Overcoming the Manufacturing Challenges of Cell and Gene Therapies
Considerable progress has been made in the development of cell and gene therapies (CGTs) over recent years. Many new CGT products have already been approved by regulators in the US, EU, and other parts of the world, and many more are in development. The most exciting part is the improvements in patient outcomes that CGTs can potentially achieve. This applies to a range of conditions, including rare and difficult-to-treat conditions such as cancer.
Furthermore, there is an expectation for many CGTs in development that they will be curative, so will replace the lifelong treatment of chronic diseases. In some cases, there is the possibility to achieve this with a single treatment.
While the potential of CGTs is game-changing, there are significant challenges and barriers to overcome – challenges and barriers that are limiting the development and widespread use of CGTs. Examples of those challenges include:
- Clinical trial challenges, including finding enough trial participants, particularly when therapies are being developed for rare conditions.
- Complex regulatory processes that are still evolving.
- Commercialisation, value realisation, and profitability challenges.
Manufacturing is another major challenge limiting the deployment of CGT therapies. It is those manufacturing challenges that we will explore in this blog.
The Challenges of Manufacturing CGTs
Manufacturing CGTs is considerably more complex than manufacturing most other pharmaceutical products.
Specialist facilities are needed, and they are in short supply.
Specialist skills are also needed, and they are in even shorter supply.
While all CGTs present complex manufacturing challenges, including allogeneic (i.e., off-the-shelf) therapies, autologous cell therapies are among the most complex.
With autologous (personalised) therapies, cells are taken from a patient, often in a hospital setting. They are then sent to specialist manufacturing facilities where the therapy is manufactured and sent back to the hospital to be administered to the patient. The challenges of this process are numerous, but the main issues include:
- Time – many autologous cell therapies are highly time-sensitive, i.e., the therapy needs to be administered to the patient as soon as possible after the cells are extracted. This can be for many reasons, including the fact that some patients are in the late stages of disease, so simply won’t survive to receive the treatment if the manufacturing process takes too long.
- Distance – the specialist facilities needed to manufacture CGTs can be located far away from the patient’s clinical setting. This is already a problem in places like the US and Europe, but it is even more of a challenge when other regions of the world are considered.
- Raw materials – many of the raw materials required to produce CGTs are non-conventional, including cells taken directly from patients. This presents supply challenges.
- Quality control – strict quality control procedures need to be followed throughout to prevent contamination, for example, or to maintain the right temperature and environmental conditions at all stages of the process.
- Logistics – even the smallest logistical hiccup can have serious consequences. In fact, the logistical precision required to produce autologous cell therapies is practically impossible to achieve consistently and at scale using traditional manufacturing approaches. Chain of identity and chain of custody processes are also complex to create and manage.
The CAR T Example
CAR T is a cell therapy where chimeric antigen receptors (CAR) enable T cells to identify and kill target cells in the body. There are multiple CAR T products currently approved for use in the US and other jurisdictions. There is also increasing evidence of the effectiveness of CAR T therapies in inducing remission in patients with cancer and autoimmune diseases.
Despite its effectiveness and potential, the rollout of CAR T therapies has been severely limited. There are a number of reasons for this limited rollout, including the challenges faced by other CGT products. There are also CAR T-specific challenges, such as patients developing toxicities and minimal clinical response rates against solid tumours.
As with other CGTs, another major challenge to achieving widespread rollout of CAR T is the complexity and cost of manufacturing.
CAR T therapies are autologous therapies. As they are unique to each patient, they need to be manufactured in what is essentially a batch size of one. Time is also a critical factor with CAR T therapies, as CAR T therapies are most effective when administered as soon as possible after the T cells are harvested from the patient. Ideally, this should be days at most.
Personalised cell therapies currently take about 2-3 weeks to produce, but that is a best-case scenario. The reality is that even a 2-3-week production timescale is rare because of the availability of suitable manufacturing capacity and resources, and the complex logistics that are involved.
That is before you even factor in financial and scalability considerations, both of which are essential to make CAR T therapies widely available and affordable for hospitals and patients, as well as profitable for pharmaceutical companies.
Overcoming the Manufacturing Challenges of CGTs
Allogeneic therapies are more compatible with traditional centralised manufacturing approaches. However, innovative solutions will need to be developed to overcome the challenges of manufacturing autologous therapies. Many allogenic therapies will also benefit from these new manufacturing solutions.
There are six main areas that need to be addressed to overcome the manufacturing challenges of CGTs.
Product Development Cycle
It is essential that manufacturing is considered early in a CGT product’s development cycle.
Additional CGT manufacturing capacity is required, although how that is developed and structured is likely to require a novel approach – see the last point below.
Increasing Skills Availability
The industry needs to increase the availability of skills to ensure manufacturers can recruit and retain the people they need. The fact there is a skills shortage at the moment is understandable as CGTs are an emerging part of the life sciences sector. However, CGTs are expected to continue growing rapidly, so it makes sense to address the skills shortage urgently.
Automate Manufacturing Processes
Automation will play a crucial role in making CGT manufacturing viable both from a practical and financial perspective.
Supply Chain Optimisation
CGT manufacturing supply chains need to be optimised to ensure a consistent supply of materials, including materials such as viral vectors and plasmids.
Bring Manufacturing Closer to the Point of Care
This solution to CGT manufacturing challenges will be the most disruptive, but it also has the potential to be the most transformative. By bringing manufacturing closer to the point of care through innovations like mobile manufacturing facilities or manufacturing-in-a-box concepts, the production of CGTs becomes much more viable:
- The time it takes to manufacture CGTs will be considerably reduced.
- The number of steps will be minimised.
- Human involvement in the manufacturing process will be minimised, particularly when combined with automation technologies.
- The logistical challenges that currently exist can be minimised and potentially eliminated.
The Starbucks Model
There will need to be detailed collaboration between hospitals and manufacturers, but taking a Starbucks approach is likely to be the best route to ensuring the widespread adoption of CGTs, particularly autologous therapies.
The highly skilled team at Starbucks’ Seattle headquarters can probably make a delicious cup of coffee. However, that is no good if you are in Boston, Dublin, Rio de Janeiro, Nairobi, Hanoi, or Auckland. By the time it reaches you, what’s left in the cup will be cold and horrible. What you need is a Starbucks close to the point of need with well-trained baristas using specialist equipment and carefully sourced ingredients.
As mentioned previously, the Starbucks approach is a disruptive model for an industry that has achieved significant success using high-speed, high-volume manufacturing. However, to keep up with the scientific advances being made in CGT development, manufacturers need to embrace this new level of innovation and disruption.
9 Game-Changing Trends in the Life Sciences Sector
The life sciences sector continues to grow as new therapies, drugs, and products are developed. New manufacturing and business technologies are also driving change in the sector, as are ever-evolving regulations and best practice guidelines. So, where is the sector heading?
Here are nine game-changing trends in the life sciences sector to be aware of:
- Cell and gene therapies
- Mobile manufacturing
- Additive manufacturing
- Decentralised trials
- Digital twins
- Growing importance of big data and analytics
- Move away from paper documentation and manual record-keeping
- Validation for the 21st century
Let’s look at each in more detail.
Growth in Cell & Gene Therapies
The life sciences industry is increasingly focusing on treating the patient rather than the condition or illness. Cell and gene therapies (CGT) have this patient focus, with significant growth expected in CGT use over the coming years.
New CGT innovations are being developed, but these types of treatment are expensive. They are also often challenging to manufacture, as CGTs are typically customised for each patient so need to be produced in small batches. Also, production processes often need to take place much closer to the patient than is currently the case with the sector’s mass manufacturing infrastructure. This leads us to the next key trend in the life sciences sector.
Companies in the life sciences sector are digitalising workflows and processes to transform business models and develop smart manufacturing operations. The next big change on the horizon is mobile manufacturing, creating an alternative to large-scale manufacturing facilities and large-batch production runs.
Mobile manufacturing solutions won’t be required for all products, but the example of CGT therapies highlights the growing need for the sector to develop new and innovative methods of production that bring manufacturing closer to the point of care. These solutions need to be scalable, practical, compliant, and cost-effective, but progress is already being made.
Mobile manufacturing will also help to improve access to critical medicines and healthcare products, reducing the geographical and societal inequalities that currently exist around the world.
Contract Development and Manufacturing Organisations (CDMOs) and Contract Manufacturing Organisations (CMOs) have been an important part of the life sciences sector for many years now. Their importance is now growing as small, medium-sized, and large life sciences sector companies continue to turn to CDMOs and CMOs, primarily for capacity, expertise, and timing reasons.
For example, large medical device companies regularly hand over elements of product development projects to a CDMO when they need specific expertise or because of insufficient internal capacity. Another example is startup companies that can bring their new product to market faster by partnering with a CMO compared to acquiring the skills, resources, technologies, and equipment that would be required to manufacture the product in-house.
3D printing technologies are already being used for prototyping in the product development process, as well as to produce prosthetics, implants, and other healthcare products. This trend is set to continue as the technologies available move beyond 3D printing to enable 4D printing.
4D printing creates 3D objects where the properties of the object can change over time in a way that is predictable. Stents are an example. Currently, stents are fixed objects with fixed properties. When 4D printing technologies become available, it will be possible to produce stents that have one shape while progressing through the body before automatically taking the required stent form when they reach the target treatment location.
Clinical trials have traditionally been centralised largely because there was no other option. One of the main problems with this approach is achieving a sufficiently diverse range of participants. It is also expensive and time-consuming. New technologies, including wearable devices, now mean decentralised clinical trials are a viable option, increasing the diversity of participants.
One model is to involve local pharmacies in clinical trials, tapping into the data they have on potential participants.
Digital twins are common in a range of situations, including production environments where digital twins can be created for individual machines or entire production lines. New technology advances are also pushing the concept of the digital twin into the field of healthcare, with many predicting there will be digital twins of people in the future.
These digital twins can be used to test drugs and medical devices, as well as to predict the outcome of various treatments. This can reduce animal testing, optimise clinical trial processes, and improve the diagnosis and treatment of patients.
Growing Importance of Big Data & Analytics
Reducing timescales while maintaining product quality, patient safety, and regulatory compliance has always been a priority for the life sciences sector. Despite this, the process to discover new drugs, develop new products, and obtain regulatory approval remains slow. The rapid development of Covid-19 vaccines showed us an alternative future, where timescales can be considerably shortened to the benefit of patients, healthcare professionals, governments, companies, and society as a whole.
Big data and analytics are crucial to shortening the lengthy timescales that exist in the life sciences sector. Here are some examples:
- Choosing clinical trial participants
- Real-time monitoring of critical quality attributes
- Accelerating drug discovery
- Improving production processes and supply chain management
- Demonstrating post-market drug safety and efficacy
Finally Moving Away from Paper Documentation and Manual Record-Keeping
The life sciences sector continues to digitalise and adopt new technologies and automation solutions. However, there are still large parts of the sector that are reliant on paper and/or manual record-keeping processes. This reliance on paper includes producing large amounts of documentation for compliance purposes.
The trend to move away from paper documentation and manual record-keeping will continue. It will also accelerate as modern technologies are adopted and processes are automated. The need for real-time data and the importance of data-driven decision-making are additional drivers for change. Furthermore, the evolving regulatory landscape is also encouraging a move away from paper, leading us to the next point.
Validation for the 21st Century
The traditional method for validating computer systems in the life sciences sector, Computer System Validation (CSV), involves manufacturers focusing heavily on the creation of documentation. The FDA has now released draft guidance on a new approach known as Computer Software Assurance (CSA).
With CSA, the FDA expects manufacturers to move away from focusing on the creation of documentation. It instead wants manufacturers to take a risk-based approach to validation with critical thinking at its core.
Staying Competitive While Pushing the Boundaries of Innovation
Some of the trends on this list are current realities, while others are emerging at the cutting edge of development. The trends also present both opportunities and challenges for companies in the life sciences sector. Understanding where the industry and technology are going can help you stay ahead of the curve.
Smart Manufacturing Strategy Overview for the Life Sciences Sector
All sections of the life sciences sector understand the need to evolve operations using the cutting-edge technologies, processes, and best practices that come under the smart manufacturing umbrella. However, it’s important to take a structured approach, with a carefully developed and implemented smart manufacturing strategy.
In this blog, we’ll look at why it is important to develop an overarching smart manufacturing strategy, as well as providing an overview for creating one. The strategy overview that we present is relevant wherever you are on your smart manufacturing journey.
Change in the Life Sciences Sector and Why a Well-Structured Change Strategy is Important
The fact the life sciences sector is experiencing unprecedented change has been a common topic of discussion for some years now. The reality goes deeper than the concept of change, though, as change implies there will be an endpoint or conclusion.
However, the more likely scenario is that the life sciences sector has entered a period of ongoing change. New technologies are now developing at such a rapid pace that continuous evolution is now a reality and a necessity. Technologies like Microsoft’s HoloLens is a good example. It is now commonly used in many industrial applications, but nothing even close to it existed 10 years ago. Examples like this will become more commonplace over the coming years.
The ongoing changes that are taking place are not just the purview of technologies, either, as the life sciences sector is also experiencing changing customer expectations, markets, and products, all of which present opportunities. There are challenges too, of course, including skills, economic, business resilience, regulatory, and geopolitical challenges.
Smart manufacturing solutions help companies in the life sciences sector take advantage of the opportunities while addressing the challenges.
The Continuous State of Change – an Example
An example we can use to highlight the continuous state of change in the life sciences sector is the emerging trend of manufacturing operations needing to innovate at speed to keep up with cutting-edge product development breakthroughs.
A specific example is personalised pharmaceutical products that need to be produced as close to the patient as possible.
This means moving away from the mass production approach that has served the life sciences sector so well for decades (and other sectors for even longer). Instead of large manufacturing facilities producing and shipping products far and wide to reach the patients who need them, many personalised medicines will require batch-size-of-one manufacturing processes and, potentially, factory-in-a-box solutions where manufacturing capabilities, rather than the manufactured product, are brought to the patient.
Adopting a smart manufacturing approach is the only way to address the needs of today (productivity, competitiveness, quality, compliance, etc), as well as keeping up with the developments of the not-too-distant future (such as those in this example).
What is Smart Manufacturing?
It is helpful when developing a smart manufacturing strategy to look at what we mean by “smart manufacturing”. Some definitions include descriptions like the digitalisation of manufacturing processes. Automation is a key component, but it is only one element. Becoming a data-driven operation is also crucial.
However, it is often better to look at smart manufacturing through the lens of outcomes and objectives. Examples include:
- Improvements in key manufacturing metrics such as OEE and productivity.
- Improved resilience to deal with market challenges as well as enhanced agility to take advantage of commercial opportunities.
- Enhanced scaling capabilities while enabling production facilities to be more flexible, including in relation to producing different products on the same line.
- Creating connected operations, whether connected across different facilities, production lines within the same facility, or equipment on a single production line. It also involves connecting manufacturing operations with other business units and departments.
All the above objectives and outcomes can be achieved with a well-structured and implemented smart manufacturing strategy.
Smart Manufacturing Strategy Overview
Step 1 – Define Objectives and Vision
The first step is to look at your production operations, wider business strategy, and corporate objectives. This includes:
- Immediate business challenges and risks
- Medium and long-term business goals
- Manufacturing and operational priorities
Step 2 – Identify a Partner
Getting the right partner is essential to help with both the planning and implementation of your smart manufacturing strategy. The qualities you should look for in a smart manufacturing partner include:
- Expertise in the life sciences sector as well as specific pharmaceutical and/or medical device industry experience.
- Broad range of technology expertise.
- A proven track record of delivering smart manufacturing projects in the pharmaceutical and medical device industries.
- Established expertise in the essential aspects of smart manufacturing projects, including change management, project management, equipment systems integration, and ongoing support.
Step 3 – Assess Digital Maturity
The roadmap to smart manufacturing success will depend on your existing situation. Therefore, assessing your current levels of digital maturity is essential. Elements to analyse include:
- OT (operational technology) and IT infrastructure
- Current processes and workflows
- Existing resources and skills, as well as resource and skills challenges
- Technology platforms, applications, and systems
- Production line equipment
Step 4 – Identify Challenges
Identifying the specific challenges faced by your organisation is also essential, as it allows the project management and implementation team to put in place mitigation measures and/or solutions. Challenges can be caused by a range of factors:
- Resources and skills
Step 5 – Define the Implementation Roadmap
This is the point where you decide on the next phase of development before implementing the project. Common steps include:
- Identify suitable project/s.
- Make sure your plans are in alignment with business objectives and adjacent projects.
- Create a business case and get organisational buy-in.
- Develop and implement a project implementation strategy, including technical and functional plans.
Taking the Next Steps
Delaying the next phase of your smart manufacturing journey will only set you further behind. The best approach is to start implementing the above strategy now to ensure your manufacturing operations evolve in step with the ongoing changes in the industry.
You don’t need to have all the answers, and you don’t even know what projects to undertake next. Looking at your business objectives and engaging a suitable partner will give you the answers you need while also ensuring you are on the path to success.
Smart Manufacturing Considerations for Pharma and MedTech Manufacturers
Most pharmaceutical and medical device manufacturers have implemented new technologies and processes over the past number of years that would come under the smart manufacturing umbrella. You are probably also now considering the next stage on your journey. What are the smart manufacturing solutions and opportunities that you should explore next?
There are a number of factors that are essential to consider when deciding on smart manufacturing projects and solutions. Crucially, those factors go way beyond the technical components, as it is important to also take into account business, resource, and process factors.
We’ll start with business and resource considerations before looking at processes and workflows. We’ll then finish off with the main technical considerations.
Smart Manufacturing Considerations – Business and Resources
It is important to look at the short, medium, and long-term market and product strategies for your facility and throughout the organisation. What new markets do you plan to target? What new products do you expect to introduce? How do you see the industry evolving over the long term and how will your organisation remain competitive? What decisions are being taken in other parts of the business that will impact what happens on the factory floor?
When answering these questions, it is essential there is alignment between your smart manufacturing strategy and the strategies, plans, and objectives of the wider business. Furthermore, there should always be a business-led approach to technology implementations.
Wider Digital Transformation Initiatives in the Business
Efforts to improve your smart manufacturing capabilities should also be aligned with the wider digital transformation initiatives of the business.
For example, smart manufacturing and digital transformation technologies are creating new business models in the life sciences sector. Examples of these new business models include service rather than product-based business models and the expected growth of personalised medicines and treatments.
Buy-In from Senior Executives
Smart manufacturing technologies and solutions will play a major role in the digital transformation of pharmaceutical and medical device companies over the coming years and decades. This is in addition to the immediate positive impacts that advanced technologies and best practices can have on your business, from productivity improvements to increases in profitability.
Therefore, it is important senior executives are involved in the process and buy into the strategy.
Embracing New Approaches
Smart manufacturing solutions and other developments present opportunities for the life sciences sector to do things differently. Traditionally, however, the sector is slow to change, not least because of patient safety, quality, and regulatory concerns.
Change now needs to be accelerated in areas such as embracing data-driven and automated decision-making or moving to modern compliance frameworks. Just because things have been done a certain way in the past doesn’t mean they should continue like that in the future.
Other Business and Resource Considerations
The other main business and resource considerations for smart manufacturing strategies and projects include:
- Supply chain resilience – given the spotlight on supply chain resilience in the pharmaceutical and medical device industries, how can smart manufacturing solutions make an impact?
- Sustainability targets and objectives – it’s important to view smart manufacturing solutions and projects through the lens of sustainability, as there are often opportunities to make small changes that can have a positive impact.
- Regulatory compliance – there is a need for ongoing regulatory compliance, but there are also opportunities to improve compliance processes by making them more efficient, data-driven, and risk-based rather than procedural.
- Investment scale – how much are you willing to invest and when? What are your ROI expectations?
- Resource availability and multi-functional teams – what resources do you have available to work on smart manufacturing project implementations? Who will be part of the leadership group, and will the teams be multi-functional with members from different departments and business units?
- Third-party partners – you will also need to decide on a smart manufacturing solution and integration partner. The best approach is to select a partner that offers an end-to-end range of services and that has specific experience in the life sciences sector.
Smart Manufacturing Process and Workflow Considerations
Process and workflow factors also need careful consideration in smart manufacturing projects. Examples include:
- Get a full understanding of existing processes and workflows, including getting the viewpoints of those on the frontline. This is essential as the theory or perception of a process can be very different from the reality.
- Conduct a critical analysis of all processes and workflows once there is a good level of understanding. It is helpful to ask why you do things the way you do, and could certain workflows and processes be done better.
- What is the level of data integrity in the organisation and what steps should be taken to make improvements?
- What repetitive and time-consuming tasks can be automated?
- What tasks that are prone to human error can be automated or semi-automated?
- How can you improve processes and workflows to make production and the wider business more agile and adaptable?
- How can you enhance collaboration, especially cross-function collaboration?
- What is the change management strategy, and how will you communicate and solicit feedback from members of the team, particularly those directly impacted by the changes that smart manufacturing solutions will bring?
- What level of staff training will be required and how will that training be delivered?
- Where automated solutions replace manual processes, how will you reallocate resources?
Smart Manufacturing Technical Considerations
Finally, we come to the technical considerations that are important in smart manufacturing projects. The main examples include:
- The existing technology and equipment infrastructure and components.
- Equipment systems integration plans, i.e., what existing systems and equipment are staying so need to be integrated, and what is being added or replaced?
- Integration at all levels of the technology stack, including at the SCADA and PLC level on the factory floor as well as Manufacturing Execution (MES) and Enterprise Resource Planning (ERP) systems.
- The level of horizontal and vertical integration you want to achieve, i.e., integration between production operations and other parts of the business, as well as integration across the supply chain.
- How you currently use data and how that can be improved, enhanced, and optimised, from collection to transmission to storage, processing, and use.
- Strategies for legacy systems that are staying, especially in relation to the standardisation of processes, terms, data, etc.
- Your cloud strategy within the operational technology (OT) environment and how that is aligned and linked with the cloud strategy within the organisation’s IT infrastructure.
- Cybersecurity and the increased level of risk that comes with connecting more devices and expanding the potential attack surface of your organisation.
Getting the Right Expertise and Support
The lists and points above are not exhaustive, as there are typically other considerations that apply on a case-by-case basis. What they highlight is there are extensive considerations that have an influence on your direction of travel and potential for success when implementing a smart manufacturing project.
Therefore, it is important you get expert advice and support. To speak to one of our smart manufacturing specialists, please contact us at SL Controls today.
Smart Manufacturing Trends for 2023 in the Life Sciences Sector
Manufacturing in all sectors and industries is changing as smart manufacturing technologies, the industrial internet of things, and Industry 4.0 best practices continue to advance. What about the life sciences sector specifically? Here are the 2023 smart manufacturing trends that pharmaceutical and medical device manufacturers need to be aware of.
Integration of MES, SCADA, and ERP Systems
The integration of MES, SCADA, and ERP systems is essential for the digitalisation of manufacturing and other business processes in the life sciences industry. Achieving this integration will be a primary focus for manufacturers in the sector in the years ahead. The benefits of doing so range from product quality and patient safety to improved business performance, reduced risks, and enhanced data-driven decision-making.
Another trend that is a feature in the life sciences sector is the move away from clunky legacy MES platforms that required a lot of customisations. Pharmaceutical and medical device manufacturers are increasingly moving to more modern, adaptable, and flexible MES solutions that facilitate end-to-end connectivity.
Increasing Focus on Data Integrity
Data integrity is essential for the digitalisation and modernisation of manufacturing processes in the pharmaceutical and medical device industries. There are multiple steps that should be taken to ensure data integrity, but two primary focus areas for 2023 and beyond will be:
- Eliminating manual intervention in data collection, updating, and processing
- Bridging data gaps, both digital and paper-based
Accelerated Innovation with GAMP 5
The summer of 2022 saw the first major update of GAMP 5 since its introduction 14 years earlier. You can read more about what has changed in GAMP 5 Second Edition on our blog. One of the main outcomes, however, is an increased facilitation of innovation.
Many in the life sciences sector implemented the original version of GAMP 5 with approaches that were prescriptive and rigid. This hampered innovation.
With GAMP 5 Second Edition, there is a recognition of agile development methodologies and critical thinking, both of which encourage a move away from previous, prescriptive methods of validation. This will result in accelerated innovation in the sector over the coming years while still protecting patient safety and product quality.
Continued Development of Batch-Size-of-One Manufacturing Solutions
R&D efforts in the pharmaceutical and medical device industries are creating new, cutting-edge personalised medicine and healthcare solutions. This means there is now an increased onus on pharmaceutical and medical device companies to develop the manufacturing solutions that will enable the commercialisation of personalised drugs and medical devices.
One exciting area of this evolution of manufacturing in the life sciences sector is the concept of the factory-in-a-box, as well as microfactories. Both have significantly smaller footprints than traditional production facilities to maximise agility and flexibility, and to enable the moving of manufacturing operations closer to the patient.
ESG Considerations Driving Change
Environmental, Social, and Governance (ESG) factors have been high on the agenda of most leaders in the life sciences sector over the past 12 months. This trend is set to not only continue, but to accelerate over the coming year and beyond as pharmaceutical and medical device companies implement strategies and initiatives that deliver tangible results on their ESG commitments.
Continued Focus on Supply Chain Resilience and Capacity Building
One of the challenges faced by companies in the life sciences sector throughout 2022 was supply chain disruption. Capacity is also a common issue, with many in the sector operating with zero or close to zero spare capacity.
Both of these factors are increasing lead times for orders of many pharmaceutical and medical device products. Patients are also impacted, while life sciences sector companies are unable to take full advantage of the commercial opportunities that exist.
The sector has been adapting throughout 2022 to these challenges, and fantastic work has been done to maintain consistency and reliability in the delivery of orders. That said, supply chain and capacity challenges remain, and they are being exacerbated by current geopolitical uncertainties and turbulent economic conditions.
Therefore, we can expect a continued focus on supply chain resilience and capacity building as we move into 2023.
Innovation and Growth
Even with the challenges highlighted in the last section above, 2023 is set to be a year of innovation and progress for companies in the life sciences sector. New opportunities to streamline and improve manufacturing lines, increased levels of vertical and horizontal integration, and the ability to make better use of data, are all going to drive innovation and growth.
There are also going to be continued R&D breakthroughs in drugs and medical devices, creating new opportunities for manufacturers. The key message, therefore, is to not stand still with your smart manufacturing initiatives, projects, and strategies.
Smart Manufacturing Solutions and the Importance of Data Integrity
Transforming your life sciences facility into a factory of the future involves implementing smart manufacturing solutions as part of a digitalisation strategy. Those solutions will ensure you get the benefits of digitalised processes while establishing the foundations for the next stage of your journey. There will be challenges, however, and one of the most important is data integrity.
Without a clear data plan that prioritises data integrity in every workflow, process, and system, your smart manufacturing solutions will struggle to reach their full potential. They could even fail.
Problems with data integrity and how you use data in your facility can also have wider consequences, whether related to a new smart manufacturing solution or not. We can see this in a study that analysed warning letters issued to pharmaceutical manufacturers by the FDA. The researchers looked at warning letters issued between 2010 and 2020 and found that 21 percent involved data integrity issues. Only process validation had a greater number of citations.
In other words, data and data integrity are crucial to the systems and processes you use today.
Data and data integrity are also essential for ensuring the successful implementation of the smart manufacturing solutions that will optimise your systems and processes for the future.
What Is Data Integrity?
To understand what data integrity means in the life sciences sector, we can turn to draft guidance published by the FDA in 2016 – Data Integrity and Compliance With CGMP.
“For the purposes of this guidance, data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate (ALCOA).”
You can read more about ALCOA and ALCOA+ on our blog.
So, data integrity is about having data that is trustworthy and reliable. Only by having reliable and trustworthy data can you get the most out of technologies like digital twins and machine learning. High-quality data is also essential to maximise the potential of your MES and ERP systems, and it is a main focus of GxP.
The Risks of Manual Data Interventions
Manual data intervention is one of the biggest issues with data integrity in pharmaceutical and medical device companies. Manual data interventions can take many forms, including when operators manually collect, store, update, and/or transfer data. If there is a manual intervention in any of these processes, problems can occur:
- Inconsistency becomes a feature of the data
- The data is prone to error and inaccuracies
- The data can be incomplete
- It is easier for the data to become corrupted
- There can be delays in processing the data
The above problems will impact the usefulness of your data, limiting its effectiveness for monitoring, analysis, decision-making, and process optimisation.
It isn’t just the manual data that is the problem either, as manual data can corrupt other data it has been integrated with. This means the integrity of your data is dependent on the weakest point.
How to Mitigate the Risk of Manual Data Intervention
There is always a risk when manual data is involved. So, when implementing smart manufacturing solutions, one of the primary aims should be to reduce manual interventions in data capture, storage, and updating.
However, it is not always possible to eliminate all manual data processes. In these situations, steps should be taken to minimise the risks that manual data intervention creates.
For example, providing pre-defined dropdown menus to limit the options when operators are inputting data. Data integrity training for operators is also beneficial.
Achieving Data Integrity When Implementing Smart Manufacturing Solutions
You should work towards achieving a reliable and trustworthy flow of data vertically through the company. This includes from and to functions within the production environment, as well as to parts of the business outside production, including finance, HR, marketing, etc.
It’s also important to have a reliable and trustworthy flow of data horizontally through each stakeholder in the supply chain.
The horizontal flow of data also includes data that must be submitted to regulators, such as the EU’s EUDAMED – European Database for Medical Devices.
Importantly, it’s helpful to remember the flow of data between systems does not happen as standard in most cases. This applies to systems within the production environment as well as systems used by other functions in the business, including MES, LIMS, and ERP systems. Therefore, integration is a central component in almost all smart manufacturing solutions.
Viewing Your Organisation’s Data as An Asset
Smart manufacturing is a journey rather than a destination, and the reality is that most companies in the life sciences sector won’t be at an advanced stage for some years to come. That said, automated data processes and good data standards are crucial for your operations today and to prepare your organisation for the future.
It is also beneficial to start viewing data as an asset in your organisation. When data is viewed as an asset, there is more of an imperative to improve and protect it.
The Importance of Systems Integration to Optimise Your SCADA, MES, and ERP Systems
Systems integration is an essential part of any modernisation and improvement project in manufacturing organisations. Systems integration is also the cornerstone of Industry 4.0, and it is central to the development of the smart factory of the future. What does systems integration mean in practice for manufacturers in the life sciences sector? One of the primary objectives is to optimise SCADA, MES, and ERP systems.
This could apply to existing systems as well as during the implementation of new platforms. For example, you might already have SCADA (supervisory control and data acquisition) and ERP (enterprise resource planning) systems and are now exploring the benefits and ROI of an MES (manufacturing execution system) platform. In this situation, systems integration is essential for the successful implementation of your new MES.
Of course, SCADA, MES, and ERP systems are not the only platforms that are important to your operations. Other equipment, applications, and platforms could also benefit from being part of your systems integration plans. Examples include LIMS (laboratory information management systems), QMS (quality management systems), and PLM (product lifecycle management) platforms.
That said, SCADA, MES, and ERP are arguably the three most important levels of integration to achieve because of where they sit in a manufacturing organisation’s technology stack. In simple terms:
- ERP – office and business operations
- SCADA – the automation of processes on the production line
- MES – the link between the two
Making Use of Data and Getting the Most Out of Your Systems
To understand the importance of systems integration for MES, SCADA, and ERP platforms, let’s first look at what each platform is responsible for. There are some overlaps depending on the system and vendor that you choose, but the following descriptions apply in most situations.
What is an ERP Platform?
ERP platforms facilitate, manage, and automate a wide range of the administrative and operational functions of your business. This includes everything from finance and purchasing to sales, customer service, and logistics.
What is SCADA?
SCADA systems control and monitor the equipment on your production line, usually through PLCs (programmable logic controllers). They collect data, monitor alarms, and enable real-time monitoring of your production lines and equipment.
What is an MES Platform?
MES platforms manage the processes involved in turning raw materials into new medical devices or pharmaceutical products. MES platforms can also optimise OEE, and they have features that can include scheduling, performance analysis, raw material tracking, quality management through statistical process control, and production optimisation.
Connecting the Three Layers
SCADA, MES, and ERP systems all produce and monitor data, and they all automate and optimise processes. Without effective integration, however, they will operate in isolation, so they will never achieve full optimisation. This is only possible when they are connected and working together, with data flowing seamlessly between the platforms.
By integrating your SCADA, MES, and ERP, you will have a single source of truth covering your entire operation – at office level, on the factory floor, and everything in between.
The Benefits of Integrating SCADA, MES, and ERP
- Reduces production line downtime and improves OEE
- Improved batch control processes
- Streamlined processes from the factory floor to office-based functions
- Improved supply chain resilience and efficiency
- Reduced waste, including waste from raw materials, overproduction, transport costs, and energy use
- Move to data-driven decision-making (both manual and automated data-driven decision-making) with the availability of real-time data streams covering production, quality, supply chain, finance, planning, procurement, maintenance, and more
- Eliminate mistakes that come from manual data handling
- Enhanced oversight at all levels, including operator, engineering, management, and C-suite
- Improved customer, user, and patient experience
- Enhanced competitiveness
- Reduced costs through efficiency savings, productivity improvements, and greater levels of automation in workflows, processes, and data handling
- Improved alignment between the different functions in your business, including those where there is currently minimal alignment
- Improved compliance processes
What About the Challenges of Systems Integration?
There are challenges to overcome with systems integration projects that involve SCADA, MES, and ERP platforms. The main challenges concern the disconnect between OT (operational technology) and IT (information technology). Both OT and IT produce, store, and use data in different ways, and they use different languages, hence the disconnect.
OT is the system used on the factory floor, while IT is for business and administrative operations. SCADA and MES typically come under the OT umbrella, while ERP is an IT system.
Therefore, solutions need to be developed to facilitate the integration of these platforms and ensure a seamless transfer of data.
Getting the Support that You Need
At SL Controls, we have extensive expertise in systems integration projects, from overcoming the challenges described above to ensuring your SCADA, MES, and ERP platforms are fully optimised. Our expertise will also ensure you get maximum returns from the investments you have made in these systems. Get in touch today to find out more and discuss your requirements.
A New Look – GAMP 5 Second Edition
GAMP 5 is one of the most important validation and computer system assurance guides for the life sciences industry. This year saw the release of GAMP 5 Second Edition. So, what’s new and what’s changed?
The original GAMP 5 was published in 2008, and it went on to become the globally accepted guide for the commissioning, operation, and retirement of computerised systems. Nevertheless, technology has moved on since its release, advancing in areas such as cloud computing, big data, artificial intelligence, and agile software development.
It was time for a refresh, and the Second Edition of GAMP 5 was released in July 2022. Here are some of the notable changes.
Non-Linear Approach to Validation
GAMP 5 Second Edition has removed the assumption of a universal linear approach to software development and, with it, the V-model terminology.
The updated edition instead supports iterative and incremental models, such as agile methodologies for software development and validation. Agile methodologies allow for the continuous delivery of working software developed in iterations.
This change shifts the focus away from the delivery of fixed and prescriptive documents. The focus is now on risk-based records of information that take into consideration modern software lifecycle methodologies.
The linear approach is still applicable for systems that are well established and where requirements are fully known. Commercial off-the-shelf applications are a good example. However, for systems that are higher in complexity and novelty, iterative and incremental models may be more appropriate.
The new Appendix D8 has been included in the GAMP 5 Second Edition to specifically address Agile Software development.
There has been great emphasis recently that validation should be increasingly based on product, data, and process knowledge. Critical thinking has become a hot topic, and it is a key pillar of the FDA’s new Computer Software Assurance draft guidelines, released in September 2022.
GAMP 5 Second Edition and the FDA’s draft guidelines have recognised that the use of rigid tables, overly prescriptive templates, and tick-the-box methods have become commonplace for many companies. This trend impedes critical thinking and can, regrettably, inhibit innovation and the adoption of new technologies.
By facilitating critical thinking, teams can move away from one-size-fits-all, document-heavy validation approaches. Teams can instead apply informed decision-making and good judgement on where and how to scale quality and compliance activities.
Critical thinking gets its own appendix in GAMP 5 Second Edition. This appendix provides many examples of where critical thinking can be applied throughout the system life cycle.
The Use of Software Tools
The replacement of paper with automated processes is transforming the manufacturing and quality landscape. To make this transition, life sciences companies are increasingly using software tools, so much so that a new Software Tools appendix has been added to GAMP 5 Second Edition. Topics such as selection, risk assessment, and life cycle management are all covered in this appendix.
GAMP guidance and the FDA CDRH Case for Quality program strongly encourage the use of software life cycle management tools and automation, which can bring great quality benefits, and have low GxP risk.
– GAMP 5 Second Edition Appendix D9
Software tools generally do not directly support GxP critical processes, plus they can be used anywhere in the business. GAMP 5 Second Edition also points out that software tools are especially prevalent in IT Infrastructure processes.
An important takeaway is that software tools are classed as GAMP Software Category 1 in the second edition of GAMP 5. This is the lowest risk category, meaning that software tools do not require computerised system validation. There should instead be a risk-based approach with the software managed through the application of good IT practices, such as ITIL.
GAMP 5 Second Edition now acknowledges that:
Much information will never exist on paper, or even in the form of a document.
The guidance also states in Appendix M9:
There is no need to create documents simply for the sake of having a document in case of regulatory inspection. If it is not useful for managing the application in a state of control and it is not needed.
Data retained in native software tools offer more robust search capabilities compared to paper records.
One of the main challenges within a highly regulated industry is to continually ensure patient safety and product quality while allowing enough space for innovation growth.
A dogmatic focus on regulatory compliance has caused companies to apply unnecessarily rigid and prescriptive approaches that are disproportionate to the infrastructure of a system and its associated risks.
Recognising that traditional linear or waterfall methodologies cannot be a one-size-fits-all approach for every case allows lifecycle activities to be scaled according to risk, complexity, and novelty. Hence, there is a welcomed introduction of agile approaches in both GAMP 5 Second Edition and the FDA’s Draft Guidance for Computer Software Assurance for Production and Quality System Software.
Additionally, GAMP 5 Second Edition has introduced additional appendices to address new software technologies, such as the blockchain (distributed ledger systems), artificial intelligence, and machine learning. This is to keep pace with the rapidly changing technology landscape that is currently unfolding within the life sciences sector.
GAMP 5 Second Edition is an important and welcome update for the sector. It is an evolutionary change that complements the still relevant key principles of GAMP 5, while also underlining important points within the guidance and introducing new topics that reflect current technology.
Technologies evolve at a rapid pace. The publication of GAMP 5 Second Edition will enable greater innovation and more cost-effective assurance of computer systems.
As a company that is involved in both the development and validation of software systems for life sciences companies, we are excited at SL Controls to embrace the changes outlined in the new GAMP 5 edition.
GAMP 5 Second Edition is a tool that can make achieving compliance easier, but for it to do so, it must keep up with the rapidly changing technology landscape. Perhaps 14 years was too long to wait for this new edition. That said, good things come to those who wait – and this new edition is certainly a good thing!
What is ALCOA+ and Why Is It Important to Validation and Data Integrity
ALCOA+ is a set of principles that ensures data integrity in the life sciences sector. It was introduced by, and is still used by, the FDA – the US Food and Drug Administration. It has relevance in a range of areas, particularly in relation to pharmaceutical research, manufacturing, testing, and the supply chain.
As well as being crucial for compliance reasons, ALCOA+ principles are becoming increasingly important to GMP (Good Manufacturing Practices). Their relevance is also growing as manufacturers in the life sciences sector continue to implement Industry 4.0 solutions and processes.
The Importance of Data Integrity
Data has always been important in pharmaceutical manufacturing and research. However, a range of different factors means the importance of data is growing exponentially. Key to this is the move away from paper-based records to digital data collection, storage, and processing.
Advances in technology, increasing levels of automation, the globalisation of the life sciences sector, and the use of contract manufacturers mean it is also now more important than ever to ensure data integrity through the entire product lifecycle.
Other drivers for the growing importance of data include:
- The importance of ensuring the quality and safety of medicines
- Enhanced regulatory requirements in relation to data, traceability, and audit trails
- The evolving expectations of consumers and end users
- The increasingly competitive nature of the pharmaceutical industry
- The fact that almost all Industry 4.0 technologies and systems rely on deeper levels of equipment integration and a vastly increased volume of digital data transfer
You can’t just have data, though, as data integrity is as important as the data itself. When you have data integrity, you have data you can use and rely on. That’s where ALCOA+ comes in.
Data Integrity and GMP Records
The ALCOA principles that ensure data integrity apply to the following types of GMP records:
- Electronically recorded – data recorded using equipment from simple machines through to complex and highly configurable computerised systems
- Paper-based – a manual recording on paper of an observation or activity
- Hybrid – where both paper-based and electronic records constitute the original record
- Other – this includes photography, images, chromatography plates, and more
ALCOA and ALCOA+ Principles
ALCOA is an acronym for the original five principles of data integrity. Those principles are:
These original ALCOA principles have since been updated to ALCOA+. The original principles remain with four additions:
Let’s look at each of the principles in more detail.
To ensure collected, generated, or updated data is attributable, the following must be recorded:
- The identity of the person, system, sensor, equipment, or device that collected, generated, or updated the data
- The source of the data
- The date and time
The above applies whether the data is collected, generated, or updated automatically or manually.
Ensuring data is attributable is not a technical issue, as all modern (and many old) systems and software applications have the above capabilities. The main challenges come with procedures and policies.
An example is password integrity, where one password is used by multiple workers. When this occurs, data that is collected, generated, or updated is not attributable.
Ensuring data is legible is about more than being able to clearly read the data, although that is important in situations where manual record-keeping takes place. Being able to make out words and figures is much less of a problem with electronic data, though.
That said, legibility still has relevance when data is digitally created, generated, or updated, as it is essential that data can be read and understood years and even decades after it’s recorded. This point is as relevant to digitally recorded data as it is to data recorded in notebooks.
So, it’s important to avoid using clichés and unusual phraseology as this may be difficult to decipher in the future without getting clarification from the originator of the data, a person who may no longer be available.
Using consistent, straightforward language throughout an entire organisation, regardless of locality, is the best approach.
One final point to consider in terms of the legibility of data is that data collected, generated, or updated must be permanent.
It’s essential that individuals or systems record data whenever an activity or action takes place. With electronic data, timestamping is usually normal practice, although there are some points that should be considered. This includes ensuring data operations are not held in a queue that could delay timestamping, while also ensuring system clocks are accurate and time zones are recorded.
In general, though, contemporaneous data recording is another point that has more relevance to manual record-keeping. The main aim is to avoid the practice of creating or updating data at some point in the future. When data is recorded after an event or action, mistakes can happen, i.e., elements can be forgotten, parts can be left out, and information can be recorded inaccurately.
Records should be original rather than copies or transcriptions. Again, this applies mostly to manual record-keeping. For example, you should not write information on a scrap of paper with the intention of completing the main record later, as this can result in errors.
Instead, the original recording of the data should be the main record, whether that record is on paper or on a digital system. With digitally recorded data, it is also important there are technical and procedural processes in place to ensure an original recording of data cannot be changed.
Any analysis, reports, or calculations based on data collected, generated, or updated should be traceable back to the original source.
Furthermore, copies of an original record should be formally verified as being a true copy, and they should be distinguishable from the original. The original version of the data should also be preserved, even when copies exist.
All records should reflect the reality of what happened and should be error-free. Also, there should be no editing of the original information that results in that information being lost.
If changes are necessary, those changes must be documented in a way that makes it possible to refer back to the original information. Nothing should be removed, blocked out, or deleted.
When recording data electronically, the system must have built-in accuracy checks and verification controls. Measurement equipment should be regularly calibrated as part of this process.
All recorded data should have an audit trail to show nothing has been deleted or lost. This doesn’t just cover the original data recording, but also metadata, retest data, analysis data, etc. There should also be audit trails covering any changes made to the data.
This primarily means ensuring data is chronological, i.e., has a date and time stamp that is in the expected sequence. Changes made to an original data recording should be timestamped.
While durability is a factor in many of the above data integrity principles, ALCOA+ places specific emphasis on ensuring data is available long after it is recorded – decades in some situations.
For digitally recorded data, specific steps should be taken to ensure data is enduring, including putting in place robust and tested data backup systems as well as disaster recovery plans and uninterruptable power supplies. Cybersecurity is also an important consideration.
Data must not only exist, but it must also be accessible. So, data storage systems should be searchable, with data properly indexed and labelled. The most efficient way of achieving this is normally by recording data electronically.
By being available, the data must be readable at any time during the retention period. This could be for a range of purposes, including audits, reviews, and inspections.
The Data Integrity Lifecycle
ALCOA+ principles apply throughout the entire data lifecycle:
- Creation – from sensors, systems, operators, etc
- Processing – during analysis, calculations, and comparisons
- Use – reviews, reporting, and analysis
- Retention and retrieval – storage of data as well as retrieval for auditing and other purposes
- Destruction – the destruction of data at the end of the retention period
The Importance of Data Integrity in the Life Sciences Sector
Data integrity is essential to all validation processes in pharmaceutical and medical device manufacturing facilities. Understanding and following the ALCOA+ principles will help you ensure data integrity, especially when selecting data solutions and implementing data recording and documentation protocols.
With data integrity now so intertwined with product quality, patient safety, and regulatory compliance, following the ALCOA+ principles should be a high priority for all life sciences sector manufacturers.