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Drug Solutions Podcast: 

Pharmaceutical packaging must advance to adapt to new, complex modalities

Out-of-the-Box Packaging Advancements

From Theory to Reality: Automating Process Development

Drug Digest: 

The Importance of Appropriate Supplier Oversight

Ask the Expert

Chris Burgess, PhD, is an analytical scientist at Burgess Analytical Consultancy Limited, ‘Rose Rae,’ The Lendings, Startforth, Barnard Castle, Co Durham, DL12 9AB, UK; Tel: +44 1833 637 446; chris@burgessconsultancy.com; www.burgessconsultancy.co

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Nomenclature is important. Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected? The purpose of this article is to provide a structured model for these terms with their definitions and their relationships in the context of analysis and testing within a pharmaceutical quality system.

The crucial concept is that data quality, including data integrity, is only attainable via data governance, as will be illustrated in the proposed model.

In a regulatory context, the establishment of measured values and subsequent reportable results of a predefined quality is an essential activity. Reportable results are then compared with predetermined acceptance criteria and/or standards and specifications. The processes, mechanisms, and control systems necessary to establish measurement values and reportable results of a defined quality are interlinked. These interlinkages may include metrological, procedural, or organizational elements.

The overall proposed model is built up using building blocks akin to the construction of Lego brick models. This approach has been used in previous papers concerning error budgets in measurement uncertainty and Monte Carlo Simulation, and data quality within a lifecycle approach (1–2).

An analytical testing flow diagram, shown in 

, gives a high-level idea of the traceability and subsequent interactions from the marketing authorization (new drug application, NDA, or abbreviated new drug application, ANDA) to the reportable result and data quality.

 A high-level process flow indicating some of the elements of data governance, data quality, and data integrity, as well as the quality management system (QMS).

This process flow has been translated into the Lego brick model shown in 

. This model is based upon a data quality within a lifecycle approach model which has been modified and extended, as will be described in some detail (2).

 Data governance model modified and extended from the data quality within a lifecycle approach model (taken from reference 2, Figure 15).

The crucial concept is that data quality is only attainable via data governance.

The data governance and data quality Lego brick model

Data quality is not an accident but a product of design. It is a combination of data integrity and the functionality and control under the Pharmaceutical Quality System, usually termed the quality management system (QMS). These aspects are underpinned by data governance and qualified information technology (IT) infrastructure services. This is essentially a sandwich structure in which the “filling” is provided by metrological integrity, analytical procedure integrity, and quality oversight. These “fillings” are described below.

 are subject to risk assessments and risk management (3), which will not be discussed here.

The qualified IT infrastructure services with cybersecurity and access control, as seen above, also will not be covered further in this article.

It is, however, necessary to discuss the key elements of data integrity and the Pharmaceutical Quality System which, when combined, generate data quality on the foundation of data governance.

Data governance is the totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (4).

The purpose of an analytical procedure is to provide a reportable result of the analytical characteristic or quality attribute being determined. Analysis and testing require a measurement system, and a procedure for its application to a sample.

Data integrity is underpinned by the first brick, the metrological integrity of the instrument or system’s operational performance, with demonstrable assurance that it is “fit for intended use” within a specific analytical procedure, which is “fit for intended purpose” over the data lifecycle.

Analysis and testing usually involve the use of an apparatus, analytical instrument, or system to make a measurement. Therefore, establishment of “fitness for intended use” for any apparatus, analytical instruments, or systems used in analysis and testing is necessary to ensure metrological integrity over the operational ranges required.

Therefore, it is essential to establish “fitness for intended use” before the analytical procedure is performed. The main resource for instrument and system requirements are the specific monographs and general chapters in the pharmacopeias. In particular, United States Pharmacopeia (USP) has a unique general chapter on the lifecycle processes and requirements for ensuring that any apparatus, analytical instrument, or system is “fit for intended use,” as seen in 

 Data quality outline for an analysis and testing quality control (QC) model using USP references.

analytical instrument and system qualification

calibration over the operational ranges of critical measurement functions

trend analysis to monitor an ongoing state of control.

The second component of data integrity is a validated or verified analytical procedure performed by a trained analyst.

It has been a requirement for more than 20 years that analytical methods and procedures need to be validated or verified (6–7). Recently, these requirements have been updated by the International Council for Harmonisation (ICH) and a new guideline on analytical procedure development issued (8–9). 

 General Chapter <1220> on Analytical Procedure Lifecycle should be consulted (10).

analytical target profile and development

trained analysts and second person review

Particular attention should be taken regarding analyst training and second person review within the laboratory.

Second person review is essential in ensuring data quality.

Quality control is the guardian of scientific soundness, whereas the quality assurance function is the guardian of compliance. To perform this duty of care, the quality assurance function requires a robust and comprehensive quality management system that enshrines the elements to provide and perform the necessary quality oversight over the data lifecycle.

Quality oversight involves both reviewing and auditing activities of QC but also includes the Pharmaceutical Quality System implementation itself to ensure that it is up to date (11).

Quality oversight covers key areas such as:

Much has been written on this topic, particularly regarding the three ALCOA models and the meanings of their acronyms (4). These acronyms are summarized below and illustrated in 

 Pictorial representation of the three ALCOA models.

It must be possible to identify the individual or computerized system that performed a recorded task, and when the task was performed. This also applies to any changes made to records, such as corrections, deletions, and changes, where it is important to know who made a change, when, and why.

All data, including any associated metadata, should be unambiguously readable throughout the lifecycle. Legibility also extends to any changes or modification to the original data made by an authorized individual so that the original entry is not obscured.

Data should be recorded on paper or electronically at the time the observation is made. All data entries must be dated and signed by the person entering the data.

The original record is the first capture of information, whether recorded on paper (static) or electronically (usually dynamic, depending on the complexity of the system). Data or information originally captured in a dynamic state remain in that state.

Records need to be a truthful representation of facts to be accurate. No errors in the original observation(s) and no editing are allowed without documented amendments or audit trail entries by authorized personnel. Accuracy is assured and verified by a documented review including review of audit trails.

All data from an analysis, including any data generated including original data, data before and after repeat testing, reanalysis, modification, recalculation, reintegration, and deletion. For hybrid systems, the paper output must be linked to the underlying electronic records used to produce it. A complete record of data generated electronically includes relevant metadata.

Data and information records should be created, processed, and stored in a logical manner that has a defined consistency. This includes policies or procedures that help control or standardize data (such as chronological sequencing, date formats, units of measurement, approaches to rounding, significant digits, etc.).

Data are recorded in a permanent, maintainable, authorized media form during the retention period. Records should be kept in a manner such that they continue to exist and are accessible for the entire period during which they are needed. They need to remain intact as an indelible and durable record throughout the record retention period.

Records should be available for review at any time during the required retention period, accessible in a readable format to all applicable personnel who are responsible for their review, whether for routine release decisions, investigations, trending, annual reports, audits, or inspections.

Data should be traceable though the lifecycle. Any changes to data or metadata should be explained and should be traceable without obscuring the original information. Timestamps should be traceable to a trusted time source. Metrological standards and instrument or system qualification should be traceable to international standards wherever possible.

Data quality is a combination of data integrity and overall control as part of the pharmaceutical quality system.

An example of a quality control data quality outline for analysis and testing, using examples from USP, is illustrated in 

Data quality cannot be assured without a data governance structure supported by a qualified IT infrastructure services with cybersecurity and access control.

The proposed Lego brick model provides a structural framework for assuring data quality over the lifecycle.

A short glossary of definitions of key terms is appended.

I wish to thank Bob McDowall and Oscar Quatrocchi for their review and helpful comments.

The totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (16).

Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable, and that these characteristics of the data are maintained throughout the data lifecycle. The data should be collected and maintained in a secure manner, so that they are attributable, legible, contemporaneously recorded, original (or a true copy), and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices (16).

All phases in the life of the data (including raw data), from initial generation and recording through processing (including transformation or migration), use, data retention, archive and retrieval, and destruction (17).

The assurance that data produced is exactly what was intended to be produced and fit for its intended purpose (17).

Articles

Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected?

Data Governance, Data Integrity, and Data Quality: What’s the Connection?

Radoslaw Kaczanowski is head of Global Project Management at Recipharm.

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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.

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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 

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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.

Other companies are also previewing their exhibits and offerings ahead of the 2024 edition of Pack Expo. Brewerton, NY-based Pacteon Group announced in a press release on Sept. 9, 2024 that it had added Descon Conveyor Systems to its portfolio, and would be displaying a Descon product at one of its booths at the event (2).

And on Sept. 26, 2024, TekniPlex Healthcare, a full-service contract development manufacturing organization for medical materials science, particularly in the minimally invasive and interventional therapy devices space, announced in a press release that it would unveil its strongest-ever reinforced paper for medical device packaging applications as part of its display (3).

 at INTERPHEX 2024, held in New York City in April 2024; that interview can be viewed 

Hapman can be located at Booth #LU-7561 at Pack Expo 2024, where the company plans to roll out the new conveyor system alongside the Bulk Bag Unloader.

1. Hapman. Hapman to Showcase Material Handling Solutions at PACK EXPO 2024. Press Release. Oct. 30, 2024.
2. Pacteon Group. Pacteon Exhibits New Packaging Automation Solutions at Pack Expo International 2024. Press Release. Sept. 9, 2024.
3. TekniPlex Healthcare. At Pack Expo, TekniPlex Healthcare to Debut Its Strongest-ever Reinforced Paper for Medical Device Packaging Applications. Press Release. Sept. 26, 2024.
4. Bigica, A. 

How TekniPlex is Expanding to Support Innovative Solutions in Materials Science.

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.

Hapman to Showcase New Conveyor at Pack Expo 2024

In this episode of Drug Digest, industry experts discuss the ongoing development of automating biomanufacturing processes, including its challenges and practical applications. 

The biopharmaceutical industry has seen the use of automated technologies in biomanufacturing processes grow over the years from theory to reality. In this episode of 

, industry experts discuss the ongoing advancement of automation in the biopharmaceutical industry. They give insight into how automated technologies are put to use today and how they are streamlining process development, with an eye toward saving cost, time, and manufacturing footprint.

John Hatzis, Global Life Sciences Industry Consultant, Rockwell Automation

John Hatzis has been with Rockwell Automation for ten years where he has leveraged his expertise using Rockwell technology in a variety of applications in the Life Sciences industry. He works with BiofabUSA consulting on control systems for new tissue therapies. John has a BSc in Chemical Engineering from Worcester Polytechnic Institute.


Smriti Khera, Global Head of Life Sciences Strategy and Marketing, Rockwell Automation

Smriti Khera is the global head of Life Sciences Strategy and Marketing at Rockwell Automation. She holds a PhD in Pharmaceutical Sciences from the University of Arizona, where her focus was drug discovery from natural products. After working as a pharma research scientist, she pivoted to strategic marketing to increase her impact. Over the past 12 years she has held strategy and marketing leadership roles with increasing levels of responsibilities at both Fortune 500 and early stage startups. She is passionate about leveraging her unique expertise to generate sustained impact across healthcare and life sciences using automation and digital technologies.

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Gefion, a new artificial intelligence (AI) supercomputer built on an AI data center infrastructure known as NVIDIA DGX SuperPOD (1), is now operational in Copenhagen, Denmark, the Novo Nordisk Foundation announced on Oct. 23, 2024. The supercomputer was launched at an event in Copenhagen, where it was symbolically “turned on” by His Majesty King Frederik X of Denmark; Jensen Huang, founder and CEO of NVIDIA, a US-based supercomputing company; and Nadia Carlsten, CEO of the Danish Centre for AI Innovation (DCAI).

Following its launch, Gefion is being readied for customers, beginning with a pilot phase in which select customers interested in using AI will use it to accelerate innovation in many areas, from quantum computing to drug discovery to societal challenges, such as the transition to green energy.

“Gefion provides our Danish enterprises with entirely new opportunities,” said Morten Bødskov, the Minister for Industry, Business, and Financial Affairs, Denmark, in a press release. “Gefion will drive the advancement of the green transition, enable tailor-made solutions, and strengthen the competitive standing of our companies in the global market. In a time of heightened international competition, this supercomputer acts as a vital enabler for advancing Danish businesses into the future. This ground-breaking technology represents an important opportunity for Danish companies and researchers to develop innovative solutions to future challenges.”

A public–private partnership between the Novo Nordisk Foundation and the Export and Investment Fund of Denmark (EIFO) resulted in the establishment of the new supercomputer. This partnership funded and established DCAI, a new company that owns and will operate Gefion.

“It’s a remarkable achievement that it has taken only six months from announcement to completion of this complex state-of-the-art supercomputer,” said Carlsten. “I am extremely proud of the team that came together and collaborated to turn this vision into a reality, delivering a leading capability that is now ready for customers to start testing new use cases. Gefion is the game-changer that many innovators had been waiting for.”

Huang added in the release, “Denmark recognizes that to innovate in AI, the most impactful technology of our time, it must foster a domestic AI infrastructure and ecosystem. The Gefion supercomputer will supercharge the scientists of Denmark with local AI computing infrastructure to drive advancements in life sciences, climate research, and quantum computing.”

Gefion is powered by 1,528 graphic processing units (GPUs) (NVIDIA H100 Tensor Core GPUs) and is interconnected using networking (NVIDIA Quantum-2 InfiniBand). According to the press release, “Denmark up to now had no GPU-accelerated supercomputers, and a thorough consulting process with relevant stakeholders across the country had identified access to computing power as a key roadblock to progress in Denmark’s otherwise thriving AI-based research ecosystem.”

Digital Realty, a global data center provider, hosts the supercomputer in an AI-ready data center facility in Denmark, which is designed and built sustainably, running on 100% renewable energy, according to the press release. Gefion was assembled and installed by Eviden, a European-based high-performance computing company.

In addition to the Novo Nordisk Foundation’s funding, EIFO contributed DKK 100 million (US$15 million) and owns a 15% minority stake in DCAI.

“Establishing Gefion is a milestone, and it fits very well with the Novo Nordisk Foundation’s vision of improving people’s health and the sustainability of society and the planet, as it creates new opportunities and can accelerate finding new solutions to pressing challenges,” said Mads Krogsgaard Thomsen, CEO of the Novo Nordisk Foundation, in the release. “In the future, we must cooperate even more with exceptional partners from all over the world if we are to solve the enormous challenges facing not only our own countries but the planet.”

Researchers will have the opportunity to engage with expert teams from NVIDIA in selected flagship project areas to co-develop solutions to complex problems. These areas include pharmaceuticals and biotechnology research, accelerating the green transition; and developing fault-tolerant (i.e., error-free) quantum computing using an open-source hybrid quantum computing platform (NVIDIA CUDA-Q).

With DKK 600 million (US$87.4 million) in funding from rhe Novo Nordisk Foundation, the new supercomputer offers the potential to accelerate innovation in drug discovery.

Denmark’s First AI Supercomputer for Drug Discovery Now Operational Under Novo Nordisk Foundation Commitment

The National Institute for Health and Care Excellence (NICE) has published a final draft guidance recommending Wainzua (eplontersen) for treatment of hereditary transthyretin-related amyloidosis (ATTR) in adults with stage 1 or 2 polyneuropathy (PN) in England and Wales, AstraZeneca announced in a press release on Oct. 29, 2024 (1).

“We are delighted that eligible patients now have access to this treatment for this significant disease which can be delivered in-clinic or in their own home to support more personalized care,” Tom Keith-Roach, president of AstraZeneca in the United Kingdom, said in the press release (1).

This recommendation of Wainzua closely followed that of the Committee for Medicinal Products for Human Use (CHMP) of the European Union for approval by the European Commission, 

announced by AstraZeneca on Oct. 21, 2024

The treatment for ATTR in certain adults with polyneuropathy had just been recommended for approval across the European Union and had gained regulatory approval in the United States at the end of 2023.

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