	In an INTERPHEX first, MG2/MG America will host an educational forum on the show floor, the MG America Processing & Packaging Summit at INTERPHEX 2019 (April 2–4, 2019, Javits Center, New York, NY). Packaging sessions are scheduled on Tuesday, April 2; processing sessions will be presented on Wednesday, April 3. Packaging topics include serialization, robotic handling of sterile-filled glass vials, and the advantages of integrated automation (1).  

	Attendees also will have the opportunity to learn about aseptic filling and closing machines and material handling for parenteral products. NJM, a ProMach product brand, plans to introduce its Dara NFL/2-RDL aseptic filling and closing machine for ready-to-use, nested vials using the RayDyLyo cap. The patented, pre-assembled plastic closure from ARaymond LIFE features a rubber stopper within the cap to minimize the “pop-off” effect and eliminate the risk of stoppers adhering to freeze-dryer plates. Available with central tear-off for injectable products and total tear-off for product reconstitution and mixing, RayDyLyo closures come in 13-mm and 20-mm diameters (2).

	The Dara NFL/2-RDL machine fills solutions, suspensions, diagnostics, or vaccines in glass or plastic vials in sizes ranging from 2R to 50R with dose volumes from 0.1 to 50 mL. Changeover to a different vial size takes less than 15 minutes with no tools required. The dual-station machine handles up to 4500 vials/hour in a footprint that measures only two meters long by one meter wide (2).

	Thomas Kessler, regional sales manager at NJM, said in a press release, “The highly efficient Dara NFL/2-RDL was developed for biotech companies and 503B pharmacies and can be used from scale-up through small batch commercial manufacturing. Utilizing ready-to-use vials massively simplifies small-scale production by eliminating the need for a washer and depyrogenation tunnel, and using the RayDyLyo cap further simplifies operations by eliminating crimping and the particulate generation that comes along with that process. After introducing the high-speed Dara SX and HSL series machines for RayDyLyo caps last year, we’re now offering this NFL system for customers with smaller capacity requirements, who will benefit from this innovative stopper/cap technology and a more compact system that has the added value of syringe and cartridge processing capabilities in the same machine” (2).

	A robotic aseptic filling and closing machine, the GENiSYS R model from AST, also works with nested, ready-to-fill containers and can handle vials, syringes, or cartridges. Designed for small-batch sterile production common to cell, gene, and regenerative medicines, the machine features a peristaltic dispensing system integrated with an electronic balance to provide 100% fill weight inspection. Modular design enables easy integration with isolator-barrier systems, lyophilizers, and other process systems. “The GENiSYS R bridges the gap for late-stage clinical and commercial products by integrating advanced aseptic features, and process and container flexibility within a simple and compact system,” reported Josh Russell, vice-president of Business Development of AST, in a press release (3).

	Another machine designed to handle lower volume aseptic requirements associated with biologics and vaccines, thecompact Asep-Tech LAB+ blow/fill/seal machine from Weiler Engineering is ideal for stability, clinical, and development batches. A small footprint, servo-driven motion controls and modular platform expedite changeover and maximize the range of products handled. Options include single-use technology or conventional clean-in-place and steam-in-place technologies, a patent-pending Grade A filling zone, and a particulate matter removal system (4). 

	The Integra machine from groninger is an aseptic filling machine with integrated isolator. Built from the ground up in conjunction with Skan as an integrated isolator-equipped filling system, the line processes toxic products while achieving shorter decontamination cycles. Its Skanfog decontamination system enables cleaning and decontamination in 30 minutes (5).

	For freeze-drying processes, Telstar is introducing the Lyogistics Zero automatic vial loading/unloading system, which can be cleaned and sterilized inside the lyophilization chamber. The unit eliminates concealed parts and features a steam-sterilizable slider, which is moved by a contact-free magnetic drive mechanism. This design allows the slider to remain inside in the chamber for cleaning and sterilization (6).

	1. INTERPHEX, “MG2/MG America to Offer Processing & Packaging Summit at INTERPHEX 2019,” Press Release, Feb. 25, 2019.

	2. NJM, “NJM Introduces Dara NFL/2-RDL Aseptic Filling and Closing Machine for Ready-to-Use Vials with RayDyLyo Caps,” Press Release, Jan. 15, 2019. 

	3. AST, “AST Unveils GENiSYS R Robotic Aseptic Filling and Closing Machine at INTERPHEX 2019,” Press Release, www.interphex.com/__novadocuments/559103?v=636863866954500000, accessed Feb. 27, 2019. 

	4. Weiler Engineering, “LAB+ Blow/Fill/Seal Machine,” Press Release, www.interphex.com/__novadocuments/558030?v=636861997071400000, accessed Feb 27, 2019. 

	5. INTERPHEX, “Revolutionary Technology and Innovation to Debut at INTERPHEX 2019,” Press Release, Feb. 20, 2019. 

	6. Telstar, “Telstar Promotes a New Generation of Automatic Vial Loading & Unloading Systems for Freeze-Drying Processes,” Press Release, www.interphex.com/en/Show-Info/Exhibitor-List/Company-Content1/?coId=498456, accessed March 1, 2019. 

Articles

Feature

At INTERPHEX 2019, MG2/MG America stages a packaging/processing summit; exhibitors offer packaging equipment innovations for parenteral products. 

Packaging Innovations at INTERPHEX 2019

	The “digital plant” and technologies that go along with digital transformation-such as robotics, data analytics, artificial intelligence (AI), and the industrial Internet of things (IIoT)-promise greater efficiency. Eli Lilly and Company is advancing in applying these technologies to its pharmaceutical manufacturing organization. 

spoke with Jim Weber, advisor for Manufacturing and Quality IT [Information Technology] Digital Manufacturing, and Wilfred Mascarenhas, advisor for Data and Analytics, Manufacturing, and Quality IT, both at Lilly, about some of the key issues. 

What do you see as the most significant drivers for making investments in digital manufacturing technologies?   

Our Lilly Manufacturing organization is united around a common purpose-to make medicines that make life better for people around the world, and to do that with safety first and quality always. Therefore, our biggest motivation is to achieve this purpose as best we can and to continuously strive to do even better. The digital plant can accelerate improvements. For example, we can reduce ergonomic risks through robotics lifting boxes and ensure quality through real-time analytics rather than after-the-fact testing. These technologies also drive cost efficiencies.

What do you see as the most valuable benefits of digital transformation technologies for pharma manufacturing?

For us, ‘value’ means anything that enables us to better make medicines. We start by asking our people who make the medicine: what are the problems; where is the waste; and what are the most frustrating things they have to do? Then, we think creatively about solutions. It’s at this ‘creative thinking’ step that the technology comes into play. We have to educate ourselves on emerging digital capabilities, so we can see the full set of possibilities to find a good opportunity. Before we start a project, we require a business case review and approval to keep value at the center, and not technology. Transformation will happen through time, replication and accumulated learning. We believe the most valuable benefits of digital transformation come from the new capabilities that our people develop. 

What are some examples of ‘low hanging fruit’/initial projects for digital transformation?

A tree has to be somewhat mature before it has any fruit, much less low-hanging fruit. Right now, technologies like robotics and advanced analytics tend to be a bit more mature, and thus these are the technologies of choice for our initial projects. For robotics, we’ve got projects to automate tedious material handling steps that are done manually today. Some examples include using a six-axis robotic arm to load material into packaging lines, transporting these materials to the line from the warehouse by autonomous guided vehicles (AGV), and even things as basic as picking up the trash with mobile robots. We’ve got projects that are using advanced analytics to help our people identify trends and patterns more quickly and effectively. Some examples of those include using natural language processing to find patterns in complaints, applying data models to predict process failures, and using artificial intelligence techniques to identify and fix errors in supply chain data. We’re doing these things in our existing facilities and leveraging data from current IT and automation systems.  At the same time, we’re trying to anticipate equipment and system lifecycle timelines, so that we can build digital plant capabilities into new installations from the start.

Data and analytics play a critical role in realizing the Digital Plant vision. It is recognized as one of the nine key technologies that is transforming manufacturing and industrial production (1). Not only is Big Data/analytics a critical capability in itself, but it is also foundational to many of the remaining eight technologies such as IIoT, robotics, etc. Our Digital Plant vision, also called Smart Manufacturing (whereby ‘smart machines’ and robots are inter-operating and self-adapting based on communication with one another and with humans) is largely driven by analysis of data and the communication of what adjustments need to be made based on the results of the data analysis. In many cases, this analysis will be done at the ‘edge’ (i.e., at the machine itself) and then communicated to other relevant machines/processes and humans. 

	Over the past 10 years or more, Lilly Manufacturing has been investing in building a strong data foundation. Due to this long-term investment, Lilly has a mature centralized global data store, which was recently modernized to a Big Data data lake. Data from all of the critical IT and OT systems flow into the centralized data lake, which allows the business to connect to a single data source to get all of the data required for analytics and perform a holistic end-to-end analysis. Time to find and integrate the data is minimized, which allows our people to focus on analysis of the data. The data lake is a fully validated system, allowing its use for both good manufacturing practices (GMP) and non-GMP usage. Lilly is considered one of the leaders in centralizing all manufacturing data for analytics and in having a GMP-validated data lake. 

What are the next steps for data and analytics and what are the challenges that need to be addressed? 

There are four different types of analytics typically conducted on data. These are broadly referred to as descriptive, diagnostic, predictive, and adaptive. Descriptive tells us what is happening. Diagnostic provides information on why it happened. Predictive helps predict what will happen so we can be proactive to avoid issues or optimize processes, and adaptive is where processes/machines are using analytics to self-adjust with minimal to no human intervention. 

	Due to Lilly Manufacturing's strong data foundation, Lilly Manufacturing has mature descriptive and diagnostic analytics. We have automated dashboards such as quality and maintenance, periodic reports, monthly metrics dashboards and process monitoring dashboards to help business users perform descriptive and diagnostic analytics on a daily basis. In the past two years, with the advancement of Big Data and analytics, Lilly Manufacturing has invested in predictive analytics. We have successfully completed several proof-of-concept projects that are now being piloted. A few examples of projects in advanced analytics include predictive and condition-based maintenance, natural language processing of unstructured data, and smart search. We are also evaluating Cloud services for data storage, as well as AI platforms for advanced analytics and machine learning. 

	We already have vast amounts of disparate data in our data lake, and it’s increasing on a daily basis. Some of the challenges in the data and analytics space are to adapt our information management practices to this huge scale. Data governance, data stewardship, automated data quality, data integrity, data catalog maintenance, and master data management are examples of those challenges. Lilly is proactively addressing these challenges to ensure the long-term maturity and sustainability of the data lake. These challenges require a strong partnership between business and IT for long-term success.

	1.     Boston Consulting Group, “Embracing Industry 4.0 and Rediscovering Growth,” 

www.bcg.com/en-us/capabilities/operations/embracing-industry-4.0-rediscovering-growth.aspx

Eli Lilly and Company experts share the vision and the value of the digital plant for pharmaceutical manufacturing.

Digital Transformation at Lilly Manufacturing

	The “digital plant” and the use of various modern manufacturing technologies that go along with digital transformation promise greater efficiency and quality. 

spoke with Bob Lenich, director of Global Life Sciences, and Dennis Belanger, director of Operational Certainty Consulting, both at Emerson, about digital transformation of pharmaceutical manufacturing.

What do you see as the most significant drivers for making these investments in pharma manufacturing?

Ongoing changes in the life-sciences industry are putting ever greater pressure on pharma and biotech manufacturers to make continuous changes to the way they operate. Expanding price controls industry-wide have created a strong need to optimize operating budgets and prioritize investment in the development of new products. To drive continuous innovation and profitability requires better use of the data they already have and the building of more efficient manufacturing processes. 

	Another driver for digital transformation is the shift from batch to continuous production. Continuous production allows for equivalent production volume with a much smaller capital investment and facility footprint. Manufacturing capital investment can be delayed until later in clinical trials when manufacturers have a clearer picture of a new therapy’s success. Higher capacity with a smaller footprint, all delivered with lower risk, makes a compelling argument for the transition to continuous manufacturing. Successful operation of these smaller, faster, and more flexible continuous manufacturing lines requires scalable digital transformation solutions.

	A third key driver for digital transformation is ensuring supply chain reliability and efficiency. As organizations take advantage of modular and scalable solutions for development in labs, clinical production, and commercial production, these organizations are finding that their systems are easier to integrate, operate, and audit. This translates directly into savings, with many organizations achieving 5–10% improvement in asset availability, as well as similar or even higher gains in production, safety, and quality. These gains can provide a competitive advantage and ensure that patient supply and safety requirements are realized.

What role do these technologies play in the trend to small/flexible facilities and personalized medicine?

Therapies produced for our unique genetic makeup are both tremendously exciting and uniquely complex for life-sciences organizations. Recent regulatory approval of the first CAR-T cell therapy product delivers incredible benefits to the public. For the first time in human history, patients can receive individualized treatments that change lives. As these targeted therapies become more and more prevalent, manufacturers will need to adapt to the new challenges created by new processes. 

	Because personalized medicine requires complete traceability to ensure that the right manufactured therapy gets to the right patient, extensive chain of identity management is becoming central to CAR-T success. To maintain the timeliness, pace, and traceability necessary to deliver life-saving personalized therapies to patients, manufacturers are leveraging electronic scheduling systems with material collection/tracking software and manufacturing execution systems to ensure the patient therapy is properly planned, manufactured, and delivered back to the patient in the required timeline. 

	In the small/flexible facilities delivering more traditional therapies, digital technologies such as digital twin modeling, [Industrial Internet of Things] IIoT integrated measurements, and augmented reality are driving improvements in quality and regulatory compliance. Additional focus will be put on alignment between maintenance, operations, quality, and planning so that data such as equipment maintenance records, batch manufacturing records, and out-of-spec quality results can be correlated across processes with context. This data integration/use leads to real-time release, optimized inventory management, and overall efficiency improvements.

What are some examples of ‘low hanging fruit’/initial projects for digital transformation in pharma manufacturing?

This question really gets to the heart of a common misconception about digital transformation projects-not all projects have to be complete plant overhauls. Many of the best digital transformation results come from pilot projects on operational units that underperform. Site-level teams and gap analysis can quickly highlight opportunities for improvement, and the discovered opportunities can jumpstart digital transformations. The benefit of performing pilot projects is that organizations can then scale these pilots up to achieve the full-scale benefits. Therefore, to get the most benefit from digital transformation projects, it is crucial to develop a scale-up strategy as part of pilot implementation.

	A great example is with process analytical technology (PAT) applications. Leveraging digital transformation technologies is allowing PAT solutions to go mainstream. Between wireless and IIoT architectures that bring in new measurements, integration of analytical devices digitally, and applying optimization models to unit operations using all these new data, organizations can deliver the promised efficiency benefits on existing production lines without new equipment capital investments.

	Moreover, these same systems often open opportunities for new or increased predictive analytics for advanced decision making. Not only can predictive data enable shorter time-to-market-accelerating technology transfer and driving more effective process engineering-it can also support next-gen workers with continuous access to the actionable information they need to better perform their tasks.

Are there any challenges specific to transforming existing plants/equipment systems (as compared to new facilities), and if so, what are some best practices in retrofitting systems?

One of the key issues that organizations face when implementing digital transformation pilots at existing facilities is filtering all the potential projects to discover the ones that are most likely to be successful. The increase in variety and decrease in cost of monitoring equipment has led to nearly limitless possibilities for digital transformation projects across a ‘brownfield’ plant.

	An important strategy for pursuing a highly successful digital transformation initiative is to focus on business enablers and drivers. Organizations that understand their key performance indicator (KPI) goals and use those goals to drive decisions on pilot program selection tend to create the digital transformation projects with high ROI [return on investment] impacts that they can easily quantify and demonstrate.

	These KPIs and business goals can be established with a thorough gap analysis starting with a digital maturity model assessment. By performing a digital maturity model assessment and associated gap analysis, organizations can clearly see opportunities for improvement in brownfield facilities. The results of the gap analysis can identify which assets and processes need performance improvement, and identify specific, measurable KPIs. Armed with such data, it is easier to build a business case that can be used to evaluate success. 

	One result of this assessment is identifying concerns and limitations of existing legacy systems. Many legacy control systems were not built with the ability to connect to IIoT applications and cloud analytics. As a best practice solution, these organizations typically look to integrated process control systems that allow the digital transformation team to install new automation controllers on top of existing equipment. 

	As these organizations adopt integrated solutions, they are also choosing systems that come pre-packaged with core components, such as OPC UA [unified architecture], and that support alarm management, advanced process control, mobile communications, and other technologies that improve decision making and operations efficiency. 

	Emerson has developed our Digital Jumpstart approach to help clients address the challenges noted above as well as other issues associated with approaching Digital Transformation in a logical, business-savvy manner. The approach takes users through discovery, vision, and planning through design and implementation. 

What do you see as the role of Big Data analytics for manufacturing? 

One of the value enablers for Big Data analytics that is often glossed over is how important domain knowledge is in providing context to the analysis. Data science skills are not as critical, as the tools on the market are making it easier for the average engineer to perform complex analysis. This ability allows those engineers and process and system experts to translate the findings in Big Data into actionable knowledge as we exploit the huge volumes of underused data we have been collecting in manufacturing and production operations for years. In the land of Big Data analytics, context is king!

	Digital twin simulations are moving into the mainstream because of their flexibility and scalability as a critical tool for improving operation of the plant as well as supporting upskilling of the workforce. Having an easy-to-use, accurate, and flexible digital representation of physical world assets allows life-sciences organizations to pre-test before first production, train operations personnel, and then improve plant operations by making it easier for personnel to successfully make production-improving decisions. 

	Like all the most important digital transformation technologies, digital twin simulation makes use of the data that already exists in an organization to drive improvement. The most advanced simulation packages take advantage of existing steady-state design models created by the project engineering team and integrate them into real-time dynamic simulation to simplify process engineering execution. As life-sciences organizations are being incentivized-or mandated-to start building new facilities closer to the populations they serve, digital twin technology will help them design and construct these locations faster and more cost-effectively. 

	After capital project completion, or in existing plants, digital twin technology provides even more benefits. Organizations are using their simulations to develop and deploy advanced process control schemes, seeing the impacts of proposed process improvements without any risk to operations. These same organizations can also use their digital twins to safely and securely train operators. Training on an exact, complete replica of the control system (DCS) or manufacturing execution system (MES) configuration they will experience in the real world ensures that operators are ready to face transient operations, new processes, or new equipment without any risk of process upset.

What challenges in data analytics need to be addressed? 

Although all organizations have a significant amount of collected data, not all of them agree on how to consolidate, contextualize, and analyze that data. Finding a way to get critical data from the plant floor systems and sensors to edge and cloud analytics systems with appropriate context for analysis is an essential step for most pharma manufacturers-even the ones that have already begun digital transformation. 

	Manufacturers have already begun leveraging a multitude of tools to overcome some of the most common barriers to moving data from the plant floor to the edge and the cloud. Integrated MES and DCS systems that are designed from the start to work together help eliminate the ‘islands of automation’ that inhibit the technology transfer process and limit the proper use and access of data across manufacturing units and processes.

	Integrating data flow and adding key context (time, equipment, order, etc.) are key new activities organizations are focusing on to make it easier to unlock untapped potential in production optimization and equipment reliability. Modular and scalable solutions for production are significantly accelerating the development pipeline. 

	As these changes drive optimized production and higher levels of facility reliability, the focus in pharma manufacturing will need to shift to understanding and maintaining critical quality attributes and critical quality parameters and cultivating a better understanding of how products and equipment operations affect and improve right-first-time production.

	Another common area of focus for machine learning and AI is around equipment and process reliability and availability. Due to the very predictive and prescriptive nature of these types of tools, we can get very early indications of potential production and reliability problems and take corrective actions to prevent them from occurring. Projects with industry clients have been shown to be able to reduce maintenance expenditure by 40–50%.  Machine learning and artificial intelligence can also reduce downtime; PM [predictive maintenance] workload;  calibration workload; and maintenance, repair, and operations inventory, for example.

	Big Data/analytics will play a larger and larger role in improving performance in an organization. As the bar is raised for top quartile performance due to the use of new Big Data capabilities, a new definition of top quartile performance will be established. The gap for late adopters will be greater and the pressure for the rest of the pack to get on board will be significant.

System connectivity and data analysis drive increased productivity in pharma manufacturing.


Equipment and Processing Report-03-20-2019

Packaging Innovations at INTERPHEX 2019

Digital Transformation at Lilly Manufacturing

Considering Digital Transformation in Pharma Manufacturing