Tag: Diagnostics

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By Dan Haworth, James Blakemore and Leigh Shelford
May 23rd 2025
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Dan Haworth

Dan Haworth

Head of Diagnostics

James Blakemore

James Blakemore

Senior Insight and Strategy Consultant

Leigh Shelford

Leigh Shelford

Consultant Physicist

Insights from ESCMID 2025: trends and future in diagnostic testing

SUMMARY

This year’s ESCMID event reinforced that access to effective diagnostic tests must increase, and that long-promised technologies are finally close to delivering clinical value.

What makes your platform different?
One recurring message: speed, accuracy, and cost effectiveness are now expected. Differentiation lies in how well a platform fits real-world clinical settings, from training and workflow to data interpretation and system integration.

Takeaway for developers: Think beyond technical specs. Differentiation increasingly depends on usability, trust at scale, and measurable impact on care delivery and operational efficiency.

ESCMID 2025 marked a shift from technology-led innovation to outcome-led development. The key question is no longer “Can it be done?” but “Does it solve the right problem, in the right way, at the right scale?”

Headline Trends

  • Antimicrobial resistance (AMR) and antibiotics use – increasing access to and uptake of diagnostic testing is key for effective interventions
    AMR remains a major global health challenge. Antibiotic use continues to rise, especially in primary care, where most prescriptions are issued without diagnostic support. Addressing this requires a broader range of diagnostic systems. These must balance performance with affordability and usability, enabling appropriate treatment decisions in both hospital and community settings.
  • The role of syndromic testing is still being debated
    While useful in hospital and acute care, syndromic panels have seen limited uptake in primary care due to cost and complexity. They support antimicrobial stewardship. But they must become more accessible and cost-effective to broaden adoption.
  • Genetic sequencing may bring a step change in clinical utility in diagnostics
    Sequencing is moving closer to clinical use. It has the potential to reshape diagnostics. Developers must build workflows and systems that integrate sequencing seamlessly. They must interpret results clearly and deliver clinical value at scale.
  • ML, AI, and automation are maturing
    AI tools are expanding from imaging to in-vitro diagnostics. They offer clinical augmentation value to tedious manual workflows, image interpretation, and data integration. The focus is shifting from innovation to implementation, embedding tools into workflows with trust, reproducibility, and regulatory alignment.

The annual congress of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) brings together clinicians, researchers, and industry leaders to explore the evolving landscape of diagnostics. This year’s event highlighted some key themes shaping the future of diagnostic impact and delivery.

AMR and Antibiotic Use: Access to Testing Is Critical

Antimicrobial resistance remains a central challenge. With an estimated 40–50 billion antibiotic doses taken daily and use projected to rise 50% by 2030, intervention is urgent. The vast majority of prescriptions occur in primary care. They are often without diagnostic support.

Improved access to diagnostics is key. Many current platforms focus on performance where demand already exists, but the biggest opportunity lies in reaching settings where no testing is currently available. Systems must support appropriate treatment decisions. They must balance speed, accuracy, and pathogen identification with usability, affordability, and integration into clinical workflows.

Syndromic Testing: Performance vs Cost

Multiplexed syndromic panels are established in hospitals and acute care, but uptake in primary care remains low. Their value in guiding antibiotic use is clear yet cost and complexity are barriers to broader use.
Developers must reduce system cost and complexity to reach more healthcare environments and fit into reimbursement frameworks. Technical capability alone is no longer enough. Integration, ease of use, and clinical decision support are now central to adoption.

Sequencing-based diagnostics are also beginning to compete with syndromic approaches, raising the bar for accessibility and performance in multiplexed testing.

Sequencing: Moving Toward Clinical Routine

Genetic sequencing is rapidly approaching routine use in clinical diagnostics. With falling costs and expanding platform availability, it holds the potential to reshape infectious disease diagnostics, particularly in syndromic or multiplexed contexts.
At ESCMID, multiple case studies demonstrated the use of same-day metagenomic sequencing for pathogen identification in respiratory and bloodstream infections. Amplicon-based approaches were also discussed, particularly for rapid variant detection and resistance gene profiling. Broader sequencing methods, including microbiome analysis, may also play a role in future clinical applications.

However, sequencing workflows are not yet plug-and-play. Upstream processes, such as sample collection, DNA extraction, host depletion, and library preparation, remain technically demanding. Downstream, interpretation, bioinformatics pipelines, and clinically actionable reporting are major hurdles. While sequencing speed and cost are improving, the challenge now lies in integrating these steps into streamlined, automated, and interpretable systems that deliver value at the point of care.

Developers must ask strategic questions. Should initial diagnostic applications focus on relatively simple, targeted sequencing? For example, amplicon sequencing for variant detection, or on broader, hypothesis-free metagenomic approaches? Will sequencing be decentralized or remain concentrated in specialized hubs? How can systems present complex data in ways that support decision-making by non-specialist clinicians? The opportunity is significant. But realizing it will require systems that balance performance, usability, and clinical relevance.

ML, AI, and Automation: From Hype to Implementation

Artificial intelligence has already made an impact in diagnostic imaging, including MRI, CT, and ultrasound. At ESCMID, attention turned to the broader use of AI and machine learning in in-vitro diagnostics, with exciting potential across three main areas:

  • Image analysis: AI is enabling rapid interpretation of high-resolution optical data in fields like digital pathology, haematology, and spectral imaging. These tools can increase diagnostic accuracy while reducing the burden on human reviewers.
  • Multi-marker data interpretation: AI models are increasingly used to integrate complex biomarker datasets, where the combined signal across markers yields diagnostic insight. Some platforms already use deterministic models; the shift toward machine learning promises greater flexibility and performance, particularly as training datasets grow.
  • Workflow optimization and automation: AI is being applied to streamline laboratory operations, reducing hands-on time, standardizing results, and minimizing error rates. This is especially valuable in high-throughput or resource-constrained settings.

Several ESCMID sessions showed AI being used to support antimicrobial resistance prediction, improve taxonomic resolution, and aid diagnostic decision-making. Importantly, the field is moving from exploratory development to real-world deployment. The emphasis is now on clinical validation, regulatory clarity, reproducibility, and integration with existing systems.

For developers, success will depend on more than accuracy. Trust, interpretability, and usability are emerging as key differentiators. Tools that embed smoothly into clinical workflows, minimize training requirements, and deliver repeatable, high-confidence outputs will define the next wave of AI in diagnostics.

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Wondering what these trends mean for your next diagnostic system? A quick conversation could help you shape a clearer path from concept to clinical impact. Get in touch with one of our team:

Dan Haworth, Head of Diagnostics
dan.haworth@cambridge-design.com

James Blakemore, Senior Insight and Strategy Consultant
james.blakemore@cambridge-design.com

Leigh Shelford, Consultant Physicist
leigh.shelford@cambridge-design.com

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Unlock the full potential of next-generation diagnostics systems with our 16-page guide. Gain valuable insights on:

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Whether it’s unlocking the potential of a groundbreaking biomarker, innovating with new assay or detection technologies, or navigating the updates needed for your system to compete with newer devices, the path ahead is undoubtedly complex.

That’s why we’ve distilled the essence of successful diagnostic system development into this 10-minute guide – it’s your compass in the intricate world of diagnostics, highlighting critical considerations essential for turning ambitious ideas into tangible solutions.

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Five hurdles to digital health innovation in the UK|||
November 30th 2023
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Five hurdles to digital health innovation in the UK (and how to overcome them)

CDP recently led an investigation into how to advance innovation in digital health in the UK for the CPI, UKRI/Innovate UK and ABHI. Our aim was to find out how best to enable the UK to be the place of choice for enabling high-risk digital health innovation, improving patient outcomes.

Our work with 50 leading healthcare professionals and entrepreneurs revealed that the UK has an enviable record in early-stage innovation, a highly regarded healthcare system and a potential treasure trove of high-quality data.

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However, we also found several hurdles that trip up many innovations before their potential can be truly realized.

In this article, we describe our top five hurdles to success and signpost the resources available to help innovators overcome them.

1. Offering the wrong product at the wrong price

Let’s start with perhaps the most obvious: you need to get your offering right. This was one of the more frequent topics to emerge in our discussions. True, it tended to come from the industry and investors, rather than entrepreneurs themselves. But perhaps this is the point; those closest to the concept are so captivated by the opportunity to solve a problem that they are rarely the best judge of commercial success.

“The biggest problem is developing stuff we don’t need, at the wrong price point.”

Life Sciences Lead, multinational consultancy

“People have struggled with finding the right balance between fixing a problem not just for the sake of it because it’s going to add value, but also there is a market attached to it.”

Medical Director, AI dermatology revenue-earning startup

Getting the right product at the right price is not easy. Regardless of how it is funded, healthcare everywhere is a complex system of separate entities with conflicting priorities. One of the biggest challenges for digital health offerings in particular is that the person paying the bills is rarely the direct beneficiary. This is as true across the NHS in the UK as it is in insurance-led services in the US.

Digital interventions are regularly shown to make significant positive impacts on diagnosis, therapy, adherence and behavior change. To date, the FDA has approved, authorised or cleared 171 AI/ML-enabled medical devices. However, digital means adding overheads (electronics, batteries, software or new digital services) to an already overstretched budget that tends to bring value much further down the pathway.

To get the right product at the right price, you need to be crystal clear about the value you bring and who you bring it to so that you can ensure the price is right.

Helpful Resources

For the UK market, we found the following resources helpful in crossing this important hurdle:

  • The NHS Innovation Service provides an innovation guide that explains how to build a value proposition
  • The NICE Advice Service provides personalized advice on the value propositions for a fee
  • The NHS Clinical Entrepreneur Programme (CEP), launched in 2016, provides training for NHS staff on the skills required to build a healthcare startup, all without them needing to leave the NHS

Indeed, this is such an important area that we at CDP are looking at how recent advances in Generative AI might make this easier to get right from the outset – not just for offering services within the UK, but how UK-based innovation can provide the right offering in the larger markets of the US and EU.

2. Neglecting the needs of key stakeholders

Digital products and services are still a novelty in healthcare. Even the regulation is taken from a device mindset – consider the terms SaMD (Software as a Medical Device) and now even AIaMD (Artificial Intelligence as a Medical Device). The digital-first mindset is to move fast, learn and repeat to get the best user insight and optimum benefit to market as fast as possible. This is not an easy marriage for healthcare, where verification and validation are critical steps to approval.

“If you are manufacturing a digital health product, you have three sets of policies to navigate right now [in the UK].”

CEO, digital health SME (referring to NHS DTAC, NICE and MHRA)

“The regs are written to cover all medical devices. They’re not very specific; it’s very high level and quite hard to interpret what we should actually be doing as an individual company.”

Medical Director, AI dermatology revenue-earning startup

This is not simply about the regulator; it is also about who will receive, who will administer and who will pay for your digital offering. On top of proving safety and efficacy, payers and adopters want to see evidence that your technology works under real-world conditions and produces sufficient benefit relative to current clinical practice to justify its cost. Not only do you need to convince your investor you have the right product at the right price point; you also need to convince them you have access to reimbursement.

This need has led us here at CDP to build a strategy and insight team that explicitly looks across the spectrum of stakeholders including the end-user, practitioner and payer.

Helpful Resources

The following resources are helpful when considering the regulatory and UK purchaser stakeholders:

  • The NHS’ AI and Digital Regulations service offers a developer’s guide that leads you through the various regulatory and NHS requirements for digital technologies
  • The NICE Evidence Standards Framework is designed to help ensure NHS stakeholders are adopting robust technologies that are likely to provide the expected performance, and are good value for money. The framework can be used by developers to understand their customer needs. NICE also offers an assessment of current/planned evidence via their META tool
  • The NICE Early Value Assessment can also help indicate the value your product can bring, and allow you to get support to understand what further evidence needs to be generated.
  • Similarly, NHS’ Digital Technology Assessment Criteria (DTAC) are designed to assess suppliers at the point of procurement, or as part of a due diligence process, ensuring digital technologies meet minimum baseline standards. The criteria can also be used by developers to understand what is expected for entry into the NHS and social care
  • FDA’s list of approved, authorized or cleared AI/ML-enabled medical devices

3. Testing, verifying and validating

The regulatory pathway will force you to verify and validate. It will be rigorous. It will take more time than you or your investors want. So, you will need to test, test and test again as early as possible to build the evidence you need for investment. And, importantly, test both the medical efficacy of your offering and its likely commercial success.

“[It] can take longer than six months, ridiculously, to build a cohort of data.
Getting people to step away from frontline service in the NHS is a fundamental challenge of getting access to that data. Even if you offered to pay, they’d say” ‘I don’t care; it’s not the money I’m short of, it’s people’.”

President, medical imaging multinational

“We really struggle to work with SMEs because we’re not able to move at the pace that they require for their cash flow.”

Director of Innovation, NHS trust

As these sentiments show, however, gathering data takes time and patience. The NHS is indeed a treasure trove of data, but unlocking it is a real hurdle. Existing NHS data typically needs preparation – cleaning and anonymizing – before you can access it. And there simply may not be the staff available to do this, meaning you may need to build additional paths to gather test data.

The fidelity of the test can start low-fi, but will need to increase as you develop. CDP typically starts with insights research and human factors studies using UI sketches/descriptions of the product to explore the true user journey, before moving onto trials with real-life samples and wizard-of-Oz demonstrators. This builds a body of evidence that reinforces your expectation of efficacy with the all-important usability and the commercial viability of your offering, before embarking on the summative human factors, clinical and market trials.

Helpful Resources

The following resources can help you prepare your plans for testing, verification and validation:

  • NIHR study support service provides guidance and advice
  • The HDR UK Gateway portal helps researchers find existing data sets and connects them to relevant stakeholders
  • Trusted Research Environments (TREs) are a new initiative to facilitate access to NHS data for R&D. Only a few TREs currently exist and there is no guarantee they will have the data you’re looking for. However, the teams involved are well placed to advise you on next steps. Even though this requires approval from HRA and notification to MHRA, consider if it might be better to just do your own trial to collect fresh data instead. This is where an experienced external innovation partner can be very helpful

4. Navigating healthcare as a ‘system of systems’

The benefits of digital health typically require systems integration. Yet, healthcare everywhere is a complex system of systems, each element with its own approaches, tools and requirements.

“[Different hospitals are] probably using different systems, different levels of maturity with different versions, with different level plugins. That probably means, even if I create it using the standard, it won’t automatically fit. It needs modification, adaptation and someone to do the translation.”

Digital Health Advisor, ex-NHSX

“You can often have very inflexible contracts with your electronic health records supplier; for example, if you want them to make one change or open up in an API or something like that, it can be prohibitively expensive.”

Director of Innovation, NHS trust

While there are only a few dominant providers of Electronic Health Record (EHR) systems, each installation is likely to be different. Moreover, the EHR providers will guard access jealously. Microsoft, Google, Amazon and others provide integration services to structure and translate data, but that is likely to be only a small part of the problem, and only useful if you are ingesting unstructured data from multiple sources.

At CDP, we encourage our clients to focus on providing easy-to-use, yet secure APIs built around well-structured data that map well to the established data standards such as FHIR. Taking ownership of your own data in this way makes it easier to deploy, integrate and support.

Helpful Resources

The following resources can help you prepare your digital health services for system integration:

5. Building a strong team

Innovation is rarely one guy in a garage. This is especially true in the digital health space. You will need to build a great team led by experienced professionals across the disciplines. Get this right and everything else will fall into place. Work out your strengths and weaknesses and actively seek resources to complement your team.

“The most useful thing to an innovator is access to an actual practicing frontline clinician who understands the problem that they want solved. It’s a real challenge to get to these people. I might spend months trying to find someone who would talk to me.”

President, medical imaging multinational

“The UK does not have enough engineering capacity… does not have enough people with product skills… does not enough people with this sort of legal regulatory skills.”

Digital Health Advisor, ex-NHSX

Helpful Resources

The following resources can help innovators looking to build a world class team:

  • Many Health Innovation Networks (HINs) and NHS trusts have innovation teams who may be able to help matchmake with clinical champions. The NHS Innovation Service is a good place to start, but it’s worth seeing what individual trusts are doing.
  • HINs and NHS trusts often also support innovation and hackathon events which are a great way to find those with a similar innovative mindset – but a complementary skill set.
  • And then there are organizations such as CDP, who bring end-to-end product development services with the hard-won experiences of how to navigate this exciting but often frustrating area of innovation.

In addition, many of your digital needs are engineering and operational ones. Recruiting experienced people from the finance and technology sectors where the UK is strong will bring you good skills and expertise in algorithm development, handling personal data and building scalable secure systems.

It’s tempting for you (and your investors) to under-resource your team and compromise in the early phases. But as the hurdles above clearly show, this rarely leads to success. Build the great team you need from the outset, to make sure you truly have the right product at the right price, that meets the expectations of the key stakeholders, is properly tested and ready to integrate into the healthcare system of systems.

Download the full action plan for digital health innovation in the UK here.

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At CDP, we continue to follow up our insights working with clients and partners to find practical solutions to complex problems. To find out more about what successful innovation in digital health looks like, please do get in touch.

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How data and AI are changing bioprocessing
October 30th 2023
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How data and AI are changing bioprocessing – and why it’s needed

After numerous insightful talks and engaging conversations with industry leaders at this year’s BioProcess International, the key theme was clear: data, data and more data.

Data has always been important, but now it is being collected to model current processes, understand how they work, and improve them. This is a trend that is only likely to accelerate in the future as AI becomes part of everyday life – both in and outside of work.

Using data-based modeling to optimize well-established industrial processes

There are many traditional processes that are used in the manufacture of antibodies, mRNA vaccines and cellular therapies. Companies are now collecting extensive data from these processes and using modeling to create their ‘digital twin’.

The processes modeled range from relatively simple tasks such as optimization of freezing/thawing product intermediates, freeze-drying and automated buffer preparation, to more complex procedures such as bioreactor scale-up. Although these used to be manual ‘craft’ processes run by a combination of experience and pre-existing data, there is now a trend for them to be tested and optimized using in silico methods.

Using modeling to improve purification methods

Bioprocessing is used to create many therapeutic products, from molecules such as protein, DNA and RNA to much larger entities such as viruses and eukaryotic cells. Their production has many different steps that often require extensive purification before the next step can proceed. Common purification methods include clarification, chromatography, ultrafiltration/diafiltration and sterile filtration.

These methods were typically used in an empirical way based on experience with similar products. Now however, use of modeling has led to a much more detailed understanding of how these separation/purification methods work. It allows the prediction of when column/membrane capacity is reached, and when “breakthrough” of contaminants is likely to occur. It has also led to the development of alternatives to standard resin-based column chromatography such as the incorporation of new reactive chemical groups on membrane filters that can then act like traditional resin-based columns.

Benefits of Process Analytical Technology (PAT)

PAT refers to on-line/at-line measurement of critical product quality and performance attributes so that real-time direct data collection can be used to control and optimize manufacturing processes.

PAT is being augmented by a much wider range of analytical techniques than before and now includes many different types of spectroscopy including variable path length, Fourier-transform infrared, Raman and Dynamic Light Scattering, as well as Nuclear Magnetic Resonance. The use of PAT for direct data collection that links to immediate process control is only likely to accelerate.

Inexorable rise of disposable closed cell processing systems

In addition to the data theme, it was clear to see that the number of automated closed cell handling and processing systems – from cell selection to expansion and harvesting – is rapidly increasing. Companies aim to offer end-to-end solutions to traditionally manual processes, either by offering modular components or a single complete system.

The options for choosing automated disposable bioreactors/cell expansion systems are also increasing, with many players recently entering the market. It is clear why this option is advantageous; traditional stainless-steel bioreactors are complex, expensive, and laborious to clean and maintain.

Just how large these systems can grow is shown by ThermoFisher’s 5000L disposable Dynadrive bioreactor, which is offered as a fast-to-install option compared to stainless-steel alternatives. However, the environmental impact of the disposable route is a long-term concern and is expected to be a point of contentious discussion over the coming years.

Bioprocessing technology is developing (but not fast enough for demand)

The technological developments described above are certainly needed as advances in eukaryotic culturing methods are allowing higher and higher cell densities to be realized, which makes purification more challenging. Furthermore, the pipeline for products that use these technologies is growing at a dizzying rate with over 1,500 cell and gene therapy and 700 mRNA trials listed on the US Clinical Trials site. New higher throughput processing techniques will need to be developed to accommodate this demand.

The industry clearly recognizes this and companies were very open in sharing their results at BioProcess International – both good and bad! They are also keen to work with the process equipment manufacturers to optimize performance. Overall, improvements have been made, but there is a long way to go.

Performance can be improved by a virtuous circle of data generation, data modeling and innovative design and engineering – something we at CDP are already doing to help our clients succeed.

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For more on how data modeling and automation can increase bioprocessing throughput and optimize manufacturing performance, contact Cambridge Design Partnership.

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January 11th 2023
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Cambridge Design Partnership and CPI launch the UK’s In Vitro Diagnostics Roadmap

Cambridge Design Partnership and technology innovation catalyst CPI today launch A Strategic Technology Roadmap for the UK In Vitro Diagnostics Industry, a major new report for industry leaders, government, and the UK’s health tech companies.

The UK in vitro diagnostics (IVD) industry has the potential to help boost UK economic growth and make the UK a global leader in the industry while improving health in the UK and for people worldwide. A new strategy, applied over the next 10 years, can see the industry transformed. The Roadmap, researched and written by Cambridge Design Partnership, in partnership with CPI, the Association of British HealthTech Industries (ABHI), and funded by Innovate UK, defines the key technologies and strategies that can place the UK at the forefront of this industry.

In vitro diagnostics – analysing biological samples outside of the body to determine health status – shows huge promise, from earlier cancer detection to the prevention and management of infectious and chronic diseases. The UK is already a global leader for science and technology. Many of the technologies at the heart of this thriving industry were pioneered in the UK, from lateral flow tests to the genetic sequencing technology used in around 80% of the world’s genetic sequencing systems. But the UK is not at the top table of the growing IVD industry, with recent estimates suggesting it accounts for just 3% of a £90bn industry. While the UK excels at research, it is held back by the commercialisation process. As a result, many UK inventions are commercialised by overseas companies – start-ups and scale-ups are acquired by global leaders, and we are yet to see the emergence of major UK IVD companies or state-of-the-art R&D centres from the global industry.

A Strategic Technology Roadmap for the UK In Vitro Diagnostics Industry sets out a programme of change to help meet the clinical needs of the future and support the UK IVD industry, government, and Innovate UK to make informed investment and capability development decisions. This Roadmap is a resource for:

  • Companies planning their strategies over the medium to long term, particularly leaders in medical technology, the pharma industry, and the investment community that supports them
  • Policy makers and broader government stakeholders shaping future UK government strategy and funding decisions
  • All those interested in and charged with the success of UK PLC.

Pari Datta, Principal Consultant in Strategy at Cambridge Design Partnership and the Roadmap’s lead author, says, “It’s hugely encouraging that the UK continues to lead the science behind all the major opportunities for the IVD industry – just as it did before for lateral flow testing and DNA sequencing. But we’ve yet to create global IVD industry leaders of our own or attract investment in UK R&D from global IVD leaders. Our strong position in research means we can change that. We can become one of the global IVD leaders of the future, boosting national economic growth and taking a global leadership role while improving patients’ lives worldwide.”

The Roadmap is part of the Health Technology Regulatory and Innovation Programme, an Innovate UK-sponsored initiative led by CPI in partnership with ABHI. This programme delivers a package of support to UK health tech companies to help them meet the regulatory requirements for developing, commercialising, and deploying their medical technology in the UK and globally.

A second report – Challenges and Opportunities for the UK HealthTech Industry – was also published today. For this report, CPI and ABHI worked with over 350 small-to-medium-sized enterprises (SMEs), Innovate UK and health tech stakeholders to identify the key challenges faced by the UK health tech SME community.

Dr Arun Harish, Strategy Director at CPI, said: “As a Catapult centre leading on HealthTech in the UK that works with many health SMEs in the sector, we understand how hard they find the navigation of the regulatory approvals process and the route to commercialisation. These two first-of-their-kind reports will help industry, policymakers, government, funding agencies and the wider HealthTech ecosystem immensely with shaping future interventions to grow the HealthTech industry in the UK. They also highlight the need for further intervention to support UK HealthTech businesses in developing and scaling-up innovative technologies, which will ultimately benefit UK populations.”

Selected Roadmap highlights

  • The Roadmap begins by defining a shared vision, acting as a focal point for stakeholders involved in the project and those going on to implement and follow the Roadmap. The vision statement is: “The UK will be the industry nucleus for world-leading businesses, with the resources, skills, and proven pathways for advancing pioneering technologies into successful data-enabled IVD solutions.”
  • Using oncology and infectious diseases as key disease states, input was collected from clinicians, publications, and patents to define nine key technology-enabled opportunities for the global IVD industry over the next 10 years. These are:
    1. Digital PCR
    2. Sequencing
    3. Cell-free nucleic acids
    4. Digital biomarkers
    5. Proteomics
    6. Combined biomarkers
    7. Single cell analytics
    8. Exosomes
    9. Metabolomics
  • The report recommends that the UK develops specific technologies in materials, enzymes, artificial intelligence (AI)/data, optics, microfluidics, and sensors. To advance in these opportunities, the IVD industry also needs to build collaborations with companies that have expertise in these areas.
  • Seven major challenges are identified that must be resolved, including lack of UK infrastructure and ecosystem for design and development, acquiring patient samples, clinical studies, commercialisation, adoption, clinical reimbursement, and financing and investment.
  • In conclusion, the report recommends that the UK needs to adopt the following strategies to overcome these challenges and realise its vision for the UK’s IVD industry:
    • Boost the IVD industry’s profile in the UK
    • Create a focused government-led strategy for the UK IVD industry
    • Support access to NHS resources during development and commercialisation
    • Assist IVD companies through a well-defined and harmonised regulatory pathway
    • Develop partnerships for high-risk IVD developments that have defined pathways to clinical use

Download the In Vitro Diagnostics Roadmap

For further information and media enquiries, please contact: media@cambridge-design.com or call 01223 264428.

WHITE PAPER

A Strategic Technology Roadmap for the UK In Vitro Diagnostics Industry

A major new report for industry leaders, government, and health tech companies

“The UK will be the industry nucleus for world-leading businesses, with the resources, skills and proven pathways for advancing pioneering technologies into successful data-enabled IVD solutions”

The UK in vitro diagnostics (IVD) industry has the potential to help boost UK economic growth and make the UK a global leader in the industry while improving health in the UK and for people worldwide. A new strategy, applied over the next 10 years, can see the industry transformed.

The Roadmap, researched and written by Cambridge Design Partnership, in partnership with CPI, the Association of British HealthTech Industries (ABHI), and funded by Innovate UK, defines the key technologies and strategies that can place the UK at the forefront of this industry.

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Download the Roadmap


Digital PCR||||||
By Richard Owen
November 10th 2022
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Richard Owen

Senior Consultant Bio-scientist

Digital PCR – a technology set to transform clinical testing?

Pregnancy screening, cancer treatment, organ transplant – digital PCR testing has the power to enhance clinical decision-making. But what is needed to take it mainstream?

Polymerase Chain Reaction (PCR) testing has reached unlikely levels of fame due to the COVID-19 pandemic. However, its latest evolution, digital PCR, could be a real game-changer for commercial diagnostics.

The power of digital PCR

When people talk about PCR testing, they often refer to quantitative PCR. This technology is fantastic at delivering binary answers, for example, whether a disease is present or not. It can also determine to some extent how much of a disease is present in a sample (though the process is inaccurate). Quantitative PCR’s quantification can be improved by calibrators, but this is complex, expensive, and time-consuming for a lab to perform.

Digital PCR advances this technology to deliver precise quantification and improves detection of low-frequency DNA targets.

The potential to transform clinical decision-making

The key benefit of digital PCR can be summed up in two words: better data. It has the power to transform clinical decision-making, for example, in the following areas:

Pregnancy screening

Highly accurate testing for chromosomal trisomies, such as Down’s syndrome, by detecting traces of foetal DNA in maternal blood. Next-generation sequencing (NGS) is an existing alternative but has extremely complex protocols, including DNA purification, DNA library preparation, sequencing, data alignment, and analysis.

Cancer treatment

Pinpointing disease progression by detecting tumor DNA in liquid biopsies.

Organ transplants

Detecting DNA sequences leaking from a donor organ (a sign that the host immune system is rejecting it).

Virus detection

Increasing accuracy in treatment of HIV, hepatitis C, herpes, cytomegalovirus, and other infections.

Disease diagnostics

Quantification of bacterial species in stools due to digital PCR’s lower sensitivity to inhibitors.

Digital PCR could also deliver accurate quantification of levels of other infectious diseases such as respiratory viruses and sexually transmitted infections. This precision isn’t currently available but could be useful for clinicians to differentiate between different stages of infection.

How does digital PCR differ from quantitative PCR?

Digital PCR is a development of ‘standard’ PCR, using the same concept of exponential amplification of template DNA with DNA primers and a polymerase enzyme. It has two crucial differences: compartmentalization and end-point data collection.

Compartmentalization

Instead of performing a reaction on a whole sample, digital PCR splits the sample across a large number of separate compartments. ‘Compartment’ could mean a microfluidics chip, or a droplet suspended in an emulsion.

Each reaction is capable of detecting a single molecule of DNA. A larger number of amplification cycles are generally run, typically 60 versus 40 for standard PCR. Just one DNA molecule in a compartment is enough to initiate a PCR amplification reaction.

End-point data collection

Unlike quantitative PCR which reads after every amplification cycle, digital PCR just needs to read once when all the amplification cycles are complete. This is an important saving, as otherwise, all the thousands of individual compartments would need to be read every cycle, which would be a significant challenge.

Digital PCR overview

What’s stopping the mass adoption of digital PCR?

Though digital PCR has been around for 20 years and is mentioned in thousands of patents, only a handful of commercial products use the technology. The primary challenge innovators need to crack for it to go mainstream is optimal compartmentalization.

Cracking compartmentalization

Compartmentalization affects key performance parameters, such as the assay’s dynamic range, linearity, accuracy and ease of use; its cost; whether it’s run as a batch or on-demand; and how many samples can be run at once. The number of compartments in the assay must be high, relative to the concentration of input DNA molecules in the sample. But if the assay uses too few compartments, the accuracy of quantification will be too low, and the assay must be repeated using a diluted sample.

Why does compartment design matter?

Digital PCR’s randomly apportioned target molecules across a large number of compartments mean there will be some compartments with no targets, some with one, and a few with two or more. As it’s not known how many target molecules are in each positive compartment, the Poisson distribution is used to determine the most likely proportions of compartments with one, two, three, or more DNA targets. Using the Poisson distribution allows accurate quantification, but relies on two important factors:

  1. The input template being randomly spread throughout all the reaction chambers
  2. All compartments being the same size

These are critical parameters, and there are two main ways to achieve them. The first is passing the sample over a microfluidic flow cell containing microwells commonly filled using capillary action. The second is encapsulating the nucleic acid in a huge number of water droplets in an emulsion of oil, with each droplet containing a separate reaction.

Microfluidic droplet generator developed at CDP

The ideal compartmentalization system would retain the ease of use of current quantitative PCR systems and have a similar lab-bench footprint and costs. However, current approaches (typically involving microfluidics or droplets), require multiple complex disposables and sophisticated optics. If a new approach was developed with lower costs, clinicians and test centers may well convert to digital PCR for all their applications. The company that manages to crack this challenge has the potential to dominate the PCR market and provide huge advances to clinical decision making.

To talk to us about our current innovation in the field of digital PCR, get in touch.

Mastering fluid flow to enhance user experience|
March 22nd 2022
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Mastering fluid flow to enhance user experience

Ice cream and blood are two things you probably don’t want to think about simultaneously. But both are full of organic proteins and fats and behave differently from a fluid like water when they’re pumped through tubes. Innovators sometimes think about these similarities when creating, for example, a novel ice cream dispenser or device that filters out platelets from donor blood .

How a substance flows is a vitally important consideration for many products, from foods to skincare to medical devices to household paints. Development teams need to keep in mind a wide range of flow behaviors (for example, flow through nozzles, non-Newtonian flow, and foaming) to hit the sweet spot: a positive user experience that makes a product stand out in a crowded market. This means thinking about the science of how liquids and gases behave (fluid dynamics), as well as how the product responds to user interaction.

Look at how the squeezable plastic ketchup bottle differs from the glass bottles that were standard before 1983. The new design completely changed the user experience – no more digging down into the bottle with a knife to get the ketchup flowing again. Things became even easier for ketchup lovers with the debut of the upside-down squeezable bottle – no more awkwardly storing ‘regular’ bottles upside down in the fridge.

Or think about how the experience of washing your hands changed after the arrival of the liquid soap dispenser. Instead of having to share the same bar of soap with others, people can now wash “without the soapy mess”, as Robert R Taylor, who introduced SoftSoap liquid soap, put it, and can take only as much soap as they need.

While the flow of some liquids is analogous to water, whose behavior is well understood, other substances behave in much more complicated ways, requiring in-depth analysis work to understand when designing new products. For example, the air bubbles in ice cream make it behave as a liquid foam. Ice cream’s flow will change depending on how you’re dispensing it: Push it at high pressure through a narrow channel or nozzle, and the air bubbles will be compressed, allowing more ice cream to flow through the nozzle at once. When the ice cream is returned to normal pressure, the air bubbles re-expand, and the ice cream returns to its original size. Because of this complex and variable behavior, designing a product to dispense ice cream relies on hands-on experiments… which can mean going through gallons of ice cream before you can create a design that works as intended. Only by conducting these experiments to understand ice cream’s behavior can you build the mathematical model required to effectively develop a high-performance machine.

While it’s a shame to use gallons of ice cream in the quest for a better product, it’s not an environmental disaster. But shipping water-based products around the world does contribute to fossil fuel consumption and climate change. Removing water from laundry detergent helps cut shipping emissions by reducing bulk and making shipping more efficient. But it also dramatically changes how detergent flows and gets used by consumers. For example, measuring out 10 ml more detergent than recommended likely wouldn’t have an impact if you’re using a product that’s mostly water. But being off by 10 ml when detergent is concentrated could make a big difference for your laundry. So, it’s vital to ensure that dispensing is accurate, which requires an understanding of flow.

There are so many flow behaviors that can affect a product’s design. For example, should a container for insecticide include a mechanism to avoid skin contact and spillage? How could a medical device for freezing tumors be redesigned to eliminate vapor locks without the use of heavy and bulky high-pressure gas cylinders? Is there a way to dispense foaming hand soap in a decorative pattern for a premium experience?

Getting the design right for a flowing substance can differentiate between a product that fails and one that creates an experience that shifts category norms and delivers breakthrough consumer delight.

References

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February 9th 2021
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CDP completes pilot manufacture of Point of Care diagnostic readers for rapid COVID-19 testing

A team at UK product and innovation company Cambridge Design Partnership (CDP) has produced highly deployable devices for COVID-19 testing. CDP has been collaborating with diagnostics tech firm QuantuMDx to refine their Q-POC™ device and produce the first batch of readers to detect COVID-19 within approximately 30 minutes. QuantuMDx is now investing over £11 million to scale up production and introduce this rapid diagnostic solution to benefit patients and frontline health workers across the globe.

QuantuMDx is developing molecular diagnostic devices for a range of diseases and has developed and launched a highly accurate lab-based SARS-CoV-2 assay. Prior to the COVID-19 outbreak, the firm had commissioned CDP to produce prototype devices for CE marking. CDP worked through the first UK lockdown to improve the design of the reader and the first units are deployed at UK hospitals for COVID-19 testing studies.

“After beginning our partnership with QuantuMDx during 2019, we were delighted to be asked to collaborate with this innovative company once again, at a critical time. The team has been highly motivated by this crucial project and proud to contribute to the national effort,” says Dan Haworth, CDP’s Head of Diagnostics.

Colin Toombs, VP Research & Development at QuantuMDx, said: “We’ve worked in partnership with CDP since April last year, to undertake accelerated pilot manufacture of our Q-POC™ device, which is a portable DNA/RNA analyser offering rapid, sample-to-answer, molecular diagnostic testing at the point of care. The QuantuMDx and CDP teams have worked in close partnership to optimise our product development and manufacture devices to deliver testing for COVID-19. They are being released initially for research use, but we are rapidly moving towards CE-IVD of Q-POC™ for SARS-CoV-2 detection. Working together with CDP, we’ve established an ongoing partnership for the future.”

The device works by processing a swab sample, amplifying the target sequence specific to SARS-CoV-2, which causes COVID-19, and then detecting whether the virus is present. This all happens within a sealed cartridge that is controlled by the reader with minimal user involvement.

“Within approximately 30 minutes from sample collection, the device will give an accurate answer to whether the patient has COVID-19” says Dan.

These first new readers have been designed and built at CDP’s HQ in Cambridgeshire, where the company has short-run manufacture capability alongside its R&D facilities.

CDP’s team working to develop the QuantuMDx device includes mechanical and electronics engineers, software engineers, regulatory experts and manufacturing engineers.

“We worked at speed to design, build and test these important devices as quickly as possible. We are all thrilled to play our part in beating COVID-19 and we congratulate QuantuMDx on moving to mass manufacture,” added Dan.

 

For further information and media enquiries, please contact: media@cambridge-design.com or call 01223 264428

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By Karla Sanchez, Alison Hart and Maru00eda Fernu00e1ndez-Martos Balson
April 3rd 2020
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Karla Sanchez

Karla Sanchez

Senior Consultant Biomedical Engineer

Alison Hart

Consultant Data Scientist

Maru00eda Fernu00e1ndez-Martos Balson

Biomedical Engineer

COVID-19 Modelling

Models are everywhere – anyone who has played a computer game has encountered a model, but in a pandemic, mathematical models are vital for understanding the dynamics of transmission, disease progression, healthcare needs and the overall outcome on the population.

Recent real world events have shown that critical decisions are being taken based on model data. Many people are still uncertain of how models work which can lead to them being met either with undue suspicion or absolute faith. In reality models are incredibly powerful, as you can test different courses of action quickly so an optimised response can be formulated, but their limitations need to be understood.

What is modelling?

Here we try and explain the basic principles of models, how mathematical models can help and what their limitations are.

Mathematical modelling involves making a mathematical representation of a system where the expected outcome of changing various parameters can be calculated. This means you can examine the outcomes of many scenarios using the same model. For example, the models reported on recently look at the spread of Covid-19 with different levels of social distancing and other interventions such as school closures. A model of a complex system is usually a collection of many separate models, each a breakdown of a different part of the system. For example, the first thing to consider when modelling the spread of disease is infection. You need to know certain things such as;

  • how likely is the infection to spread person to person with each contact?
  • how often do people come into contact with each other?

These values are called parameters and can be changed depending on the situation. The equations behind epidemiological models for the spread of infection are well established, but the parameters will vary between countries depending on things like the breakdown of age and population density. Crucially, epidemiological models rely on data. Sourcing these parameters is all part of modelling and assumptions have to be made.

For recent UK modelling this data came from a number of diverse sources. Census data could give a good indication of the age and distribution of households, data on social connections broken down by type (work, school, home) and age came from a BBC citizen science project. Even knowing this, data on class sizes, commute distance and company size were then needed to create a virtual population to simulate the spread of the disease.

Models can also show where collected data may be inaccurate. Recent models monitoring the situation in Spain found that the number of COVID-19 deaths reported appears to be significantly underestimated, given existing data on the expected seasonal number of deaths in a normal year and data on the total number of deaths recorded in the past few weeks (by any cause).

Simple demo of infection model.

This is a very simple model purely looking at infection.  When ‘infected’ balls come into contact with others susceptible to the disease there is a probability that the other balls will become infected. There is no death rate, everybody recovers and is then immune.  The speed of the balls represents the number of social contacts. The probability of infection, the proximity for infection, the length of the infection all need to be set.  Even in this toy model many assumptions have been made.

What changed with the recent modelling?

Having modelled infection and the population, the outcome needs to be considered. What proportion will recover and develop immunity? What proportion of people will become hospitalised? Of them, crucially, how many will need intensive care and specialist equipment like ventilators. All these parameters depend on the disease itself. Unfortunately, Covid-19 is practically unknown, researchers have had only a few months to study it. That means there has been a degree of uncertainty with the parameters fed into the models.

One widely reported model that looked at the impact on the UK population was led by Imperial College London. Having updated their models with better information from Italy on the proportion of patients requiring  intensive care beds, it found that the UK’s National Health Service would not be able to cope without further action, which led to the recent dramatic change in government policy.

In the report, the team presents a pandemic curve for different degrees of potential government intervention. The different measures that they considered the impacts of were: no intervention, household isolation, social distancing, and school closures. With no intervention, the model predicted a need for hospitalisation thirty times what the current UK healthcare system can manage. Only by combining all of the measures would the healthcare system not be overwhelmed.

This is one of the graphs from the Imperial paper [1]. The vertical axis shows the number of critical care beds needed through time in each scenario. The blue region shows the time period on the horizontal axis where various social distancing is applied. The horizontal red lines show the maximum number of NHS critical care beds available. The various lines, explained in the key, show the difference between doing nothing,  applying some social distancing and with full school and university closures. This graph shows that without all measures being taken the number of available beds would be exceeded.

What happens next? What about the large peak when the measures are withdrawn?

We need time. Time to get better testing, time to find new treatments and more ventilators. The current model suggests the current restrictions should keep the number of cases at a manageable level for the next few months.

The Imperial model makes important assumptions. Firstly, it assumes that measures put in place to control the spread of the virus are all lifted at the same time, which is neither realistic nor advisable. Secondly, it assumes that recurrences of the outbreak after the initial lift of restrictions continue for an indefinite period. In reality, this may not happen due to people acquiring immunity or the availability of a vaccine. Finally, it doesn’t account for infected cases that have gone undetected or for tools such as contact tracing, which can help break the chain of transmission.  All of this will affect the number of people who become infected once the measures are lifted.

A competing model by Oxford University followed the publication of Imperial’s model. This model stated that under-detection of cases could be high, indicating that a significant part of the UK population could have already contracted the virus. This caused a big media response, however, given the data available it seems an unexpected conclusion to draw, as epidemiologist Adam Kucharski pointed out.

The Imperial model has since been refined and other competing models have been published, but the consensus remains.

It’s important to remember that the model is only based on what we know now. Models are continuously updated. Research teams are now focusing on measuring the impact and preparedness of healthcare systems by predicting the number of hospital beds, ventilators and testing kits needed based on what the models are telling us. They are also looking at the big question of the length of time needed before lifting restrictions and how to prevent a second wave of the outbreak, and models will help us to understand this better.

So in a few months the situation will have changed, the model will be updated with more information and the curve may look very different. Data should start to emerge to confirm level of immunity gained from recovering from the disease. More hospitals are currently being built and there is a national effort to produce more ventilators. Doctors have already identified the response that causes some patients to develop severe symptoms while most have a mild version, which may make it possible to screen people to detect who is most vulnerable or identify better treatments.

Given the rate of learning in the last 3 months the picture may be very different when more accurate assumptions  are fed into the models.

Once a vaccine has been developed, we may  require modelling for rolling it out to best effect, as well as monitoring changes in the virus. This will feed into the ongoing body of research for this pandemic and will help us prepare for future pandemics.

While models are not always accurate, they help build a consensus of understanding that informs policy and helps save lives. Taking the right measures at the right time is key in the fight against Covid-19 and through modelling we can make the best-informed decisions possible from the data available.

References

[1] https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf
[2] https://www.gov.uk/government/groups/scientific-advisory-group-for-emergencies-sage-coronavirus-covid-19-response