Cell by Design (CbD)
The Importance of Risk-Based Development
Advanced therapy medicinal products (ATMPs) are innovative therapies with high potential to treat debilitating diseases, which are untreatable at this moment. Unfortunately, together with this enormous potential comes a corresponding complexity of products and processes. Today, with a dozen EMA-approved ATMPs, the field is building momentum. The only way to unlock further potential while keeping the complexity under control is through systematic risk-based development.
Traditionally, these ATMPs are developed through an empirical or trial-and-error approach. In these approaches, the producer relies on end-of-line quality testing for batch release during large-scale manufacturing, while the in-process controls are limited and fixed. As a result, the end product quality varies due to variability in raw materials and process parameters. As the variability is not controlled at any step, it accumulates as the process progresses from one step to the next, with the final product quality varying significantly.
This variability is especially challenging in the case of ATMPs, due to differences in raw material characteristics (donors, number of cells,...) in addition to a desired variation of the target product profile (i.e. personalized medicine). Furthermore, the trial-and-error approach is very time-consuming and can enormously increase process development costs. Therefore, alternative development methodologies are needed, such as Quality by Design with integrated process analytical technology (PAT) tools.
Quality by Design & Process Analytical Technology
Quality by Design is a knowledge- and risk-based process development methodology, aiming to establish product quality as an inherent part of the manufacturing process, and this from the early start of the development phase. The European Medicines Agency (EMA) has written guidelines on how this methodology can be used to define strategies for product development in a systematic approach (ICH Q8 [R2] Step 5 Pharmaceutical Development).
This methodology starts with defining the quality, safety and efficacy characteristics of the desired end product, also known as the Quality Target Product Profile (QTTP). In function of the QTTP, critical quality attributes (CQAs) that have a potential impact on product quality can be identified. The product and process knowledge, acquired with Design of Experiments (DoEs), is used to create a design space based on risk-based process analysis (Figure 1). The critical quality attributes should be within the defined limits of the design space to ensure the desired product quality.
This process is to be facilitated by Process Analytical Technology (PAT), a strategy that describes the design and implementation of process monitoring and control tools.
Fig. 1: Quality by Design principle & Process Analytical Technology (PAT): focus on quality is implemented throughout the complete manufacturing process
Naturally, the primary goal of PAT is to monitor the process Critical Quality Attributes (CQAs). The complexity and personalized focus of ATMP products results in quite stringent demands for these analytical tools. First, ATMP products are often produced in single treatment batch sizes (autologous or personalized therapies) with no or only limited material available for destructive release testing. Therefore, the implemented analytical tools must be able to quantify complex biological attributes non-destructively and ideally even non-invasively. Furthermore, ATMP processes are preferably executed in closed systems. This lowers the production cost since no grade A/B clean rooms are needed for the production process and additionally, the therapy can be produced closer to the patient, which reduces transport costs.
Beyond Traditional Analytical Approaches
Thus, it is of high importance that the integrated Process Analytical Technology tools are compatible with closed systems and provide real-time automated readouts of the complex Critical Quality Attributes. The need for real-time, non-destructive and automated read-outs implies moving away from traditional biological assays as much as possible. Hence, a PAT-based monitoring and control system enables both a higher degree of process control and optimization in a cost-effective way, as well as the ability to overcome the need for destructive release testing of the final therapeutic product. Currently, in many cases there are no suited off-the-shelf readout or sensor options available to cover the full range of Critical Quality Attributes of a process (cell density, viability, identity,…) to be monitored in this manner.
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This forces today's cell therapy developers to look beyond the traditional analytical approaches. The onset of artificial intelligence (AI) and machine learning (ML) presents a promising solution in the form of data-based analytical methods, combining data from common, readily available hardware sensors (e.g. pH and O2 probes) with inferential software process models to monitor or predict key process states, such as its Critical Quality Attributes (CQAs).
Overall, the Quality by Design principle (with knowledge gathering as one of its cornerstones) in combination with PAT tools (which provide the means to gather this data effectively and efficiently) increases the robustness of the process.
The enhanced process knowledge captured in the design space and translated to a dedicated monitoring and control system, results in increased process robustness and product quality (Figure 2).
Fig. 2: The figure compares the traditional approach for cell-based products with a PAT + QbD approach. In traditional approaches such as 2D and 3D cell culture systems (state-of-the-art), only limited monitoring, cell control, and robustness are achievable: the result is a heterogeneous cell population. Quality by Design in combination with PAT, on the other hand, increases process robustness and product quality through enhanced monitoring and control actions compared to traditional process methodologies, where end-of-line quality testing takes place.
However, at the moment, the practical framework to guide scientists and engineers for process development according to Quality by Design principles is often not available, resulting in a relatively low number of processes with quality as an inherent part of their design.
In this context, the Cell by Design (CbD) platform was created to provide the needed framework to tackle the R&D complexity of cell-compatible in-process monitoring and control.
Cell by Design Framework
Cell by Design is a cloud-based platform that guides the user step by step through the application to develop a robust and controllable process on a knowledge- and risk-based principle. The various steps of the CbD framework are described briefly below and are shown schematically in Figure 3.
Step 1 - Quality Target Product Profile & Critical Quality Attributes
After creating a new project in the CbD software, the first step is a proper definition of the Quality Target Product Profile (QTPP) by defining the quality characteristics the end product must meet.
Therefore, the Quality Target Product Profile forms the basis of design for development of the product, such as enabling the definition of Critical Quality Attributes (CQAs).
A CQA is a physical, chemical biological, or microbiological property or characteristic that should be within an appropriate limit, range or distribution to ensure the desired product quality, safety and efficacy. Some typical examples of CQAs in ATMPS are cell yield, cell viability, percentage of transfected cells and percentage of specific target markers on the cell membrane.
Fig. 3: Schematic overview of Cell by Design framework
Step 2- Process Mapping
In the next step, the manufacturing process is mapped by dividing it in unit operations. A unit operation is a basic step in the process in which a physical or (bio)chemical change is involved, such as apheresis, cell sorting, filtration, cell transfection, cell expansion, harvesting, etc. As indicated in Figure 4, the CQAs of each unit operation are affected by the Process Parameters (PPs) and Material Attributes (MAs) involved during the execution of each process step. Therefore, proper determination of these PPs and MAs is of crucial importance in the control of the critical quality attributes. For example, changing the cell density, time of cultivation or cultivation medium/volume during cell culturing, may have an effect on the viability and yield after harvesting.
In this process mapping step, it is also possible to use a clustering option, in which several unit operations can be clustered because the same Process Parameters/Material Attributes are applied. Additionally, this cluster can be investigated as a sub-process, which is particularly interesting for very long and elaborate manufacturing processes for which it is too time and resource-consuming to carry out the entire manufacturing process for each separate experiment.
Fig. 4: Schematic overview of a unit operation with its Process Parameters and Material Attributes as input variables and the Critical Quality Attributes as output
Step 3 - Risk Assessments
To prioritize process developmental efforts, it is important to quickly and efficiently determine the most critical attributes/parameters involved in the process and especially those that demonstrate a possible synergistic effect. Therefore, Cell by Design uses a risk assessment approach divided into two sections. In the first section of the risk assessment, the most critical unit operation is determined by scoring each Critical Quality Attribute. This scoring is based on the knowledge of the process within the development team.
A rationale for each decision can be provided in the Cell by Design framework. Once the most critical unit operation has been identified, a second risk assessment is carried out. In this second section, the most critical unit operation is selected to create a risk assessment matrix in which each CQA is scored against all process variables (the PPs and the MAs). As a result, this matrix provides a ranking or prioritization of the most critical unit operation and its most critical parameters to be investigated.
The design space created can then be used to develop a control strategy. For example, a collection of operating ranges for the Critical Process Parameters/Material Attributes within which their values must be held to keep the Critical Quality Characteristics within their desired ranges. For optimal use of the design space during process development, the Cell by Design platform uses algorithms for decision-making tools.
If a source of variability was not taken into account in the DoE variables, this will result in noise in the gathered data and ultimately lead to uncertainty in the statistical model. In turn, this results in impractical and narrow operating ranges of the control strategy obtained from the design space. It is a tell-tale signal that a new DOE is required with additional variables. When such an issue occurs, it is important to revisit the risk assessment to update it and change it according to the findings of the DoE (Figure 3).
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Step 4 - Deisgn of Experiments / Design Space
The most critical variables from the risk assessments are selected to perform Design of Experiments (DoEs). These DoEs are used to efficiently gather data to build a design space, which is a databased statistical model that captures the relationship between the Critical Process Parameters/Material Attributes and the Critical Quality Attributes.
As indicated in Figure 3 and briefly mentioned in the section above, data obtained from the Design of Experiments are used to adjust the risk assessments if relevant. Based on the altered risk assessments, new Design of Experiments can be set up to improve the design space. In this manner, the risk analyses function as process lifecycle documents, forming the center of the Cell by Design methodology. They centralize and harmonize all the available knowledge, facilitating goal-oriented and risk-based decision making. This reiterative process is repeated until the optimization of the Critical Process Parameters and Material Attributes is concluded and a robust and controllable process has been developed.
ATMP process development faces challenges that come with the development of highly complex processes, by multidisciplinary teams. Cell by Design enables centralised, risk-based and goaloriented process development. The platform utilizes risk analyses and Design of Experiments, which work together in an iterative manner to build process knowledge, by prioritising the most critical aspects of the process. Capturing the methodology in a soft - ware facilitates the adoption by process development professionals, and enables centralization of all the risk analyses and gathered data. This facilitates the compilation of an IMPD or marketing authorization application, for which a risk-based approach to process development is considered almost essential by regulatory authorities.
About the Authors:
Johan Van den Bergh
... PhD is a pharmaceutical consultant in the life sciences at Quality by Design, working on the optimization of production processes in the quality sector
... has a background in biomedical engineering. At Antleron he focuses on the application of Quality-by-Design to ATMP process development, as well as the development of innovative process monitoring and control technology.