How cell and gene therapy innovators find certainty in a world of unknowns

March 16, 2020
A process simulation on transitioning R&D activities to large scale production.

Transitioning from R&D activities to large-scale commercial production is complex for any drug manufacturer. When autologous cell therapies enter the formula, that complexity quickly multiplies. 

If you’re a manufacturer in this new, uncharted space, you’re a pioneer by necessity. Without volumes of historic data or a tried-and-true template to illuminate the way forward, you’re designing in the dark, aware of the potential risks ahead but unsure of how to navigate around them. You know that you need a robust process to meet your throughput objectives, but you also need built-in flexibility so that you can respond to unknowns. How can you find that balance?

A focused, strategic process simulation study, based on a dynamic computer model, is the solution. In other, more established areas, process simulation works by applying what we know already about a given scenario. In the case of cell and gene therapy facilities, process simulation works differently. It uses those variables and relationships to measure your facility’s sensitivity to risk and to find opportunities to bolster resilience, improve efficiencies and guarantee a successful business outcome.

In other words, a process simulation turns that initial complexity into insight, helping you blaze a confident path forward with answers to the questions that impact your project. Questions such as: How long will it take to achieve our desired throughput? 

The stakes are high for cell and gene therapy labs that transition to manufacturing. Shareholders and business leaders need a return on their large investment. Front-line operators need a safe and efficient working environment. And then there are patients, the most important stakeholders of all, who need access to lifesaving therapies before they succumb to illness. Responding to these pressures is a matter of upholding quality while moving quickly. 

Moving quickly is complicated when so much of your timeline is driven by external factors that you cannot control (the convenience of the patient, the availability of a surgical center, and the efficiency of transportation are a few examples). To succeed, you need to identify the opportunities that you can control and leverage them to your advantage. For example, if you know how long a certain step in your process takes, you may be able to flex the incubation period accordingly; in this way, you can proactively manage the velocity of your manufacturing workflow, even as new samples arrive daily and unpredictably.

To accurately calibrate these moving pieces — the manual processes, the incubation times, and so on — you need to understand each of those pieces in as much detail as possible. This is especially true given that cell therapy processes are so novel. A simulation is your best opportunity to explore that novelty before you’ve invested in real-world applications; you can use your dynamic process model to test scenarios, identify bottlenecks, and make accurate predictions in a low-stakes environment, giving you the data you need to use allowable flex times strategically in order to control throughput.

Here’s what process simulations can reveal about your facility’s workflow and productivity potential:

At the granular level: You can use simulations to examine what’s required at specific moments throughout the process. Maybe you want to know exactly what’s going on eleven days after the patient’s sample arrives. The simulation will take you to that moment in time, showing you what tasks are required, what equipment and supplies are necessary, and how many operators are needed to get Day Eleven’s work completed efficiently.

At the macro level: The simulation is equally good at providing a “big picture” perspective, inviting you to examine your facility’s overall design and thereby optimize its operational efficiency. What’s the best location for your warehouse? How can you design your staging spaces and production suites to minimize travel time? Where are your cross-contamination risks, and how can you mitigate them through design? Use the simulation to uncover answers to these “macro” questions, and much more.

At the level of operational organization: Simulations can help you test and tweak your staffing approach, ensuring the best possible balance between cost, risk, and operator availability. A 24/7 production cycle might minimize risk, for example, but it’s a very expensive model to maintain. What if you could de-risk operations equally well by running a larger first shift, and a second and third shift with fewer operators? The simulation will tell you if and how that scenario would work, including factors like how long it takes each shift to gown up, or how breaks and vacations will impact productivity, and so on.

At the level of departmental efficiency: In a manufacturing environment where batch samples are small in scale but potentially enormous in number, the QC lab can sometimes rival the production area in size, staffing and complexity. Simulations can help you proactively design around that complexity. What will it mean to scale your test load from 100 to 3,000 batches per year, for example? Use the simulation to understand how many QC technicians you’ll need, and how efficiently they can work.

How reliable is the equipment?

Much of the technology in play is well-known in the auspices of an R&D lab, but how will that technology scale to dozens of manufacturing suites and hundreds of discrete operations? 

In other types of manufacturing spaces, this tech transfer from clinical to commercial follows a well-established formula; engineers can scale processes with reasonable confidence, following (and improving on) the path blazed by their predecessors. Here, though, there are no predecessors. The realities of such a tech transfer are relatively unknown, leaving manufacturers vulnerable to the risks of underperformance. 

A process simulation can help minimize these risks by giving you the tools to run worst-case scenarios and discover proactive workarounds. Use the real-world simulation to answer questions such as:

  • What if the equipment is half as reliable as the vendor claims?
  • What happens if we add an additional suite to mitigate such an eventuality?
  • What is our sensitivity to variabilities in the equipment’s performance, and how can we minimize that sensitivity?

How can we defend against contamination?

Like your timelines, much of what affects the purity of your material is outside of your control. How a patient’s sample is extracted, how it’s packaged, how it’s transported — all of these factors impact the quality and safety of that sample on its way to your facility, which in turn impacts productivity. Without proper mitigations in place, accepting a contaminated sample is like catching a live grenade; your whole operation could be disabled for an undefined period while you scramble to clean up the mess that results.

Simulations can protect against this risk by helping you develop resilience against contamination events in the most efficient and cost-effective way possible. Using process simulations, you can determine:

How to balance risks with costs. Extensive use of segregation may be the best solution for quickly confining and eliminating contamination without impacting other manufacturing areas, but this comes with great expense. It requires many airlocks (lowering the profitability of your factory footprint), extensive air handling systems (increasing your energy load), more travel/transition time for operators moving in and out (limiting operational productivity), and so on. Running scenarios that increase or decrease the number of isolation rooms will help you make a strategic decision based on real-world costs and benefits.

How to make the best use of lab safety technology. Technology designed to protect both operators and the samples they work with is evolving quickly. Simulations are a low-cost way to assess the viability of these new and emerging biosafety technologies (like cabinets, fume hoods, and isolators) before making a purchase, helping you proactively design a network of safe, productive manufacturing suites.

The strategy of simulation

Designing a manufacturing facility as complex as this, with stakes this high (a patient could live or die, based on your facility’s productivity) requires planning and risk mitigation of the highest degree. But it requires equally careful cost control and reliable throughput in order to remain viable.

The correct balance between these interests (complexity vs. cost, quality vs. life-or-death speed) cannot be determined in a static spreadsheet; such a sophisticated business problem requires an equally sophisticated and dynamic solution. That’s where process simulations come in.

Process simulations based on appropriately detailed computer models are your best defense against the forces of uncertainty that darken the path ahead. But to be useful, simulations have to be done at a strategic point in the evolution of your project, before real-world scale-out has begun. This gives you time to make proactive decisions about everything from facility design to equipment selection and hiring strategies, setting you up for long-term success. Win-win for your business and for the patients who are betting their life, quite literally, on the speed and quality of your operations.

About the Author

Philip Lyman | Director