating data from sensors with the CQA of the process has been proven to improve CQA prediction with an error of less than 5%. 

In two column capture chromatography, a model-based adaptive control strategy can be used to achieve dynamic control, resulting in a process yield exceeding 85%. Combining data from high-performance liquid chromatography (HPLC) with chromatographic equipment can enable control decisions. 

In addition, the final required charge variant composition can also be obtained. For flow-through chromatography, tray models, mass balance models, and general rate models can be used to capture phases, generate breakthrough curves, and predict product concentrations. 

These models can be encoded in MATLAB, CADET, or ChromX platforms, helping to generate insights that can be transformed into actionable control decisions.

PART/2 Manufacturing Digitalization In the biopharmaceutical industry, distributed control systems (DCS) and programmable logic controllers (PLC) with SCADA systems are two commonly used automation platforms. DCS has centralized set point management, control, and data acquisition functions. 

The PLC/SCADA platform provides local control for unit operations and centralized set point management and data collection.

The digitization of PART/3 quality assurance/quality control also affects quality assurance and quality control. Its implementation has brought higher quality standards and efficiency. 

It helps to improve the transparency and traceability of the process chain, automate data collection and management, and enhance the visibility of the platform. Compliance with regulations is crucial in the biopharmaceutical manufacturing process. 

One of the important FDA guidance documents applicable to digital work is Chapter 21, Part 11 of the Federal Regulations (Electronic Records; Electronic Signature). 

This document involves the maintenance of the process data obtained. In addition, QbD and Good Manufacturing Practice regulations require clear digital methods in virtual process design and its acceptance by regulatory agencies; Intellectual property and information technology security; And possible methods to address regulatory barriers.

PART/4 Outlook: The biopharmaceutical industry is embracing Industry 4.0 and digitizing, aiming to shorten product time to market and improve process efficiency, output, and productivity. 

However, the widespread adoption in manufacturing workshops still needs to be successful, and some major challenges need to be successfully addressed. 

Implementing digital solutions requires close collaboration between multiple functional departments and experts. 

Regulatory agencies are also familiarizing themselves with various aspects of digitization in order to provide effective guidance. Therefore, it is currently necessary to design flexible, modular, and agile processes and develop strategies for implementing PAT solutions during the development phase.

It should be pointed out that the biopharmaceutical industry has not yet fully adopted automation systems, which is a requirement of Industry 3.0 and therefore will also become a requirement of Industry 4.0. 

So far, most research on developing digital twins, model-based control, and soft sensors has focused on the operation of individual units. 

It is necessary to establish smooth communication between unit operations, transmission, and storage in a standardized data format. 

At present, this is an activity that requires a significant amount of manual intervention, partly due to a lack of standards from process and analytical equipment suppliers. 

The Internet of Things and cloud computing initiatives provide interesting possibilities for digitization.