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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It provides a secure, scalable way to manage patient records, streamline medical research, and enhance data security across the healthcare ecosystem. Crucially, blockchain enables a unified system where patient records can move seamlessly across providers, across continents, ensuring a continuity of care wherever the patient is located. By offering an immutable and interoperable ledger, blockchain enables healthcare stakeholders—from physicians to researchers and pharmaceutical companies—to trust the accuracy and security of their data while maintaining compliance with industry standards.

Protecting and Empowering Patient Health Data

Cyberattacks on healthcare systems are becoming more frequent, directly impacting patient safety and trust. Last year, the NHS faced multiple cyberattacks, including those affecting NHS Dumfries and Galloway and Synnovis, disrupting essential services. Many healthcare providers still rely on outdated, fragmented storage systems, making them more vulnerable to breaches. Blockchain technology offers a much-needed alternative acting as a secure, time-stamped log of all interactions with sensitive data, making it easier to track changes and prevent tampering. Companies like BSV Blockchain are already leading the charge in applying blockchain to healthcare, ensuring secure solutions that provide greater control and security over vaccination records and other verified health data.

At the same time, patients deserve greater control over their own medical data. Blockchain allows them to set access permissions for their records, ensuring only authorised providers can view specific information. By eliminating third-party data custodians, blockchain restores trust in patient privacy and enables seamless, secure data sharing across healthcare platforms.

Patients can even grant temporary access to their records when needed, keeping control over who sees their data. This feature enhances interoperability within healthcare systems while ensuring that personal information remains protected. Additionally, blockchain’s scalability enables hospital networks to manage vast amounts of medical records efficiently and cost-effectively.

Accelerating Medical Research

Medical research thrives on data, but too often, that data is scattered and inaccessible. Scientific literature, clinical trial data, and genetic research are typically siloed, making collaboration difficult and slowing the pace of innovation. Blockchain simplifies this by enabling real-time data aggregation and secure sharing, all while preserving patient privacy.

Blockchain simplifies research agreements—like those between hospitals and pharmaceutical companies for clinical trials—by securely recording and automating them. This reduces paperwork, speeds up approvals, and makes collaboration between institutions more seamless. Researchers can gain access to verified datasets without compromising data integrity or patient confidentiality. This means faster breakthroughs, smoother trials, and life-saving treatments getting to patients more quickly.

Managing the Medicine Supply Chain

Beyond securing patient records and advancing research, blockchain is also making a tangible impact in pharmaceutical safety and supply chain management. Counterfeit medicines pose a serious risk to patient safety. In fact, a study from The Pharmaceutical Journal found that around 15,500 falsified medicine packs were identified in the UK’s authorised medicines supply chain over just two years. Ensuring the authenticity and traceability of medical products is crucial for manufacturers, healthcare providers, and patients alike.

Blockchain enhances supply chain security by creating a permanent record of every transaction, from raw material sourcing to distribution. Each medicine can be assigned a unique, time-stamped identifier, allowing healthcare professionals to verify its authenticity before administration. This level of traceability helps manufacturers and distributors maintain accountability while keeping counterfeit drugs out of the market.

The Future of Healthcare Security with Blockchain

Blockchain technology is already making healthcare more secure, scalable, and interoperable. By ensuring real-time, trusted data access for providers, researchers, and patients, it has the potential to redefine digital healthcare infrastructure. As cyber threats and data privacy concerns grow, the need for robust, blockchain-based solutions is more urgent than ever.

For healthcare professionals and organisations looking to enhance security, streamline research, and improve patient experiences, blockchain offers a proven and scalable solution. Now is the time to explore its potential and lead the next wave of digital healthcare transformation.

By Calvin Ayre, Founder at Ayre Group

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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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Individual doctors saving an hour each day on admin. Nurses halving the time spent on handover preparation. And informed staff leveraging key information, whilst eliminating paper. This describes just some of the immediate impact witnessed on the shift from paper records to digital noting at South Tees Hospitals NHS Foundation Trust: results that are enduring as digitisation continues to progress.

“The general ward state is undoubtedly more efficient and safer because we now have availability of standardised, legible, and complete notes,” says Dr Andrew Adair, an emergency department consultant and the chief clinical information officer for the trust. “We have links into regional systems, all accessible through one window. Our healthcare teams are not having to leave the electronic patient record to look at x-rays, radiology reports, endoscopy, outpatient letters, or to look at other hospital attendances on the Great North Care Record.”

His comments reflect benefits being realised following a South Tees Hospitals decision to deploy Alcidion’s Miya Precision platform as a trust-wide electronic patient record.

Patient flow, e-observations and assessments, electronic prescribing and medicines administration, and clinical messaging were some of the first priority areas to be digitised in the EPR programme, with significant positive implications for patient care.

But the digitisation of clinical noting that Adair describes has become one of the most significant achievements in the programme to date.

“It’s that visibility right across the system of information for the people who need it, when they need it,” says Niki Idle, digital business change manager.

The trust has so far prevented the creation of 1.8 million paper documents as a result of digitised clinical noting. “That’s 1.8 million documents through the system so far, including 102,000 discharge letters that can automatically be sent to GPs electronically,” says Idle. “We are not building up notes that require physical storage. And other than for business continuity purposes, specialist notebooks that were used to capture notes are not being printed.”

‘I can’t believe we have never had this before’

South Tees Hospitals has worked with Alcidion to effectively reinvent noting at the trust. Intuitive technology has helped with clinical buy-in.

“Compared to other digital systems I’ve used, it just looks nice. It has been laid out with clinical teams in mind. It seems like a little thing, but this is important as your first impression of the system as a clinician,” says Adair. He describes Miya Noting, a component of the EPR platform.

Deeper under the hood, nurses and clinicians at the trust have fed back positively on a system built, configured, and deployed around their needs.

“All grades of medical staff, the nursing body, and allied healthcare professionals have all been really receptive of it,” says Dr John Greenaway, a consultant gastroenterologist and the trust’s deputy CCIO.

He recalls that in other trusts clinicians had left their positions at the thought of an EPR deployment. At South Tees Hospitals one clinician who was approaching retirement had voiced similar reservations.

“As we went live, she realised that there wasn’t much she was going to have to do, she saw the advantages of it, and there were big smiles over the next few days,” says Greenaway. “‘I’m not going to retire”, she told us.”

Another nurse, initially fearful about being able to cope with new technology, changed her mind by the end of her first shift. “I can’t believe how we’ve not had this before,” she said.

Now deployed across nearly all of the trust, and with plans to soon deploy to a remaining four areas out of 38 wards, clinical noting has had widespread engagement – with further configuration ongoing to respond to the evolving needs and requests of specialty noting. In the early stages of deployment, Idle recalls how clinical educators who were there to support staff, were told they could leave early because wards intuitively understood the system.

“We’ve been fairly swept away by how people have taken to this”, adds Greenaway. “We’ve not really had the ‘hard time’ often faced in large healthcare IT deployments. That’s partly a testament to the system and partly because clinicians do not find it too painful to input the electronic information that will be so beneficial further down the line.”

Collaboration to reinvent noting

A collaborative approach between the trust and specialists at Alcidion in designing how data is captured, has helped.

The design process delivered alongside frontline clinicians has meant that the noting has been configured around user needs from day one. Comments from staff that the system “lightens the workload”, that it has “made life a lot easier” and has released “far more time to care”, have resulted.

“It feels like you are in it with your mates,” says Greenaway. “We have a common goal, working through things together.”

The intention is that around 70% of the data required on many forms could eventually be auto-populated, either from existing parts of the patient record such as demographics, or pulled through from notes captured at earlier points of the patient’s hospital encounter.

For example, ‘pull through’ of comorbidities data is not only expected to save time, but aide in clinical decisions, and in accurate coding for financial purposes, says Idle: “Every time the patient is admitted, the system will present the clinician with a list of comorbidities, asking ‘are these still all valid and present?’ It’s prompting the clinician with information that already exists.”

“We are taking the brain power out of remembering what to do and where. We get to concentrate on important patient care decisions,” adds Greenaway.

The system has been configured to create efficiencies beyond the point of care too, for example supporting data requirements for national clinical audits. This is expected to prevent the need for clinicians to manually search for information for mandatory audits, so that they can then spend more time on quality improvement.

“We can just pull that data out of the system,” says Idle. “We’ve designed forms to ensure we collect as much pertinent information as possible, rather than somebody sifting through notes to then type into another computer system. This is freeing up time to ensure audits are complete and to address concerns raised in audit data.”

The availability of data for reporting is also supporting patient safety. “Within 14 hours of an emergency admission patients should have a senior clinical review,” Idle explains. “We’ve never been able to quantify that before without searching manually through paper notes. Now we can, just as we can examine where VTE assessments have been completed. We can now pull that data, see where it’s happening, where it isn’t, and then follow up with education.”

AI: The near future

Despite positive results, Greenaway insists more is to come and soon. “I don’t think we are anywhere near realising the benefits of the Alcidion system,” he says.

AI and other smart technologies are expected in the “near future”. “I don’t think this will be long,” says Greenaway. He refers to demonstrations already made to the trust, where a clinician can dictate a summary into a microphone for AI to then populate a form, or a plan, for clinicians to approve. And he describes “ambient listening”, where AI tools can listen to consultations in the background to generate notes.

Initially the trust intends to structure options to allow staff to ask AI to generate a handover document, or a discharge letter, or to pull through certain information from multiple encounters.

Adair concludes: “What we have now is already undoubtedly so much better. Now we are planning to introduce robotic process automation to be able to bring in additional data from our comorbidities system. And we are working to integrate more data from pathology. Not having to go into a separate system for that information – for our clinicians, that’s massive.”

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Liver disease is a rapidly growing global challenge that often advances without obvious symptoms until it becomes life-threatening. According to the British Liver Trust, mortality rates from liver disease have quadrupled since 1970, a stark contrast to declining death rates seen in other major non-communicable illnesses such as heart and lung diseases. Even more concerning, premature deaths from liver disease and liver cancer in England have risen by 64% over the past two decades. When the COVID-19 pandemic hit, liver-related deaths increased by an additional 21% between 2019 and 2021 – evidence that external stressors can amplify an already dire situation.^[1]

Yet the reality is that most chronic liver conditions remain undetected until they are in advanced stages, leaving patients with few treatment options and the looming possibility of needing an organ transplant.^[2] Unfortunately, a limited donor pool and lengthy waiting times mean that, for too many, the window for intervention closes before suitable solutions are found. While traditional animal models have played a helpful role in the early exploration of liver disease, they often fail to capture subtle shifts in human cellular behaviour – the very changes that might unlock novel preventive or therapeutic strategies.^[3]

Why human-first models are essential

Many promising therapies stumble during clinical trials because preclinical testing rarely reflects the intricacies of human liver disease.^[4] Subtle fibrotic processes and small yet pivotal shifts in hepatic cell populations often go unnoticed in these models – early cues that could pave the way for effective early intervention. By the time these types of changes are detected in patients, the disease may already be too advanced for treatments to make a meaningful impact.

Shifting to human-centric platforms such as organ-on-a-chip systems and donor organ perfusion devices – combined with computational modelling of gene behaviour in human models, powered by large-scale causal datasets – offers much-needed insight into the real-time dynamics of liver function. In these near-physiological platforms, researchers can track early markers of liver injury – signals that can be overlooked in animal studies. This perspective is especially key for advanced conditions like cirrhosis, where a human-focused approach might allow us to intervene long before irreversible scarring takes hold.

Turning data into early interventions

One of the most promising human-focused tools is the ex vivo perfused liver system. In this setup, a donated liver is connected to a machine that circulates an oxygen-rich blood substitute, preserving the organ’s architecture and metabolic functions for several days. Researchers can monitor a wide range of health indicators, including pH, nutrient levels, and markers of liver function such as bilirubin or lactate. If any signs of inflammation or reduced liver function are measured, they know immediately that the liver is under stress.

This environment also provides a platform to test RNA-based therapies in conditions that closely resemble real human biology. Fibrogenic markers such as collagen I and alpha-smooth muscle actin can be measured through histology and gene expression assays, giving a window into how well new treatments suppress the scarring process. Meanwhile, tools such as RNA sequencing can map out shifts in thousands of genes at once, revealing broader mechanisms of action and highlight any off-target effects. By collecting and analysing these data, researchers can observe precisely how a therapy performs in a human liver before entering clinical trials.

Marrying machine learning with lab insights

Then comes translating these organ-level insights into effective therapies through precision therapeutics that target gene modifiers of disease. Precision therapeutics demand a holistic approach that unites patient-derived tissues, computational modelling, and advanced analytics. Machine learning (ML) can help sift through enormous volumes of transcriptomic and imaging data, pinpointing which genes and pathways might drive disease progression.

The real power, however, emerges when ML insights are tested iteratively in physiologically relevant models. Instead of a single pass – where a computational pipeline yields a list of possible targets – investigators can take those leads, evaluate them using ex vivo liver perfusions, organ-on-a-chip systems or multicellular co-cultures, then feed the results back into the algorithm, creating a ‘lab-in-a-loop’ approach. Over time, this cycle refines the most promising targets, discarding less viable options early and focusing resources on therapies most likely to succeed in clinical settings.

Targeting the hardest, underserved stages of  liver disease

Cirrhosis, one of the final stages of liver disease, emphasises why earlier intervention is critical. At this point, the organ is heavily scarred, making it extremely difficult to reverse damage. Research efforts that centre on perfused donor livers with varying degrees of fibrosis, aiming to pinpoint what drives scarring at the molecular level, are not common. But, by examining real-time indicators – tying histopathological scores to advanced transcriptomics – scientists can identify precisely where and when RNA-based therapies might disrupt the fibrotic cascade.

Additionally, the scarring process makes delivery more challenging due to the reduction in liver function, a consequence of which is to impact the accessibility of therapies to their target cells. Studying failing livers and testing potential therapies on machine perfusion devices enables the distribution and efficacy of the therapy to be quantified within the disease setting, improving the chances of success in subsequent clinical trials.

A dynamic, human-focused system

However, moving from traditional drug development to precision therapeutics requires an ecosystem that is both technologically advanced and continuously adaptable. Clinical data can be fed back into computational models, which inform further ex vivo experiments, sharpening each subsequent round of candidate screening. This ongoing, iterative process stands in contrast to older pipelines that risk committing to the wrong targets for too long.

This approach can bring a new standard of care to liver disease – one where advanced platforms and data-driven human models collaborate to intervene earlier, specialise treatment, and minimise transplant dependency. Rather than hoping for incremental gains, the field can drive towards genuinely transformative outcomes.

Changing the odds for patients

Ultimately, the aim is to re-assess how we address this global crisis in liver disease treatments. By fusing human-focused science, AI-driven insights, and rigorous ex vivo validation, we can detect and treat liver disease sooner, tailoring interventions to the genes involved. This comprehensive approach offers the best chance of improving survival rates and easing the burden on transplant systems.

The question now is how quickly and successfully pharmaceutical companies will seize this momentum and open up new possibilities for precision liver care. Done correctly, these breakthroughs promise to change the odds for millions of people worldwide facing one of healthcare’s most pressing challenges.

By Kenny Moore, Head of R&D at Ochre Bio

References

^[1] https://britishlivertrust.org.uk/information-and-support/statistics/

^[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC7829073/

^[3] https://www.frontiersin.org/journals/drug-discovery/articles/10.3389/fddsv.2024.1355044/full

^[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC2864134/

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To address these challenges, Resonance Health developed FerriScan®, a system where MRI centres worldwide could send image data scanned either by 1.5T or 3T scanners to expert analysts for accurate LIC measurement. This system has been invaluable for hospitals treating patients and pharmaceutical companies developing and accessing drug efficacy with conditions like thalassemia, who require frequent and precise iron measurements.

Introducing FerriSmart®: The Future of Automated MRI Iron Measurement

In a significant leap forward, Resonance Health has harnessed artificial intelligence to create FerriSmart®, an automated system for calculating LIC from MRI images. This system simplifies the process: radiologists can upload image data into a secure portal hosting the software, which then provides a LIC report within seconds. FerriSmart® maintains the high standards of FerriScan® and is compatible with a wide range of MRI scanners, ensuring broader accessibility.

Liver iron concentration measurement through MRI is a powerful tool in diagnosing and managing conditions like hereditary hemochromatosis and thalassemia. With the advent of FerriSmart®, we are making this technology more accessible, offering rapid, reliable results at a lower cost. As we continue to expand the availability of these advanced imaging solutions, we remain committed to enhancing patient care and supporting clinicians with precise, non-invasive diagnostic tools.

Measuring Iron in Other Organs

MRI technology is also capable of assessing iron in the heart using the CardiacT2* technique which has regulatory approval for routine clinical practice. This method is frequently used in patients with thalassemia to monitor cardiac iron levels. Similarly, for investigational purposes, MRI can measure iron in the bone marrow, another suspected target organ. While not many studies have focused on this, there is evidence that patients with hereditary hemochromatosis can have excess iron in their bone marrow.

The spleen plays a crucial role in recycling iron from old or damaged red blood cells. Excess iron can be found in the spleen, due to different conditions, such as, hemochromatosis, and regular blood transfusion dependent patients, which can lead to several health issues. Accurate spleen iron measure can be important for proper diagnosis and treatment. Early intervention can help manage the condition and prevent complications.  The pancreas is another organ under investigation. Historically, hereditary hemochromatosis has been associated with diabetes, suggesting a link to pancreatic iron overload. Although MRI techniques for measuring pancreatic iron are still investigational, they hold promise for future clinical use. Additionally, iron accumulation in the spleen is typically not expected in hereditary hemochromatosis but is a significant marker in ferroportin disease, which can initially present similarly to hemochromatosis.

The Role of MRI in Assessing Liver Fat

An important complementary measurement to liver iron concentration (LIC) is the assessment of liver fat with HepaFatSmart®. Using MRI, we can quickly determine the presence of fatty liver, which can influence serum ferritin levels. This dual measurement can clarify ambiguities in diagnosing the cause of elevated ferritin, providing a clearer picture of a patient’s condition.

What to Expect During a FerriScan® or FerriSmart® MRI

For patients, the procedure is completely non-invasive, with no need for injections or contrast agents. Patients lie on a scanner bed with a radio antenna placed on their abdomen, wearing earmuffs or headphones to protect against the scanner’s noise. The data acquisition takes about 8 minutes, during which patients must remain still and breathe gently.

We understand that some patients may feel nervous or claustrophobic. To mitigate this, the scanner tube is open at both ends, with fresh air circulating, and patients can always communicate with the radiographer. Patients can also hold a device to alert the radiographer if they feel uncomfortable, and they can enter the scanner feet first if preferred. Closing eyes or using a blindfold, along with listening to relaxing music, can also help ease anxiety.

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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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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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Today, nearly one billion people worldwide suffer from OSA^[4], 80% of whom are still undiagnosed.^[5] Additionally, 10 to 15% of the global population are affected by insomnia^[6], with some of these individuals potentially having comorbid OSA. Undiagnosed sleep disorders aggravate and accelerate NCD symptoms. We should, therefore, always seek to innovate to address sleep issues and disorders that burden individuals, impact chronic diseases as well as tax the healthcare system.

Adopting a technology driven mindset for clinical research and health solutions

Evaluating underlying factors and developing technology-driven solutions opens new opportunities for improved patient outcomes, prevention, and the promotion of healthy ageing across Europe. Through strong engagement with patients and continuous partnerships with public, academic and other stakeholders, we aim to accelerate the adoption of digital health technologies (DHTs) to scale the availability of solutions and treatments for many sleep conditions including OSA and other sleep disorders, including insomnia, which directly impact NCDs.

A holistic, multi-faceted approach is needed to improve the patient pathway, and digital health solutions must be a part of this. The WHO recently published a report^[7] that discusses the benefits of Digital Health Technology and how boosting digital health can help prevent millions of deaths from non-communicable diseases in the next decade, as well as help address the current shortage of hospital staff.

Healthcare companies can also embark on clinical and healthcare outcomes research to determine the effectiveness of their innovative solutions on patient outcomes and the broader healthcare system. For example, ResMed’s supported ALASKA study – a large-scale analysis of a cohort of 176,000 individuals treated with CPAP in France – based on data from the French National Health insurance reimbursement system for new CPAP users. Through the analysis, the impact of CPAP treatment on long-term survival was explored and it was found that those who continued CPAP had a 39% better chance of survival than those who stopped their CPAP within the first year.

Along with tracking the effectiveness of new solutions, digital health has bolstered the ability to diagnose and elevate the quality-of-care patients receive. In the UK, the National Health Service’s strategic partnerships with private technology firms helped the rapid deployment of remote care solutions, allowing Health Care Professionals to monitor over 12,000 COVID patients remotely within just three months of implementation, promptly addressing health concerns, and reducing the strain on hospital resources.

OSA and technology driven strategies for sleep health

In the US, the cost of undiagnosed OSA is approximately $150 billion, highlighting the importance of accessible diagnosis-to-treatment pathway for these patients. By treating 80% of OSA patients with digital health solutions, we could save up to $50 billion annually worldwide in healthcare costs and regain productivity.^[8] But despite their effectiveness, we are continuing to face challenges when it comes to the adoption of DHTs. Lack of reimbursement structures that incentivise prescribing new solutions, and a low level of awareness among medical professionals about the importance of sleep as a foundational pillar of health are two important factors that can delay the adoption of DHTs.

As digital health technologies continue to evolve, new approaches and innovative solutions are emerging that hold tremendous potential for advancing healthcare overall. I believe it is up to healthcare leaders and policymakers to spread the word about enhanced innovation and the benefits of the deployment of digital health technologies that can help address the burden of sleep disorders and non-communicable diseases more broadly. By weaving sleep health into the digital health conversation, public health strategies, and everyday practice, well-being will be positively impacted. Not only will this help improve patients’ lives, but it will also reduce pressure on national health services and contribute to a more sustainable and efficient health service for decades to come.

By Carlos Nunez, Chief Medical Officer, ResMed

References

^[1] Redline, S., et al. (2010). The effects of obstructive sleep apnea on cardiovascular disease and stroke. Nature Reviews Cardiology, 7(5), 277-285.

^[2] Kent, B. D., et al. (2014). Obstructive sleep apnea and diabetes: Epidemiology and pathophysiologic insights. Chest, 146(4), 967-973.

^[3] Nutt D, Wilson S, Paterson L. Sleep disorders as core symptoms of depression. Dialogues Clin Neurosci. 2008;10(3):329-336. doi:10.31887/DCNS.2008.10.3/dnutt

^[4] Benjafield, A. V., et al. (2019). Global prevalence of obstructive sleep apnea in adults: Estimation using currently available data. The Lancet Respiratory Medicine, 7(8), 687-698.

^[5] Goyal M, Johnson J. Obstructive Sleep Apnea Diagnosis and Management. Mo Med. 2017;114(2):120-124.

^[6] Kaur H, Spurling BC, Bollu PC. Chronic Insomnia. [Updated 2023 Jul 10]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK526136/

^[7] World Health Organisation : Boosting digital health can help prevent millions of deaths from noncommunicable diseases. September 2024

^[8] Frost & Sullivan Report (2016) – Hidden Health Crisis Costing America Billions: Underdiagnosing and Undertreating Obstructive Sleep Apnea Drains Healthcare System.

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