March 12, 2019 PAP-Q1-2019-CL-016
The number of cell therapy treatments in development has exploded in recent years. While only a few have received regulatory approval to date, many are moving rapidly from late clinical stage to commercialization. From 2017–2023, the cell therapy market — including stem cell and non-stem cell (some modified via gene editing) autologous (personalized) and allogeneic (off-the-shelf) drugs to treat musculoskeletal, cardiovascular, gastrointestinal, neurological, oncology, dermatology, ocular and other diseases and conditions — is expected to expand at a compound annual growth rate of over 10%.1
Capacity for the production of cell therapies is currently generally limited to laboratory scale, with most processes involving highly manual operations. As the market expands and volume demands increase, current practices must be modified for scale-out (autologous) or scale-up (allogeneic). New facilities will also be needed to implement larger-volume manufacturing.
Designing new facilities for cell therapy manufacturing is a challenging task. Commercialization remains in the nascent stage, with only a few facilities constructed to date. Numerous uncertainties remain, from the potential demand for any given new therapy, equipment technology and equipment reliability, and learning curves for the analysts and operators, to the possible lead times for key raw materials, and many more. All of these factors impact facility design, including plant footprint, the types and numbers of pieces of equipment, the required staff and the flow of people and materials.
One approach to managing these uncertainties at the facility design stage is to develop operational models and perform computer simulations.
Computer modeling and simulations from an operational perspective can support the development of an optimal facility design that will enable cell therapy manufacturers to meet the needs of patient populations.
The first step in modeling involves defining the objectives and the metrics that will be captured from the analysis. The overarching objective is to meet patient demand in the most effective manner. That will require certain resources, such as equipment, personnel, utilities, logistical capabilities and space for production, intermediate and finished goods staging and support functions (e.g., quality assurance/control, warehousing, maintenance, administration).
Once the objectives and metrics have been clarified, data must be gathered to construct a baseline model. Since most cell therapies remain in clinical trials and have not yet been commercialized, “best guestimates” from subject matter experts (SMEs) and laboratory research data must be established. Assumptions must be made. To characterize the uncertainties involved, it is highly recommended to use a range of values (e.g., min–max, min–mode–max) or fit the data to probabilistic distributions, instead of using average values or point estimates. Discrete event simulation (DES) models are best suited to capture these variabilities and uncertainties. Since the inputs are stochastic, the outputs will also be probabilistic. This allows end-users to make decisions based on their appetite to handle risk.
The baseline model results must be verified and/or validated. Verification involves getting buy-in from SMEs, while validation involves statistical comparisons of actual data to the modeled results. After completing the verification/validation phase, the model can be used to perform different “what-if” analyses to determine how changing different variables affect the modeled metrics. Sensitivity analysis can also be performed in order to challenge and fine-tune the assumptions/constraints.
Once a model has been tested and shown to be reasonable (verified and validated), simulation can serve as an excellent tool for identifying bottlenecks and key areas of concern. This information can then be used to develop risk-mitigation plans to help manage the uncertainties associated with the design and construction of facilities in an emerging field.
For instance, given certain facility design and operational characteristics/constraints, it is possible to estimate the level of throughput that can be achieved. On the other hand, given a desired throughput, it is possible to determine the resource needs (e.g., equipment, personnel, space, utilities) and other design attributes that will be required. In models with well-defined workflows and established constraints, it is even possible to work backward to determine what type of facility and resource consumption combination will achieve an optimal cost of goods.
Simulations can be built to focus on a single unit operation in great detail, or they can be constructed at a more strategic level to provide assistance in making long-term decisions. To make the most effective facility decisions, models must consider not only the key production unit operations, but also the QC and warehousing requirements. Personnel and material flows should be studied as well. All of these operations and functional areas influence and are influenced by the facility footprint. The information that is generated via these simulations enables management to make data-driven decisions.
Once the model has been constructed, trained engineers and analysts can conduct further operational simulations (scenario analysis). The objectives will be different for each case, however. Start-up companies may use a model to determine whether they should outsource production or QC testing or keep it in-house; while a company taking a cell therapy from the clinic to the market may want to quantify equipment and headcount needs or shift strategies to deliver the projected demands; an established company may be looking to optimize its existing facility to achieve cost reductions. Some other examples of how simulations are used to address different objectives and outcomes have been previously discussed.2
Another equally important aspect of using DES is the visual communication component. For example, DES allows the modeler to import a facility layout, place equipment in desired locations and define travel paths, among other uses. Though simulations can be used to quantify metrics, such as headcount, equipment needs within a suite and number of trips made by personnel, they prove to be very helpful in visually communicating results with upper management. The 3D animations can help better visualize traffic within key corridors, any congestion points, adequacy of intermediate staging spaces, appropriate adjacencies needed, and other factors. Additionally, such visualization makes it easier for the SMEs to verify the model results. Figure 1 shows a snapshot of a DES model with the visualization added.
Effective simulation of cell therapy facilities requires construction of good models. As with any simulation, the model will only be as good as the data inputs used to build it (garbage-in–garbage-out).
Because data on commercial cell therapy production are currently limited, the inputs for operational models for cell therapy facilities are scaled up from research results, which introduces uncertainties.
Those uncertainties will differ depending on the type of product that will be manufactured. Autologous therapies involve the use of cells taken from a patient, modified and then sent back to that patient only. To increase the volume of production of such personalized medicines, scale-out or numbering-up is required, which involves the addition of more, very small production systems. Allogeneic or off-the-shelf cell therapies are produced from a limited number of cell donors. Scaling up — moving to larger-scale equipment — is therefore required to achieve higher production volumes.
These factors must be taken into consideration when developing the operational model for a cell therapy facility. They will have a direct impact on the number, types and sizes of equipment and the personnel required to operate them. For autologous manufacturing, a goal may be to optimize production with the minimum number of additional pieces of equipment; for a facility producing allogeneic cell therapies, optimizing the process to minimize the number of additional personnel required could be an objective.
Beyond the four walls of the manufacturing facility, the logistics of cell therapies adds additional challenges, particularly for autologous therapies that must be returned to the specific patient from whom the cells were initially drawn. Ensuring an effective cold chain (temperature controlled and monitored) while guaranteeing chain of custody for these materials is absolutely crucial, and modeling can help ensure patient safety while minimizing lead times and any potential penalties.3
The latter is also crucial, because these materials often only remain viable for a limited time and must be rapidly transported within a narrow time frame in a cryogenically frozen state from the collection site to the plant and then back to the patient once in final product form. Cold-chain issues must therefore also be considered in any logistics simulation.
One important recommendation for anyone considering the use of operational modeling and simulation for cell therapies — or any drug manufacturing facilities and associated activities — is to define the objectives and metrics for the simulation as early in the project as possible. It is also important to limit the number of scenarios to be simulated, which accelerates the decision-making process. Prioritizing a handful of scenarios leads to a practical project. Aiming to evaluate 50 different scenarios is unwise, difficult, time-consuming and costly.
For facility design problems, simulations should ideally be performed at the concept or even the feasibility stage in order to determine if the right type and size of facility is being considered. For companies looking to purchase an existing structure, this information is needed in order to be able to make the right decisions when evaluating their options.
Because models can only be as robust as the data used to construct them, excellent communication with the SMEs is another critical component of operational simulation. It is essential to translate computer-engineering language into results that can be understood by the people providing the data on which the model will be based. The right questions must be asked to ensure that the right data are obtained and that the model will address their needs.
Similarly, the model builders must be able to properly translate the information provided by SMEs into a computer-generated model that accurately represents the data. Documentation is also essential. All assumptions must be clearly detailed and an explanation of how they were vetted, verified and approved by the appropriate experts must be recorded. Finally, communicating the results generated by a simulation in the manner that the customer is expecting is equally important — the audience must be considered when preparing reports.
Like quality documents, simulation models should be considered as living documents. Before making a change to a facility or operation within it, simulations can be run to determine the impact of the change. Once any change is made, it is important to modify the model to reflect that change. The simulation can then be rerun to confirm that the desired result was obtained. Updating the model is also essential so that it continues to reflect the current state of the facility.
In addition, when additional data is obtained that can inform the model, such as for a specific unit operation, the data should be added to the model. The more real data points that can be included in a model that was initially constructed with uncertain data, the better the model will perform and the more accurate its predictions will be. For instance, once a cell therapy facility has been constructed and is in operation, actual data on the process cycle time for a particular step can be fitted to a probabilistic distribution. The distribution that best captures the variability in and explains that data set should then be the input to the model.
CRB is committed to providing as much information to our clients as possible. That includes the development of models that can be used to simulate potential facility designs for cell manufacturing. Rather than provide basic deterministic calculations on a process level, we offer more in-depth models and simulations that help customers answer questions about overall facility design and even go beyond the four walls of a cell therapy production facility to address logistics and supply chain concerns.
We have been involved in the design, engineering and construction of many biologic production facilities, including facilities intended for the manufacture of next-generation drug products like cell and gene therapies. We apply this experience to the development of models that incorporate the most appropriate variables and practical constraints.
These models offer our customers tremendous value with respect to understanding and quantifying facility aspects — where bottlenecks might exist and what might be done to relieve them and what sorts of equipment, people and supply networks need to be in place to achieve certain distribution goals, improve cost of goods and enhance overall decision making.
Niranjan is the Director of Operations Improvement at CRB. He holds a doctorate and master’s degree in industrial and systems engineering from Binghamton University. He is also a certified Lean Six Sigma Master Black Belt. Niranjan has over 15 years of experience in business process and data modeling, operations and process simulations, process improvements, layout optimizations and supply chain management. He has worked with the pharmaceutical, biotech, food, chemical, semiconductor, electronics assembly and packaging, manufacturing and financial industries.