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/

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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/

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Within just 2.5 years, Generative AI has disrupted entire industries. For a time, its potential was constrained by the materials the technology was exposed to; then the ability to understand this in context. But with escalating momentum those limitations are being overcome. This is presenting a challenging duality: a future that is already here, yet still largely unknown. In life sciences, where an advantage lost could mean patients missing out, companies are wondering how on earth they move forwards?

The unstoppable force of AI

GenAI is the branch of artificial intelligence that uses everything that is known already to create something new. From early conversational capabilities, through reasoning, the technology is already delivering ‘agentic’ capabilities (goal-driven abilities to act independently and make decisions with human intervention only where needed).

There are early signs too that “innovating AI” is emerging. That’s as AI becomes capable of creating novel frameworks, generate fresh hypotheses, and pioneer new approaches. This creative potential pushes AI from merely processing information to actively shaping the future of scientific discovery, applying it to problems yet to be solved.

At the core of the latest GenAI advances is the accelerated pace of large language model (LLM) development. These deep learning models, trained on extensive data sets, are capable of performing a range of natural language processing (NLP) and analysis tasks, including identifying complex data patterns, risks and anomalies. A growing movement towards open-source GenAI models, meantime, is making the technology more accessible and customisable (alongside proprietary models).

Reimagining scientific discovery and deep research

In life sciences, there are persuasive reasons to keep pace with and harness latest developments as they evolve. GenAI is poised to become a gamechanger in scientific discovery and new knowledge generation – at speed and at scale.

In human intelligence terms, we have already reached and surpassed human expertise levels[1]. Recent advancements in Agentic AI models have even led to the need for a new benchmark[2].

The advanced reasoning promise, a highlighted benefit of DeepSeek’s latest AI model, has enormous scope in science (enabling logical inferences and advanced decision-making). Google and OpenAI both have Deep Research agents that go off and perform their own searches, combining reasoning and agentic capabilities. As reasoning capabilities continue to improve, and as the technology becomes more context-aware, the potential to accelerate scientific discovery becomes real through the creation of new knowledge. The ability to project forward, and consider “What if?” and “What next?”.

Already OpenAI’s Deep Research is optimised for intelligence gathering, data analysis and multi-step reasoning. It employs end-to-end reinforcement learning for complex search and synthesis tasks, effectively combining LLM reasoning with real-time internet browsing.

Meanwhile Google has recently introduced its AI co-scientist[3], a multi-agent AI system built with Gemini 2.0 as a “virtual scientific collaborator”. Give it a research goal, and off it will go – suggesting novel hypotheses, novel research and novel research plans.

Which way now?

With all of this potential, the strategic question for biopharma R&D becomes one of how to keep pace with all of these technology developments and build them into the business-as-usual; how to prepare for a future that is simultaneously already here yet continuously changing shape?

Up to now, most established companies have experimented with GenAI to see how it might address everyday pain points in Safety/Pharmacovigilance, Regulatory, Quality and some Clinical and Pre-Clinical processes. These activities been largely about becoming familiar with the technology, and assessing its trustworthiness and value. Others have gone further, creating lab-like constructs for experimentation.

Yet the hastening pace of technology development, and the intangibility around what’s coming, means that the industry now needs to embed AI more intrinsically within its infrastructure and culture. This is about proactively becoming AI-ready rather than simply “receptive to” what the technology can do.

Being discerning as “experts” hover

In the past, a popular approach to a hyped new technology or business change lever has been to “pepper” associated champions across the business. In this case, some organisations are taking a venture-capital like approach of bringing in non-native AI talent to key roles – visionaries and master-crafters from other industries. But AI is moving so quickly, and its likely impact is so fundamental to life sciences, that experts need to be “neck deep” in it to be of strategic value.

One of the biggest challenges now is the duality companies are now grappling with: the simultaneous need to be ready for and get moving with deeper AI use today, while gearing up for a tomorrow that is likely to look very different. This has widespread “change” implications: at a mindset and method level; and from a technical and cultural perspective – both today and tomorrow.

For this reason, strategic partnerships are proving a safer route – with tech companies that are fully up to speed with the latest developments, are enmeshed in it and its expanding application, and are actively building sector-specific solutions. Even so, companies will need to choose their AI advocates wisely, as “AI washing” is commonplace among consultants and service providers now, as new converts to the technology inflate their credentials in the field.

The good news is that internal IT and data teams are well versed in AI technology today, and have high ambitions for it. The challenge is bringing the technology’s potential to fruition where it could make a difference strategically. This is likely to require involve sitting with an organisation’s real problem areas, and understanding if and how emerging iterations of AI might offer a solution.

About the author

Jason Bryant is Vice President, Product Management for AI & Data at ArisGlobal, based in London, UK. A Data Science Actuary, he has built his career in fintech and healthtech, and specialises in AI-powered, data-driven, yet human-centric product innovation.

[1] Measuring Massive Multitask Language Understanding, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021): https://arxiv.org/pdf/2009.03300

[2] Humanity’s Last Exam, November 2024, https://agi.safe.ai/

[3] Accelerating scientific breakthroughs with an AI co-scientist, Google Research blog, February 2025: https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/

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This period can reduce the profitability of drugs as the market opens up to other companies focusing on manufacturing generic drugs, driving down the price. However, it also makes life-saving medications accessible to a wider population, particularly in low-income countries who rely on medicines that are off patent or ‘generic’. This balance is crucial. Patents encourage investment in new drug development, while their expiration makes life-saving medicines more affordable.

This raises an important question: how can manufacturers manage patent cliffs to sustain their business and continue providing life-saving medications?

Managing product lifecycles to avoid patent cliffs

Pharmaceutical companies use several common strategies to manage the challenges of patent cliffs. One approach is to time new product launches to coincide with the patent expiration of existing products, maintaining a steady income.

But this strategy is fraught with challenges because of the unpredictable nature of drug discovery and development. Developing a new drug is a high-risk, high-cost endeavour, with only 1 in 500 drugs making it to market. The extensive research, clinical trials and regulatory approvals required often take over a decade and cost billions. Aligning product launches with patent expirations, while ideal, is easier said than done.

Another strategy is to further develop the existing drug to extend its lifecycle. For example, companies can develop new formulations or delivery systems for existing drugs, such as shifting from an oral tablet to an injectable form to reach more patient groups. Constant reinvention also allows companies to provide high-quality, best in class products.

A different approach involves expanding the use of an existing drug to treat a new patient group, which can result in additional approved indications. For example, the diabetes drug Ozempic was further developed and studied in other patient groups, eventually launching as a treatment for obesity, which helped secure extended patent protection.

Logistics after patent expiration

As drugs reach the end of their patent life, a thorough review and adjustment of logistics becomes essential to adapt to the new reality of increased competition.

One key consideration is likely changes in shipping volumes in line with new market conditions. The intense competitive landscape post-patent expiration puts even greater pressure on finding cost-effective solutions that do not compromise product integrity. Manufacturers might look for lightweight, durable packaging that maximises cargo space as this will help reduce shipping costs without sacrificing product protection.

While high-performance solutions are vital throughout a drug’s lifecycle to maintain product quality and patient safety, packaging choice may also be guided by stability data and risk assessments depending on the stage of the product being shipped.

For example, a product being newly introduced to the market may have little stability data available or may be shipped on a relatively unknown tradeline. In such cases, a solution with real-time monitoring is crucial for immediate intervention should the unexpected occur, helping maintain temperature control, product efficacy, and cost-effectiveness. This approach can also accelerate time-to-market and provide a competitive edge.

In contrast, a mature product approaching patent expiry may have a well-established shipping history and extensive stability data so its behaviour during transit is far more predictable. In this instance, a solution with real-time monitoring and extended autonomy, for example, may not be necessary but the focus may be instead on improving logistics efficiencies.

Another key consideration is partnering with trusted distribution partners with efficient logistics operations and a large enough fleet size to ensure timely delivery and reduced transportation costs. For example, regions with many generic manufacturers may experience increased competition, requiring even more efficient and cost-effective distribution solutions. Collaborating with logistics partners that can adapt to these regional challenges and offer flexible, scalable services becomes vital in sustaining profitability.

Preparing for unexpected events, such as the disruptions in the Red Sea, is essential. In a post-patent environment, the focus should shift to building resilience in the supply chain to handle increased competition and potential shocks. This means finding alternative transportation routes and using advanced risk management tools to keep the supply chain robust and adaptable to real-time changes in demand and transport conditions.

Additionally, managing inventory levels at distribution centres becomes critical to prevent stockouts or accumulating excess stock. By carefully analysing sales data and market trends, companies can adjust their distribution network to better match demand fluctuations after patent expiration. This helps keep products available when needed without overstocking, which would tie up valuable resources.

Looking ahead, technologies like IoT, blockchain for traceability and AI for predictive analytics will significantly enhance logistics operations, making pharma distribution more robust and reliable. Using data analytics to simplify processes, cut costs and boost performance will be especially important in the post-patent cliff landscape. These advancements will help companies better navigate the complexities of increased competition and market pressures, helping them stay competitive and efficient.

As the pharmaceutical landscape evolves, efficient logistics, particularly cold chain logistics, will become increasingly important. Currently, about one in three medicines requires cold storage[i], and this proportion is expected to rise. Nearly half of all new medicines in the next three years will require cold storage and distribution[ii].

Manufacturers need to bear this in mind and use the coming decade to prepare for the patent cliff. By establishing robust, adaptable cold chain processes now, companies can help future-proof the drugs’ cost-efficiency and product integrity when these reach the end of their patent life.

Beyond the patent cliff

As medicines approach the end of their patent life, pharmaceutical manufacturers must make critical decisions on how to manage these patent cliffs. The goal is to continue providing quality medications while sustaining their business to be able to support the next generation of life saving medicines

In this context, improving logistics is one reliable and effective method for navigating this challenging period. The end of a patent may appear daunting, but it doesn’t have to mark the end of a product’s lifecycle. With smart logistics and innovative strategies, it can be an opportunity for a fresh start.

By Diane Onken, Head of Sales, Americas at Envirotainer

References

[i] Pharma’s Frozen Assets: Cold chain medicines – IQVIA whitepaper, 2023
https://www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/iqvia-pharmas-frozen-assets_final.pdf

[ii] Outlook for medicine use and spending through 2027 – IQVIA infographic, 2023

https://www.iqvia.com/-/media/library/scientific-posters/fip-global-outlook-poster-vertical-orientation_final.pdf

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The biotech market was estimated to be worth $1.55 trillion globally in 2023, and is due to grow at a compound annual rate of 13.96% between now and 2030[1]. Yet not all markets are equal; conditions are dynamic too. To delve deeper into biotech companies’ geographic strategy, and assess their changing perceptions against the experienced reality, Arriello recently commissioned a transatlantic survey among 200+ Regulatory, Safety and Quality Directors at small/medium biotechs, split 50/50 between the EU (Ireland) and North America (the US).

The companies polled already had a strong and well-balanced international presence, typically a blend of direct representation and indirect, via local partners. Direct representation is currently highest proportionally in Switzerland, followed by Norway and Austria, and lowest in Turkey, Mexico, Argentina, the Middle East, Japan, New Zealand, and France, where in-country partnerships are more common. In the US, where overall representation is highest (97% penetration), activity is split roughly 50/50 between direct and indirect. A similar ratio is reflected in the UK, where 87% of respondents cited an active presence.

Target markets

Looking at companies’ next priorities either for first-time entry or for active expansion, the survey revealed a consistent focus on the US, UK, and Canada in both categories – the US leading both as an immediate priority and of high interest in the near future.

The picture becomes more interesting when contrasting respondents’ current priorities versus their relative focus five years earlier (in 2019). Brexit has elevated the perceived importance of entering or expanding into the UK, so that it is at least as important a market as Canada now (where five years ago, Canada was a greater priority).

Brazil is now deemed an immediate priority, meanwhile, along with the Middle East which is also a strong interest for the near future. Five years ago, Brazil was sixth down the list (two places below Mexico, which no longer features as a priority target), while the Middle East did not feature as a top-10 market of interest.

The biotechnology market in Brazil is expected to reach revenues of over $69,140 million by 2030, if it achieves its forecast compound annual growth rate of 14.2% between now and then[2]. The country today accounts for the largest share of Latin America’s biotech market, which Grand View Research attributes partly to Brazil’s improving infrastructure and domestic biotech innovation.

The Middle East, which country by country is looking to reduce its dependence on oil as set out in a series of high-profile new economic national ‘visions’, has become a region of growing interest for life sciences, too. Saudi Arabia, for instance, is proposing possible market entry within as short a period as six weeks, as long as companies have the supporting infrastructure in place.

Barriers and green flags: perceptions versus reality

There is a general perception that it is still relatively complex and onerous to enter a new market, whatever efforts are in place to simplify processes. Respondents specified competition structure; political; and then legislative factors as the top three conditions for smoothing the way. By contrast, respondents ranked tax factors; resources/logistics; and cultural factors as contributing to a challenging experience.

When trying to enter new markets, respondents cited the following issues, in order of experienced negative impact:

1. The scale/degree of quality-related challenges (e.g. required licences/authorisations; associated roles such as Qualified/Responsible Persons; and Quality Management System requirements)
2. Pharmacovigilance-related infrastructure and expectations
3. Significantly lengthier timescales to approval
4. Substantially greater costs of market entry
5. Failure to understand the specific requirements of the new market.

Asked which markets had presented substantial additional access issues, respondents most commonly cited China, followed by Brazil. US-based companies were more likely to be affected by higher-than-anticipated costs when new market access plans went awry. Over a third of companies (36%) withdrew from the opportunity altogether, not going on to enter the market.

Learning the hard way

Where companies had encountered difficult market access experiences, respondents put this down to a range of contributing factors. Key lessons learned included:

• That Regulatory and/or Quality experts need to be involved much earlier and more intrinsically in initial decision-making and planning around target markets
• That Pharmacovigilance experts need to be involved much earlier and more intrinsically in initial decision-making and planning around target markets
• That other markets should have been considered instead
• That the European opportunity has changed, in terms of which countries are optimal to target
• That market size alone does not determine revenue or profit success.

Measures of success in the biotech market

Finally, the survey identified an evolution in the measures of perceived success in biotech over the last five years. Reflecting on the gauges applied in 2019, respondents deemed these to have been revenue performance; followed by the number of patients reached – then profitability. Today, by contrast, divestment or investor payback are the main considerations.

The role of problem-free compliance has risen too. Getting products successfully past regulators is clearly a critical step in achieving a good return for the business and its stakeholders. Compliance performance is particularly pronounced as a measure of success for respondents in the US: 43% of survey respondents here cited this as a main measure of success today.

With pressures likely to worsen before there is significant progress towards global harmonisation of requirements (almost 40% of survey respondents expected accelerated routes to market to increase the pressure), ambitious biotechs will need to allow more time and apply more scrutiny as they fine-tune their global expansion plans.

About the research

Conducted independently by Censuswide in September 2024, the quantitative research polled 210 senior managers or directors in regulatory/safety/pharmacovigilance/quality roles at small/medium biotech companies in the US and Ireland (101 and 109 respondents respectively). The full report can be accessed here .

About the author

Sam Tomlinson is Vice President of Global Drug Safety at Arriello.

References

[1] Biotechnology Market Size, Share & Growth Report, 2030, Grand View Research: https://www.grandviewresearch.com/industry-analysis/biotechnology-market

[2] Brazil Biotechnology Market Size & Outlook, 2023-2030, Grand View Research: https://www.grandviewresearch.com/horizon/outlook/biotechnology-market/brazil

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Despite these advancements, the sector still faces challenges in fully realising the potential of digital maturity compared to other industries.

Looking ahead to 2026, several key trends will shape the future of biotech, driven by the integration of digital technologies and advanced analytics.

Artificial Intelligence (AI) in Biotech and Drug Discovery

AI is accelerating the discovery of novel therapeutics by streamlining the identification of promising drug candidates.

Machine learning algorithms analyse vast biological datasets to identify viable molecules, significantly reducing R&D costs and timelines. AI-powered platforms enhance target identification, lead optimisation, and preclinical testing, improving efficiency in biotech research.

Cloud Computing for Biotech Innovation

Cloud and edge computing are revolutionising the scalability and innovation potential of biotech firms.

With enhanced data sharing, real-time collaboration, and seamless AI integration, cloud computing enables faster drug development cycles and robust data security. Companies leveraging cloud-based platforms will gain a competitive advantage in operational efficiency and scientific breakthroughs.

Machine Learning (ML) for Drug Development

Industrialised machine learning is transforming every stage of drug development. From predictive modelling in clinical trials to optimising biologics formulations, ML enhances data-driven decision-making. Advanced algorithms refine predictions, minimise trial failures, and accelerate regulatory approval processes for new therapies.

Real-Time Analytics in Clinical Trials

The demand for more efficient and effective clinical trials has led to greater adoption of real-time data analytics. AI-powered data processing enables biotech companies to monitor patient responses, detect anomalies early, and optimise trial designs. This trend is particularly critical in rare disease research, where patient recruitment and retention remain key challenges.

Investment in Digital Health Technologies

Venture capital is flowing into digital health solutions, particularly those that enhance patient engagement, remote monitoring, and commercialisation strategies. Biotech firms are increasingly partnering with health tech start-ups to develop wearable devices, mobile applications, and AI-powered telemedicine solutions that improve patient outcomes and treatment adherence.

Data-Driven Decision Making

Biotechnology companies are leveraging big data to optimise research, clinical development, and commercial operations. Advanced analytics provide deep insights into patient behaviour, biomarker discovery, and market dynamics, enabling more precise business and scientific strategies. Organisations that successfully utilise data-driven decision-making will drive innovation and maintain industry leadership.

Synthetic Biology and Precision Medicine

Synthetic biology is rapidly emerging as a disruptive field for engineering novel biological systems. By designing customised treatments for genetic disorders, regenerative medicine, and vaccine development, synthetic biology offers unprecedented potential for addressing unmet medical needs with precision and efficiency.

Decentralised and Virtual Clinical Trials

The shift towards virtual and decentralised clinical trials is improving patient accessibility, recruitment, and trial efficiency. AI-driven analytics, remote monitoring tools, and telemedicine solutions allow biotech companies to conduct trials with greater flexibility while ensuring data integrity and regulatory compliance. This trend is redefining the clinical trial landscape, making drug testing more patient-centric.

Quantum Computing in Drug Discovery

Quantum computing is poised to become a game-changer for biotech. By simulating molecular interactions at an unprecedented scale, quantum computers could dramatically accelerate drug discovery. While still in its early stages, this technology holds immense promise for solving complex chemical and biological challenges beyond the capabilities of traditional computing.

AI-Powered Diagnostics and Personalised Medicine

AI is transforming diagnostics by enabling early disease detection and precision medicine. AI-driven imaging, pathology analysis, and predictive algorithms are revolutionising how diseases are diagnosed and treated. As healthcare shifts towards personalised medicine, AI-powered diagnostics will play a crucial role in advancing targeted therapies and improving patient outcomes.

AI-Driven Scientific Research Assistants

AI-powered research assistants are becoming indispensable tools in biotech and life sciences. These digital assistants automate data analysis, literature reviews, and experiment documentation, significantly enhancing productivity. By integrating with cloud computing and real-time analytics, AI-driven assistants foster collaboration, accelerate discoveries, and reduce the workload for human researchers.

Conclusion

As we move towards 2026, the integration of digital and AI-driven solutions in biotech is not just a trend—it is a necessity. Companies that invest in these innovations will lead the charge in scientific and medical advancements, driving faster drug development, improving patient care, and optimising research operations. The future of biotechnology is digital, and those who embrace this transformation will be at the forefront of innovation and discovery.

Kevin Cramer, CEO, Sapio Sciences

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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.

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“The only thing that was impossible was to do nothing.” These are the words of Terry Pirovolakis, CEO of Elpida Therapeutics, and father to Michael who was diagnosed as an infant in 2019 with an ultra-rare neurological condition, SPG50. Despite being told there was no cure, Terry moved mountains over four years to find a breakthrough gene therapy that his son and other affected children all over the world could benefit from.

Michael, Terry, and their family are not alone in facing a long, daunting journey to access life-enhancing therapy for a disease with no cure. In the European Union, less than five percent of rare diseases have at least one approved treatment.[1] It can take up to five years for adults to receive a diagnosis, and half of patients will receive a misdiagnosis first.

By 2030, the goal is to support the development of 1,000 new therapies in Europe.[2] However, rare diseases challenge traditional ways of doing business. Once a medicine is approved, organizations must launch and scale quickly, often across multiple markets. Field medical teams must keep track of complex science at a time when the volume of medical knowledge is doubling every 73 days.[3] They need to identify, engage, and provide education to relevant healthcare professionals (HCPs) and experts even though they’re harder to reach via traditional methods (Veeva Pulse data shows that in-person meetings with physicians have declined in Europe in the last 12 months).

Despite these difficulties, life sciences organizations are improving how their teams organize market access and educate prescribing doctors on new medicines. Companies with comprehensive, accurate healthcare ecosystem data and insights relating to medical experts can prepare the market pre-launch and hit the ground running to accelerate execution after launch. This helps all customer-facing teams to deliver a consistently high-value scientific exchange in which every interaction builds off the last.

Greater agility during launch

Emerging biopharmas play a key role in delivering treatments for affected patients. Of the 192 orphan medicinal products (OMPs) authorized by the European Medicines Agency (EMA) between 2010 and 2022, one in ten successful applicants was designated an ‘SME’.[4] Facing more resource constraints than their competitors (which may have sales organizations multiple times their size), early-stage companies are increasingly seeking the best customer reference data and key opinion leader (KOL) insights to become more nimble when launching new treatments.

Rich information on experts, available in CRM, supported Sweden-headquartered Sobi to launch the first and only medicine for hemophagocytic lymphohistiocytosis (HLH) — a rare, hyperinflammatory condition affecting one in 50,000 resulting in a two-month life expectancy. In the U.S., MSLs at the company now routinely use real-time intelligence to find the right KOLs, understand their interests, priorities, and activities, and consolidate scientific information and updates. “90% of MSLs found new insights for their next engagements, which is critical for a rare disease company with lean teams,” says Rich Palizzolo, executive director of CX and advanced commercial capabilities at Sobi.

An added complexity is that rare diseases often involve several specialties. MSL teams have to get up to speed quickly on multiple areas before meeting experts — while also being responsible for other (more mainstream) therapeutic areas. ADVANZ PHARMA, which focuses on specialty, hospital, and rare disease medicine, found having scientific resources and activity data in one place helped MSLs use their time efficiently. Head of CRM and Digital Solutions for Global Commercial Excellence, Andy Eeckhout, notes: “Our customer-facing teams need to be agile communicators and effectively switch to a more patient-oriented, in-depth scientific discussion than with generics. Pre-call planning is crucial for MSLs before and after launch. The more data they can find, including on past interactions, the better.”

Other companies use customer reference and patient data to improve operational agility as they launch and scale their first products across Europe. For example, one late-stage biotech leveraged its data on the healthcare ecosystem to get a head start on launch by identifying market access roles in Spain, Benelux, and the Nordic countries.

One voice to the physician

After identifying the right experts, teams can engage them more effectively by ensuring that each HCP interaction builds off the previous one. However, sales, marketing, and medical teams often use disconnected technology. As a result, 65% of HCP engagements are not synchronized.

When these teams are connected in the same system handovers are smoother, and HCPs can find answers quickly or connect with MSLs if needed. ADVANZ PHARMA introduced a pre-launch module in its CRM to help market access, medical, and commercial teams share information compliantly. Eeckhout explains: “Physicians need a direct line to the industry, so they know who to contact when they have questions. Medical and commercial teams need to talk to each other and remain agile across customer conversations.”

ANI Pharmaceuticals, which delivers treatments for certain rare autoimmune and inflammatory conditions, only had 75 days to commercialize following swifter-than-expected regulatory approval. By using an industry-specific CRM with master data management, it consolidated its view of each HCP to include interactions with medical and sales reps. “Having this information accessible within the CRM system facilitates more thoughtful and helpful conversations with providers, as well as sales teams’ success and high click-through email rates,” explains Bob Acropolis, executive director of operations and analytics at ANI.

The foundation of successful interactions is accurate reference data — on physicians, healthcare organizations (HCOs), or affiliations. If applied across functions as part of a complete life sciences-specific CRM, it helps companies speak with one voice. In most cases, data change requests (DCRs) can now be made (and resolved) in hours, so reps and field medical don’t duplicate attempts to modify account information and instead work from the same database. With greater trust and confidence in reference data, teams save time so they can focus on high-value scientific exchange.

Sharing medical content that engages

Europe’s fragmented regulatory landscape and evolving local requirements intensify the pressure on marketing teams: they must provide highly personalized, compliant medical content that field teams can share at scale with scientific experts and physicians. With a single view across the entire content lifecycle, biopharmas can streamline and speed up medical, legal, and regulatory (MLR) reviews.

To deliver highly personalized content across a large rare disease portfolio, marketing teams need clarity on which content to recommend, and when. One global biopharma uses data analytics on its global repository of promotional and medical engagement tools to support content use across 17 areas of focus (and a growing global footprint). As its head of marketing and customer engagement noted, “How we engage with HCPs is critical. We need to know what percentage of our content is being developed and relevant to support different HCPs, whose patients rely on them for their rare disease diagnosis and management.”

As a new generation of digitally-savvy HCPs comes through, companies are considering the most effective tactics to engage them. More scientifically active than their peers and four times more likely to adopt a new treatment, younger HCPs require a different mix of scientific channels and content. They seek medical insights to inform their clinical and commercial decisions, which requires close coordination between medical affairs and field medical. “Gone are the days when medical could just focus on the top-tier scientific thought leaders. The range of stakeholders has broadened, and it’s imperative to expand our engagement strategies beyond traditional experts,” says Angela Smart, director of global medical excellence and operations at ADVANZ PHARMA.

Defying the odds, then beating them

Scientific discovery continues to bring hope to patients affected by rare diseases and their families. Life-changing conditions will eventually become chronic illnesses, thanks to the efforts of organizations willing to launch in a high-risk commercial environment. Companies ranging from emerging biotechs to global biopharma are using high-quality customer reference data, deep data on scientific experts, and connected technology to identify, engage, and provide medical education to the most relevant HCPs and KOLs.

Every rare disease patient faces a daunting journey. When Terry Pirovolakis’ son, Michael, was diagnosed, his family was told he was the only child in Canada with SPG50. Life sciences will do its part to help patients defy overwhelming odds — and eventually beat them.

By Chris Moore, President, Veeva Systems Europe

References

[1] “The building blocks to make rare disease treatments more common,” European Commission, February 2022

[2] “What is Rare Disease,” EURODIS, 2024

[3] “Challenges and Opportunities Facing Medical Education,” American Clinical and Climatological Association, 2011

[4] “Trends in orphan medicinal products approvals in the European Union between 2010–2022,” Orphanet Journal of Rare Diseases, 2024

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AI is transforming medical treatment in many areas and across many diseases, not just cancer. Within oncology however, which is a particularly complex and challenging disease, AI is making an even more significant difference.

Technology is accelerating the way molecules are designed and developed in new approaches against cancer, allowing us to invent innovative molecules that could work better and safer in a shorter amount of time. This stands to bring improved treatments faster to patients.

Findings published by the WHO last year revealed that in 2022 there were an estimated 20 million new cancer cases and 9.7 million deaths. Globally, about 1 in 5 people develop cancer in their lifetime, and approximately 1 in 9 men and 1 in 12 women die from the disease. This is the scale of the challenge.

Pharmaceutical and biomedical companies are now using increasingly powerful computational methods to fight this battle, strengthening their discovery efforts with AI to help find new compounds for killing cancer. In this early-stage, pre-clinical setting, AI is being used to search for, analyse and develop compounds aiming to eliminate the disease. As such, these new computational methods are gradually increasing accuracy, speed and reliability of treatments.

How does AI drug discovery work?

Our computational platform, called Synth AI, achieves acceleration by computationally producing molecules that meet three key objectives simultaneously.

Firstly, the platform ensures that prospective molecules can be synthesised using known chemical methods. Many computational approaches suggest potential drugs candidates that can’t actually be made or have significant challenges in their synthesis. Synth AI avoids this, ensuring the molecules can be not only made but also scaled using known technologies. This significantly boosts the chances of these treatments being successful when they reach clinical or commercial use.

The third key criterion is that Synth AI optimises the chances that these molecules have the desired biological effect. The platform delivers molecules that are not only synthesisable and scalable but also stand to be biologically effective, critically increasing the overall accuracy, speed and reliability of the drug discovery process.

These three metrics – ensuring the molecule can be active biologically, that it can be made in the laboratory, and that it may be scaled cost-effectively – are making these treatments more likely to come to market. This potentially more cost-effective drug development is a positive prospect for both patients as well as for investors in the space.

Balancing efficacy with side effects in oncology

AI drug discovery is playing a crucial role in improving efficacy, dose response and toxicity in cancer treatment as well.

Considering the unmet need and the lethal consequences of cancer, efficacy is typically prioritised, namely how effective the treatment is at killing cancerous cells. However, the market is being populated with an arsenal of anti-cancer agents which have side effects that remain a major burden for patients.

Our efforts, through the use of AI, are focused on improving efficacy while overcoming the side effect disadvantages that plague many existing treatments.

This comes back to the precision of AI.

As a concrete demonstration of how computationally sourced molecules are being validated, our initial tests against cancer in mice have produced clear evidence of tumour regression and a good safety profile. Remarkably, this is being achieved with a drug candidate that has not even been optimised in the lab.

This points to the chemical scaffolds identified using the proprietary AI computational component already producing molecules with significant advantages that would normally be far behind in the optimisation curve. The benefit for patients, physicians and also investors, is that because treatments are starting their journey to the clinic at a more advanced point, the time and capital at risk during the optimisation phase is reduced overall, speeding up the process and prospectively providing faster economic returns.

The fact that AI drug discovery can achieve such superior results with an unoptimised drug candidate means that we’ve jumped ahead in time compared to what would have normally been required to reach this point.

What AI means for patients and cancer treatment

AI is therefore getting better, more effective and safer treatment to cancer patients more quickly with the associated reduced risk for investors.

When such a validated, strong technology is devised and applied by a leading scientific team, the result can be potential treatments which begin their life cycle as if they’ve been optimised over many months or years. The saved time and valuable resources for drug discovery and development companies means investment then goes further and money can be used more efficiently within the industry.

Importantly, this new approach can positively impact disease areas beyond oncology. Within our own pipeline we have additionally demonstrated the versatility of AI in creating new potential treatments also against resistant infections.

And it isn’t just us. Drug discovery efforts of other pharmaceutical and biotech companies in numerous other conditions are demonstrating the broad applicability of AI computational technology in drug discovery.

Many novel AI methods being utilised within the industry today have already been found to work for a diversity of targets, independently of the therapeutic area or the nature of the target itself.

By Dr. Alan D Roth, CEO of Oxford Drug Design

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Building on my recent exploration of the top medical education technologies trends of 2024, here, we’re diving into the most anticipated next-generation technologies that will take medical education to new levels in 2025.

Immersive Mechanism of Action and Pathophysiology Training Tools

The future of HCP training is immersive. Immersive training tools include everything from virtual and augmented reality to holovision technology—interactive holograms of physicians or patients that guide HCPs through clinical and real-world data, clinical practice scenarios, and patient stories. These technologies can be used to dive deeper into a drug’s mechanism of action or the underlying pathophysiology, gain experience with new devices or techniques, guide treatment decision-making, and practice role-playing or objection-handling for complex clinical situations. While they might not be feasible to have in every clinic or hospital quite yet, these technologies are starting to become more and more common at congresses, conferences, and other large events.

Clinical Data Visualization and Interactive Datasets

Other immersive technologies that might be more accessible to a greater number of companies and HCPs include animated leave-behinds for HCPs and patients, such as interactive infographics, posters, and brochures. These are visually-heavy, interactive materials designed to simplify complex concepts and allow HCPs to actively engage with the content. Interactive leave-behinds may include clickable elements, animations, pop-up videos, quizzes, polls, and more, ensuring that learning is engaging and tailored to everyone’s preference, whether they are a “skimmer,” “swimmer,” or “deep-diver.”

In addition to leave-behinds, other educational materials will also become increasingly interactive and immersive in 2025, including treatment/diagnostic guidelines, patient journey maps, and treatment sequence algorithms. Leveraging a microlearning approach (discussed more below), guidelines and treatment algorithms can be broken down into digestible steps and easily accessed on demand, while the incorporation of interactive elements will help the content stand out and improve memory retention.

Microlearning Modules

Microlearning has been shown to have a positive effect on the knowledge and confidence of HCPs. Virtual microlearning modules remain a crucial technology for medical education delivery and are the most effective when used in conjunction with other formats. This approach is suitable for education on disease awareness, treatment options and guidelines, and adverse event management. By delivering bite-sized, focused lessons on specific topics, HCPs can engage with the content in manageable segments, anywhere, anytime it fits with their schedule. As a result, they can learn at their own pace, without the need for extended sessions.

Another benefit of this format is the fact that any updates to guidelines or new clinical data can be seamlessly integrated into the relevant micromodules as they become available. As a result, HCPs always have easy access to the latest recommendations without having to attend an entire lecture or retake the whole course for up-to-date information.

Incorporating Case-based and Gamified Learning

For all of the above technologies, it is recommended to incorporate case-based and/or gamified elements, as these are always appreciated by HCPs, help put new information into clinical context, and can be used to improve real-world skills. When done right, case-based learning ensures that content is relevant and tailored to the participants’ specialty, aligned with guidelines, based on logic, and allows repetition of key points guiding treatment decision-making. If combined with microlearning modules, this might look like progressive case modules, where more and more information is presented as the learner moves through the activity, changing how they might approach diagnosis, treatment, and management. If knowledge checks or quizzes are added throughout, the experience can be further enhanced. Case-based learning can also be incorporated into holovision patient avatars or in the form of interactive patient journey maps, among many other technologies.

Likewise, gamified learning—leveraging a combination of points, levels, and/or rewards to encourage participation—can make learning not only fun but also more engaging and motivating. Gamification can easily be incorporated into next-generation educational tools such as microlearning modules, virtual reality, and interactive leave-behinds or guidelines. It can also be used to enhance more traditional learning formats such as webinars and in-person presentations.

The Bottom Line for Medical Education Technologies

By leveraging next-generation medical education technologies, life science companies can increase interactivity and engagement while ensuring up-to-date and relevant learning for their HCP target audience. No matter which approach is chosen, the key is to move away from static, didactic presentations and offer something that is personalized and stands out from the crowd. These next-gen tools are already being used by leading pharmaceutical companies in 2025; at the risk of otherwise falling behind, now is the time to make the switch.

By Natalie Yeadon, President & CEO Impetus

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