Jennifer Markarian is manufacturing reporter for Pharmaceutical Technology.

Jennifer Markarian

	A successful transfer of a drug manufacturing process-whether from pilot to commercial scale or from developer to contract manufacturing organization (CMO), for example-requires a thorough understanding of the product, the process, and the equipment by both the sending and receiving site teams. Quality-by-design (QbD) approaches are increasingly used for developing this understanding and for conducting verification of a transfer. As with any project involving multiple teams of people, good communication is crucial.


	Alignment between the receiving and transferring sites is one of the most significant challenges of a technology transfer. “More than the technology or the knowledge itself, the alignment of systems (for example, information technology and quality systems) and mentality, culture, and approaches to issues between these two main stakeholders are the most important factors for success,” says Mirko Gabriele, technology transfer manager at Patheon’s Ferentino, Italy site and co-leader of the Parenteral Drug Association’s (PDA’s) tech-transfer interest group, which was formed in July 2015. An external technology transfer, which requires aligning two groups from different organizations, is typically the most challenging to coordinate. Gabriele notes that it is crucial to face the issue of alignment head-on at the beginning of a project, as well as to evaluate and quantify alignment of systems throughout the project life by establishing and tracking key performance indicators. Evaluating system alignment is a crucial part of selecting a receiving site, says Gabriele; “If a receiving site is close to the transferring site-same systems, same approach, same mindset-it will be easier to work with them and there will be less risk associated with the project.”

	Early communication between the sending and receiving sites at the beginning of a transfer process is crucial. “We prefer to have a face-to-face kickoff meeting at the beginning of a transfer to establish a robust transfer process,” says Stefanie Kortenbreer, deputy head of product transfer for Rottendorf Pharma. A QbD approach involves experts from various parts of the company, including development and production, who perform a risk analysis and determine the critical process parameters (CPPs), for example. “It is better to spend more time in the beginning to avoid changes later,” Kortenbreer explains.


	Sharing knowledge is crucial for technology transfer success. “Some can debate that intellectual property protection is needed for transferring site safety and, therefore, the less you share, the more you protect yourself. However, the knowledge of what you are doing is a ‘condition 

’ of technology transfer. You must know what you are doing in order to put in place an appropriate governance of the process,” says Gabriele.

	Knowledge about the development of a product and process makes data points more useful. For example, “transfers typically include the appropriate process settings, but it would be useful to know how those parameters were selected and what flexibility might exist,” says Susan J. Schniepp, fellow at Regulatory Compliance Associates. She advocates sharing information about the results and conclusions related to investigations and deviations encountered during product development, because this knowledge can help save time should issues be encountered during the technology transfer activities. “The more knowledge [the receiving site has] about the product going into the validation activities, the smoother the activity will go. Bottom line--sometimes we learn more from our failures than our successes, and this information should be shared during a technology transfer project.”


	Aligning the process equipment and systems is no small feat. Site readiness in a GMP environment is a logistical challenge that requires a significant amount of up-front work, says Bill Randolph, vice-president of technical services at Janssen Supply Chain. This preparation includes identifying and setting up data-processing systems, preparing for documentation requirements, planning cleaning validation for the introduction of new APIs, transferring production and cleaning methods, qualifying manufacturing equipment, and ensuring that laboratory equipment and capacity are prepared. An organized system to coordinate these activities is essential. As part of its supply-chain reliability program, Janssen established a taskforce dedicated to building a technology-transfer checklist. “Developing a tech transfer checklist pre-defines all transfer procedures and identifies functional responsibility and ownership of each operation,” says Randolph. “It clearly documents the protocols for the sending and receiving site and summarizes the description of the manufacturing process, process flow, materials, and equipment of the sending site and proposed manufacturing strategy for the receiving site. Having a system like this in place is important because it outlines the global standard for equipment, including instrumentation and data collection, to reduce development work during transfer. Additionally, identifying equipment-independent process control parameters for tech transfer is helpful when the equipment trains are different.”

	Product transfer between manufacturing sites can be particularly difficult if there are differences in equipment, such as size/capacity, material handling, and automation/process controls, notes Randolph. Translating a process to new or alternate equipment requires understanding differences in parameters such as mixing intensity, heat transfer, and airflow.

	Performing a gap analysis early in the planning stages can identify any differences in equipment, adds Kortenbreer. “Once the gaps are identified, the design group members, which include experts from various departments, put their knowledge together to identify risks, using risk analysis tools like failure mode and effects analysis (FMEA). The group can decide if engineering batches are needed and how many at what scale, as well as whether a design of experiments (DoE) is needed to verify CPPs.” Rottendorf has found that running an engineering batch, at full scale but prior to the GMP validation runs, is useful in identifying any deviations that were unforeseen in smaller-scale batches. “For example, we found, in one case, that a longer compression time was needed at the full scale. We also discovered that the equipment became warmer because of the longer dwell time and that this temperature change affected compaction. Parameters needed to be adjusted to compensate,” explains Kortenbreer.

	When the equipment is unfamiliar to the receiving site, as may be the case with new technologies such as continuous manufacturing, process analytical technology, multi-layer compression, or micro-tablet technology, additional personnel training is required for existing and new employees to ensure that they understand the new operations and can deliver the same level of product quality and reliability, says Randolph.

	Older products and processes, on the other hand, might need to be updated. For legacy products, “analytical methods may not meet current regulatory standards, reliability needs, and/or best practices, and may require further development,” comments Randolph.

	When scaling up from pilot-plant to commercial scale, challenges “are often technology specific and usually occur when intermediate quality attributes are influenced by process scale or equipment differences (e.g., blending and material handling),” notes Randolph. Solutions employed by Janssen Supply Chain include using process models to assist in establishing process parameter estimates and standardizing equipment if possible. Continuous manufacturing provides benefits for scale-up (see "

Continuous Manufacturing Eases Scale Up for Solid Dosage

	Whether processes are new or old, risk-based approaches are commonly used to identify how processing steps and equipment will impact critical quality attributes (CQAs). “Evidence from previous experience, process development, and the literature helps define CPPs to be monitored and validated. Mitigation plans must be put in place where needed. The purpose is to reduce, as much as you can, the risks to the quality, safety, and efficacy of the product,” explains Gabriele. A risk-based approach will also highlight areas of high uncertainty, where additional knowledge is needed, as discussed in PDA’s technical report on technology transfer (1).


	Traditional process validation, which calls for three validation batches, is required by European Union GMPs (2) and is still widely performed globally, even though FDA has moved to continued process verification based on a lifecycle approach and on monitoring CPPs to ensure that a process remains in control (3).

	“We have been performing technology transfers based on a QbD approach and have seen increasing requests for this approach over the past two years,” says Marcus Spreen, head of strategic planning at Rottendorf. “We expect to also see more requests for continued process verification in the near future, although most still want three validation batches.” He explains that Rottendorf already tracks CPPs and evaluates them annually, and expects that continued verification will be similar, perhaps with more frequent reporting.

	Even apart from government regulation requirements, the industry is changing its view of process validation from a limited evidence of the end result of the development and transfer process to a more holistic view of the “overall chain of events that brings a molecule from the lab to the patient,” adds Gabriele. “The industry is moving from a standard, fixed number of batches to a scientific approach in which data coming from several process stages are evaluated and used to plan, and later on confirm, that a robust, consistent, and safe process has been transferred.” In the approach laid out by FDA’s 2011 process validation guidance (3), the commercial manufacturing process is based on knowledge gained through development and scale-up, and continued process verification ensures that a process remains in a state of control and changes, improvements, additional information from operations, quality, and development are properly taken into consideration, notes Gabriele.

Technology Transfer: Technical Report No. 65 

Eudralex Vol. 4, EU Guidelines for Good Manufacturing Practice for Medicinal Products, Annex 15: Qualification and Validation

Guidance for Industry: Process Validation: General Principles and Practices

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	When referring to this article, please cite it as J. Markarian, "Technology Transfer Connections," 

Articles

Transferring the manufacturing of a drug from one scale to another or between manufacturing sites presents both technical and business challenges.

Technology Transfer Connections

	Continuous manufacturing can make scale-up and technology transfer easier in several ways. With some continuous process technologies, the same primary equipment can be used for both the smaller and larger scale, and the equipment is simply run longer to obtain the greater volume. Another advantage is that a set of experiments can be run more quickly on a continuous line than in a batch process, making it easier to define scale-up parameters. 

 spoke with Bill Randolph, vice-president, Technical Services, Janssen Supply Chain, about some of the considerations for technology transfer of a continuous, solid-dosage manufacturing process and what he sees as the outlook for continuous manufacturing.

 What needs to be considered in a technology transfer from a pilot-scale site to a commercial-scale site when using a continuous solid-dosage manufacturing process?

If the pilot and commercial operations are the same scale, the systems are 'mirrored' and equipment is directly transferable. Changes would only apply to local operational considerations, such as changes to finished product hold or bulk shipment.

	In other situations, the same design of equipment is used at both sites but scale up is needed. For example, for a wet-granulation process where the pilot scale was a 25-kg/h line but the market volume requires a 50- or 100-kg/hr line, scale-up will be needed. The scale condition, particularly the screw configuration and wettability of the blend in the case of screw-granulation technology, needs to be tested in a technology-transfer study. Thermodynamic conditions on a segmented dryer also need to be tested, although thermodynamic and mass transfer equations can be used to establish scale equivalency. 

	Continuous processes like direct compression or dry granulation roller compaction do not require significant technology-transfer studies for increased line speeds.  Areas that could require some level of development or confirmation in these cases, however, include changes in scale for feeders, blenders, granulators, compression, coaters, and process analytical technology (PAT)/in-process control.

	The idea is to have an understanding of which equipment characteristics are most important (e.g., residence time distribution) and to have a good way of characterizing powders and their mixtures. The behavior of the product-specific materials must be characterized in the new set of feeders, blenders, and other equipment. There may also be a need for adjusting PAT chemometric models for a new dynamic environment.  With this in hand, we can perform design of experiments on the line quickly, on a single continuous run, to nail down the design space, which will vary some from line to line.

	For a scale-up or transfer onto a different design (including a blender, granulator, or dryers), assessments would be needed on differences in equipment trains and the complexity of the continuous manufacturing process.

Future of continuous solid-dosage manufacturing

What role do you expect continuous solid-dosage manufacturing to play in the near/long-term?

In the near-term, continuous manufacturing will foster more strategic collaboration between industry, academia, and healthcare agencies to establish the guidance, development processes, and science needed to implement proper operations at each manufacturing site. This collaboration will elicit alignment with the FDA to create regulatory pathways for the use of continuous manufacturing in pharmaceutical production.

	Through the use of continuous manufacturing, products will be monitored for quality using real-time release testing, which will greatly reduce processing time and deliver products to patients sooner. It also allows for batch size flexibility by eliminating the need to discard an entire batch when a correction is needed.

	In the long term, continuous manufacturing offers the potential for an enhanced ability to control manufacturing. Through the incorporation of PAT, manufacturers will have the ability to ensure product quality and improve knowledge management for lifecycle validation. Operational benefits could potentially include, for example, improved risk profile/commercial reliability, on-demand manufacturing, improved batch/process yields, and reduced investigations.

	I believe the implementation of continuous manufacturing will lead to a mixed pharmaceutical production environment where some products are still developed and manufactured in batch process, while others transition from batch to continuous or from continuous to batch depending on market demand requirements and product lifecycle. There will also be more end-to-end continuous process implementation (from materials to packaging) and introduction of potential blockbuster new products directly using broad application of continuous manufacturing technologies. 

Pharmaceutical Technology spoke with Bill Randolph, vice-president, Technical Services, Janssen Supply Chain, about some of the considerations for technology transfer of a continuous, solid-dosage manufacturing process and what he sees as the outlook for continuous manufacturing.

	The International Organization for Standardization (ISO) published the long-awaited revisions to its standards for classification and monitoring of air cleanliness in cleanrooms on Dec. 15, 2015. ISO 14644-1:2015 “Cleanrooms and associated controlled environments Part 1:Classification of air cleanliness by particle concentration” (1) replaces ISO 14644-1:1999, and ISO 14644-2:2015 “Cleanrooms and associated controlled environments Part 2: Monitoring to provide evidence of cleanroom performance related to air cleanliness by particle concentration” (2) replaces ISO 14644-2:2000.

	The 2015 editions are the result of a systematic review and include changes made in response to requests by users and experts in the cleanroom community. In particular, the requests for reviewing Part 1 were related to “the basis for the number of sampling locations and, most importantly, the whole statistical basis of classification of cleanliness using the Student T-test for one to nine sampling locations,” notes Gordon Farquharson, convenor of the ISO TC209 working group 1, which performed the review and revisions. Because Part 2 is closely aligned with Part 1, the committee reviewed both parts together.


	The addition of “by particle concentration” to the title of the standard is a long-overdue clarification, comments Karen Ginsbury, CEO at PCI Pharmaceutical Consulting. “For years, I have heard cleanroom contractors and practitioners alike wrongly describe ISO 14644-1 and 2 as cleanroom ‘validation’ or ‘qualification’ standards.” She notes that the standards only address airborne particles, not other factors crucial to cleanroom qualification, such as smoke tests to determine airflow patterns.

	The introduction of Part 1 explains, “This part of ISO 14644 specifies classes of air cleanliness in terms of the number of particles expressed as a concentration in air volume. It also specifies the standard method of testing to determine cleanliness class, including selection of sampling locations” (1).


	The primary changes to Part 1 involve the number of samples and the selection of sampling locations. “The number of samples will increase from what was required previously,” explains Marsha Stabler Hardiman, senior consultant at ValSource. “The minimum number of samples is now determined from a lookup table (instead of an equation), and that number is set to be statistically significant.”

	According to ISO, the new method for selecting the sites and number of sampling collections uses a more consistent statistical approach based “where samples are drawn randomly without replacement from a finite population. The new approach allows each location to be treated independently with at least a 95% level of confidence that at least 90% of the cleanroom or clean zone areas will comply with the maximum particle concentration limit for the target class of air cleanliness. No assumptions are made regarding the distribution of the actual particle counts over the area of the cleanroom or clean zone; while in ISO 14644-1:1999 an underlying assumption was that the particle counts follow the same normal distribution across the room” (1). The sampling locations are to be chosen representatively, meaning that “features such as cleanroom or clean zone layout, equipment disposition, and airflow systems should be considered when selecting sampling locations” (1).

	This representative selection of sample locations is a big change from the previous random selection, says Hardiman. “A company now has to have a rationale and justification for sample site location to ensure that the sample locations selected are representative of the characteristics of that section.  Companies will have to look at the new number of sample locations and then determine where the representative sample locations will be collected.  If contracting out the classification activities, you should make sure that your contractor is now using your new, representative sample locations.” 

Classification of macro and nano-particles


	The revision makes a change regarding large (≥ 5 µm) particles, which are required to be measured for some classifications in the EU Annex 1 GMP guidelines (3) and others. “The experts working on the revision of ISO 14644-1 were of the opinion that particles ≥ 5 µm diameter should not be used to classify ISO class 5 and cleaner environments because of the uncertainty associated with particle collection efficiency and accuracy of counting low concentrations,” says Farquharson. “In order that the European Union (EU), the Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation Scheme, World Health Organization, and Chinese GMPs are not left without a classification tool for their Grades A  (at rest and operational) and B (at rest), ISO 14644-1:2015 provides a mechanism of extrapolating the macro-particle descriptor for class limits of 20 and 29 particles ≥ 5 µm.”

	The new document does not address nano-scale particles, which were formerly defined as ultrafine particles  in ISO 14644-1:1999, but will address these under a new Part 12, notes Farquharson, who explains, “These particles are measured using a different particle counter, and industries such as semi-conductor monitor for concentration of these very small airborne particles at critical control points. These particles are not generally of interest to the pharmaceutical and life sciences industries.”


	ISO 14644-2:2015 now requires monitoring to provide evidence of cleanroom performance, explains Farquharson. The standard addresses airborne particle concentration, airflow, and device pressure difference. New topics include monitoring of critical parameters and setting action and alert alarms.

	The revised standard now allows companies to use risk management to set their periodic classification testing schedules, notes Hardiman. “In the past, the retesting was prescribed in a table and the timing was based on the ISO class of the cleanroom or clean zone. Now, companies can put more emphasis on the day-to-day data that they generate in their own facilities to help determine the appropriate testing and frequencies needed for continued cleanroom compliance. If a company is generating great data and the risk to continued cleanroom compliance is low, then they can set a longer periodic classification frequency,” explains Hardiman. She notes that risk-based sample site selection is crucial for environmental monitoring. “The key is understanding your unique products and processes and selecting sites that best address the risks that your products and processes present. It is important to be able to identify all of the potential contamination sources in each cleanroom and to select environmental monitoring sample locations in close proximity to these sources. It is also very important to understand the people, material, and waste flows,” concludes Hardiman.

	Pharmaceutical cleanrooms typically already have monitoring plans, which are required by Annex 1 of the EU GMPs, says Ginsbury (3). She notes that users should, however, check with their cleanroom contractors to determine whether a contractor is qualified and familiar with the revisions, because the regulators reference the ISO standards and they must be followed, says Ginsbury. “I would recommend having the discussion now so that, by 2017, your contractors and in-house staff are fully up to speed and following the new standard,” she notes. “Even if you use contractors, the responsibility for review and approval of their results and compliance with regulatory standards lies with you. ISO may not require 5 µm particles and may let you use risk assessment to determine frequency of classification. However, EU Annex 1 still requires measuring 5 µm particles as part of classification, and current industry practice (EU [3] and FDA [4]) is to perform cleanroom classification twice a year for aseptic core. We haven’t heard the regulators on this one but I wouldn’t rush to reduce that frequency based on risk assessment,” cautions Ginsbury.

ISO 14644-1:2015, Cleanrooms and associated controlled environments – Part 1: Classification of air cleanliness by particle concentration

		ISO, ISO 14644-2:2015, Cleanrooms and associated controlled environments – Part 2: Monitoring to provide evidence of cleanroom performance related to air cleanliness by particle concentration (Geneva, 2015).	

EudraLex Volume 4: Good manufacturing practice Guidelines

, “Annex 1, Manufacture of Sterile Medicinal Products,” (Brussels, 2008).	

Guidance for Industry: Sterile Drug Products Produced by Aseptic Processing-Current Good Manufacturing Practice 

Revised versions of ISO 14644 adopt changes to sampling procedures and monitoring plans for cleanrooms.

Revised ISO Cleanroom Standards Improve Air Cleanliness Classification

In the past, process control data were used simply to control the process. With the explosion of data collection and analysis capabilities, however, data can be harnessed to do much more. 

 spoke with Dr. Hartmut Klocker, vice-president of the Pharma Market Development Board at Siemens, about how data from process control systems can be used to optimize manufacturing processes.

 What can be accomplished by more effectively using process control data?

: In the past, process control data were collected in the distributed control system archives and dedicated plant information systems, and their only purposes were to control the process and visualize the status of the process in real-time and historically. Today, however, pharma companies try to gain additional information by bringing together all different types of data from many sources including control systems, programmable logic controllers (PLCs), analyzers, building management systems, and laboratories.

	The analysis of key performance indicators (KPIs) using all these data will open new insights into manufacturing processes and provide the basis for process optimization while keeping the quality within tight specifications. In addition, new technologies, such as continuous processes for tablet manufacturing or the production of personalized medicine, would not work without integrating process and analyzer data and even parameters of the scheduling and planning tools. In the future, the industry will focus on using process data to predict the end-product quality and thus the expected therapeutic effect using model predictive control.

	Some data generated within PLCs, smart sensors, and complex analyzers are not collected and not used at all. Collecting these data, transferring them to a database, and applying tools to generate valuable information out of the pile of data are key challenges. A cloud solution may provide sufficient storage capacity and calculation power. The data analysis can then be performed by the customer or even outsourced to the suppliers of the automation systems or the machine manufacturers by giving them access to the relevant cloud data. An example, available today, is equipment monitoring, with the purpose of getting a better understanding of the condition of the machines and the overall production process. Data could be then used for energy management and condition-based maintenance.

 How can data from different sources be integrated and presented to users?

: Data integration platforms are key to bringing together these data. The networking capabilities of devices are increasing steadily in line with the general industry trends of digitalization. Key technologies that allow this integration are the open standards OLE for Process Control, Unified Architecture (OPC UA) and OPC Analyzer-Device Integration (OPC UA ADI), which is for complex analyzers with large quantities of multivariate data.

	Cloud technologies can provide a basis for such data platforms. Different experts can then be granted access to a subset of these data to provide data analytical services. For example, equipment experts can be shown data for predictive maintenance, and process engineers can be shown data to support continuous process improvement. In addition, the common problem of overloading plant operators with control data that are not directly relevant to monitor the process can be avoided. Data can be hidden on the operators’ screens but transferred into the cloud, where other experts can look at various issues, such as machine health and performance or the relationship between climate and process quality parameters. The traditional operator screens will change, and instead of colorful graphics of process flow diagrams, process parameters and alarm messages with more focused views will prevail. The intent is to give a fast and clear picture of what is really going on, while hiding irrelevant details. Another tool is smart-phone apps that can be used to present the information needed to deal with specific tasks.

What are the security concerns with storing data in the cloud? 

Cyber-attacks are a challenge that is mentioned in every industry, especially when storing process data and accessing it broadly. To create the highest level of security, it is important to modernize and integrate the automation environment, with security built natively into the system from the field-level up. Siemens uses a ‘defense-in-depth’ concept that covers plant and network security as well as system integrity.

 How can the collected data be used for determining KPIs, such as overall equipment effectiveness (OEE)?

KPIs, such as OEE and others, must use the whole range of data sources from automation systems, planning tools, building management systems, and laboratory management. There are two principal methods for achieving this. The first strategy is to collect all data in one large database and apply the KPI calculations to this database. This strategy is straightforward as long as the intelligent device can be easily integrated into the plant information database using an open communication standard. In the alternative strategy, the data are not copied into a database, but accessed from all the distributed databases on request. The databases accessed might be plant information systems and historical data from control systems, recipe-control systems, enterprise resource planning data, and others. Intelligent caching of data to allow quick access using internet browsers is important. The advantage of this concept is to avoid copying of the original data, which might make the proof of data consistency (according to GMP regulations) cumbersome.

 What are some best practices for using process data to control the manufacturing process?

: Critical quality attributes (CQAs) determine the final product quality and are the basis for predicting therapeutic effect. Today, many CQAs can be measured in-line/on-line by applying process analytical technology (PAT). Data collected from experiments using the in-line/on-line measurement of the CQAs in the different unit operations of a production chain are the basis for process data modeling. Correlation of the data provides a basis for understanding the process and determining the critical process parameter (CPP) settings to obtain the right quality. Furthermore, the application of PAT, combined with model predictive control in the process control system, allows us to adapt CPP settings dynamically and produce within specification all the time.

	A good example of the need to combine data from several sources is personalized medicine, which is of increasing importance to the pharmaceutical industry. The same principles in terms of manufacturing, quality control, and release of the drug apply to personalized medicine as to conventional drugs, but instead of large batches of active ingredients, a batch size becomes the drug product for a single patient. Without tight data integration and a fully paperless production process, manufacturing costs would exceed any acceptable limit. To overcome this challenge, process automation data and data from the laboratory information system have to be shared with the information technology (IT) systems for batch recording, reviewing, and releasing. Furthermore, scheduling systems are a key part aligning all production steps and ensuring planning principles like first-in/first-out prioritization rules. Extensive integration of these data sources by IT and automation systems will allow production costs to be lowered by at least an order of magnitude.

	Another case that presents some challenges is transforming a batch pharmaceutical process into a continuous process. A continuous process requires a robust process design and continual monitoring, typically using online analyzers. Multivariate analyzer data and other process parameters from simple sensors must be combined and treated by statistical engines to give the operators a clear view of the process so that they can ensure that the process always stays within the quality boundaries. In the next generation of continuous production processes, it will become important to use these quality parameters to automatically adjust the process, thus closing the loop. The core of such an advanced process-control system is a data platform handling a large quantity of multivariate and univariate data in real time and applying statistical methods like principle component analysis. The easy integration of multivariate analyzer data (e.g., spectral data) following open standards is an important requirement. An equally important feature is the real-time integration to the control system for adjusting the set-point dynamically. Advanced process control methods, especially model predictive control strategies, can add the benefit of controlling the overall quality at the lowest variance within the lower and upper limits.

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	When referring to this article, please cite it as J. Markarian, "Gaining Insight from Process Control Data," 

Integrated data and cloud-based solutions can be used for process optimization.

Gaining Insight from Process Control Data

	Just as emails can be accessed anywhere from a smart phone, pharmaceutical manufacturing process data can now be made accessible remotely through new network solutions. And remote access is not limited to data on a screen. Smart glasses have moved beyond a consumer novelty to a business tool that can be particularly valuable in manufacturing. One of the primary uses today in pharmaceutical manufacturing is telepresence, by which remote personnel can literally see what an on-site person is viewing and can perform remote troubleshooting, for example. Both large pharmaceutical companies and CMOs are using or experimenting with smart glasses for various uses, including troubleshooting, maintenance, and training.


	The smart glasses concept was popularized by the Google Glass for consumers, but several other companies are also producing smart glasses. Some of these are believed to be better than Google Glass for industrial applications in the functions of information access, real-time communication, and documentation, according to a Lux Research Report published in December 2015 (1), which looked at enterprise deployments of smart glasses. Some glasses on the market for industry include Epson Moverio, Vuzix M100, Osterhout Design Group (ODG) R-7, and others.

	Developers continue to improve the glasses. “How to balance between a long battery life time and the limited available space within a pair of smart glasses will continue to be a major challenge for smart glass developers,” notes Tony Sun, the lead analyst of the Lux Research report. “Developers also need to improve the durability of the overall device construct for industrial environments, such as incorporation of smart glasses into a helmet.”


	Other companies are developing the applications needed to make smart glasses useful in manufacturing, including pharmaceutical manufacturing. Pristine focuses on the business value found in “virtualizing the expert.” “Smart glasses can multiply the availability of experts, because an operator in a plant can wear smart glasses and be connected to the expert in another plant or an office,” notes Pristine’s Lucas Schlager. Connecting two people with a video stream is relatively straightforward, but making this connection for hands-on workers in the field, such as in a manufacturing environment, comes with specific needs. “First, the user needs to easily contact the expert. Problems are usually unplanned, so a preset conference set up via email may not provide a fast enough response,” says Schlager. “Apps are designed to perform ‘pre-call magic’ to get the call from the field to the expert quickly, while the operator is in the plant with gloves on and can’t dial a phone, but can say out loud, ‘Need help.’ After connection, the expert may need to demonstrate to the operator what might need to be done, by drawing on top of the video, for example.” Data about the call or about the problem and solution can be recorded for evaluation to make the system work better or to identify trends in manufacturing problems. “We are on the ‘slope of enlightenment’ of adoption of these technologies. Early adopters have experienced the benefits of virtualized experts and others are starting to try it,” observes Schlager. He suggests that augmented reality or immersive experience (e.g., having instructions pop up on the glasses based on what the operator is viewing) will grow in the near future as smart-glass technology becomes more lightweight and higher resolution and as enterprises gain more experience with the technology.

	Apprentice Field Suite, which uses smart glasses and other mobility technology to improve processes for its pharmaceutical and biotechnology clients, designed three technology modules specifically for pharmaceutical manufacturing and R&D. The Tandem module allows an engineer to have a “telepresence” for remote troubleshooting, which minimizes travel and gives instant access to a problem. “The operator in the facility will wear the glasses and the engineer at a remote location can see the problem that the operator is looking at; this feature currently is being used at all of our global pharma clients,” explains Gary Pignata, partner at Apprentice. “If necessary, an equipment manufacturer or subject-matter expert can also be given simultaneous access. Additional tools allow the remote user to ‘draw’ on the field of vision of the glasses-wearer (e.g., to show what button to push or to indicate the specific part being discussed) so that communication can be visual as well as verbal.” The glasses are also useful for a CMO–client project relationship, notes Pignata, "A CMO protects the proprietary nature of other projects by only turning the glasses on for a specific client, while at the same time allowing that client access to their process without traveling." The Gauge module can alert operators to the condition of equipment (i.e., if a reactor is in use) and capture data from analog (and other) gauges. A Manuals component can be used during training or maintenance, for example, so that safety notices and standard operating procedures can be viewed with the glasses in a heads-up display, allowing the users' hands to remain free to perform operations.  Apprentice co-founder Angelo Stracquatanio remarks, "We are seeing significant efficiency gains and operational improvements at each of our four top-10 pharma clients, as well as the other biotechs, CMOs, and equipment manufacturers we work with".

	Software provider Vital Enterprises sees the main advantage of smart glasses in manufacturing as allowing hands-free operation with access to information and remote collaborators. The company currently has commercial applications in air and space assembly and in medical, and is beginning to work with pharmaceutical manfuacturers. “We are working with pharmaceutical innovation centers to gestate this new technology and deploy it more broadly,” says Ash Eldritch, co-founder and chief science officer of Vital Enterprises. “Companies are deploying at the pilot stage to look for cost and efficiency benefits. The gains are largest for geographically distributed teams that can benefit from telepresence and for cases where there are long process checklists, such as in technology transfer procedures.”  Another possible use is smart laboratory notebooks. “Some are using electronic notebooks already, but smart glasses have the benefit of being worn. They can also capture point-of-view video and audio notes that can be transcribed,” says Eldritch. Similarly, in a manufacturing environment, the glasses can capture expert knowledge for training or for quality checks. Smart glasses are currently being used for hands-free instructional support, with text or schematics put into the wearer’s view while they are carrying out a complex task. “Next we plan to incorporate videos, in addition to the currently available static images and text that can be read to the wearer. The challenge is creating and transferring the video content, because the traditional way of doing things is text based,” says Eldritch.


	Smart glass technology has only been in existence for a few years, and experts note there is still relatively little knowledge in the market about the potential benefits. Even so, the technology continues to get smarter. “Smart glasses are advancing in ‘contextually awareness’-rather than the operator telling the glasses what checklist is needed, for example, the glasses will leverage the sensors in the industrial internet of things (IIoT) to sense where the user is standing, who they are, and what task needs to be performed,” says Eldritch. “The convergence of IIoT and augmented reality will be the next step forward from where the technology is now, as a head-mounted display. In five to ten years, smart glasses will be able to project digital imagery directly onto physical objects, meshing digital information seamlessly and intuitively into the world around us,” he predicts.

	The use of smart glasses is expected to grow. “We expect the use of smart glasses will eventually expand from manufacturing to other roles or functions within the same company, such as customer service and online sales support,” predicts Sun. “For this to happen, smart glasses first need to scale up within manufacturing roles by improving ergonomics, learnability, and data safety.”

		T. Sun, “Better than Google Glass: Finding the Right Smart Glasses for Enterprise” Lux Research State of the Market Report, Dec. 22, 2015, 

https://portal.luxresearchinc.com/research/report_excerpt/20864

Smart glasses enhance remote troubleshooting and process management for pharmaceutical manufacturing.