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The evolution of cell and gene therapy from discovery to commercialization
The evolution of cell and gene therapy from discovery to commercialization. The field of cell and gene therapy (CGT) is maturing rapidly. Manufacturing remains a major challenge, and manufacturing failures and restrictions are important reasons why CGT products do not meet specifications or cannot obtain regulatory approvals.
In order to ensure that products remain compliant and competitive throughout their life cycle, CGT process development requires continuous improvement of technologies suitable for use to allow and support well-designed development research, as well as the development of methods that enable these research to be effectively carried out during the key product period movement.
We describe a continuous innovation framework for CGT process development, manufacturing, and clinical testing based on the quality of design (QbD) method.
1. The manufacturing of cell and gene therapy products requires a framework that supports continuous innovation
The field of cell and gene therapy (CGT) includes advanced treatment methods, including living cells, tissues, viral vectors, and non-viral genetic modification components. The CGT industry is growing rapidly. As of 2020, it is reported that there are more than 1,000 ongoing CGT clinical trials, and the revenue of the CGT market is expected to grow at a compound annual growth rate (CAGR) of more than 30% between 2019-2025.
Most of the recent growth has come from gene therapy for single-gene diseases, as well as gene-modified cell therapy in the field of immuno-oncology, especially chimeric antigen receptor T cell (CAR-T cell) therapy. In the past 5-10 years, as the company strives to achieve positive milestones, the positive clinical results and interest of investors and the pharmaceutical industry have led to an accelerated pace of activity.
A key challenge posed by this rapid growth is the need to manufacture CGT products in a robust and stage-adapted way throughout the product development life cycle, while still allowing innovation to be incorporated.
Manufacturing failure is a significant reason why CGT products do not meet specifications and are not delivered to patients, and CMC issues are the main reason why CGT products cannot obtain regulatory approval. Several companies have publicly acknowledged that there are CMC-related challenges in recent post-interactions with regulators, including refusing to submit Bristol-Myers Squibb’s 2020 biologics license application for its ide-cel CAR-T product ( Documents received in BLA). For autologous or patient-specific products, manufacturing failure may mean that the patient cannot receive treatment, or it may use substandard products without company reimbursement .
Novartis reported that due to manufacturing failures or products that do not meet specifications , as well as additional substandard products, about 10% of their CAR-T product Kymriah on the market has not been shipped to patients. Management without reimbursement. For allogeneic CGT products, producing large quantities of products to treat many patients, manufacturing failure may not have an immediate clinical impact, but it may have a huge financial impact . During product development and clinical research, the combination of manufacturing innovation and new technology solutions is very important to ensure that products remain compliant, competitive, and cost-effective after they are approved in the market.
The manufacturing solutions for CGT products are non-standard. They are still emerging and usually evolve with industry products and clinical stages. Further innovation around manufacturing is a key area of research and development (R&D) for academic and tool and reagent companies, an important differentiating factor between products, and a core part of the company’s product line and intellectual property strategy. Several types of manufacturing challenges are common in CGT products.
CGT products usually have complex components that are highly specific to individuals, some of which are composed of the patient’s own cells or precisely matched donors. These in vivo drugs are usually made from highly variable cell-based raw materials, and the manufacturing steps may require skilled manual operations and strategies to address the inherent biological variability in cell growth and characterization. The final cell or gene product is usually fragile, has a short shelf life, and requires careful control of the supply chain. The biological mechanisms of these products are also very complex and highly dependent on the in vivo interactions when they are delivered to patients [8, 9], making it difficult to define relevant manufacturing release standards.
As the CGT field matures, methods are being developed to reduce manufacturing complexity and variability in a variety of ways. An example is the increasing focus on allogeneic cell therapy products as a step in the production of ready-made products [10, 11]. These products have the potential to simplify manufacturing by standardizing input materials and reducing the complexity of the supply chain. Raw material suppliers are developing to focus on chemically determined and animal-free products to improve consistency and ensure adequate quality standards [12, 13], and supply chain solutions support extensive distribution with appropriate traceability [14, 15], industrial bioprocess strategies adapted from the more mature biologics field began to be implemented in CGT manufacturing [16, 17]. The combination of these strategies is more stringent, which is essential for CGT to develop into a more mature and powerful field.
To complement the maturity of CGT manufacturing, process development methods must also be mature. This will require continuous improvement of technologies suitable for use to allow and support well-designed development research, as well as methods to enable these research to be effectively conducted in key product development activities. Here, based on the quality by design (QbD) approach and its inherent iterative nature, we propose a framework for appropriate innovations in the ongoing stages of CGT process development and manufacturing to better combine technology-based improvements and discovery-based The biology of insight throughout the life cycle of product development and commercialization.
2. The QbD structure guides the iterative refinement of the process design space through the product development life cycle
In the early development stage of CGT products, the understanding of the mode of action (MoA) and target product profile (TPP) is incomplete or limited. Early clinical data is used to improve the TPP, thereby continuously enhancing the understanding of the product, as well as the ability to identify and hone the critical quality attributes (CQA) of the manufacturing process. However, most knowledge does not appear until later in the product development cycle. To guide preclinical development, the initial early draft of the TPP and related CQA should be defined. However, TPP and CQAs are not fixed in the early development, but should be iteratively refined. Among them, TPP1, which was originally very extensive, was refined and reduced through iterative cycles, resulting in TPP2, TPP3,… Multi-data collection and understanding, the increase of TPP in the final cell product (Figure 1A).
Figure 1 Iterative refinement of target product overview, critical quality attributes (CQA) and process design space. (A) Target Product Profile (TPP) has undergone a series of improvements during the process and product development phase. With the collection of clinical data and increased understanding of cell products, TPP has become more focused and directly related to clinical efficacy (TPP1 ➔ TPP2 ➔TPP3). (B) With the refinement of TPP and related CQAs, the process design space has also narrowed, allowing more intensive development in the next iteration.
QbD is a useful framework to guide the development and manufacturing of CGT [18, 19]. The developer uses the determined TPP and CQA to establish the process design space. This refers to the parameter space in which the process can be optimized to achieve CQA. At the beginning of the product development cycle, this design space may be very large, because the details of CQA may be unknown or poorly understood. For example, developers may be exploring a wide range of cell densities, multiple media and additive components, and various feeding solutions for cell culture processes.
In this initial stage, data should be collected to better understand how these process parameters affect cell yield, viability, phenotype, gene expression profile, and functional output. At this stage, the minimum acceptable TPP standards are quite extensive. As these correlations between process input and unit output develop, key process parameters (CPP) (usually the parameters whose sensitivity has the most significant effect on TPP) can be determined, and the CQA can be further refined through a feedback loop.
This in turn reduces the size of the design space, and the next iteration of process optimization can focus on improving a smaller number of parameters. Reflecting the improvement of TPP, the design space has become more concentrated over time, although it will not shrink to the point where there is no room for fine-tuning even in the advanced manufacturing stage (Figure 1B). Although beyond the scope of this review, statistical frameworks for design space optimization exist in other fields and may be applicable to CGT [20, 21].
To support this evolving approach to design space frameworks, three iterative CGT product development phases can be considered: (1) process discovery, (2) process characterization, and (3) process development (Table 1).
Process discovery refers to the identification of key process parameters that affect the basic biology of the system. Process characterization includes constructing the input and output data set of each unit operation and its sensitivity to process parameters. Finally, process development involves using the results of process characterization to optimize a component of the manufacturing process in order to achieve CQA more robustly (or vice versa, to make the process more efficient, more consistent, or more cost-effective, while maintaining the current state of CQA) .
By doing so, process knowledge can be obtained, and the design space becomes better defined and aligned with TPP and CQA. These steps can then be repeated, starting from this more refined design space, and continuing through this cycle.
3. Extensive and high-resolution analysis is the cornerstone of iterative development
Analysis is essential for process discovery, process characterization, and process development. Treatment developers can invest a lot of effort early in development to build cellular and molecular analyses for all stages of their manufacturing process. In the initial stage, characterization analysis needs to be broad enough to span large, multi-dimensional design spaces. It is very useful to characterize target and off-target cells, capture run-to-run variability and general system robustness, and explore all the characteristics of cells that may affect efficacy. Prior to process development activities, key tests should be implemented and standardized to the greatest extent so that tests will not develop while exploring process changes. Although the tests used early in the development cycle are often unqualified, it is important that they are sufficiently robust and consistent to ensure that it can be determined whether the measured differences between process conditions are relevant and significant.
Cell characterization in the field of CGT is usually performed in a fairly low-throughput and low-resolution manner, where a small number of parameters (such as the expression of surface markers in flow cytometry) are used to track a large number of cell populations without a good understanding of which parameters have Responsive process variability and process quality. In order to explore the design space more extensively in a more detailed manner, more sophisticated high-resolution and single-cell-based analysis techniques, such as single-cell RNA-Seq, and proteomics and metabolomics analysis of cells and culture components should be used It is more often used in the field of CGT [22-24]. The most relevant CQA can be found using extensive feature analysis, which can then be routinely monitored and used as the basis for process development. In addition to a diverse set of analyses, a strong testing team should also be multidimensional in nature, using multiple methods to evaluate the same CQA. This orthogonal approach (that is, the use of more than one independent method to measure similar attributes) is even more important when developing an understanding and definition of drug efficacy.
Powerful analysis creates the ability to track and trend the implementation process to monitor CQA through development and identify intentional changes and unintentional drifts. These data sets form the basis for early process characterization and help define process optimization strategies. CGT developers may tend to defer process characterization to the later stages of clinical development, but it is a valuable tool for early implementation to build development strategies.
In the pre-clinical development phase, simple research and additional data collection can be combined to begin building process understanding. By making it the focus of early process development work, over time, it will be easier to make risk-based decisions on process changes and integrate technical solutions. In the future product development process, a very concentrated subset of these extensive analyses will form the basis for a set of minimal clinical release tests. In addition, if the product format allows, developers should retain samples from key early development batches (for example, samples used in non-clinical and early clinical studies for investigational new drugs [IND]) for retrospective analysis in the future , In order to understand the quality and robustness of products and product analysis and characterization in the development process.
Cell characterization data can also be used as a supplementary tool for exploring and optimizing the design space for process simulation [25-27]. The output of the calculation model will help to more effectively and quickly determine the key parameters for experimentation and optimization by highlighting the sensitivity. Dealing with different parameters, for example, modeling the differentiation process based on initial experimental conditions may lead to the assumption that maintaining a specific intermediate cell phenotype within the upper and lower limits is important for effective subsequent differentiation and mature cell function. This will then guide the next experimental step and limit the design space surrounding this fine goal. The calculation tool provides a way to transfer the multi-parameter high-resolution data set generated during the discovery phase of the process to a reduced number of specific parameters to be evaluated during the development of the experimental process. How analysis and calculation methods support the further formalization of design space refinement is an important area of CGT’s continued strategic development.
4. Process modularity allows faster iterative optimization
The CGT manufacturing process is usually a multi-stage process with many unit operations, spanning days to months. In some cases, cells must go through very specific intermediate stages, whether through differentiation processes, activation steps, or the selection or consumption of specific cell types. With the growing interest in ready-made cell products, the manufacturing process may include additional genetic modification steps and lengthy cell expansion stages. Having a modular manufacturing process with clearly defined intermediates and process pause points is important to allow innovation.
In a highly modular process, a complex manufacturing scheme can be viewed as a set of simplified building blocks, each of which can be optimized independently (Figure 2). In order to achieve this, each module must be clearly defined with CQA that controls its input and output. This allows improvements made to a single stage to be inserted into the entire process, with the confidence that re-optimization of subsequent stages may not be necessary.
Some process steps are easily suitable for this modular consideration, such as cryopreservation steps or magnetic cell enrichment. The multi-stage cell differentiation process also greatly benefits from modular optimization strategies. Enabling this method requires a sufficient understanding of the cell identity at each stage, which requires high-quality analysis (may include developmental lineage tracking  and other technologies) in the early stages of the development plan.
Each modular unit can be considered to have a defined intermediate cell population as the output of the module, and a target cell profile can be developed for it. In this way, the same design space refinement iteration framework can be applied to these smaller process modules.
Figure 2 Modularization is conducive to process development. In a complex manufacturing process, the process can be decomposed into modular units composed of unit operation sets, for which different intermediate input and output totals can be defined. For each intermediate, a target cell profile that captures the key characteristics of the purity and identity of the intermediate population can be determined. By aligning these intermediate groups with process pause points, each module can be optimized independently and simultaneously.
The process complexity of many CGT products also limits early R&D activities because the research team is limited in the amount and consistency of materials that can be used for functional testing and detailed characterization. Modularity and built-in process suspension points provide a strategy for internal supply chain optimization by generating consistent products or intermediates to produce in larger quantities and/or robustness. Cell material can be produced for optimization studies at multiple points in the process, so that the supply of input materials does not limit discovery efforts.
5 While considering commercial manufacturing and comparability requirements, the development strategy suitable for the stage remains agile
As the CGT product progresses in the development phase, the discovery team will generate new biological insights and learning to enhance, modify, and improve therapies under development. The generation of preclinical and clinical data completes the understanding of drug MoA and TPP, enabling CGT developers to further innovate to improve the efficacy, safety and/or consistency of their products. However, iterative discovery of the benefits of biology is difficult to coordinate with the structure and standardization required for process development and manufacturing.
Due to the importance and challenges of developing a robust manufacturing strategy for CGT products, the field tends to advocate the view that manufacturing solutions must be developed, implemented, and finalized as early as possible in the product development schedule. If substantial process changes are made, the developer must be able to demonstrate how these process changes will affect or change the safety and effectiveness characteristics established using earlier processes, and when TPP and CQA are still under development, it may be difficult to identify and evaluate this An appropriate indicator of impact. This burden of justifying process changes becomes more onerous in the later stages of clinical development. Therefore, CGT developers usually avoid making substantial manufacturing changes or improvements.
However, this strategy of focusing on lock-in manufacturing solutions has significant limitations. The manufacturing strategies usually used when CGT products enter the market are not optimal in terms of quantity, quality, and logistics requirements to serve the expected patients, market size and distribution, which may limit the product’s potential clinical efficacy. Newer manufacturing technologies may introduce process consistency through automation, enhanced monitoring and control, and improved data generation. Even though they may provide obvious benefits, they are not utilized due to the responsibility of changing the process. New biological insights and discoveries may not be integrated into product development, because in typical process development methods, there is no simplified way to do this.
We suggest that according to the ICH drug development guidelines , the CGT field should shift its thinking from the need to establish the final manufacturing process as early as possible to use an iterative QbD refining framework to determine the process design space and CQA through process discovery, process characterization, and process development . There is no need to design the commercial manufacturing process of the product in early development. On the contrary, appropriate processes at the stage should be designed and improved to achieve later development and commercialization.
Rather than trying to avoid the inevitable assessment and determination of comparability requirements, the CGT field will benefit from forward-looking design comparability studies to plan transitions between processes that are appropriate for the stage. Comparability does not require a segmented approach, and if an appropriate research design is combined, multiple changes can be addressed simultaneously . The two most common misconceptions about comparability studies are that comparability should also be defined as the same, and it is sufficient to set comparability acceptance criteria for release specifications.
When trying to prove comparability, the value of different orthogonal analysis teams was again emphasized. This is especially true of the value of analysis matrices when constructing narratives describing effectiveness (and MoA). The more design space knowledge gained from extensive and high-resolution analysis and characterization, the greater the confidence in the comparability of the product. In addition, the well-known in-process analysis will allow real-time tracking of comparability throughout the modularization process.
The planned successful comparability study usually includes forward-looking communication with regulatory agencies in order to obtain early support for the proposed research design and statistical analysis plan. Obtaining this expected consent will greatly reduce the risk that the final data package is found to be insufficient to support the proposed process change. The final key consideration in planning for future comparability studies is to maintain a robust retention plan during the early development process. Retaining samples will prove invaluable in comparing new process materials with a large number of old process batches, preferably including specific batches used to collect critical non-clinical and early clinical data. In addition, when introducing new or improved analyses, reserve samples can be used to bridge data between two different test methods.
Although the burden of comparability does increase with the development stage, the magnitude and expected impact of one or more specific changes and the strength of the available analysis packages can have a significant impact on the nature (and magnitude) of the comparability required. . Process changes in early clinical development are very common and can usually be resolved with paper-based and data-driven reasons. As clinical development progresses and products enter Phase 3 or key trials, these changes may become more challenging.
In view of the fact that continuous innovation in the manufacturing industry is beneficial and value-added, the cost and time of comparability research in the well-run stage should be part of the forward-looking plan. The iterative improvement of the QbD framework on which TPP, CQA and the design space are based means that as the product matures and the burden of comparability increases, the increased knowledge of the process enables strict control and concentrated comparability research under the guidance of robust CQA . Using these methods, developers can integrate new technologies and new biological insights learned from current research into the development and improvement of their processes, and CGT manufacturing can be opened up, allowing continuous innovation and improvement from discovery to commercialization.
(source:internet, reference only)