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Jonathan Haider, j.haider@susupport.com, is product line manager at Single Use Support.

In the course of the significant transformation of the biopharmaceutical industry, modular manufacturing emerges as a game-changing approach to fluid management. This innovative methodology is reshaping how pharmaceutical companies and contract development and manufacturing organizations (CDMOs) produce and handle biopharmaceuticals, offering unprecedented flexibility, efficiency, and scalability.

Modular manufacturing in biopharmaceutical fluid management (

It allows for rapid adaptation that is due to either changing market demands or product specifications.

It enables manufacturers to optimize their production processes, reducing costs and improving product quality.

It facilitates easier compliance with regulatory requirements.

The latter is true when technologies provide better control and traceability throughout the manufacturing process. The integration of modular systems with manufacturing execution systems (MES) helps ensure seamless operations, real-time monitoring, and data-driven decision-making.

Overall, the approach to add modularity is particularly valuable when dealing with diverse product portfolios, varying batch sizes, and the potential need for quick scale-up or scale-out capabilities.

Process flexibility: a necessity in modern biomanufacturing

In today’s dynamic biopharmaceutical landscape, process flexibility is not just an advantage—it’s a necessity. CDMOs, in particular, face the challenge of producing a wide array of products with different specifications, volumes, and handling requirements.

One of the primary drivers for process flexibility is the diverse nature of biopharmaceutical products. From monoclonal antibodies (mAbs) to cell and gene therapies, each product may require unique processing conditions, volumes, and handling protocols. Such a volatile demand necessitates rapid scalability.

Will the COVID-19 pandemic remain unique? Uncertain. However, it has prompted manufacturers to be more prepared for potential further outbreaks, such as monkeypox. With pandemic preparedness comes the importance of being able to scale up production swiftly. Modular manufacturing systems provide the agility needed to respond to such sudden surges in demand. They also allow CDMOs to quickly reconfigure their production lines to accommodate these varying needs without significant downtime or retooling or alternatively piling equipment for all possible events and scales.

Why would CDMOs invest in a full set of multiple technologies when they can have one modular system for many scenarios?

Fluid management in manufacturing: the shift to automated closed systems

Fluid management is all about transferring liquids in biopharmaceutical manufacturing between different process steps. This can be the case in upstream, downstream, fill/finish, or in between. The industry is witnessing a transition from manual, open handling to automated closed systems, in large part due to Annex 1 of the European Commission’s guidelines, which favors closed systems. This regulatory requirement has been effective in shifting the interest of manufacturers and CDMOs to automated closed systems (1). The shift in attitude is primarily driven by the need for increased sterility, reduced contamination risks, and improved process control. Even for aliquoting small volumes into single-use bags, closed systems are increasingly being used (see 

 Aliquotation of biopharmaceuticals into small single-use bags.

Why would a manufacturer or CDMO want to close the fluid path system? Responses to this question include the following:

Enhanced sterility: by minimizing human intervention and exposure to the environment, closed systems significantly reduce the risk of contamination.

Improved process control: automated systems allow for precise control over filling parameters, ensuring consistency across batches.

Increased efficiency: automation reduces manual handling, leading to faster processing times and reduced labor costs.

Better traceability: closed systems integrated with MES provide comprehensive data logging, facilitating easier compliance with regulatory requirements.

Six most current pain points and challenges in fluid management

Despite the advancements in fluid management processes, several challenges persist in the biopharmaceutical industry.

 Many current systems struggle to accommodate varying production volumes efficiently. So many aspects can change, be it the batch volume or the size of the primary packaging. Does the manufacturer fill 100 L of antibody-drug conjugates into five single-use bags of 20 L? Or does the manufacturer dispense 20 L of viral vectors into 40 single-use bags of 500 mL? Do mAbs even need to be filled into 20 1-L bottles?Scaling up, down, or out often requires significant changes to equipment or processes, leading to increased costs and downtime.

 There is one closed system that CDMOs do not like—the closed ecosystem of technologies. A vendor lock-in and the resulting incompatibilities can restrict flexibility and increase costs for manufacturers. If Solution Provider A only accepts its own single-use assemblies, single-use bags, and sometimes even platform systems and not those of Vendor B or C, it limits both the drive for optimization and productivity and simply prevents CDMOs from following suit with their customers’ mandatory equipment.

 The need for different systems to handle various scales of production leads to increased capital expenditure, larger footprints, and more complex training requirements for operators, even more when platform systems are not modular. Adding a number of technologies does not necessarily equal scalability. More equipment is just a problem with regards to footprint in manufacturing suites.

 As the industry moves toward advanced therapy medicinal products (ATMPs) and personalized medicines, the need for precise handling of small volumes becomes critical. Many existing systems lack the required accuracy for these applications into single-use bags. In most cases, scales are integrated to measure larger filling quantities, while flow sensors are used to aliquot smaller volumes.

 Some biopharmaceuticals, particularly cell-based products, always require precise temperature control. Otherwise, inaccurate temperatures reduce cell viability, such as when cells are held too warm for too long. Most current systems struggle to maintain optimal temperatures in process steps before the cooling process.

 Most existing systems often leave significant residual volumes in tubing and containers, leading to product waste and reduced yield. This waste can be limited by draining single-use assemblies post filling with negative pressure.

Streamlining requirements: a (liquid) path forward

All of the mentioned pain points are issues that every manufacturer would want to be solved. But do all these pain points make sense for a specific manufacturer’s production? These points are of importance when biopharmaceutical manufacturers and CDMOs carefully consider their project criteria and priorities when selecting or designing fluid management systems.

What are the key project criteria, and in what order should these criteria be prioritized?

 Answering these questions helps a manufacturer find the most suitable filling platform and allows them to make trade-offs between some options. Examples are illustrated in 

; if it is important to fill small volumes (e.g., ATMPs) as accurately as possible, then speed of filling might not be the biggest priority. If it is the speed that counts, then the speed may reduce the accuracy and the option to homogenize drugs before the filling process to ensure aliquot-to-aliquot consistent product quality throughout.

 Examples of project priorities for aseptic filling.

Advanced filling platform systems adapting to criteria.

 Modular, adaptable platform systems are more likely to meet manufacturers’ defined criteria and priorities. Most innovative automated aseptic filling platforms offer several key features, such as:

Unified platform communication: implementing a standardized communication protocol across different scales enables seamless changes in production capacity without major system overhauls. Whatever MES is used, the platform systems require an interface to seamlessly be integrated into the current system, such as withOpen Platform Communication Unified Architecture (OPC UA).

Plug-and-play architecture: a single control unit that can accommodate different racks or modules, allowing for easy reconfiguration based on production needs. Even multiple racks can be attached to the control unit for large-scale operations.

Flexible pumping systems: incorporating pumps that can accurately handle a wide range of volumes, from microliters to liters. Large peristaltic pumps can be as accurate as +/-2 g filling accuracy, for example, but may not be fully suitable for certain drug substances. Depending on the product characteristics, different pump setups can be considered.

Advanced sealing technologies: predominantly performed manually, there is also a possibility to implement an automated sealing mechanisms to maintain system closure and sterility. Certain pinch valves can automatically seal the tubing right after the filling process, as shown in 

, which helps skip one process step of aseptically decoupling fully.

Cooling liquids for aliquotation process: connecting cooling functions directly to the filling platform helps to maintain product integrity for temperature-sensitive biopharmaceuticals. For example, cell-based products are cooled prior to the filling process during homogenization and mixing to maximize cell viability (2).

Optimized tubing design: enlarging the inner and outer diameters of tubing where appropriate to accommodate higher flow rates and reduce flow rates for low amounts of residual volumes. Integrated pinch valves need to be compatible to tubing of different sizes and characteristics. This allows a wider spectrum of scales and filling accuracy for manufacturers.

 Sealing pinch valves for aseptic decoupling.

Why modular filling platforms can no longer be overlooked by CDMOs

Modular manufacturing in biopharmaceutical fluid management marks a significant advancement in the industry, offering a multitude of benefits that are essential for modern production demands. One of the most compelling advantages is increased flexibility, which allows manufacturers and CDMOs to swiftly adapt to varying product requirements and batch sizes. This adaptability is crucial in a landscape where market needs can change rapidly.

Moreover, these modular systems contribute to improved efficiency by reducing setup times and facilitating easier changeovers between products, ultimately leading to higher overall equipment effectiveness. The cost-effectiveness of modular platforms cannot be overstated; by consolidating multiple dedicated systems into a single, versatile platform, companies can significantly lower capital expenditures and optimize their manufacturing footprint.

Quality control is enhanced through the implementation of closed, automated systems that minimize human error and contamination risks, ensuring consistent product quality. Additionally, the integrated data management and traceability features simplify regulatory compliance, making it easier for manufacturers to meet stringent industry standards.

As the biopharmaceutical landscape evolves, the adaptability of modular systems provides a vital degree of future proofing, allowing facilities to upgrade or expand as new technologies and regulations emerge. Furthermore, the ability to quickly scale up production can substantially shorten the time required to bring new products to market, giving companies a competitive edge.

In fact, those who embrace modular manufacturing will be well positioned to navigate the complexities of the biopharmaceutical industry, respond to changing demands, and thrive in an increasingly competitive environment. The future of biopharmaceutical manufacturing lies in modularity.

The Rules Governing Medicinal Products in the European Union Volume 4 EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use

. 8.127 of Annex 1 (2022).
2. Moloney, B. Homogenization of Cells & Aliquotation in Single-use Bags. 

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Vol. 48, No. 11
November 2024
Pages: 17–19, 30

When referring to this article, please cite it as Haider, J. Automate Aseptic Filling: Closing the System at Full Modularity. 

Articles

Shifting to automated, closed modular manufacturing systems is growing increasingly crucial for biopharmaceutical production.

Automate Aseptic Filling: Closing the System at Full Modularity

Chris Stumpf is director of Drug Discovery Informatics Solutions, Revvity Signals.

Many pharmaceutical and biotechnology organizations are rich in data but need more insights. Scientific data are often siloed, lacking context, and, therefore, inaccessible. Artificial intelligence (AI) and machine learning (ML) are promising tools for enhancing R&D. In drug discovery, AI can perform a variety of tasks, from identifying potential drug targets to predicting drug efficacy and optimizing treatment strategies. However, there is often a missing link between drug discovery data and AI outcomes. The integration of AI in drug discovery hinges on a crucial need for robust data management.

Why proper data management is essential for evolving research needs

Demand for traditional drugs and new modalities has increased data digitization across the pharmaceutical industry (1). Meanwhile, the latest AI and ML methods are transforming the R&D process within drug discovery, delivering on the promise to accelerate the journey from laboratory insights to lifesaving therapies. This transformative power of AI inspires optimism for the future of drug discovery, but much of the potential is trapped in isolated and undefined data.

AI is only as powerful as the data it consumes, and it works best with data that have the proper quality, detail, and context. While there are abundant scientific data in pharmaceuticals and biotechnology, the data are often siloed, poorly modeled, and ultimately inaccessible. A meticulous approach to capturing and curating R&D data is indispensable to unlock the full potential of novel AI methodologies. Examining how data are ingested, stored, organized, and maintained is crucial. A strong AI-ready data management strategy can help drug discovery leaders leverage AI to deliver innovative drugs to patients faster.

As AI has matured, many industries have experienced the benefits of automating tedious, data-heavy tasks. Now, the drug discovery industry is facing a high demand for finding insights from high volumes of data, while cutting-edge AI technology is becoming more widely available just in time.

 Pharmaceutical R&D has always been data-intensive, involving many data types from various sources, including laboratory instruments, clinical trials, and real-world evidence.

These data are often stored in disparate systems, leading to silos that make it difficult for both humans and machines to access and utilize the data. What’s more, modern drug therapies are increasingly complex. Drug modalities such as antibodies and cell and gene therapies comprise large, intricate molecular entities, and developing these complicated solutions requires large amounts of data. This complexity is an opportunity for innovation, but it also underscores the need for AI’s interpretive capabilities in drug discovery (see 

 Newer drug modalities comprise large, intricate molecular entities, and potentially terabytes and petabytes of data, that artificial intelligence can interpret.

As science grows in complexity, researchers need new tools and different approaches to curating data to ensure quality and consistency. Simple tools for storing and organizing data were adequate for managing the work that previous generations performed, but they lacked the capabilities required for today’s complex research needs. Fortunately, AI can reduce human workload and quickly achieve targets (2). When paired with proper data management, it can rapidly find patterns and discover hidden insights that humans would otherwise miss.

 AI can consume and analyze complex datasets, and ML can identify valuable patterns within the data. Much like how the manufacturing industry has outsourced heavy lifting and repetitive tasks to robots, the pharmaceutical industry can delegate complex computing tasks to AI systems, enabling humans to find insights, make decisions, and explore new capabilities. Tools such as generative AI and ML algorithms give back quality time to scientists and fuel what scientists do best: think, experiment, and discover new therapies faster.

There are already many use cases for AI in drug discovery. In drug design and molecule generation, generative AI models can analyze existing databases of chemical compounds and learn how to generate novel molecules that are likely to have the desired efficacy and safety profile. In this way, researchers can employ AI to design new molecular structures with desired properties for treating specific diseases. Additionally, AI models can analyze biological data to understand the complex interactions within biological systems and identify points where therapeutic intervention could be beneficial.

Generative AI can also aid in discovering new biomarkers for diseases, which can lead to the development of more precise and targeted therapies. By analyzing patient data, including genetic information, AI models can predict how individual patients might respond to certain treatments, leading to more personalized and effective therapy plans.

Furthermore, AI models can simulate the interaction between drugs and biological systems, predicting drugs’ efficacy and potential side effects before real-world testing. Researchers can also use AI to design clinical trials, identify suitable participants, and predict outcomes, thus speeding up the development process and reducing costs. Another benefit of AI is its speed in finding, analyzing, and summarizing large volumes of external literature. For example, researchers can employ techniques such as natural language processing (NLP) and semantic search to quickly find and understand relevant issues from large bodies of work.

AI is clearly making a tremendous impact on the pharma industry, and the future holds even more promise. However, as the volume and complexity of research data grow, AI’s success depends on data management. Take ML, for example. An ML algorithm’s effectiveness depends on the quality of the training dataset. As AI capabilities continue to advance, there will be even more opportunities to streamline the scientific process and unite disparate sources of information—as long as organizations practice strategic data management.

 As technology advances and labs continue introducing more automation to their processes, the volume of data will increase, thus underscoring the need for proactive data management. Traditional on-premises infrastructure struggles to keep pace with the demands of modern labs, often leading to inefficiencies and increased costs. As a result, many research organizations have embraced cloud computing, which provides data storage and computational power for processing it. But simply storing data in the cloud is not enough. No matter where the data are stored, it needs context in order to be useful. Metadata—or in other words, data about data—are essential. Without metadata, data are like a cabinet full of unlabeled chemicals. In order to utilize AI, researchers must label data with information about the experiment, such as experimental methods, conditions, and the nature of the data.

By moving data management to the cloud, pharmaceutical companies can leverage the extensive infrastructure, security, and scalability that cloud service providers offer. This shift allows organizations to focus on their core expertise—drug discovery and development—instead of troubleshooting technical problems.

While simply moving existing applications to the cloud (a process known as “lift-and-shift”) can offer some benefits, it often fails to unlock cloud computing’s full potential. The real advantages are realized through the adoption of cloud-native applications—those specifically designed to exploit cloud computing principles and technologies from the ground up.

Cloud-native solutions provide a rich ecosystem of services and connected capabilities that are indispensable for modern pharmaceutical R&D. They facilitate seamless integration of data from multiple devices, enable the application of advanced analytics and AI, and support real-time collaboration across geographically dispersed teams.

Cloud-based Software as a Service (SaaS) platforms seamlessly integrate with existing information technology (IT) ecosystems, providing a unified and flexible approach to data management. These integration-ready SaaS applications enable pharmaceutical companies to streamline their R&D workflows, reduce manual processes, and enhance collaboration across global teams. Plus, the cloud-based approach allows for frequently updating software with new releases and capabilities. The ability to update is essential as research needs change, AI capabilities advance, and new security vulnerabilities develop.

Electronic lab notebooks (ELNs) provide a central tool for data capture, analysis, and reporting. Cloud-based ELNs offer robust integration capabilities, allowing data from various sources to exist in a single, accessible platform. This centralization not only simplifies data management but also breaks down silos, fostering a collaborative environment where researchers can share insights and work together more effectively.

They also enable the creation of seamless workflows that connect different stages of the drug development process. For instance, integration between an ELN and a sample management system can automate the flow of substance registration data, minimizing the need for manual data entry. This approach saves time and reduces the chance of human error, ensuring that ML algorithms are working with accurate data.

 Like many technological advances, AI will only be effective if users embrace it. Data entry must be simple enough to encourage researchers to adopt AI-based research methods. User-friendly interfaces can help scientists quickly learn to navigate these platforms, manipulate data, and customize workflows to suit their needs. Features such as inventory tracking and integration with familiar tools make it easier for researchers to embrace new technologies and drive innovation.

As pharmaceutical R&D evolves, security and compliance are critical aspects of data management. Ensuring robust data security and compliance is crucial for safeguarding sensitive research information, especially as AI becomes integral to the industry.

While scientists leverage AI to accelerate discoveries, cybercriminals are also using AI to develop sophisticated attack methods. The benefits of using AI far outweigh the risks, but organizations must take proactive measures to prevent attacks. A layered approach to security includes data encryption and rigorous access controls, continuous monitoring and proactive threat intelligence, and regular vulnerability scans and risk assessments.

 The Findability, Accessibility, Interoperability, and Reuse (FAIR) principle, defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations, states that data should be findable, accessible, interoperable, and reusable. A given data point must have metadata and a unique identifier. Furthermore, humans and machines should be able to read the data, which must be registered or indexed in a searchable resource (2) (see 

 Findability, Accessibility, Interoperability, and Reuse (FAIR) data principles describe the concept that data should be findable, accessible, interoperable, and reusable.

Evolving data frameworks: adapting to future research needs

Scientific inquiry is unpredictable, and the data collected today might need to answer unforeseen questions tomorrow. Rigid data schemas risk losing valuable context and potential insights. A different methodology called “late binding of schema” offers the flexibility that scientific discovery requires.

This methodology involves capturing data in a way that allows for its structure to be defined and modified closer to the time of analysis. By enabling data to be restructured as new hypotheses and analytical needs emerge, late binding of schema preserves the data’s full informational content and contextual richness. This flexibility is especially critical in the context of AI and ML, where the quality of training datasets directly influences model performance.

It is not only the output of today’s research that has changed, but also the way research is conducted. Collaborating with outsourced partners and colleagues in different locations is not only possible, but common. The modern way of working requires seamless, secure data sharing across teams and scientific disciplines. As teams become more distributed, there is a need for continuous integration with a globalized scientific community.

Ushering in the next era of drug discovery with AI

As AI progresses, so will the way researchers capture, integrate, and analyze data. Gathering data and transferring these data into programs for analysis and review will continue to become more streamlined and automated so that, eventually, data could automatically flow from across systems and algorithms and onto predictive models.

In the drug development industry, unifying data and contextualizing data are paramount for advancing scientific research and innovation. By annotating data with rich metadata and employing flexible data modeling, researchers can transform overwhelming volumes of information into structured, navigable resources ready for complex analysis by today’s AI tools and new tools that will become available in the future.

The collaborative nature of scientific research necessitates a data management system that facilitates seamless and secure data sharing across teams and disciplines. This approach prevents data silos and allows for the collective expertise of the scientific community to enrich and expand upon the available data. Integrating diverse data types—such as medicinal chemistry and biochemical data, screening data, and pre-clinical observations—becomes increasingly critical for a holistic understanding of therapeutic outcomes as research becomes a global endeavor.

Flexibility in data management is also crucial, particularly in the context of AI and ML. Adopting methodologies such as late binding of schema allows for the data structure to be defined and refined as close as possible to the analysis time. This ensures that data remain agile and adaptable enough for modern scientific discovery. By enabling the reorganization of data and correction of errors without restarting the process, researchers save time and maintain the integrity and utility of their data, ultimately accelerating R&D progress and driving breakthroughs in the pharmaceutical industry.

Effective data management may have previously been considered an operational decision, but it is now a strategic decision. When data are contextualized, centralized, and accessible, AI can enable pharmaceutical companies to accelerate innovation and improve patient outcomes.

1. Paul, D.; Sanap, G.; Shenoy, S.; et al. Artificial Intelligence in Drug Discovery and Development. 

 (1), 80–93. DOI: 10.1016/j.drudis.2020.10.010
2. Wilkinson, M. D.; Dumontier, M.; Aalbersberg, I. J.; et al. The FAIR Guiding Principles for Scientific Data Management and Stewardship. 

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Vol. 48, No. 11
November 2024
Pages: 27–30

When referring to this article, please cite it Stumpf, C. Untethered Data: Unifying and Contextualizing Drug Discovery Data. 

Capturing and curating R&D data are crucial to realizing the full value of advanced analytics.

Untethered Data: Unifying and Contextualizing Drug Discovery Data

Radoslaw Kaczanowski is head of Global Project Management at Recipharm.

Man touching a knowledge sharing concept | Image Credit: ©thodonal - stock.adobe.com

Outsourcing in the biopharma and pharma industries is on the rise, driven by increasing competition, the rising number of emerging start-ups, and the demand for affordable medicines. While outsourcing offers numerous advantages, including access to specialized facilities and expertise, technology transfer remains a critical challenge.

Common hurdles in the complex nature of tech transfer may be overcome by data-driven approaches, digital tools, and effective communication.By embracing these strategies and collaborating with experienced contract development and manufacturing organizations (CDMOs), companies can navigate the complexities of tech transfer and bring innovative therapies to market faster.

The increasing complexity and cost of drug development, coupled with intensifying market competition and the demand for faster time-to-market, have driven a significant rise in outsourcing within the biopharmaceutical industry during the past 10 to 15 years (1).

As of 2023, over 60% of biopharma companies outsource at least some activities, contributing to the remarkable growth of the CDMO market, which is projected to outpace overall pharmaceutical industry growth through 2028 (2,3). This surge in outsourcing is fueled by factors such as the need for specialized expertise and infrastructure, the rise of resource-constrained start-ups, and the pressure to deliver affordable therapies.

While outsourcing to CDMO partners provides many advantages for pharma and biopharma companies, such as helping to mitigate risks and accelerate development timelines, a critical phase in the process can pose significant challenges: technology transfer. Seamless tech transfer ensures that the manufacturing process is faithfully replicated at the receiving site, safeguarding product quality, maintaining regulatory compliance, and minimizing the risk of costly delays or disruptions. However, this process is far from a simple exchange of physical assets; it involves the intricate transmission of knowledge and expertise required to replicate a complex manufacturing process.

Common tech transfer challenges include the following:

Misaligned expectations and project scope definitions.

 The drug developer or marketing authorization holder (MAH) may have preconceptions about their product based on initial information. These assumptions can influence the project plan and the tech transfer process itself. If these initial assumptions prove inaccurate, it can lead to costly delays and rework. Common examples of such discrepancies include unexpected product stability issues arising from changes in analytical methods, necessary product modifications to comply with evolving regulations (e.g., nitrosamine or elemental impurities guidelines), or misinterpretations of regulatory requirements at the project’s outset.

 Effective technology transfer hinges on a comprehensive understanding of the product and its manufacturing process. However, there can be gaps in technical knowledge that may hinder the creation of a robust transfer process, particularly with legacy products or those in early development stages. Some originating sites may lack experience in tech transfer, leading to uncertainties about the necessary information or its sources. Additionally, inadequate documentation practices can result in the loss of valuable technical knowledge, potentially leading to repeated challenges and delays during the transfer, impacting project timelines.

A lack of standardization at the receiving site.

 Challenges can arise during tech transfer without standardized internal processes and documentation at the receiving CDMO site. Multiple tech transfers from diverse clients with varying requirements can lead to inconsistencies, potentially impacting project timelines and efficiency. Implementing standardized procedures and documentation across projects streamlines the tech transfer process, enhancing productivity and fostering operational excellence. This ensures a smoother transition, regardless of the client or project complexity, and ultimately benefits both the CDMO and its partners.

 Effective tech transfer management and coordination rely on assembling dedicated teams at both the originating and receiving sites. However, this can be challenging, especially for smaller organizations or those with limited experience in tech transfer. The originating site may lack personnel with specialized knowledge in handling the transfer process, requiring additional support in gathering and organizing the necessary information for handover. Similarly, the receiving site may need to tap into expertise beyond its core tech transfer team for complex or novel projects. This ensures the development of a comprehensive and tailored plan that addresses the unique challenges of the specific technology being transferred.

Failure to address these challenges and navigate tech transfer effectively can have far-reaching consequences, impacting the overall success of the end product and potentially hindering its timely delivery to patients. Pharma and biopharma companies should always look closely at the tech transfer processes and capabilities of a potential partner when selecting a CDMO, ensuring that the right strategies are employed to successfully develop and manufacture their therapy.

Strategies for an efficient and effective tech transfer

Experienced CDMOs will employ a range of proactive strategies to ensure a seamless and successful tech transfer process while simultaneously mitigating risks to streamline timelines.

 A dedicated team of data scientists and statisticians equipped with advanced modeling and statistical tools can significantly enhance the tech transfer process. By harnessing the power of data-driven insights, development timelines can be accelerated, API consumption optimized, and tech transfers de-risked, leading to consistent, high-quality results throughout the manufacturing process.

Statistical modeling has emerged as a powerful tool for mitigating risks and ensuring a seamless transition throughout tech transfer. Harnessing a holistic, integrated approach to modeling across the drug development lifecycle, unlike standard processes which often focus on isolated aspects, drug developers can gain a comprehensive understanding of the product and its manufacturing process, allowing for confident decision-making and proactive risk mitigation.

Through the creation of sophisticated predictive models, developers and manufacturers can establish clear connections between material attributes, process parameters, and critical quality attributes (CQAs) of the drug product. Using a broad range of data sources information, including data from specialized equipment such as those used for powder characterization and simulation, historical data from previous projects, literature data, and real-time process data, developers can generate more robust and comprehensive models with enhanced predictive accuracy. This enables the identification of key process parameters that significantly impact product quality, empowering targeted optimization and control during tech transfer.

Predictive modeling capabilities also allow for the simulation of process performance under various conditions, aiding in the selection of optimal operating parameters and anticipating potential challenges during scale-up. This foresight minimizes the risk of costly deviations or failures, ensuring a smoother and more efficient transfer process.

By defining the “design space”—the range of operating conditions where product quality is assured—statistical models provide a clear framework for process transfer and robust control strategies. This data-driven approach reduces reliance on empirical methods, fostering a more predictable and reliable tech transfer process.

 In addition to statistical modeling, technological advancements are also revolutionizing the way tech transfer is conducted, offering innovative solutions to address issues surrounding data flow and connectivity. Traditionally, data flow during tech transfer has involved cumbersome processes, relying heavily on the physical transfer of documents, spreadsheets, and presentations, coupled with in-person meetings and limited digital communication. This approach presents challenges in terms of efficiency, security, and version control, often leading to delays and potential misinterpretations.

Traditional methods of data storage and transfer are now being replaced by advanced digital knowledge management systems. These systems enable automated information capture and ensure centralized storage of all relevant data, promoting efficient and transparent information sharing between the originating and receiving sites. By standardizing data organization and facilitating seamless transfer between systems, these tools significantly expedite the tech transfer process, saving valuable time and resources.

Digital tools also play a crucial role in overcoming geographical barriers. Video conferencing and project management platforms enable real-time communication and collaboration, even when teams are located in different parts of the world. This enhances transparency, fosters effective teamwork, and ensures that all parties are aligned throughout the tech transfer journey.

A foundation of transparency and collaboration. 

Open communication and transparent information exchange are pivotal for achieving seamless technology transfer. At the onset of the process, it is essential to share comprehensive process documentation, encompassing not only successful runs but also any documented failures or challenges encountered at the originating site. This complete picture empowers the receiving site to anticipate potential hurdles and proactively implement preventive measures, minimizing the risk of delays and ensuring a smoother transition.

Providing opportunities for the technical team from the receiving site to observe processes at the originating site can significantly enhance understanding and facilitate the transfer of tacit knowledge. Similarly, conducting engineering runs under the observation of peers from the sending unit allows for real-time feedback and validation, ensuring that the process is accurately replicated, and any undocumented nuances are captured and shared.

Established tech transfer process methodology.

 The receiving site must have defined and established processes for tech transfer projects. These should include predefined project phases with clear milestones and deliverables pivotal for achieving an efficient tech transfer process and establishing a robust production system. A clear gate system between project phases allows developers and manufacturers to access the transfer process in each phase of the transfer and ensure that deliverables with the required quality are achieved. Leveraging a gate system, developers and manufacturers can identify and quickly address any gaps in the tech transfer process, enabling efficient and rapid production process transfer to the receiving site. Clearly defined roles, responsibilities, and deliverables for each step further enhance the efficiency of the tech transfer system.

Tech transfer in the era of accelerated development

As the biopharma and pharma industries continue to rapidly evolve, the demand for swift and efficient tech transfers will only intensify. The pressures to bring innovative therapies to market faster, coupled with the increasing complexity of drug development, necessitate a proactive and adaptive approach to technology transfer. Embracing data-driven strategies, leveraging digital tools and advanced modeling, and fostering a culture of transparency and collaboration will be essential for success in this new era.

By working in partnership with experienced CDMOs that harness these strategies, biopharma and pharma companies can navigate the intricacies of tech transfer with confidence, ensuring that their innovative products continue to reach patients swiftly and safely.

Khanna, S. 2022 Outsourcing Trends in Biopharmaceutical Manufacturing. 

20th Annual Report and Survey of Biopharmaceutical Capacity and Production 

Kansteiner, F. 2024 Forecast: As CDMOs Come Off Pandemic’s Highs and Lows, Employment and Funding Hitches Persist. 

®
Vol. 48, No. 11
November 2024
Pages: 31–33

When referring to this article, please cite it as Kaczanowski, R. Strategies for Overcoming Common Challenges in Tech Transfer. 

The complexities of tech transfer may be overcome by data-driven approaches, digital tools, and effective communication.

Strategies for Overcoming Common Challenges in Tech Transfer

Mike Hennessy Jr. is the President and CEO of 

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The 2024 fall season has already seen perilous weather conditions impacting many areas of the world, none more so than the southeastern coastal states of the United States. Towards the end of September, the US was hit by Hurricane Helene, which underwent rapid intensification as it traveled over warm ocean waters, and was then swiftly followed by five further hurricanes, including Hurricane Milton (1).

These recent storms have caused significant damage, with major efforts already underway to provide assistance to affected areas. However, for the healthcare industry, there are wider-reaching issues arising from the devastation of the storms.

In North Cove, Marion, NC, Baxter’s largest manufacturing facility was flooded, and bridges accessing the site were damaged, resulting in the facility being forcibly closed. This facility is, in fact, the largest manufacturing site for intravenous and peritoneal dialysis solutions in the US, and its closure has led to nationwide shortages of certain medicines (2).

“Our goal is to begin restarting North Cove production as soon as possible and in phases by the end of this year. We do not yet have a timeline for when we expect production to be fully restored to pre-hurricane levels,” Baxter stated in a progress update press release (3).

Responding to the shortages, FDA has added three new parenteral products to its drug shortages list and released guidance for the temporary compounding of certain parenteral products to help fill any supply gaps. Additionally, FDA is assessing potential imported products to help meet patient needs within the US (4).

However, the disruption being caused by this weather-related emergency should further intensify governmental assessments of the fragility and opacity of the US pharma supply chain—facets that have been under scrutiny for some time (5).

1. Poynting, M.; Dale, B.; Rivault, E.; Rogers, L. How Unusual Has this Hurricane Season Been? 

, Oct. 11, 2024.
2. Baxter. Baxter Provides Update on North Cove, N.C., Facility and Hurricane Helene Relief Plans for Community. Press Release, Sep. 29, 2024.
3. Baxter. Hurricane Helene Updates. Press Release, Oct. 21, 2024.
4. FDA. 

Hurricane Helene: Baxter’s Manufacturing Recovery in North Carolina

Short Supply: The Health and National Security Risks of Drug Shortages

Mike Hennessy Jr is president and CEO of MJH Life Sciences®

When referring to this article, please cite it as Hennessy, M. Weathering the Storms. 

Recent hurricanes in the US close Baxter plant, shining a spotlight on supply chain fragility again.

Weathering the Storms

Siegfried Schmitt, PhD, is Vice President Technical at Parexel International, Siegfried.Schmitt@parexel.com.

Q. According to our quality management system, we assess the suitability of all our suppliers in one way or another. Is this sufficient with regards to supplier oversight?

A. The supply chain for pharmaceutical products is complex and involves many actors, often in different countries and jurisdictions. Importantly, the ultimate responsibility for the product and its quality always remain with the Marketing Authorization Holder (MAH) (i.e., the license holder). Therefore, the MAH must have a supplier oversight system in place that assures that any materials (e.g., excipients), equipment (e.g., laboratory instruments), or services (e.g., transport and storage) meet the appropriate quality requirements and specifications. Such a supplier oversight system, which obviously is part of the pharmaceutical quality system (PQS) (1), must comply with the applicable rules and regulations for the good practices (e.g., good manufacturing practices). That this is by far not always a given is demonstrated by the regulatory agency inspection observations, such as those made public in warning letters by FDA (2).

The question is not whether we can trust a supplier, but what controls one must put in place to be able to trust said supplier. A risk-based approach (3) is required. This is where companies can do too little (non-conforming with the regulations) or too much (waste of valuable resources). This is best demonstrated with two examples.

Company A relies on the Certificates of Analysis (CoAs) from their suppliers for raw materials, including excipients and APIs without performing any incoming tests. The company argued that they only buy from reputable suppliers. However, FDA insists on at least identity testing and additional testing depending on the criticality of the material: “Without adequate testing, you do not have scientific evidence that your raw materials conform to appropriate specifications prior to use in the manufacture of drug products. As a manufacturer, you have a responsibility to sample, test, and examine drug components before use in production to assure adequate product quality” (4).

Whereas, Company B performs a large amount of testing. For example, the microbiology laboratory purchases media and performs several incoming tests, including identity and mycoplasma testing. The media is in use with a large number of pharmaceutical companies globally and comes with a CoA that covers mycoplasma testing. The company needs to assess if the risk from the media warrants mycoplasma testing. A review of testing data showed that there never had been an issue with the media. Equally, an Internet search found no evidence of any issues with that media. Furthermore, the supplier is qualified by the contract giver, and an identity test is always performed when the media is received. Thus, this testing is not necessary.

It requires experience and expertise to devise a supplier oversight system that is compliant and optimized. Such a system assures that all activities, including supplier audits, material testing, etc., are risk-based, meet the minimum legal requirements, and are of a manageable complexity. This means, for example, that suppliers are assigned to risk categories. Therefore, risk assessments don’t have to be performed for each supplier, only for each supplier category. Such systems need to be reviewed, revised, and updated based on the company’s evolving needs and any changes in the regulations and their interpretation.

Becker, G. The FDA Warning Letter Report 2023. 

Siegfried Schmitt, PhD, is vice president, Technical at Parexel.

®
Vol. 48, No. 11
November 2024
Page: 34

When referring to this article, please cite it as Schmitt, S. The Importance of Appropriate Supplier Oversight.

The product license holder must have a supplier oversight system in place, says Siegfried Schmitt, PhD, vice president, Technical, Parexel.

The Importance of Appropriate Supplier Oversight 

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FDA’s Patrizia Cavazzoni, director, Center for Drug Evaluation and Research (CDER), and Peter Marks, director, Center for Biologics Evaluation and Research (CBER), provided an update on the agency’s 

 (the Hub) in a Nov. 1, 2024 FDA Voices blog post (1). The Hub was created in July 2024 (2) as a cross-center program that operates as a single point of engagement with stakeholders in the development of treatments for rare diseases. The Hub is the agency’s way of creating a community that can share new approaches for drug development for rare diseases.

In establishing the Hub, FDA gathered feedback from stakeholders through a public meeting, held on Oct. 16, 2024 in collaboration with the Reagan-Udall Foundation. The meeting included advocates from rare disease patient groups, academics, regulators, and other stakeholders who discussed how the Hub can work with the community to prioritize work.

A Hub director was also appointed. Amy Comstock Rick will be the Director of Strategic Coalitions for the Hub and be accountable to both Cavazzoni and Marks. Previously, Ms. Rick worked as a consultant at Leavitt Partners and was president and CEO of the Food & Drug Law Institute.

“Ms. Rick brings to this new role more than 20 years of experience as a leader and trusted ally in advocating for policies throughout healthcare, biomedical research, and nonprofit communities, which includes extensive engagement with patient communities and government agencies on issues related to rare diseases. We are excited to have her join and begin creating the important partnerships needed to strengthen the Hub’s engagement throughout the rare disease community,” stated Cavazzoni and Marks in the blog post.

As part of their work with the Hub, Marks and Cavazzoni plan on collaborating with other FDA centers that work with rare diseases, such as the Center for Devices and Radiological Health, the Oncology Center of Excellence, and the Office of Orphan Products Development. In addition, a Rare Disease Innovation Hub Steering Committee has been established to bring the groups together, and a new CBER-CDER Rare Disease Policy and Portfolio Council has been created. The council will bring experts from the different FDA centers together to discuss scientific, regulatory, and policy issues.

“The collaborative model of the Hub will build upon the knowledge and skills of staff within their respective centers, promote adoption of best practices, and avoid confusion over decision-making authorities within those organizations. Meetings between the FDA and sponsors relating to specific drug or biological products will remain the same, as will the way decisions are made about specific programs and how advisory committee meetings are structured and run,” Cavazzoni and Marks stated. “By working together, we believe that we can further facilitate the development, evaluation, and availability of safe and effective therapies. We are committed to ongoing and open communication and look forward to continuing this dialogue with the rare disease community.”

Cavazzoni, P. and Marks, P.FDA Takes Exciting Steps Toward Establishing the Rare Disease Innovation Hub. 

https://www.fda.gov/news-events/fda-voices/fda-takes-exciting-steps-toward-establishing-rare-disease-innovation-hub

FDA. FDA Rare Disease Innovation Hub to Enhance and Advance Outcomes for Patients.

CDER Director Patrizia Cavazzoni and CBER Director Peter Marks provided an update on the rare disease innovation hub in a new FDA Voices blog post.

FDA Rare Disease Innovation Moves Forward

Patrick Lavery is an Editor for the MJH Life Sciences brands 

. Previously at MJH, he filled the same role for 

Text sign showing Industry News. Business photo text delivering news to the general public or a target public | Image Credit: © Artur - stock.adobe.com

Hapman, a Kalamazoo, Mich., company focused on the design and manufacture of custom bulk material handling equipment, announced in a press release on Oct. 30, 2024 that it would be introducing a new conveyor at Pack Expo 2024, to be held in Chicago from Nov. 3–6, 2024 (1).

After a yearlong, intensive R&D initiative, Hapman said, the conveyor system—emblematic of the company’s nearly eight decades of expertise in tubular drag conveyors—represents Hapman’s first major product launch since 2008 (1). Hapman said the unveiling of the conveyor will serve to fill a gap in the market for material handling operations in the packaging and processing sectors and offers enhanced performance and versatility (1).

The uniqueness of the demands of Hapman’s customers, the company said, requires that the solutions provided to those customers be efficient and customizable, and designed to ease packaging and processing operations. Additional considerations include safety, productivity, user-friendly design, and the minimization of product loss. Further details about the conveyor are being kept “under wraps” by Hapman until the exhibition gets underway, according to the press release (1).

Hapman additionally plans to show its Bulk Bag Unloader, which features heavy-duty, 3” tubing construction offering adjustable heights that accommodate numerous bag sizes, as well as high durability (1). Dust control systems, vibration devices, and optional, 1- or 2-ton hoists and trolleys with certified, load-tested bag adapters are listed as some of the other features.

The system is Hapman’s first major product launch since 2008, following a year of intensive R&D that builds upon the company’s eight decades of tubular drag conveyor expertise.

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