A Quality by Design approach optimizes automation in aseptic production

Advanced (bio-)pharmaceuticals as well as cell and gene therapy offer new ways to treat pathologies in areas such as cancer or autoimmune diseases. This is driving a paradigm shift in drug development. Because terminal sterilization is not possible, these protein-based injectable drugs require aseptic manufacturing. For the design and commissioning of aseptic production facilities, new regulations, such as the EU GMP Annex 1 for the Manufacture of Sterile Medicinal Products, provide guidance. However, for many manufacturers, this poses challenges that require new solutions.

Reducing human intervention as well as applying automation and digitalization are crucial for complying with the regulations. Isolators have become standard practice in the design of aseptic facilities. These rely on filtered air with unidirectional airflow in a sealed cabinet to provide a protective barrier around exposed materials. However, they require large investment and operational effort. To ensure Quality by Design (QbD), a holistic and systematic design approach includes technical and operational aspects, and provides a scale-up path from the early stage. It also saves costs and improves yield.

New opportunities for the design and operation of aseptic lines are also offered by new technologies such as machine learning (ML). While the potential of ML to improve pharmaceutical manufacturing is huge, its use in highly regulated environments requires considerable know-how for its validation.

Helbling has expertise in developing complex equipment and automation solutions, with a focus on challenging requirements in regulated industries such as pharma. Its experience includes viability analysis of client-specific aseptic lines, conceptual design of potential solutions, the efficient execution of technical projects, and commissioning. The systematic approach applied in the design of automation solutions is inherently in line with QbD. Current projects have a particular relevance to QbD.

The following points discuss ML applications for automating operations, robotics and automated processes in isolators, and a transient CFD simulation with a Helbling case study to reduce Time-to-Market of automated aseptic cells.

1. Application of Machine Learning (ML) for automating operations

There are benefits to using ML in aseptic manufacturing: established use cases that provide value include ML-based work item identification, robot grasp point generation and the quality control of work steps by a human or robotic arm. ML also opens up specific use cases inside an isolator, such as automated visual inspection of defects in primary containers during the fill & finish process. It can also detect robotic motions breaching the First Air principle.

As well as solving technical challenges when developing an AI/ML model, there are regulatory requirements to meet in a Good Manufacturing Practice (GMP) environment. Currently, there are no specific regulatory requirements on the use of AI in pharmaceutical production. However, regulatory frameworks including the EU GMP Annex 11 on Computerized Systems and GAMP5 provide general guidance. For example, the use of computerized systems should result in consistent product quality, process control, quality assurance and a stable risk profile across the process.

It follows that the process of developing and validating an AI/ML model requires a systematic approach for its successful application in a controlled manufacturing environment. It is essential to have a validated model for decision-making autonomy, as well as adaptability and adequate quality control measures. To ensure successful deployment, an end-to-end process must consider the complete lifecycle of an AI/ML model and its validation. These three points are particularly relevant:

1. Lifecycle from concept to development and operation/monitoring (see Fig. 1)
2. Deployment with level of control and autonomy (see factbox 1 & table 1 on the maturity model)
3. Required validation measures

Application of Machine Learning

In the context of aseptic production, a practical application could include an automated visual inspection (AVI) quality control task, such as identification of cracks and particles (see Fig. 2). Different levels of control and autonomy are possible here.

For a better overview of the relationship between the level of control and model autonomy, the following table summarizes the maturity model with validation levels. It also outlines the support Helbling can offer.

Helbling has experience in implementing AI and ML solutions in many industries, including the conception and acquisition of real-world or synthetic data, identifying the model architecture, and validation activities. With early-stage applications, regulatory agencies are open to discussion and Helbling can support the client’s dialogue with the authorities. This is crucial for the application of AI/ML in a regulated pharmaceutical environment.
 

2. Robotics and automated processes in isolatorsIn manually operated isolators, each step is carried out by trained personnel operating through hermetically sealed gloves so the throughput of a manual process is limited. There is also a higher risk of contamination due to variability in operator movement, touching product contact surfaces with gloves, and human error. Automation is equally becoming increasingly important for small-batch production of high-value drugs as it supports higher throughput, repeatability, and process traceability.

For small batches, there is a preference for robotic arms because they meet process quality requirements while offering flexibility for different primary packaging formats. Points to consider when choosing a robotic solution include:

• Tradeoff between speed and flexibility of 6-axes robots compared to classical XY stages
• Payload handling
• Compatibility with cleaning and sterilization processes (e.g. vaporized H2O2)
• Sealed design to minimize particle generation through wear
• Design of trajectory to minimize disturbance of First Air

The drug development and manufacturing process is long and complex. Drugs must go through lab, clinical, and pilot phases before finally reaching commercial scale. The must also avoid manufacturing process changes along the way. As a result, including scalability and modularity aspects from the start provides huge benefits that can generate tremendous time-to-market acceleration and cost savings.