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Susan Haigney is managing editor of BioPharm International, firstname.lastname@example.org.
Industry experts discuss challenges and best practices for scaling up manufacturing under a short deadline.
Scaling up a drug manufacturing process is demanding under the best circumstances. The added pressure of meeting accelerated deadlines during a pandemic presents additional challenges for process development, materials supply, equipment acquisition and installation, and process validation.
Representatives from API and equipment manufacturers, as well as contract development and manufacturing organizations, shared with Pharmaceutical Technology their perspectives on challenges and strategies for the scale up of different drug types under pressure.
PharmTech: What kind of challenges occur when scaling up manufacturing within a short time deadline (e.g., when trying to meet demand for drug shortages)?
Sripathy Venkatraman, vice-president, Global Chemical Development, AMRI: When you need to scale-up quickly, several challenges to quality and safety come to mind, particularly inadequate data from small-scale experiments, especially around process safety. Another is poor understanding of the impurity profile and how that translates at scale. Shortened timelines can affect process development and understanding of the edge of failure, which is important for transfer. For example, not understanding the mass transfer and heat transfer can result in repeated rework of batches in order to meet specifications.
Prasad Raje, CEO of LGM Pharma: I would say that all the normal scientific challenges we face, which chemists and engineers work to solve, are amplified under compressed timelines. For example, a common problem—if you don’t set the right specifications for scale up—is impurities. Setting specifications, whether for drug substance or drug product, is challenging and if not done properly can result in deviations or out-of-specs at scale up and regulatory delays. From a supply-chain perspective, getting all the raw materials for the product is challenging; you might not have enough batch history or know the vendors and their manufacturing record regarding quality.
Todd Stutzman, director of Pharmaceutical Development, Catalent, Kansas City: During scale up, it is common for scientists to have only limited quantities of API to use in process qualification. Experience of model design and in the identification of potential critical process parameters (CPPs) are crucial to the successful scale up of new chemical entities (NCEs). Process optimization has benefited from the use of a design-of-experiment (DoE) approach using fractional factorial software for decades, but recently, new approaches in modeling have allowed these experiments to be conducted whilst consuming significantly less API. This provides an opportunity to complete the development design space earlier and accelerate the introduction of NCEs.
Tim Gardner, director–Manufacturing and New Product Introduction, Vectura: For inhaled medicines, immediate challenges in the short term revolve around speed of supply due to increased demand for device components, as well as excipients and APIs, with potential pinch points around lead times that need to be overcome. The ability to be agile in balancing existing projects, and those that require urgent short time delivery, can be challenging. However, with the right team focus and clear equipment utilization and KPIs [key performance indicators] in place, demand can be met. The ability to repurpose or procure additional equipment and resources with speed is also key.
Ilan Avni, vice president, Business Development, Marketing & Intellectual Property at Wavelength Pharmaceuticals: When demand earlier in 2020 required that we triple and quadruple output of several urgently needed APIs for critical care of COVID-19 patients, we were well prepared for the challenge. That included having properly designed processes, a highly experienced team, and a robust supply chain. In-depth knowledge of process design, comprehensive process characterization, and control of critical parameters were essential to ensure scale up is successful the first time, both in terms of product quality and yield. Supply chains must secure sufficient supply of additional raw materials to meet expanded production.
Ralph Landau, head of Development, Drug Product at Cambrex: Our vast experience as a contract development and manufacturing organization (CDMO) has taught us that although the challenges that are faced during such situations vary from case to case, there are three common challenges: safety, supply chain resilience, and communication/transparency with customers.
Communication with customers is a crucial challenge to overcome. For instance, we have deployed Realwear technology from the start of COVID-19 lockdowns at Cambrex to help clients envision scale up manufacturing of their product(s).Realwear technology is one of the cutting edge mobile interactive video systems in which customers can see and communicate with the entire manufacturing facility as if they were walking through [it] themselves. They can safely observe the manufacturing process without physically being there.
Tania Pereira Chilima, deputy chief technology officer, Univercells Technologies: Technology selection is extremely important and will highly affect process scale-up. Manual technologies are typically easier to adopt as companies are rather familiar with these systems. However, these technologies are not scalable, which has a significant impact on COG [cost of goods], CAPEX [capital expenditure], and capacity given the footprint and skilled labor numbers required to support products with very high manual demands.
When it comes to bioreactors, achieving the same performance at large scale as in small scale under pressure can be very challenging and require significant expertise, and hence, companies selecting between manufacturing technologies must be aware of the trade-offs associated with them.
Byron Rees, senior manager, Process Development Services, Pall Biotech: While the principles of scale up are universal, each product comes with its own unique set of challenges. This is particularly true of therapies with less established platforms such as viral vectors used for gene therapy and any viral vector based therapeutic, or, most pertinent now, viral vaccines. Long cell-doubling times for mammalian systems slow down upstream process development (PD) and the ability to generate material for downstream PD to optimize for yield. Analytical cell-based assays with long turnaround times, low throughput, and product complexity may also present challenges, so efforts must be made to identify rapid surrogate assays that can be used for PD purposes. Many suppliers do provide small-scale PD devices that allow for high throughput and DoE experiments.
PharmTech: What is the best strategy for scaling up under a deadline? How can quality be ensured during scale up?
Avni (Wavelength): Ultimately, the best strategy to ensure product quality during scale up is quality by design. When process design is done properly at lower scale, it inevitably paves the way to more efficient scale up—right first time. An experienced team will be well-practiced in identifying any process design gaps and will make sure to complete any missing data before moving to scale. Having the internal capability and experience to troubleshoot is also vital to keep such projects on track.
Raje (LGM Pharma): Quality by design is essential here, building quality into your processes. And teamwork is essential to that. You cannot anticipate all the challenges if, for example, you don’t involve the late-stage production staff early enough and ask, ‘Can you scale this process up?’ Well-staffed QA/QC [quality assurance/quality control] can ensure that process assays, cleaning method validations, and raw materials—and final product-release—happen on time. A robust, electronic QMS [quality management system]—not paper—brings efficiencies and quality enhancements. Lastly, it is important to accept only projects that fit the capabilities of the organization to avoid failure, especially in times of drug shortages.
Venkatraman (AMRI): One strategy is running process development and analytical method development in parallel, versus sequentially. A common mistake, under tight timelines, is to push analytical method development to the very last stage. But to succeed in rapid scale-up, once a process is set, you must move quickly to understand the edge of failure for critical process parameters. Another strategy relates to availability of raw materials and identifying suppliers early on. You must qualify suppliers and set specifications for the materials. Understanding the quality tolerance of the materials sooner, and purging impurities, will help in consistently meeting the critical quality attributes.
Stutzman (Catalent): Time can be reduced by the approaches mentioned previously; however, there is a chronological order which must be followed in order to ensure that quality is built into the process. Process optimization test results and proper interpretation of the data must be completed prior to embarking on scale up/confirmation activities, and sufficient API quantities must be available for a minimum number of confirmation batches, and should include provisions for boundary batches, which are processed at a scale representative of commercial production. These batches establish the upper and lower limits defined in the master batch record, which is derived from the process optimization studies. The confirmation and boundary batches provide a complete picture of commercial manufacturing ranges and minimize risk to a successful pre-approval inspection (PAI).
Landau (Cambrex): Every scale-up is under a deadline, so a plan becomes the critical pathway to quality. We use baseline engineering runs at full scale whenever possible to learn what to expect upon scale-up. This is confirmatory in nature and allows our team to collect as much info as possible to tweak the final process for registration scale-up batches. We allot for raw materials timings as part of our supply chain resilience, to avoid delays due to starting material hang ups.
Rees (Pall Biotech): Scale up goes fastest when teams have deep knowledge and experience of both the product they are trying to scale up and the equipment that they are using. Of course, it is usually not practical for groups to have deep knowledge of their drug product or expertise with the manufacturing equipment: Therefore, some process development is essential for achieving a successful scale-up.
Suppliers often have deep engineering knowledge and operational bioprocess experience of their equipment and how it performs in lots of different applications. Therefore, partnering with your equipment suppliers can often significantly accelerate process development.
Gardner (Vectura): Strategic selection of cross functional, dedicated teams [is] need[ed] to manage the synergies required to scale up at speed. Streamlined protocols that ensure all CQAs [critical quality attributes] and CPPs are captured are preferential, to maximize the time spent gathering data for quality assurance and good practice (GxP) compliance purposes. Simplicity is paramount, and reduction of complex documentation is the key to meeting tight deadlines for demand when drug shortages are faced.
PharmTech: What are some best practices for streamlining scale up?
Raje (LGM Pharma): There are many, starting with superb project management—bringing the team together, with excellent coordination between chemists and engineers, to provide insights earlier in the process. You want team ownership of success, which requires seamless handoffs between early development and large-scale manufacturing teams. I’d add heightened supply chain control, from excellent vendor management to avoid issues with raw and starting materials not meeting specifications. Another would be having both the right equipment and personnel to study and evaluate scale-up parameters.
Landau (Cambrex): Streamlining scale-up effectively is essentially a large planning exercise that considers all the moving parts of a process. Cutting-edge equipment and capacity are extremely critical, particularly for processes scaling into larger batches. We start with confirming the target scale-up equipment needs, from both a technical and staffing perspective, verifying that we have the right technologies that enable scale-up and the right people to operate the equipment. We plan for any points of changeover, and if there are disposables involved, ensure that all those resources have been ordered and are available to keep the process moving.
Each process is different, which means different potential problems. Development work should be well-documented, including any specific requirements for scaling of batches. And this work cannot ever be viewed as ‘complete’.
A focus on communications and continuous monitoring allows for threats to be identified early and dealt with before they cause disruption. Now more than ever, this also includes planning for capacity, with the availability to spread processes over multiple similarly equipped plants and conduct parallel manufacturing if/as required.
Avni (Wavelength): Acknowledging the many different challenges of scale up is first. This translates to performing rigorous process design; using equipment and methods representative of commercial processes during scale up; and meticulously qualifying suppliers of raw materials to ensure a robust reliable supply chain. Wavelength continuously invests in our supply chain, carefully selecting our suppliers based on their quality processes and regulatory track record. We qualify at least two suppliers from different geographies for key starting material, keep sufficient stock on hand, and plan on a rolling 12-months-out so that we have a back-up in crisis situations. Another best practice is developing additional synthetic steps for manufacturing strategic products from simpler, more readily available starting materials. This paid off during the COVID-19 crisis, as we could apply these steps in-house or transfer the knowhow for quick implementation by an alternate supplier.
Pereira Chilima (Univercells Technologies): When it comes to technology selection, the best practices include using scale-down models that are representative of the target manufacturing scale. This will highly accelerate the scale-up process as well as reduce manufacturing costs as companies can identify optimal process designs at small scale and transfer these processes to the larger scale without significant adaption.
Venkatraman (AMRI): Data-rich experiments can minimize or mitigate risk during scale-up. Because it is likely fewer experiments can be performed due to time constraints, they must produce maximum usable data. This requires advanced technologies that can capture data in one shot, speeding up process research without sacrificing quality. Also, a DoE approach, which is critical to understanding the design space, can give you an understanding of the space around the reaction that is very important. Modeling and simulations are also helpful in predicting and addressing issue that cannot be seen in small-scale experiments.
Gardner (Vectura): Ensuring as much critical data can be captured, evaluated, and defined within the early-phase development is crucial. Early-phase development equipment and processes should be synergistic with those at the scale-up phase, thereby reducing required data capture for CPPs and CQAs. Involving late-phase manufacturing teams early in the development phases, to ‘debug’ any potential challenges early in the product lifecycle, really helps streamline scale up.
Stutzman (Catalent): Recent advancements in compaction simulators can not only mimic tablet presses, but also roller compactors. Such simulators demonstrate how process parameters can now be established with a fraction of the API that would have been consumed using more traditional approaches. In addition to less API being required, simulation can also reduce the total time required to complete such activities. Although these new approaches reduce total API required in establishing the manufacturing design space, they do not preclude the necessity to confirm the results at commercial scale prior to product process qualification. Simulation minimizes API requirements and the risk associated with scale up activities, and confirmation batches solidify the target process parameters evaluated during the process optimization activities.
Volume 44, Number 10
When referring to this article, please cite it as S. Haigney, “Scaling Up Fast,” Pharmaceutical Technology 44 (10) 2020.