Jennifer Markarian is manufacturing reporter for Pharmaceutical Technology.

Jennifer Markarian

The variety of drug-device combination products continues to grow as the trends toward self-administration and personalized medicine drive development of these dosage forms, such as prefilled syringes and auto-injection devices. 

 spoke with Adam Lambert, PhD, vice-president of product and process development at Pharmatech Associates, about current regulatory considerations and best practices in developing combination products.

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When referring to this article, please cite it as J. Markarian, “Understanding Combination Product Regulations,” 

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Considering interactions and understanding regulatory requirements are crucial when developing a drug or biologic product for use with a medical device.

Understanding Combination Product Regulations

Titanium dioxide is widely used for adding opacity and brightness in many products. A May 2021 evaluation from the European Food Safety Authority (EFSA), which declared that titanium dioxide is unsafe for food products, alarmed other industries that rely on the ingredient. Although changes are not required in pharmaceutical products, some supplement and drug manufacturers and their suppliers have considered potential alternatives. 


Considering Titanium Dioxide Regulatory Changes

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When referring to this article, please cite it as J. Markarian, “Considering Titanium Dioxide Regulatory Changes,” 

EFSA’s evaluation of risk in food products raises implications for drug product formulations. 

Considering Titanium Dioxide Regulatory Changes

For inhalation drugs that are dosed via pressurized metered dose inhalers (pMDIs) and dry powder inhalers (DPIs), characteristics of the formulation and of the device are both crucial for optimal drug delivery. 

 spoke with Recipharm’s Robin Heath, head of New Product Innovation and Commercial Management, and Peter Hirst, commercial vice-president, about trends and best practices in manufacturing and filling these combination drug-delivery devices.

 What do you see as key trends in inhalation drug formulation that are affecting manufacturing processes?

 We are seeing increasing use of engineered particles, such as spray drying, rather than traditional micronization in the formulations for pMDIs and dry powder inhalers DPIs. For DPIs in particular, we are seeing the filling of large volumes of engineered particles.

Traditionally, many existing drugs are low molecular weight crystalline substances, which can be micronized into a targeted size suitable for inhalation product development. These drugs are stable and well understood, and they are relatively low dose products in the range of micrograms. Many new drugs being developed, however, are complex biomolecules, which have issues with physical stability and are often delivered in higher doses owing to their low potency.

pMDIs are arguably at their simplest when prepared as a solution, but this [method of formulation] can be limited by the solubility of the drug and excipients. In addition, a recognition of new global pressures towards more sustainable products is driving the investigation into alternative propellants in pMDIs, providing new potential formulation opportunities.

The infrastructure to handle these complex drug products successfully is significant, with health and safety requirements, environmental conditions (temperature and humidity), and use of solvents to consider along with the physical handling of these free-flowing powders when it comes to device filling. These may pose a challenge for drug developers, and contract development and manufacturing organizations can bring their expertise and facilities to the forefront in this new wave of challenging inhalation drug development.

 What are the considerations for choosing DPIs or pMDIs?

 Bespak by Recipharm manufactures both DPI and pMDI devices for a wide range of drug developer customers. We also manufacture clinical and commercial-scale finished products in DPI and pMDI formats at our Recipharm manufacturing facility in Holmes Chapel, Cheshire, United Kingdom. When selecting an appropriate device for a specific drug formulation, several factors must be considered:

pMDIs as a platform are limited by the volume of the formulation in the metering chamber (typically <100 µl), and there are considerations with drug solubility if making a solution.


In terms of protection from the environment, a pMDI formulation is contained within a sealed aluminium cannister, but there are opportunities for the formulation to interact with the elastomeric components of the valve. Reservoir DPIs contain bulk formulation, which is dispensed in small doses each time the patient actuates the device. The inherent design of this device type potentially leaves it more exposed to the elements, and it can be challenging to dispense doses accurately.


Excipient compatibility is one of the most critical aspects during development for any inhalation platform. Although there are a limited number of approved excipients for inhaled products, they must be managed and screened for early in the process.

In addition, if a drug developer wishes to utilize a bespoke delivery device for patent protection purposes, then a DPI will be selected. These devices tend to be bespoke, rather than ‘off the shelf’, often requiring simultaneous development of the device, the formulation, and the filling and assembly process, all of which adds complexity into the product development and approval processes. 

Conversely, the design of a pMDI is generally more universal and more cost-effective. It does not require the design of a bespoke device, and the device is typically able to be filled using already installed filling equipment.

 For pMDIs, what are the current trends in propellants, and how do these affect the overall drug delivery device? Does this also have an effect on the drug product formulation?

 In accordance with the United Nations 2016 Kigali Amendment to the Montreal Protocol to phase out hydrofluorocarbons (HFAs), there is a transition away from the current HFA propellants used in inhalation pharmaceutical products. The current HFAs have a high global warming potential, contributing to climate change.

There are at least two potential replacements currently undergoing safety evaluations: HFA152a and HFO1234ze. Both are compatible with surfactants and solvents used in inhalation drugs, meaning they pose few problems when it comes to formulation development.

However, there are issues that need to be tackled to ensure their future success. The relative flammability of HFA152a poses safety challenges on production lines. The compatibility of these replacement propellants with container closure systems must also be evaluated to ensure valve functionality. In addition, the industry must study the propellants’ ability to interact with APIs and excipients to ensure the drug formulation maintains optimum stability and acceptable extractable and leachable profiles.

 What are some best practices in designing and manufacturing this type of combination drug-delivery device? What are some of the ways that the drug developer and delivery device manufacturer need to collaborate on the initial design or for ongoing manufacturing?

 When designing and manufacturing an inhalation device, it is essential to consider both the interaction between the drug formulation and the device, and between the device and the patient, as well.

It is important to identify, optimize, and control the parameters that will influence the finished product’s performance, in terms of dose delivery and therapeutic effect. These include API particle size, device component dimensions, and the formulation. Manufacturing process parameters, such as time and temperature, are also important to consider.

 What are some of the key considerations when scaling up a development process to a commercial-scale, good manufacturing practice process?

 Above all, it is important to design development processes with the commercial stage in mind.

Scale-up often entails moving to multiple items of equipment and the involvement of multiple people, which means a process that was originally designed at small scale using single items of equipment and a restricted group of people has to be replicated multiple times.

Developing robust processes, including robust analytical test methods with minimized variation of critical parameters, is key from the very beginning of the project to streamline scale-up. The ability to automate or at least semi-automate processes needs to be considered during the product development process. 

In addition, it is critical to ensure a robust manufacturing strategy is in place prior to industrialization. An understanding of the factors that influence the finished product batch size and manufacturing throughput is fundamental in determining both capacity and unit price.

Where commercial-scale equipment already exists, which is more often the case for pMDIs, product development should consider the process that is already installed and, where possible, design the new product accordingly. This forethought will save considerable development time and capital outlay associated with modifying existing equipment or procuring new.

When designing and manufacturing a pMDI or DPI combination drug-delivery device, the commercial stage should be kept in mind from the beginning of development.  

Best Practices in Manufacturing Inhalation Drugs

A digital twin is a model of a process that allows process development experiments to be run 

—on a computer—rather than on actual lab or manufacturing equipment. Although models can be developed using experimental data, mechanistic models, based on first principles, can be developed with only minimal experimental input, explains Nick Whitelock, EngD, sales specialist at GoSilico, which was acquired by Cytiva in June 2021. The company provides software as a service, as well as consulting and training services to help users build expertise within their organizations. 

 spoke with Whitelock about how digital twins are used in biopharmaceutical process development, scale up, tech transfer, and manufacturing, particularly chromatographic polishing as an example.

 Are digital twins typically used for modeling individual unit operations in biopharmaceutical manufacturing? Can they be used for continuous operations? What are the benefits and challenges?

 Modeling a single operation is the first goal of mechanistic digital twins, as this represents the low hanging fruit in which little experimental effort provides profound process understanding. A typical first project would include calibrating and interrogating a model describing a single operation, often a chromatographic polishing step, as this is often straightforward.

A powerful aspect of a mechanistic approach is the ability to extrapolate to unseen process conditions. [These conditions] may include a changing feedstream quality with respect to product or contaminant concentrations, variations in material quality, such as adsorber capacity and buffer composition, or indeed simulating a process comprising of multiple chromatographic steps, continuous or not.

With a mechanistic approach, relatively sparse experimental data are used to describe the full dynamics of the system at hand, using deceptively simple fundamental natural laws from the fields of fluid mechanics, kinetics, and thermodynamics. With these base processes understood, the complex, emergent behavior can be simulated to high precision by linking these underlying phenomena, systems and operations, 

. Tedious, physical experimentation can, therefore, instead be performed in mere seconds within a computer.

The challenges of mechanistic modeling include the need to characterize the behavior of these systems, and the expertise required to use them effectively. Companies such as GoSilico (now part of Cytiva) provide intuitive tools, intensive training, and expert consultancy to help develop this expertise and lower the barrier of entry. Additionally, the field has reached a maturity where experimental workflows are often straightforward, and there is an ever-growing library of models and use cases applicable to a wide range of activities.

 How are digital twins used in process development?

 All mechanistic models describing preparative chromatography have the eventual aim to understand and improve manufacturing-scale processes. It is typically the development lab, however, that has access to the data needed to characterize system dynamics, considering the small operating window at manufacturing scale and prohibitive expense of testing scale-up conditions. Models are typically calibrated using lab-scale data for material and cost efficiency, with the model then scaled to predict manufacturing scale by accounting and simulating changes to scale, operation, systems, and material quality.

 scale-up and scale-down of chromatography is that only the fluid dynamics outside the pore system change. Once a molecule enters the pore system, its behavior follows the same mechanisms in an adsorber bead packed in a filter plate and a production column. To allow for a prediction of the complete process on a new scale, only the basic fluid dynamic properties need to be known that can be derived from, for example, column qualification runs.

 What types of models/data would be needed for scale up/tech transfer?

 A digital twin is an invaluable tool for transferring a process, whether to different scale, equipment, or facility. By explicitly describing all components within a process, from raw material to method, one can account for any changes.

To predict the impact on process performance, a profound understanding of the differences with respect to material, system, and operation is required, though fortunately obtaining this required information is often easy. The impact of the chromatographic system used, whether lab-scale or manufacturing skid, can be assessed with pulse experiments, or knowledge of the machine’s geometry. Data on column packing quality [are] often available as part of column qualification, and conductivity transition data may provide an insight in fluid mechanics often overlooked. Data on buffer compositions and adsorber quality should also be available.

A particularly powerful application of these tools to manufacturing, aside from predicting the influence of scale, is for root cause analysis; whilst a mechanistic model enables one to find optimal conditions for process robustness, if deviations do occur, such a model is also well suited to determine the root of the issue. In this case, if a model describing the process is extant and describes the process when controlled, data for the out-of-spec chromatogram is fed back into the model. Process parameters are then changed 

 to search for potential differences, such as raw material quality, operational changes, or feed stream variations. This enables one to isolate the mechanisms responsible for changes, therefore making informed decision during root cause analysis based upon data and fundamental principles.

 Now that GoSilico is part of Cytiva, how are the digital twins being used with the processing equipment?

 GoSilico’s software has a seamless workflow for importing and processing files [from Cytiva’s chromatography columns]; method and result information are available for modeling directly from the .res or .zip files. Cytiva has also recently released 

 columns, which come precalibrated for mechanistic modeling, reducing the already sparse experimental burden even further.

GoSilico’s chromatography software DSPX provides the tools for customers to build models describing their own processes; established modelers rarely require aid, but first-time users often benefit from access to GoSilico’s support.

 Can digital twins be used in manufacturing to control a process? If so, what are the benefits and challenges of doing this?

 Mechanistic models can and have been used as part of a sophisticated control strategy, though often we see models being used to better understand the process and, therefore, find more robust conditions. In a situation where thousands of experimental conditions can be evaluated 

, including variation in upstream feed or material quality, one can mechanistically understand the dynamics and develop a design space that will function with simple controls even in the face of process variability.

The challenges in implementing model-based control strategies are the availability and quality of data, as well as required timelines. A mechanistic model can easily predict the influence of a process change; variations within the process need to be quantified for such a prediction to be valid, and often these data are not easily available, which is a limitation in all modeling approaches. If such data are available, a mechanistic model would allow not just a prediction of process performance, but also whether this variation can be mitigated by varying operational conditions, such as chromatographic method or pooling strategy.

 What do you predict for how digital twins will be used in biopharma in the future?

 Over the past few years, we have seen great enthusiasm and rapid adoption of this technology across the industry, largely by process development groups. In the near term, we expect this trend to continue, as has occurred for statistical models; mechanistic model driven process development will continue securing itself the standard approach for improved process performance, intensified development, and quality-by-design compliance.

Additionally, mechanistic modeling has an ever-growing research and development pipeline itself; in recent times, we have seen adsorption models based upon colloidal theory, with a greater basis in physics, emerge as revolutionary tools. We expect this direction to continue, in which increasingly sophisticated mathematical descriptions of bioprocesses become available through strong collaboration with both industry and academia.

Into the longer term, we expect to see manufacturing groups further embrace this technology; continued process verification, improved control strategy, and root cause analysis are just three facets that greatly benefit from a fundamental understanding of the underlying mechanisms of the process. Accordingly, fully developed digital twins will foreseeably play an important role in handling raw material variability, batch control, and real-time release testing in the future.

One of the largest obstacles to the adoption of such a revolutionary tool is the perceived complexity; certainly, the equations are obtuse, and that is often enough to scare away non-engineers or computer scientists. However, the art and tools have matured to the point where user-friendly software are available, there’s a community of experts and an ever-growing library of papers and case studies—one doesn’t need to be a mathematical genius with advanced coding skills to use this technology effectively. Any chromatographer can pick up the practical skills rapidly; and the fundamental principles are very intuitive.

Mechanistic models provide process understanding for developing robust manufacturing processes and for scale up and tech transfer.

GoSilico Digital Twin Models Downstream Bioprocessing

Digital twins of manufacturing processes are being explored in pharmaceutical manufacturing for the benefits that can be harnessed from a real-time simulation of the actual process. In 2020, GSK partnered with Siemens and Atos (a digital transformation company and a long-time Siemens partner) to pilot a digital twin for their vaccine manufacturing process that modeled and controlled the adjuvant particle manufacturing process. The partners succeeded with the project goal of proof of concept, and GSK is now seeking to replicate the digital twin concept on an industrial scale for its vaccine development and manufacturing platforms.

“Industry 4.0 concepts, such as digital twins, are generally thought to be a good idea, but this project created belief that it can really be used in vaccine development and manufacturing,” says Matt Harrison, head of Sciences, Digital Innovation, and Business Strategy at GSK Vaccines. “Now we are developing a vision for using it for multiple products and platforms, and we are deciding which opportunities will best deliver value and have the most impact.”

A significant benefit of the digital twin was that the model allowed developers to better understand the relationships between process variables to control variability.

“Controlling variability allows us to improve quality and make product ‘right first time’ every time,” says Harrison. “The model and the high degree of process control also allows us to better predict and optimize product yield. The aim was to build the twin during process development and then use it in manufacturing. The digital twin is an additional ‘deliverable’ of the development process.” Doing the work to understand and optimize the process “

” in the digital twin, rather than actual experiments with real material in a lab or test facility, saves a significant amount of time, materials, and cost.

Although the team has not yet gone through a tech transfer between manufacturing facilities using the digital twin, in theory, the ability to model any differences in scaling up or moving from one facility to another will reduce tech transfer risks, says Harrison.

Factors in the success of the pilot project include strong backing from GSK leadership and good teamwork both between GSK departments and with partners Siemens and Atos, notes Harrison.

Another key to the project’s success was that we started small and focused on our goal of demonstrating the concept. The next challenge is industrialization—using digital twins at scale in both development and operations throughout the organization. This effort will require continuous improvement, and we expect we will need to continue to make software and hardware changes as we further develop our capabilities,” says Harrison. “Although there are certainly technical challenges ahead, the aim is for digital twins to become routine.”

Harrison asserts that digital twins will be useful not only for process control in the operation of manufacturing facilities, but for the future of vaccine development. “When designing a vaccine, manufacturability is crucial. Having a digital twin to develop understanding of the manufacturing process is a key benefit,” he explains.

Modeling the manufacturing process benefits both development and operations. 

GSK Pilots Digital Twin for Vaccine Manufacturing 

Traditionally, compounding facilities—also called 503B outsourcing facilities in the United States in reference to the section of the US regulation under which they are registered (1)—have conducted drug manufacturing operations manually; “on demand” production of a wide range of drugs needed by hospitals and pharmacies as well as drugs on FDA’s drug shortages list did not lend itself to automation in the way that longer-run, scheduled production did. Robotic automation technologies available today, however, offer benefits of greater efficiency and higher quality even for small volumes.

“Robotics offer several significant advantages for aseptic compounding. Most notable is that the greatest source of contamination, the person, is removed from the process,” says Chris Smalley, an independent compounding pharmacist advisor. “There is a robust traceability to what is performed, including volume, drug barcode scan and more, and it does not matter what hour or day of the week the robot operates,” explains Smalley. He notes that, in some cases, a robot is set up to operate on its own overnight (i.e., “lights-out” manufacturing), and the completed products are unloaded and checked the next day.

“Minimizing risks associated with aseptic processing is crucial when working with compounding systems,” agrees Randy Fraatz, vice-president of North American Operations at Steriline, which produces aseptic manufacturing equipment. “The primary benefit of robotics and automation in general is safety, coming from the reduction of human-related mistakes in the entire process.” Other benefits, says Fraatz, include the accuracy, efficiency, and reliability that result from automation. “If errors are reduced, process reliability and quality improve,” he explains. 

Despite these advantages, uptake of robotics is slow, and many 503B outsourcing facilities continue to have technicians working in laminar flow hoods or biological safety cabinets to handle beakers and flasks for solution compounding and fill vials manually using syringes, notes Smalley. He points out that some large teaching hospitals have adopted robotics for large-volume parenterals and for oncologic compounding and filling. “Smaller hospitals that focus on a specialty, such as orthopedics or oncology, appear to be the second wave,” Smalley adds.

An early example of a hospital compounding facility that adopted automation was the overall winner of the International Society for Pharmaceutical Engineering (ISPE) Facility of the Year Awards in 2019 (2). The Kantonsapotheke Zurich (KAZ) supplies oral, dermal, and parenteral formulations to the Canton of Zurich hospital system under current good manufacturing practice (CGMP) conditions. Exyte, the engineering firm for the project, said in the award announcement: “This facility raised the bar for quality performance to CGMP levels, which had never been done before in a hospital compounding facility” (2).

Although robotic systems have many advantages, one of the challenges is designing and programming for flexible operations. Since it takes time to set up the systems to manage different material combinations, speed can be limited. “For a compounding combination that requires a lot of flexibility, such as handling a wide range of materials to prepare one compound, the system will need more time to process materials,” says Fraatz. “In many cases, humans are typically able to handle different types of materials and combinations faster than robotics, but contamination risks are higher.” He explains that planning ahead is important for using robotics, so that the right tools, equipment, and programming instructions are ready for a specific process.

“The user requirements need to be clearly and completely defined,” agrees Smalley. “Robotics can only work with the tools that they are constructed with, for instance vacuum-assisted suction cups, pincers, barcode scanners, syringe operators, and the like. Additionally, they can only perform tasks that were included in their programming. Refitting and reprogramming can be problematic.”

Another challenge is the need to have barcodes that can be identified by the automated system. “Not all vials have barcodes, and new products or new brands of generic drugs result in barcodes not in the robot’s database. Software updates are constantly needed,” notes Smalley.

New developments are focusing on improving software and improving material handling. “Changes are being made to software to enable the robot to operate more smoothly and efficiently, as well as to make the user interface more intuitive. For material handling, some robotics manufacturers are focusing on loading cells, while others are looking at the ‘hands,’ as it were, trying to make the way the robot grasps items more securely without the danger of breaking the item,” reports Smalley.

A best practice for robotics users is periodic service by the equipment manufacturer that includes maintenance and adjustment, as well as software updates to address operational and security needs, notes Smalley.

“Routine maintenance and attention are absolutely required for robotic automation in the compounding space,” agrees Fraatz. In his experience, various hospitals tend to be familiar with using high-tech equipment such as robotics (as in some surgical settings), but the overall compounding industry is not yet as aware of how process automation can be used in compounding environments.

In particular, integrating robotics inside isolators is a relatively new technology for the compounding environment, says Fraatz. “True isolators have become very common in the pharmaceutical processing industry, and are the safest containment solution available for the compounding market as well,” he says. “From a best practice perspective, compounding organizations need to evaluate and adjust their quality risk management philosophy, including process and validation protocols for this type of automation. Evaluating [the use of isolators and robotics] is a multi-department effort, as there are also facility considerations in terms of what they may need, concerning cleanroom design or availability of utilities, for example.” Fraatz notes that it is also important to evaluate the process flow, including how materials are introduced to the compounding environment and handled after compounding. He points out that robotic equipment can be designed to be compatible with cleanroom conditions. “Cleanroom conditions require easy to clean tools, low particulate generation, and equipment designed to respect the clean airflow required,” notes Fraatz. 

A pharmaceutical manufacturing company adopting cutting-edge robotic technology for its CGMP 503B operations is Nephron Pharmaceuticals in South Carolina. The company, which specializes in producing generic respiratory medications using a fully automated process with blow-fill-seal (BFS) technology, launched its division for sterile compounded drugs in 2017 and began with manual operations, in which pharmacy technicians worked inside laminar-flow hoods to fill parenteral solutions coming from sterile filtration into intravenous (IV) bags or syringes. Now, Nephron is moving to robotic systems inside of the laminar-flow hood to perform these fill/finish operations. The company worked with the University of South Carolina (UofSC) and Clemson University in two separate projects to custom design robots for this application. In April 2021, Nephron validated the UofSC system and began commercial production, says Lou Kennedy, CEO of Nephron. A second robot is already being built, and more are planned. “We’re producing drugs on FDA’s drug shortages list, and this 503B space is growing,” she says. Robotic systems will improve productivity, reduce the burden of repetitive physical work for operators, and provide better accuracy and precision. Future projects will seek to increase speed of the robots to obtain higher throughput.

In addition to being used in commercial production at Nephron, Kennedy would like to see the robotic systems licensed to hospital compounding facilities. “What I love about both the Clemson and UofSC projects is that as we collaborate with both undergraduate and graduate students, we’re helping develop future industry employees,” says Kennedy.

Although a common fear is that robots will eliminate people’s jobs, Kennedy says this concern is unfounded. “Robotics is not eliminating people; it’s teaching people to have new skills to operate automation,” she notes. “Operators need to learn how to service the robot and work with it, to solve a jam or replenish components, for example. Operators are also needed for quality functions.”

Kennedy notes that Nephron sees a need for more pharmacy technicians, and the company works closely with local schools to help develop the future workforce. For example, Nephron built a sterile compounding lab on the nearby UofSC campus that is used to train pharmacy students at the university as well as two-year students from the local technical college in using robotics in sterile compounding.

Both developing an understanding of how robotics works and specific training with the equipment and its functions are key, adds Fraatz. “It is a new approach, which means trust needs to be gained, starting from education and understanding, so people can appreciate the purpose of robotic automation. Once they believe in the purpose, they can grow their familiarity and comfort with it.”

1. FDA, “Information for Outsourcing Facilities,” 

2. Exyte, “ISPE’s 2019 Facility of the Year Award for Operational Excellence & Facility of the Year Overall Award won by partners Exyte and Kantonsapotheke Zurich,” Press Release, Oct. 30, 2019.

Sterile manufacturing of small batches, in facilities including hospital compounding pharmacies and 503b outsourcing facilities, benefits from automation.