In today’s dynamic healthcare landscape, where scientific information evolves rapidly and access to medical experts is crucial, the need for highly trained MSLs is paramount. Yet, while the vast majority of MSLs want training, only two-thirds report that these opportunities are available to them, and almost 40% state that they lack time for professional upskilling.²

Current Pain Points in MSL Training

In addition to a lack of time, MSL training is plagued by a range of issues, including geographical barriers for global companies, which can make it challenging to deliver consistent training if opting for in-person delivery. Traditional in-person training also comes with substantial cost barriers and logistical hurdles, as well as the risk of information overload if delivered as multi-hour or multi-day live events. In turn, this can reduce MSL engagement and negatively impact knowledge retention.

Further, with rapidly evolving scientific information, data, and guidelines, there is a clear need for continuous learning and knowledge reinforcement. However, considering how busy MSLs are with other priorities, this can be challenging if opting for a traditional approach to training.

Finally, accurately measuring and demonstrating the effectiveness of training in terms of its impact on MSL performance and knowledge retention can prove a challenge.

Training Needs for MSLs in Today’s Evolving Landscape

For training to be effective, the Medical Affairs Professional Society (MAPS) identified that it needs to be:³

• Practical and able to be used immediately.
• Technology-based, flexible, and available whenever, wherever the trainee wants to access it.
• Available in multiple formats.
• Consumable as individual, low-commitment offerings but placed within a larger framework.
• Collaborative and engaging, with learning enhanced by mentoring, gamification, accountability partnering, collaborative case study reviews, and more.
• Relevant and streamlined to reduce the cognitive load and improve the learning experience.

With these points in mind, the importance of leveraging digital tools and cutting-edge technologies becomes evident.

Digital Tools for MSL Training

Luckily, there is no shortage of digital tools available for MSL training. Some important examples include:

1. Asynchronous e-Learning Platforms
   These ‘over-time, anytime’ platforms offer versatile learning opportunities for MSLs: from hosting on-demand videos, podcasts, and written materials to interactive infographics and posters, digital journal clubs, quizzes, online case studies, and more. Adding a well-maintained resource center to serve as a knowledge repository/central information hub further helps ensure consistent and accurate information dissemination.

2. Mobile Learning and Microlearning

Mobile apps and micro modules—short, focused learning modules—are ideal for providing on-the-go knowledge reinforcement and just-in-time learning. Having this option available alongside other, more comprehensive tools enhances accessibility and convenience for busy MSLs.

3. Virtual Reality (VR), Augmented Reality (AR), and Holovision Technology

VR, AR, and Holovision technology represent novel tools that can be used to create a variety of immersive training experiences, both for MSL- and external healthcare provider (HCP) education. Examples include simulation of patient or HCP interactions, including objection-handling, as well as disease state, mechanism, and clinical data visualizations. These technologies are also ideal for case-based learning.

4. Artificial Intelligence (AI)-powered Tools

AI-powered learning paths can be used to tailor education to individual needs and preferences. For example, AI chatbots can provide instant answers to MSL queries, and AI avatars can be used to enhance case-based learning. While still in its infancy, there are vast opportunities for AI-based MSL training.

Benefits of Digital MSL Training

Leveraging digital technologies for MSL training comes with numerous benefits, including:

1. Increased Accessibility and Flexibility

Digital platforms eliminate geographical barriers, allowing MSLs to access the same high-quality training irrespective of location or time zone. This ensures consistency in knowledge and skills across global teams. The asynchronous nature of virtual training technologies means that MSLs can access training materials at their convenience, without the need to travel to a central location for training. This flexibility is crucial for MSLs—who often spend substantial time out in the field—to fit learning into their busy schedules.

2. Enhanced Engagement and Knowledge Retention

Interactive training modules that incorporate simulations, quizzes, and gamified learning experiences are known to increase engagement and improve knowledge retention compared to passive learning formats. The digital learning format also allows for the incorporation of videos, animations, and interactive visuals, which further enhances the learning experience and caters to different learning styles. In addition, by leveraging novel tools such as VR, AR, and holovision technology, MSLs can practice real-world scenarios, such as objection handling or presentations at medical conferences, in a safe and controlled environment, leading to enhanced learner confidence.

3. Improved Training Effectiveness and Performance

Digital platforms can track MSL progress over time and identify areas where they need additional support much easier than what can be done with traditional learning approaches, allowing for more personalized learning and continuous support. Likewise, data analytics can be used to assess the overall effectiveness of the training, enabling easy identification of potential areas for improvement.

4. Up-to-date, Consistent Information

Digital platforms and centralized knowledge hubs allow for the rapid dissemination of new scientific information, clinical trial data, and treatment guidelines, ensuring that MSLs are always up-to-date with the latest developments. The use of digital tools also ensures that all MSLs in a company receive the same consistent messaging, regardless of their location.

5. Enhanced Collaboration and Communication

Through the use of online forums and discussion boards, MSLs can seamlessly share best practices, ask questions, and collaborate with colleagues on their own schedules. Virtual meetings and webinars can be added as needed to enhance knowledge-sharing and collaboration further.

6. Reduced Training Costs

Lastly, digital training largely eliminates the need for physical training materials, travel, accommodation, and venue rentals, thereby significantly reducing training costs. Digital platforms can easily be scaled to accommodate large MSL teams, making them a cost-effective solution for global pharmaceutical companies.

Implementation Considerations

Before implementing a new digital training course, life science companies should first evaluate and select the appropriate digital training platform and technology based on their specific needs. Depending on the technology chosen, there may be a need to proactively promote user adoption and address potential resistance to digital training. After its launch, it will be key to conduct ongoing evaluations of the program and technology to optimize the training. Companies may also want to consider working with a single, one-stop provider for all training needs—technological, strategic, and content—to further streamline the process.

By Natalie Yeadon, President & CEO Impetus

References

1. https://themsls.org/what-is-an-msl/
2. https://www.onemsl.com/wp-content/uploads/2022/05/One-MSL-Global-Survey-2022-Findings-Report-MSLs.pdf
3. https://medicalaffairs.org/training-medical-science-liaisons-msl/

The post The Future of MSL Training – How Digital Technologies Are Shaping Success appeared first on .

Across the modern pharma organisation, overstretched clinical, regulatory, quality, and pharmacovigilance teams are rapidly becoming overwhelmed by the multiplying digital processes and IT systems they are required to use. The systems are increasingly sophisticated and complex, too, reflecting the latest tech capabilities and the rapidly-evolving regulatory requirements they map onto.

For teams to fully harness the potential of these advanced systems (e.g. next-generation regulatory information management systems, quality management, or safety systems), they must first know how to use them correctly. More often than not now, that requires more than designated upfront training then habitual system use. That’s if teams are to use new capabilities confidently, reliably and in a compliant manner.

It is for this reason that digital adoption platforms (DAP) are gaining traction in a pharma GxP compliance context, guiding users for instance in exactly how to input data at each step of a process via a series of handy in-app prompts. These platforms sit on top of cloud/web-based IT systems. These online overlays to target applications help to steer users through tasks such as form-filling and data entry, nudging the desired action at each stage, such as how to format data correctly, or adhere to a particular naming convention. DAPs can halve the investment needed in regular training, or e-learning materials creation; the same with post-go-live helpdesk support, say the main vendors.

An asset many companies already have

DAPs are already proven in centralised business functions like HR, where they have been shown to significantly boost user adoption of important applications such as Workday (total DAP spend was worth $621.5 million in 2023, and is forecast to reach $3.86 billion by 2032[1]).

This existing enterprise use means that many companies will already possess DAP licences. Extending the reach of those platforms would simply involve developing suitable materials for each new respective application.

DAPs can have a particular impact where users are faced with sophisticated systems, or those with complex features. In pharma clinical, regulatory, quality, and pharmacovigilance functions, their potential is significant. The current transition from document-based operations and health authority exchanges to data-driven decision-making, and the need to conform with comprehensive new and expanding standards such as ISO IDMP, compound the need to use new or updated systems correctly and to input good data reliably and consistently. Upfront training alone won’t ensure that individuals consistently conform to agreed naming conventions or data structures when inputting critical information. Use of DAPs/in-app prompts can drive a minimum 20% improvement in data accuracy[2].

Administrative considerations

DAPs do not touch or interact with a system’s data, which means they don’t present a new risk to security or data protection. Because the platforms are an overlay, they are infinitely agile and adaptable too. Where traditional e-learning materials often rely on liberal use of screenshots to highlight what to do where (soon rendered out of date as systems, fields, or data requirements are updated), DAPs can be readily amended on the fly. Given how frequently cloud applications can be refreshed, even within a year, this is a consideration worth keeping in mind.

DAP guides will ideally be tailored to particular roles, so that users are presented only with relevant prompts as they interact with a system. Individuals can opt to turn off the guides once they understand what’s required and are using the software routinely. Where issues remain, and help continues to prove of value, built-in HEART analytics (tracking Happiness, Engagement, Adoption, Retention, and Task Success) offer a useful source of feedback about specific points of difficulty in a system (handy for refining the system, or for improving initial training).

Optimising DAP for compliance impact

The value of each DAP guide depends on the quality and relevance of its content, so this should be created and honed for each user group or role, and refreshed as required. A popular feature of all DAPs is that they can be used very effectively to make system-related announcements to users as they log in, e.g. about changes to the system, or to data input requirements (rather than hoping a blanket email will reach all of those affected).

The platforms are also coming into their own as AI is introduced to a whole range of operational software. In common with any new technology, AI needs to be used carefully and correctly to ensure compliance and elicit reliable results. DAPs are ideal as a mechanism for this, to provide essential, step-by-step, in-app guidance.

Budget-wise, DAPs can boost the value of traditional training, allowing more of this to be elevated to more of a strategic role around the purpose of a new system, for example. This creates scope for companies to allocate their budgets and materials expenditure more efficiently. It is for all of these reasons and more that 2025 is expected to be a pivotal year for DAP in a pharma GxP compliance context.

More than anything, this is about bringing a more intuitive experience to users that complex systems may lack (but which is necessary to ensure consistency and compliance). If automatic, timely intervention saves guesswork, or avoids a delay as assistance is sought, the payback is self-evident.

Finally, DAP support is likely to become increasingly predictive, anticipating what users are trying to do – e.g. “It looks like you’re entering new drug substance and product information, do you need help?”; further ensuring they do what’s required. Interventions like these will be invaluable in the modern pursuit of consistent data of the highest quality.

About the author

Sabine Gölden is eLearning & training lead at MAIN5, a European consulting firm delivering digitally-enabled change in Life Sciences. Gölden can be reached via email at sabine.goelden@main5.de.

References

[1] Digital Adoption Platform Market Size, Share, Industry Analysis, Trends, Growth, 2032, Zion Market Research: https://www.zionmarketresearch.com/report/digital-adoption-platform-market

[2] Value of a DAP, white paper, Whatfix: https://whatfix.com/resources/whitepapers/value-of-a-digital-adoption-platform/

The post A New Tool for Fostering GxP System Compliance – Introducing the ‘DAP’ appeared first on .

However, by definition, the market for such treatments is limited, so the business case for a drug developer is often, at best, unclear; without government intervention it would be untenable. Having said that, new technologies and a deeper understanding of the mechanisms of rare diseases is driving investment in the sector.

Patient advocacy groups have played a key role in raising awareness and investment, and are increasingly involved in the design and implementation of clinical trials.

In this article we explore the rare disease landscape, focusing on the emerging and hugely promising technology of ‘gene silencing’. Since around 80% of rare diseases are monogenic, caused by a mutation in a single gene, the ability to stop that mutation from forming toxic proteins is a game-changer.

We will discuss in some detail one form of gene silencing technology, antisense oligonucleotides (ASOs), which is proving to be particularly effective; ASOs are currently being trialled for treating a wide range of conditions, from Alzheimer’s and Parkinson’s to Motor Neurone Disease.

Finally, we speculate on what 2025 might bring for the biotech industry. Will AI advance rare disease diagnosis, drug research and clinical trial design?

Landscape painting

According to the European Commission, there are between 6,000 and 8,000 known rare diseases, with new conditions being discovered regularly.

And according to a report from May 2024 by Global Market Insights (GMI), the market for ASOs was worth $4.4 billion in 2023 and is predicted to grow at a Compound Annual Growth Rate (CAGR) off 18% to reach $19.7 billion in 2032.

GMI cites the increasing prevalence (and diagnosis) of neurodegenerative and genetic disorders, growing investments in research related to gene expression and delivery technologies, and the growth in regulatory approvals for antisense therapeutics as the key drivers behind this growth.

Rare diseases are rare individually but collectively add up to a significant number; up to 400 million people worldwide are affected, and about 90% of those have no current treatment.

But bespoke ASO therapies can be very expensive. For example, a five-year treatment of the ASO drug ‘nusinersen’ costs over $2 million for one patient.

The cost for healthcare systems is also very high; over the last ten years the cost to NHS England of rare disease patients up to the point of a diagnosis was greater than £3.4bn, according to a report from 2018 by Imperial College Health Partners.

Familiar Stories

Getting the right diagnosis, early, is widely agreed to be the most serious challenge faced by those affected by a rare disease. On average, it takes over four years to get an accurate diagnosis, according to Rare Disease UK.

Rare diseases often exhibit a wide variety of symptoms that can overlap with more common conditions, making them difficult to distinguish.

A major challenge in getting the right diagnosis is simply the limited knowledge of rare diseases, especially at primary care level. Clinical knowledge is often lacking, being available only at specialist centres.

The critical problem of a delayed diagnosis is made more acute as the impact of ASOs is greater the earlier they are administered; as time goes on some symptoms may cause irreversible damage. For some the hope may only go as far as delayed progression, though there is some evidence starting to emerge of symptoms being reversed and patients recovering capacities.

For perhaps obvious reasons, the rarer the condition, the less chance of diagnosis and the longer that can take.

Cell out

Messenger RNA (mRNA) is a copy of DNA that leaves the cell nucleus for the ribosomes, where mRNA genetic code is translated into amino acids. These then grow into long chains that fold to form proteins.

ASOs are short, single sequences of nucleotides, designed to bind to mRNA – stopping it from completing its function. It is the ‘sense’ part of mRNA that results in a protein. ASOs are called antisense because they bind to the sense part of mRNA in a complementary manner.

If a gene is known to have a specific mutation that leads to the production of a toxic protein, then the associated mRNA can be targeted by an ASO, leading to a reduction in the volume of toxic protein produced.

‘Gene silencing’ is different from gene editing in that the gene itself in untouched; only its expression as a protein is affected. ASOs are highly targeted and produces much fewer side effects than gene editing.

In trials at University College London Hospitals an ASO is being used to target the mutated gene that results in the Tau protein, one of the two proteins (the other is Amyloid) that are known to be prevalent in patients with Alzheimer’s. The trials have recently been extended after initial success.

Trials are also now underway for gene silencing ASOs that treat Parkinson’s and Motor Neuron Disease. ASOs have shown particular benefits in the treatment of neurodegenerative diseases, including Duchenne muscular dystrophy.

Perhaps most importantly, ASOs target the molecular causes of disease, rather than just treating the symptoms. This is what makes them game-changing.

Patient voice

Patient advocacy groups have historically played a vital role in lobbying for change; in the US in the 1980’s it was advocacy by the National Organization for Rare Disorders (NORD) that resulted in the passage of the Orphan Drug Act (ODA) in 1983, a seminal moment in the history of rare disease drug development.

The ODA included provisions for 7‐year market exclusivity for orphan drugs, tax credits, development grants, fast‐track approval, and the waiving of some fees. These incentives help offset the high costs and risks associated with developing therapies for diseases with small patient populations, making it more feasible for smaller biotech companies to undertake such projects.

In the UK, to take one example, the H-ABC Foundation supports patients and families affected by the disease, advocates on their behalf, and raises money to help fund vital research. They also maintain a map of patients and their specific symptoms, disease progression and more – all vital input to clinical trial design.

Rare disease drug development in 2025

What will this year bring? In a world controlled by economic mantras, rare disease drug development would likely never occur; so government intervention has, in this case, proved vital to the life-chances of millions of patients worldwide.

In 2025, advances in gene therapies will accelerate the pace of drug development; treatments will be developed that are highly targeted and easier to deliver.

But perhaps most encouragingly there is the promise that rapid advances in AI for data analysis, pattern recognition, decision-support systems, genomic analysis, image analysis, and much more, will mean that rare disease identification is much more rapid.

AI tools could help distinguish rare disease symptoms from more common ones, getting to the very heart of the challenge – early identification.

A key factor is learning from real-world data. In a typical consultation, it may be near-impossible to research historical records to look for patterns, tell-tale signs and other data-based clues – precisely what ‘trained’ AI tools can do.

There are issues of course, not least around patient confidentiality and data security. These are paramount in any health system, so the use of powerful algorithms to search vast (real -world) data sets inevitably causes concern.

Ethical and legal issues include questions around accountability; who gets the blame if an AI diagnosis turns out to be wrong?

By Dan Williams, PhD CEO SynaptixBio

About the author

Dan Williams is CEO of SynaptixBio. He has spent over 20 years in the industry after studying at the University of Dundee for a degree in biochemistry and physiology, and a PhD. After his PhD he entered the industry, where he worked his way up to senior scientist. Dan then took over management of a cell research group, initially running a cell biology research and then preclinical development.

Following this he moved to drug development, focusing on the organisation and management of both manufacturing and clinical trials. After that particular therapy went into the clinic and was progressing within clinical trials, he moved to Adaptimmune and switched from biologics to developing cell therapies. He set up the development groups within Adaptimmune, while project managing some of the preclinical research and the move from the partnership with an academic group for their clinical trials, to taking on those clinical trials as a company.

He then managed the larger research group, and moved from that position to the VP of Research Operations. From there, Dan moved to Meatable as the Chief Product Officer. Dan co-founded SynaptixBio Ltd. in 2021 with the aim to push leukodystrophy therapies through to clinical trials.

The post Making a Difference – The Business of Rare Disease Drug Development appeared first on .

The pharmaceutical cold chain is becoming a cornerstone of global supply chains, shaped by new regulations, innovation, and the need for resilience.

This year, the industry witnessed remarkable innovations across the broader pharmaceutical distribution landscape. One standout development was the rapid growth of direct-to-patient logistics. Pharmaceutical companies have made strides in delivering treatments directly to patients’ homes. These advancements not only enhance patient access but also reflect a wider trend towards personalised, patient-centric supply chains.

Hard on the heels of a year shaped by innovation, sustainability, and security, 2025 now promises the pharmaceutical industry even greater transformation.

Strengthening supply chain resilience

Over this past year, global tensions and geopolitical conflicts have created ongoing logistical disruptions and bottlenecks. These challenges can vary from port closures to sanctions or trade restrictions, all of which impact delivery efficiency and cost. Such issues expose vulnerabilities in the pharmaceutical supply chain, leading to significant delays or, in worse cases, spoiled or cancelled shipments.

In response, companies in 2025 will need to diversify logistics strategies and focus on working with partners who are able to quickly adapt in response to changing circumstances or risks in the supply chain. Cold chain solutions will also evolve, with a growing emphasis on redundancy and alternative routes to avoid disruptions and support the reliable delivery of critical medicines.

Elevating sustainability standards

Following on from COP29, the call for sustainable practices in pharmaceutical delivery is louder than ever. This summit marked a pivotal moment, as countries are now urged to integrate health considerations into their Nationally Determined Contributions, which will significantly impact the pharmaceutical industry.

This increased pressure will lead pharmaceutical companies to evaluate and adapt their ESG requirements, making sustainability a non-negotiable factor for suppliers. Building on this expectation, advanced cold chain providers are shifting their focus from merely avoiding waste to finding the most sustainable way to distribute essential medicines. This includes both reusable equipment and optimised single-use solutions that are lighter, use fewer raw materials, or are biodegradable, enabling effective resource use even when disposability is necessary.

As environmental awareness continues to grow, sustainability standards across the industry will continue to rise. The next step for the industry is to review and address supply chain emissions, which are often the hardest to reduce. Companies can achieve this independently, but submitting emissions targets to the Science Based Targets initiative provides a public demonstration of their commitment to taking meaningful action.

Expanding into emerging markets

Despite global tensions and geopolitical conflicts, the pharmaceutical industry is increasingly expanding its reach into underserved markets – and this reach will continue to grow next year. This push is essential for bringing critical medicines to new communities, yet logistics and storage infrastructure in these regions can create additional complications.

Cold chain logistics rely on stable, temperature-controlled environments to protect sensitive medicines. However, outdated infrastructure in emerging markets often means smaller storage spaces, unreliable electrical supplies, and no return logistics for reusable packaging. These factors make it problematic for cold chain providers to maintain the necessary conditions consistently.

To overcome these challenges, market expansion will require specialised packaging and logistics solutions that address local infrastructure limitations. These adaptations will be key to enabling successful market entry and supporting long-term growth.

Rising mergers, acquisitions, and strategic partnerships

We have witnessed an increase in merger and acquisition activity in the pharmaceutical industry in 2024, and we expect to see this continue to rise in 2025. Pharmaceutical companies are also increasing their strategic partnerships with CMOs and CDMOs to accelerate innovation and reduce time-to-market. By pooling resources and expertise, companies will be able to streamline the R&D process, paving the way for faster production and delivery of high-demand drugs.

The beneficial outcomes will be set to multiply if companies integrate cold chain logistics into these new partnerships. Not only will this support the rapid scale-up of new therapies, but it will also help build a robust supply chain for new markets.

Adapting to small-batch shipments

As the demand for smaller, high-value pharmaceutical shipments grow, cold chain logistics will need to become more agile and adaptable to meet these evolving needs. These solutions are essential to ensure highly sensitive medicines, such as personalised treatments, are stored and transported under precise temperature-controlled conditions.

Additionally, AI-driven clinical trials are accelerating drug development, leading to faster production cycles that also require efficient, responsive cold chain solutions to handle smaller but high-value, sensitive shipments.

Finally, the growing prevalence of decentralised clinical trials adds to the demand for robust cold chain logistics. These trials, which involve direct delivery of experimental therapies to patients’ homes, rely on precision cold chain networks to maintain the integrity of highly sensitive medicines.

Optimising pharmaceutical logistics cold chain with technology

The potential of AI will extend beyond R&D to supply chain management next year. Organisations will begin to use the technology to minimise risk, reduce costs and boost efficiency. Cold chain providers can learn from tech giants like Amazon and Alibaba, who use AI and data-driven insights to improve logistics and access to treatments. While their focus isn’t exclusively on the cold chain, their innovations may offer valuable ideas for delivering sensitive medicines more efficiently.

Finally, blockchain’s potential to increase supply chain transparency through decentralised, immutable records makes it a key technology to watch. Deloitte’s case studies already demonstrate its practical application in ensuring product integrity, underscoring the importance of blockchain in advancing pharmaceutical logistics.

2024 has seen steady progress in pharmaceutical logistics, with innovations continuing to shape the future. As we move into 2025, the focus will be on smarter packaging, agile supply routes, and solutions that address the growing complexity of global healthcare demands. The year ahead holds significant opportunities for companies willing to adapt, collaborate, and lead the charge in delivering medicines to underserved communities, improving availability, and saving lives.

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Nowhere is quality more important than in healthcare. What challenges do biopharmas currently face in maintaining high quality?

There are many challenges in the pharmaceutical industry, and quality is a critical factor. Patients must trust that medicines are safe, meet high standards, and comply with regulations. With personalised medicine and other modern therapies, this becomes even more challenging. Unlike traditional medicines distributed through pharmacies, personalised therapies require extra layers of quality control. A good example is the public discussion around vaccines during the COVID-19 pandemic. People knew some vaccines had to be refrigerated, but they likely didn’t understand the extensive effort required. You can produce a high-quality medicine, but for it to remain safe and effective, you must monitor it until it reaches the patient. The more personalised the therapy, the more crucial this becomes.

How does Veeva help biopharmaceutical companies maintain quality?

There has been a significant shift in recent years. In the past, companies manufactured and tested medicines in-house. Now, manufacturers must pay more attention to the quality of their suppliers, and logistics plays a growing role as more of it is outsourced. While some companies specialise in logistics, the manufacturer remains responsible. This shift requires solutions that not only manage internal business processes but also integrate the entire supply chain, including partners. That’s why cloud-based, industry-specific solutions like Veeva Vault Quality have become critical. Cloud technologies play a key role in maintaining quality because companies need to communicate with suppliers and partners—and even patients—in real-time. This requires easily accessible, secure, and traceable data, all while staying compliant with regulations like GDPR.

Where do connected cloud platforms help the most, particularly with transparency?

Here’s an example: when a raw material is produced in another country, the quality decision requires approval based on the data the company receives. If this data is handled on paper, it causes delays. Connected cloud platforms provide real-time insights, and they ensure data integrity. With cloud solutions, everyone in the supply chain can access the same data simultaneously, reducing redundancies and improving decision-making.

How does Veeva help keep data entry clean and secure?

You have to look at the entire supply chain. In production, for example, some machines have sensors that provide data relevant for quality decisions. Another example is patient complaint management. If a patient experiences side effects, a doctor or healthcare professional enters this data manually. Ideally, they only need to enter it once, and the system ensures secure entry. Artificial intelligence can help ensure data is entered correctly, and quality checks can occur as soon as data is available. Connected cloud technologies, like those from Veeva, also bring together different data sources, whether from other digital systems or manual inputs, to maintain quality control.

Has Veeva developed a streamlined way to achieve this?

Yes. Veeva’s Quality applications are scalable and easy to access online. Companies can define which groups of people need convenient access to the database, and we can configure the system accordingly, always adhering to security requirements.

Looking to the future, what challenges could the industry better address in the next three to five years, possibly with AI? 

There’s a constant tension between efficiency and compliance. Regulations must be met, but at what cost? The key for the pharmaceutical industry is balancing GxP compliance with streamlining quality systems to improve both cost-efficiency and quality outcomes. The focus should shift from managing quality issues to preventing them. AI can help predict potential risks and prevent them before they occur. Additionally, the definition of a pharmaceutical product will evolve. We often think of medicine as a pill or physical product, but future therapies will be more complex and personalised. Homecare and clinical trials are moving from hospitals to patients’ homes, where wearables will report data. This brings new quality challenges, such as qualifying devices and validating processes, which are easier to manage with cloud solutions.

What does the industry expect from Veeva in this regard?

We develop our solutions in collaboration with our customers. We aim to understand their challenges and work with our experts to develop innovative solutions that align with their needs. It’s a combination of customer input and our strategic vision, alongside regulatory requirements, particularly in the area of quality.

The post How Connected Cloud-based Platforms are Advancing Quality in Biopharma Manufacturing appeared first on .

Healthcare is more than a service, it’s a fundamental human right!

And if we care about health in a holistic fashion we need to think about access from root to branch. At ClinicalTrials.EU we believe that access to healthcare should also mean the opportunity to find, research and explore the opportunities presented by new, potentially life-saving treatments that may not yet be available on the open market.

However, at the moment, access to clinical trials across the EU is fragmented, with significant disparities based on geography, socioeconomic status, and other factors. This ultimately leads to a system that is unfair, favouring those in more developed societies and wealthier backgrounds who have the time and resources to seek out new and innovative treatments.

The Current Landscape for Clinical Trials in Europe

Clinical trials are not just a beacon of hope for patients but a cornerstone of medical research, providing the data needed to bring new treatments to market and improve patient outcomes.

As of late 2023, the European Union Clinical Trials Register lists more than 43,000 trials in total and approximately 14,474 live trials, with a significant portion involving pediatric participants. These trials cover a broad spectrum of therapeutic areas, including oncology, cardiology, and rare diseases, reflecting the diversity and scope of medical research conducted across Europe.

Unfortunately, the current clinical trials ecosystem sees significant challenges for patients trying to access them. For many patients, particularly those living in rural or underserved areas, the opportunity to participate in a clinical trial is limited. This disparity is especially pronounced for patients with rare diseases, who often have few options outside of experimental treatments offered through clinical trials.

One of the major issues is that many potential patients just don’t know these trials exist or how to be recruited onto the right ones. There are a number of platforms with information on ongoing trials such as the Clinical Trials Information System (CTIS). However these are often clinician-facing, rather than patient-facing with technical details that are difficult for a layman patient or caregiver to understand if it is relevant for their condition. This barrier prevents many patients from participating in trials that could benefit them.

The Complexities of Participation

Once you are over the information barrier, you may well then encounter the logistical barriers. Trial sites are often concentrated in major cities, where clinicians or research sites are located, making it difficult for patients in rural or remote areas to participate. This urban-centric model not only limits access but also exacerbates existing healthcare disparities. Patients in these areas may face long travel distances, which can be particularly challenging for those with limited mobility or those suffering from severe illnesses.

The design of clinical trial protocols is also problematic as they often exclude patients who do not meet a very specific criteria. For example, patients with multiple comorbidities or those who are older and more frail are frequently excluded from trials due to concerns about data variability or adverse events. This rigidity in trial design can prevent large segments of the population from accessing potentially life-saving treatments.

Democratising Access to Clinical Trials

Addressing the opportunity gap for patients and researchers requires democratising access to clinical trials in its nature. Greater visibility, comprehensive but simpler communication and a more flexible approach are all required to connect patients with complex or rare conditions with researchers needing a large, qualified pool of participants.

This could be created through:

Increased Awareness and Visibility

This can be achieved through targeted public health campaigns, partnerships with patient advocacy groups, and improved use of social media and other digital platforms. For example, studies have shown that more than 85% of EU citizens regularly use the internet, with health-related information being one of the most searched topics. By making information about clinical trials more accessible online, we can reach a broader audience and increase participation rates.

Simplifying Language and Communication

To make clinical trials more accessible, it is essential to simplify the language used to describe them. This involves not only translating technical jargon into plain language but also ensuring that information is available in multiple languages. Tools like interactive websites, explainer videos, and patient-centered communication strategies can help bridge the gap between healthcare professionals and potential participants.

Decentralising Clinical Trials

Traditionally, clinical trials have been centralized in major urban centers, making them inaccessible to those living in remote areas. To democratise access to clinical trials, we need to shift towards a more decentralised model, where trials are conducted closer to where patients live. Decentralised clinical trials (DCTs), which leverage digital technologies to allow patients to participate from their own homes, are an effective way to overcome geographical barriers and make trials more accessible.

Fostering Patient-Centricity

Patient-centric clinical trials focus on the needs and preferences of participants. This approach involves engaging patients in the design and implementation of trials, ensuring that their voices are heard and their needs are met. By adopting a patient-centric approach, clinical trials can become more responsive to the needs of participants and more effective in recruiting and retaining patients.

Leveraging EU Initiatives

The EU has launched several initiatives aimed at improving access to clinical trials. For example, the EU-X-CT initiative (https://eu-x-ct.eu/) focuses on facilitating cross-border participation in clinical trials, while one of the HORIZON projects aims to promote equitable clinical research across Europe. These initiatives represent important steps towards democratising access to clinical trials and ensuring that all EU citizens can benefit from the latest medical advancements.

Enhanced Access to benefit all

By improving access to clinical trials, we can ensure that patients across Europe, regardless of their location, socioeconomic status, or medical condition, have the opportunity to participate in scientific research and benefit from new treatments. This is vital as in order to advance medical science and understanding we need a wider pool of appropriate patients to take part in research. Only by democratising access to connect patients to clinical trials researchers will we be able to accelerate the development and production of potentially life-saving new treatments.

By Lukasz Izbicki, CEO, ClinicalTrials.EU

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In a new survey conducted by Censuswide with 100 senior regulatory professionals in US pharma and biopharma organisations, 97% said they had seen their regulatory obligations soar over the last five years, with three in five (60%) putting the increase beyond what would be expected as the result of company growth. Expectations are that this growth in work throughput will continue over the next five years: 95% indicated this would be the case; 41% predicting next increases would be significant.

The various regulatory process challenges are numerous, including excessive time spent producing submissions/dossiers; maintaining labelling compliance; inputting data/documents into IT systems; verifying submission correctness/completeness; performing regulatory impact assessments; and locating data or documents in existing IT systems. More than a quarter of the research base indicated each of these issues. Further barriers to efficiency include responding to agency queries; inadequacy of current IT systems; and time lost to administrative tasks (such as data quality checks, and assessing submission readiness).

Interestingly, a lack of qualified people was least likely to be registered as a serious challenge or concern, suggesting that preferred strategies do not involve allocating more people to processing regulatory workloads. Rather, pharma and biopharma regulatory functions are looking to smarter use of technology to ease the impact of their rising workloads, while ideally enhancing the output and impact of existing processes.

The case for AI in a regulatory context

By use case, almost all respondents could see direct potential for AI in transforming labelling compliance and deviations maintenance; capturing, searching, filtering the latest regulatory requirements; automating the intake of Health Authority interactions; automating regulated content translations for different markets; automating the authoring of responses to Health Authority queries; suggesting improvements to submissions/dossiers; performing regulatory impact assessments; authoring submission documents; automating document summarization; and generating entire regulatory submissions.

Although specialist AI tools and applications for targeted regulatory use cases are only now coming to market, over a third (35%) of respondents claimed to be using AI for regulatory purposes in some form already, while 42% plan to invest in the next 18 months. A further 15% are looking at a timeframe beyond that, but do also have plans to roll out AI within the regulatory function.

While no respondents were ignoring AI entirely, 6% were not yet convinced by the technology’s potential for regulatory purposes and had no current plans to invest in AI.

Barriers to adoption

Asked what might be holding back initial or further investment in AI for Regulatory purposes, respondents most commonly cited outdated existing IT landscapes (45%); a belief that risks currently outweigh the benefits (44%); and inadequate availability/quality/consistency of data or content resources to derive the value from AI (42%).

In addition, 39% of respondents felt the technology remained too immature/unproven; similarly, that the tools do not exist today to address their particular regulatory pain points. Sixteen per cent blamed a lack of trust in AI currently. This was ahead of budget challenges: only 15% named a lack of budget as a barrier to AI investment.

The research also identified the factors most likely to convert interest and inertia into active projects. Here, respondents most commonly cited the discovery that their competitors are using the technology (41%); soaring workloads/continued resource pressures (40%); advances with the technology/its being more mature and proven (36%); the availability of specific tools geared to the tasks regulatory teams find most challenging or expensive (35%); and relevant IT systems becoming easier and more affordable to deploy (33%).

Beyond those drivers, 31% said updating their upgrades to existing IT set-ups (making it possible to use AI reliably) would prompt investment. Endorsement or recommendation of AI by regulators would inspire investment also for just under a third of respondents.

Financial constraints do not feature

Budget constraints did not appear to be a particular barrier to investment plans: just 18% indicated that the availability of new budget would unlock AI investment.

That budget constraints are not a major barrier is encouraging, because hesitancy linked to “a lack of confidence to deploy” is surmountable and readily addressable now. AI technology, including Generative AI (GenAI) is maturing and advancing at an accelerating pace, and specialist applications for target use cases in a life sciences regulatory context are being actively developed and piloted today, showcasing what is possible. This is in keeping with Gartner’s prediction that, by 2027, more than 50% of the GenAI models used by enterprises will be specific to either an industry or business function, up from just 1% in 2023[1].

Regulatory AI aids will become increasingly essential

Finally, the research sought a sense of respondents’ expectations of AI in a regulatory context over the longer term. Almost half (48%) of respondents agreed that, in time, AI would transform a lot of routine regulatory work and considerably streamline processes. Over 2 in 5 (43%) felt AI would drive up accuracy and quality in the information they produce for regulators and patients. Almost 2 in 5 (39%) respondents believed AI would be critical to the regulatory function’s ability to keep pace with market demands. And over a third (35%) of respondents agreed that AI would save a lot of time and money.

This supports the finding above that pain points are multiple and diverse, and suggests that ideally an investment in the right AI capability should ultimately enable all of these to be targeted.

The full research report is available to download at https://www.arisglobal.com/resources/regulatory-industry-survey/

[1] 3 Bold and Actionable Predictions for the Future of GenAI, Gartner, April 2024: https://www.gartner.com/en/articles/3-bold-and-actionable-predictions-for-the-future-of-genai

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Imagine a world where AI seamlessly optimises the pharma cold chain. Predictive analytics prevent temperature excursions before they occur and real-time adjustments guarantee timely delivery of life-saving medications. Such capabilities would dramatically improve operations and patient care. But these advancements hinge on establishing consistent and reliable data practices .

Currently, the adoption of AI in the pharma cold chain remains more aspirational than operational. Without uniform data practices, AI technologies cannot effectively learn from past incidents or accurately predict future challenges.

The essential role of data standardisation

To turn the promise of AI applications into reality, addressing the data issue must be the top priority. Failure to do so risks undermining the entire AI integration process and jeopardising patient safety.

For example, imagine a scenario where different pharmaceutical distribution centres record temperature excursions in varying ways. Some centres might log that an excursion occurred without specifying the degree of deviation, while others provide precise temperature details. Additionally, the duration of these excursions might be recorded inconsistently, sometimes in hours, minutes or even through text descriptions.

As a result, the AI system may struggle to accurately assess the risk of product spoilage, potentially leading to inaccurate risk evaluations. When the impact of a temperature deviation on the quality of the medication isn’t clear, products may be discarded unnecessarily, causing avoidable waste and increased cost. The absence of uniform data can also complicate the attempts to identify the root causes of these excursions, making it harder to implement effective preventative measures.

By establishing a standardised framework for data across the industry, these inconsistencies can be eliminated, paving the way for AI systems to function effectively. This means developing clear guidelines for data terminology, measurement units and data recording practices. Once these standards are in place, AI can begin to analyse and predict with greater accuracy, providing better outcomes for the entire supply chain.

To maintain the quality and reliability of the data, all stakeholders in the supply chain must rigorously validate their data, maintain transparency in AI decision-making, and conduct regular audits. These steps will help build a foundation of trust in AI systems, which is crucial for their successful adoption and operation.

Adopting AI as a long-term strategy

Effective AI adoption will be a gradual process focusing on long-term gains rather than immediate results. The goal is to make sure that each phase of implementation is grounded on a solid foundation of reliable data. Viewing AI adoption as a long-term journey means we can make steady improvements over time. Each step forward builds on previous progress, leading to significant benefits in the long run.

For instance, initial phases might focus on collecting and standardising data. Later phases could involve implementing basic AI systems for monitoring, gradually advancing to more sophisticated predictive analytics and real-time adjustments. This continuous improvement requires collaboration among pharmaceutical companies, logistics providers and technology partners to coordinate and effectively integrate all efforts.

The benefits of AI in pharma cold chain for all stakeholders

The advantages of integrating AI into the pharma cold chain are extensive and impact everyone involved. AI-driven insights will optimise routes, reduce waste and lower costs. This means pharmaceutical companies can streamline their logistics, distributors can better achieve timely deliveries and patients receive their medications reliably.

Real-world examples illustrate these benefits vividly. Take a pharmaceutical manufacturer that has adopted AI-driven route optimisation. By analysing historical data and predicting traffic patterns, AI can suggest the most efficient delivery routes, reducing fuel consumption and supporting timely deliveries. This not only cuts costs but also minimises the environmental impact.

But the future of AI in the pharma cold chain depends heavily on collaboration and partnership. Sharing data and insights among stakeholders can enhance the entire supply chain. For instance, logistics partners can share real-time data on transportation conditions, such as temperature and time spent in each location, with pharmaceutical companies, helping them understand and mitigate risks in the supply chain. Pharmaceutical companies can use AI to analyse this data and make informed decisions about their shipping strategies and packaging choices.

Collaborative efforts can lead to innovative solutions that no single player could achieve alone. By working together, stakeholders can develop reliable and effective AI systems, improve data quality and ultimately ensure the safe and efficient delivery of vital medications.

Building trust in AI systems

The journey towards fully integrating AI in the pharma cold chain begins with data that is fed to the systems. Without high-quality data that flows as the lifeblood of AI systems, we cannot achieve the accurate and reliable predictions needed to instil trust in the technology.

To achieve the required level of standardisation, pharmaceutical companies, logistics providers, and technology partners must work together to implement rigorous data validation processes, maintain transparency in AI decision-making, and conduct regular audits. By focusing on these long-term strategies and fostering a collaborative environment, the industry can achieve significant advancements in efficiency, reliability and patient care.

As technology continues to evolve, the strategic implementation of AI over time will be crucial in meeting the growing demands of the pharmaceutical industry. Embracing this marathon with a clear vision and cooperative spirit will lead to a brighter, more efficient future for all involved.

By Otto Dyberg, Chief Information Officer at Envirotainer

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The current problem

Drug shortages are a complex issue with cascading effects. They can occur due to various factors, including manufacturing disruptions, quality control issues, and economic pressures. When a critical medication becomes unavailable, healthcare providers scramble to find alternatives, potentially compromising treatment plans. Patients may experience delays in care, anxiety, and even adverse effects from switching medications. More drastic effects occur when patients are left without medication alternatives altogether.

The problem is recurrent and widespread: in 2022 and 2023, national pharmacy bodies across 26 European countries all reported shortages, with the picture worsening last year. In the UK specifically, 99 generic drugs were short in January, double the number counted two years ago, according to the British Generic Manufacturers Association. This has affected supplies of drugs like hormone replacement therapies and ADHD treatments, partly due to spikes in demand.

Lessons can be taken from retail

The retail industry provides a valuable lesson in managing complex supply chains. Retailers seamlessly track inventory based on real-time demand, ensuring products are readily available on shelves. Pharmaceutical companies can emulate this approach by embracing real-time data throughout the manufacturing process and supply chain.

Imagine a system that tracks everything –  from the origin of raw materials, to the temperature fluctuations within shipping containers, all in real-time. This can be a reality and this level of transparency empowers companies to:

• Optimise stock levels: Real-time data allows for accurate forecasting of demand, enabling companies to maintain optimal stock levels and avoid oversupply or understocking.
• Anticipate shortages: Early detection of potential issues in the supply chain, such as raw material scarcity or production delays, allows proactive measures to be taken before shortages materialise.
• React swiftly to disruptions: Real-time data provides immediate alerts to disruptions like transportation delays or equipment failures. This allows companies to reroute shipments, secure alternative sources, and minimise the impact on patients.

By employing real-time data, enabled by event-driven architecture, companies can move away from reactive responses to disruptions and become proactive in managing their supply chains. This proactive approach has the potential to significantly reduce drug shortages and their associated consequences.

Continuous monitoring: ensuring quality and safety throughout the journey

It’s important to address that numerous medications require constant vigilance – especially temperature-sensitive drugs like insulin or vaccines. Traditional, periodic monitoring methods may miss crucial fluctuations that compromise product quality, which can lead to shortages. Real-time, continuous monitoring throughout the entire pharma supply chain addresses this limitation.

By using a real-time data model, it’s possible to track temperature fluctuations within shipping containers. This allows for immediate adjustments to maintain the optimal environment for medications. Real-time data eliminates the risk of human error associated with periodic checks and ensures medications stay within the necessary range, safeguarding patient safety.

The role of automation and AI: intelligent solutions for a complex industry

The pharmaceutical industry operates under strict regulations, ensuring the safety and efficacy of medications. However, this shouldn’t hinder progress. Automation, combined with real-time data and AI-powered analytics, can be a powerful force for good.

Automation can handle routine tasks like data collection and analysis, freeing up human expertise for higher-level decision making. Algorithms can analyse vast datasets, identifying patterns and predicting potential issues before they occur. For example, think about the benefits that come from a system which automatically alerts to temperature deviations or predicts equipment failures before they happen. This allows for preventive maintenance, ensuring product quality and patient safety.

By embracing intelligent automation, pharmaceutical companies can achieve so much more.

Building a future with a secure medication landscape

Real-time data, modern manufacturing practices, and intelligent automation hold immense potential to transform the pharmaceutical industry. By embracing these advancements, companies can move towards a future free from drug shortages, ensuring a stable, secure supply of essential medications and ultimately, prioritising patient safety.

It’s important to acknowledge that the pharmaceutical industry already makes significant efforts towards data management and quality control. However, further action can build upon these existing efforts, further enhancing efficiency and ensuring a more proactive approach to managing secure medication supply chains.

The road ahead involves collaboration between industry leaders, regulatory bodies, and technology providers. By working together, they can develop and implement robust real-time data systems that empower the industry to prevent drug shortages and consistently deliver life-saving medications to patients.

By Jamil Ahmed, Distinguished Engineer at Solace

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The Challenge

Contract managers face numerous challenges, from handling complex and varied contracts to ensuring compliance with stringent regulatory requirements. The current manual process of finding and analyzing datapoints in literally thousands of contracts and amendments can no longer be done manually. The tedious, time-consuming discovery of specific datapoints and manual extraction to spreadsheets is dragging down the potential of rebate management while increasing risk of compliance. That is why the pharmaceutical manufacturing industry is now looking to AI-driven technologies that accelerate the speed of analysis to better determine business position in real time.

Changes in healthcare policies, both domestically and internationally, are also continually reshaping the landscape of rebate contracting. Policy changes, such as alterations to drug pricing regulations or shifts in reimbursement methodologies, necessitate rapid adjustments to rebate contracts. While rebate programs open new avenues for collaboration and partnerships, allowing companies to access a broader range of resources and expertise, they also introduce added complexities in contract management, as they are often amended and changed.

The AI Solution

Automating many of a contract manager’s tasks with AI can vastly improve efficiency and accuracy. AI can quickly and accurately extract and analyze contract data, freeing contract managers to focus on strategic activities such as vendor relationships or negotiations. Robust AI technologies support effective data use in contracts, preserving profitability and enhancing distribution. Natural Language Processing (NLP) and Machine Learning (ML) technologies can automate the extraction and analysis of contract data. This not only reduces the manual effort involved but also ensures consistency and accuracy across all documents.

The pharmaceutical industry is turning to AI-driven technologies to speed up analysis and better determine business positions in real time. For contract managers, this means eliminating tedious data entry, unlocking the potential of rebate management, and reducing compliance risks.

For instance, a leading pharmaceutical company recently utilized AI technology from my company to develop a digital transformation ‘smart search’ capability. This solution involved enhancing Optical Character Reading (OCR) document processing and integrating it with the company’s record management system. The result was a system capable of extracting and visualizing key contract information in real-time. This more efficient system led to over 3000 hours saved in one year. For context, contract landscape updates can take roughly 400 hours per quarter across 10 full time employees.

AI technologies emerge as a catalyst for streamlining operations and enhancing efficiency, enabling contract managers to stay ahead of these policy changes and evolve their approach to contract management at scale. Through supervised machine learning and AI-powered analytics, contract managers can access and localize key data points from vast datasets, and compare pertinent rebate data such as drug names, eligible product units, average selling price, contract start and end dates, termination periods, shipping and liability terms with ease.

Additionally, AI-powered analytics can revolutionize rebate contract management by identifying patterns to predict future market dynamics, optimize rebate strategies, minimize revenue leakage, and enhance negotiation outcomes.

Key Benefits

Implementing AI in rebate contract management brings several significant benefits:

1. Efficiency and Time Savings: AI dramatically reduces the time required for tasks, enabling managers to respond to contract queries in minutes instead of weeks.
2. Improved Data Quality: AI-driven systems ensure accurate and consistent data extraction, minimizing human errors and missed opportunities. High-quality data is crucial for effective AI tools.
3. Enhanced Decision Making: Real-time insights and analytics allow managers to make informed decisions, shifting their focus from routine tasks to strategic activities.
4. Scalability: AI solutions manage increasing volumes of contracts and data efficiently, accommodating growing workloads without additional resources.

The Impact

Rebate contract management play a pivotal role in the financial ecosystem of pharmaceutical companies, often representing significant portions of their revenue and spending. However, managing rebate contracts effectively requires more than just paperwork and a can-do attitude. It demands a sophisticated organizational approach that leverages data analytics for better decision-making. Manually addressing this task—even with highly trained teams—puts contracts at risk of inaccuracies that can have huge consequences in revenue management down the line. In such a scenario, the integration of quality data and AI-powered technology solutions can equip contract managers with the tools they need to thrive in the ever-changing world of rebate contracts.

By adopting AI, pharmaceutical companies can create new business value, improve operational efficiency, and enhance collaboration across teams. AI not only augments human judgment, but also frees up valuable time for contract managers to focus on more strategic activities. The transformation brought about by AI is redefining rebate contract management, making it more efficient, accurate, and scalable.

The Future of Rebate Contract Management

At EncompaaS, we strongly advocate for the rapid adoption of AI-driven solutions to streamline the analysis and visualization of contracted agreements. When applied in the complex world of pharmaceutical rebate contracts, it provides an automated way of tracking the performance of drugs against contracted terms—enabling pharmaceutical manufacturers to maximize the revenue potential of every rebate contract. With EncompaaS, contract managers don’t need to learn another software application; they can spend their time learning more about their rebate data to drive better outcomes for their organization.

The integration of AI into rebate contract management in the pharmaceutical industry offers a powerful tool for addressing the challenges faced by managers. It provides a scalable, efficient and accurate solution that enhances decision-making and operational efficiency. As AI technology continues to evolve, its impact on rebate contract management will only grow, driving further innovation and improvement in the industry.

By David Gould, Chief Customer Officer, EncompaaS

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