Prioritize Innovation and Commercial Viability in Surgical Robotics with Intelligent Architecture

September 15th 2026

Jack Hornsby

Deputy Head of Surgical Robotics

Functional density: why intelligent architecture definition matters

The surgical robotics companies we partner with are constantly looking to squeeze greater functionality into ever-smaller form factors, a combination I’ll call functional density. Our partners understand that functional density is critical to success in a competitive market, where form-factor influences clinical adoption (e.g., Is there room in the hospital? Does it obstruct workflow?) and functionality directly impacts clinical outcomes (e.g., capability of instruments, energy and visualization). The baseline of what hospitals and surgeons expect is constantly increasing as device companies push these boundaries. The devices that break new ground, exceeding function or form, have a critical differentiator in the eyes of customers.

From the perspective of the hospital buyers, introducing surgical robots into existing operating rooms and storage spaces poses novel challenges, which discourages the adoption of large footprint robots that may hinder workflow and occupy extensive storage space. Yet surgeons expect greater functionality, particularly for high-acuity procedures: instrument performance, improved feedback, integration with advanced energy systems, and access to AI augmentation.

This tension shrinking form factor while expanding capability is driving the need for functional density and will play an important role for future winners in surgical robotics.

Advances in materials, actuator power density, embedded sensing, and novel manufacturing processes support functional density. However just adding or optimizing technologies can only get you so far. The greatest impact on functional density is made early in the surgical robot development process when defining the system architecture. Failure to recognize this, and the cascading impact of early architectural decisions, is a common pitfall, but one that can be avoided with the right expertise and experience.

Intelligent architecture

The greatest impact on functional density is achieved with intelligent system architecture. Intelligent architecture prioritizes clinical value by only including features and functions necessary to maximize clinical outcomes and ensuring they are electromechanically implemented in a manner that reduces friction to clinical adoption. Without intelligent architecture, you might create a capable system, but it won’t have a feature set that really catches the eye of users and enables them to do more. Here are three areas we think about when creating an architecture for functional density:

Clinical insights that ensure focus and eliminate functionality that doesn’t add value.

Insights that provide critical clinical context inform the range of motion required to access the necessary anatomy, the resolution of each joint to give instruments the proper control, the dimensions and layout of the operating theatre to inform workflows, and many other critical elements of the system.

Knowing the clinical context inside out before putting pen to paper to design system architecture is imperative.

Deep understanding of clinical context guides what an architecture needs, or more importantly, what it doesn’t need; striking the correct balance significantly improves the clinical value the architecture can deliver through functional density.

Front-end Insights teams can build and refine this deep understanding of clinical context by capturing insights early and often, through discussions with key opinion leaders and structured interviews with clinical stakeholders to build robust requirements and simplify decision-making. Throughout development, and as our working hypotheses mature, frequent testing and feedback serve to validate decision-making, allowing early course correction or doubling down to ensure clinical value is always the priority.

Optimizations that have nonlinear impacts on clinical value.

Consider the motors that drive laparoscopic instruments. Working volume, lifting capacity and end effector capability are all impacted by motor selection. Motors also significantly drive the form factor of the instrument and drive unit, so there is a clear trade-off between the size and capability of the instrument. Here, a step change in clinical impact could be realized by increasing motor specification to drive a high torque instrument, (e.g. a stapler), which unlocks a suite of procedures for a minimal increase to drive unit size, making it a good trade-off for functional density.

Besides selecting which features and functions make up the architecture, there’s also a quantitative element of ‘how much’ of those features or functions are implemented (e.g., the reach and range of the robot, the torque output of instrument drive motors, or the resolution of force sensors). The key to optimizing the quantity of a function is a deep understanding of the relationship between the ‘cost’ of scaling the function versus the clinical value. There may be critical inflection points where step changes in clinical value can be unlocked as you increase or decrease functionality, and conversely, there are step changes in cost that don’t add meaningful value. Recognizing the inflection points is key to enabling significant, non-linear impacts to functional density and clinical value.

Multi-disciplinary input that maximizes the potential of how technology is selected and integrated.

Think about the many different elements of a surgical robot: mechanical linkages, drive units, resposable instruments, vision systems, advanced energy, disposable draping, etc. Now compound this with all the different ways of achieving those functions, and you quickly discover how broad a pool of expertise is needed to understand which technologies are available, how to use them and which ones are best suited for each of those elements.

The last element of intelligent system architecture is the technologies selected and how they are integrated. Integration, done well, leverages technology capability, availability and compatibility from a broad range of industries and disciplines, a process that takes multidisciplinary, multi-industry expertise. The challenge, for many companies, lies in accessing this pool of knowledge and experience to bring all these things together. For a smaller company, funding may not support a larger, multidisciplinary team, and for a large strategic organization, silos are often a barrier to cross-functional pollination.

To address this, teams can adopt two main practical strategies.

Technology landscaping: Systematic exploration of relevant technologies, before committing to architecture decisions. By casting the net wider than the obvious and involving expertise from adjacent industries and technologies, teams can identify solutions and innovations from adjacent industries that may offer performance or cost advantages. It provides a much larger pool of ideas to pull from and qualifies these against the experience of seasoned experts.
Leveraging third-party subject matter experts to augment internal capabilities: Partnerships with specialists in areas such as advanced sensing, novel materials, or niche manufacturing techniques allow teams to access deep expertise without permanently expanding headcount. This flexible model enables teams to scale knowledge as needed, ensuring that each subsystem has the necessary breadth and depth of thinking behind it.

Conclusion

In a space like surgical robotics, where expectations of performance are constantly advancing while form factor is limited by the physical constraints of hospitals, functional density is critical to competitiveness. The teams that succeed will be those that accurately understand the user’s needs and resist the temptation to over-engineer and instead anchor every architectural decision in clinical value, system-level thinking, and a clear understanding of technological trade-offs.

At Cambridge Design Partnership we’ve been supporting our partners’ surgical robotics development teams with exactly this. Our teams deliver system architectures that prioritize clinical impact and create robust foundations for progression into engineering design and industrialization.

It’s an exciting time to be in this space, and we’re proud to be supporting and accelerating the next generation of surgical robotics. Get in touch to find out more.

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Our Surgical Robotics team works with clients to develop innovative, high-impact devices. Reach out today, and a member of our Surgical Robotics team will get in touch.

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Design Verification for the Real World: Complex Drug Delivery in Uncontrolled Environments

September 11th 2026

Dr Matthew Roberts

Consultant Physicist

Andrew Fiorini

Senior Consultant Healthcare Devices Engineer

Featured in September’s issue of ONDrugDelivery, Dr Matthew Roberts and Andrew Fiorini, explore the key steps involved in building a successful design verification programme for ambulatory electromechanical infusion systems and share the lessons they have learned along the way.

Large-volume drug delivery is a rapidly growing industry, as more biologics, oncology therapies and treatments for chronic conditions move from hospital-administered infusion into home and self-administered settings. Many of these drug products require a subcutaneous infusion pump, changing the way infusion pumps are being used. This has resulted in increased regulatory scrutiny of how these devices perform once they leave controlled testing conditions (Figure 1).

Figure 1: The real-world performance of modern, complex drug delivery systems can differ significantly from that indicated by standard laboratory pump tests.

Design Verification Testing of Ambulatory Infusion Pumps

For electromechanical ambulatory infusion pumps, design verification (DV) typically begins with the International Electrotechnical Commission (IEC) standard IEC 60601-2-24. However, demonstrating safe and effective drug delivery in real-world use extends beyond the standard’s scope. The Association for the Advancement of Medical Instrumentation (AAMI)’s Technical Information Report (TIR) 101:2021 addresses this gap, with particular relevance to ambulatory, self-administered systems, where the gap is widest.

It is noteworthy that, although this report provides extensive guidance as to how to test and report system performance, it leaves manufacturers responsible for defining the conditions under which performance should be verified. Self-administration conditions introduce additional complexities, and success depends on understanding the delivery system before testing begins.

Beyond IEC 60601-2-24

IEC 60601-2-24 is the internationally recognised standard for electromechanical infusion pumps, syringe pumps and volumetric infusion controllers, providing a well-established framework for accurate and consistent drug delivery in both clinical and ambulatory settings.

The most complex pumps that the standard defines are Type 5 profile pumps that can combine basal and bolus delivery of drug products to manage symptoms that can fluctuate through the day. Insulin pumps for diabetes and apomorphine pumps for the management of Parkinson’s disease are two such products, delivering a basal rate for most of the day and patient-controlled boluses to manage mealtimes (for diabetics) or “off” episodes for patients with Parkinson’s disease. Whereas most pump testing described in IEC 60601-2-24 should be conducted with Class III water, pumps intended for use with a specific drug should be tested with that drug.

Water for injection behaves differently from many modern drug products, which may be highly viscous or exhibit non-Newtonian behaviour. At-home use introduces further variables that can affect performance and the dose received by patients:

Pronounced temperature swings that, compared with clinical settings, can affect both formulation properties and catheter behaviour
Delivery profiles often include patient-triggered bolus doses or variable flow rates, making transient performance important
Pumps may be worn at different heights, orientations and locations on the body throughout the day.

To address the gap between IEC60601-2-24 and real-world conditions, the AAMI published TIR101 in 2021, introducing test methods and reporting metrics that better reflect ambulatory and self-administered use conditions.

The sponsor is expected to define, justify and execute a statistically robust DV programme that demonstrates that the system performs as intended and the level of risk presented to patients is within acceptable limits. This flexibility is valuable but creates challenges: sponsors must consider which variables matter most for their drug-device combination and ensure that their testing strategy is both scientifically justified and regulatorily defensible.

DV testing of complex drug products using the AAMI TIR101 guidance can appear daunting, but it can be effectively managed by following three key steps:

Define the test matrix and reporting metrics
Design and de-risk the testing programme
Execute the DV testing.

From experience, more than 80% of the work lies in designing and de-risking the test programme itself – this is particularly true under AAMI TIR101. Unlike many traditional standards, with tightly defined test conditions, AAMI TIR101 leaves sponsors responsible for the test matrix, environmental conditions and acceptance criteria. Success depends heavily on the characterisation work performed before formal testing begins. Failure to prepare adequately can jeopardise a programme, resulting in repeated testing and delaying submissions by several months. However, with the right planning and a risk-based approach, even highly complex systems can be verified efficiently and with confidence.

Establish the Test Matrix and Reporting Metrics

Despite provision of details on how to measure, what to report and how to structure the data, TIR101:2021 does not contain acceptance criteria. It is up to the sponsor to justify which tests are applicable, what results are acceptable and how confidently those results can be trusted.

Establishing acceptance criteria starts with understanding patient dosage sensitivity, including minimum effective dose and clinically meaningful dose variation. Unlike IEC 60601-2-24, which focuses on pump performance, TIR101 views this through the lens of patient self-administration, evaluating drug concentration fluctuations using pharmacokinetic coefficient of short-term variation (PK-CV) methods. This should also drive the pump specification. TIR101:2021 requires testing at the lower and upper limits of pump flow rates – allowing the pump to operate outside clinically useful windows can significantly increase the complexity and timescales of DV (Figure 2).

Figure 2: In the PK-CV mode, drugs with a longer half-life will have a higher active volume in the patient and will be less sensitive to variance in pump flow rate.

The test matrix should reflect all clinically relevant operating conditions. Every claimed operating environment increases the DV burden; therefore, unnecessarily broad specifications can lead to significant additional testing. A pump with an established performance history and existing approvals can carry a smaller, better-understood test matrix than a newly developed platform, if its specifications are updated to meet the specific use case.

"A pump with an established performance history and existing approvals can carry a smaller, better-understood test matrix than a newly developed platform, if its specifications are updated to meet the specific use case."

Design and De-risk the Test Process

As with all DV processes, characterisation (or pre-DV) directly influences the efficiency of formal test execution. For ambulatory infusion devices dealing with complex formulations, unexpected behaviours arising during test execution can derail the entire programme. The following steps detail how to efficiently design and de-risk the DV protocol:

Risk identification
Risk elimination and mitigation
Define data analysis and integrity.

Risk Identification

For complex infusion systems, risks may often not be visible in product requirements or design documentation, so pre-DV activities should focus on uncovering underlying physical properties that may influence measurement performance (Figure 3).

Figure 3: Efficient testing begins before execution, with extensive pre-DV planning and preparation work.

Examples encountered during infusion pump verification programmes can include:

Trapped air bubbles created during cartridge assembly, including their presence and migration with pump orientation
Air bubbles becoming dispersed into catheters during fluid transfer
Non-Newtonian formulation properties (e.g. shear thinning) behaving drastically differently during infusion versus bolus delivery
Fluid evaporation through catheters and beakers.

These effects risk being misinterpreted as pump performance issues if the root causes are not fully understood. Flow-rate instability and end-of-dose issues could be incorrectly attributed to a pump’s drive mechanism, when the true causes are formulation – rather than pump-limited. For example, different air bubble locations along a catheter can produce distinct, repeatable flow signatures that closely mimic a mechanical fault, and separating the two typically requires careful analysis. Pre-DV activities should focus on identifying and understanding such mechanisms before they appear during formal verification.

Risk Elimination and Mitigation

Once identified, risks should be eliminated, controlled or characterised using targeted experimentation and engineering judgement. Effects such as balance drift, fixture design problems and temperature gradients can introduce significant measurement errors. For ambulatory pumps, this includes ensuring gravimetric balances have settling times long enough to resolve genuine flow transients without mistaking balance noise for a delivery event. Effective mitigation activities are those that eliminate sources of uncertainty.

Ambulatory pumps operate across broad parameter spaces, including temperature, viscosity, back pressure, orientation and start-up conditions, all of which affect performance. Design of experiments is an effective tool for systematically exploring these spaces, identifying interactions and sensitivities, and determining whether behaviour remains within acceptable limits across the intended operating range, thereby ensuring that an appropriate subset of testing conditions, representing potential worst-case scenarios, are chosen for the formal DV testing.

A recurring challenge in infusion pump verification is that the root cause can be misidentified. Formulation behaviour requires close attention and verification, as material properties on a certificate of analysis may be insufficient for predicting how a formulation behaves inside an infusion system. Shear rates and residence time can differ substantially from the conditions under which such data were generated – for example, density versus temperature.

"Formulation behaviour requires close attention and verification, as material properties on a certificate of analysis may be insufficient for predicting how a formulation behaves inside an infusion system."

As a result of investigations such as these, the mitigation strategy for a programme shifts towards understanding the physical mechanisms responsible and quantifying their impact. By the time formal DV commences, experimental signatures observed during the pre-DV phase can be accounted for and correctly assessed, reducing the risk of inefficient post-event root cause investigations and repeat testing.

Define Data Analysis and Integrity

During a DV programme, the characteristics of the data generated and how they are analysed are equally as important as the design of the physical test step. For example, a transient flow-rate disturbance may appear dramatic in raw data but have negligible impact on delivered dose, while a seemingly insignificant disturbance may accumulate into a clinically meaningful error over time. Distinguishing between the two requires careful analysis to identify clinically relevant issues from avoidable experimental artefacts.

This becomes particularly important with automated analysis workflows. Automation improves repeatability and processes large datasets efficiently, but introduces risks of incorrect data manipulation and interpretation. Validation should focus not only on software calculations but also on checks to ensure that the correct data are selected and analysed. Defining analysis, acceptance criteria and anomaly classification rules during characterisation ensure that formal DV answers meaningful questions about device performance as opposed to asking new questions of the data.

"Defining analysis, acceptance criteria and anomaly classification rules during characterisation ensures that formal DV answers meaningful questions about device performance, as opposed to asking new questions of the data."

Execute the DV Testing

Before executing the full DV matrix, teams must be trained, confident in procedures and clear on how to respond to unexpected results. Training should be documented, with the lab operating under a vigilance-driven, blame-free culture focused on catching issues early.

Planning how to respond to an out-of-specification (OOS) result beforehand is a key part of this culture, signalling to the reviewer that the process is robust and root cause analysis procedures are followed (Figure 4). OOS investigations all follow a simple logic:

If the behaviour reflects the real drug delivery performance, the failure must be accepted and included in the statistical analysis
If the behaviour reflects some aspect of the methodology, careful argument may justify the exclusion and retest of an erroneous result, if it can be proven as such
If the behaviour is ambiguous, capture evidence (e.g. environmental data, balance data, photographs), learn as much as possible and document it for further investigation. This will ultimately allocate the result to one of the first two categories.

Figure 4: Decision framework illustrating the primary categories of causes considered during investigation of an OOS result.

Following these guidelines significantly reduces the burden of root cause analysis and reporting, and, even where acceptance criteria are not met, the resulting understanding will support strong, evidence-based recommendations for product improvement.

A genuine product failure and a system effect may only be distinguishable after careful investigation, demonstrating why agreeing the OOS logic in advance is so important. The objective of characterisation is to ensure that formal DV contains no unexpected lessons so that execution focuses on demonstrating device performance rather than investigating previously unknown phenomena.

Conclusion

For ambulatory infusion systems, successful DV may not be limited by the ability to execute the required tests but by how well the underlying system is understood before testing begins. Modern delivery systems combine electromechanical pumps, consumables, complex formulations and patient-driven use conditions, meaning that performance is frequently governed by interactions between these elements rather than the pump mechanism alone. Trapped air bubbles, formulation rheology, start-up transients and fluid-path compliance can all generate behaviours that resemble pump failures, which, through characterisation work, can be correctly classified.

Successful programmes treat DV as a confirmation exercise by defining clinically meaningful metrics, identifying hidden sources of variability and establishing robust interpretation methods before commencing formal testing. By following these principles, sponsors can reduce programme risk while generating evidence that is both scientifically robust and regulatorily defensible.

ACKNOWLEDGEMENT:

With special thanks to Manuel Acha for his assistance with the article.

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For more on how a well-designed verification programme can reduce risk and build confidence in complex drug delivery systems, contact Cambridge Design Partnership.

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James Harmer: Leading CPG Into Its Next Growth Phase

August 4th 2026

James Harmer

Head of Consumer Packaged Goods (CPG)

We’re pleased to announce that James Harmer has stepped into the role of Head of Consumer Packaged Goods (CPG) at Cambridge Design Partnerships leading the team through its next phase of growth. To mark the appointment, we took the opportunity to sit down with James and talk about why now, and the challenges our clients are currently facing.

“For more than 25 years, I’ve worked alongside some of the world’s leading consumer brands, helping bring new products, packaging and experiences to market. Today, the conversations I’m having with clients are changing;

Consumer businesses are navigating an unprecedented combination of challenges. Regulation is accelerating. Resources are becoming more constrained. AI and digital technologies are transforming how products are designed and managed. Supply chains are evolving. Expectations from consumers continue to rise. These aren’t isolated trends. Together, they’re reshaping how businesses create value.

The question is no longer “How do we make a better product?” Increasingly, it’s becoming: “How do we create and keep more value?”

James Harmer | Head of Consumer Packaged Goods

One of the biggest misconceptions I see is that circularity is positioned as simply another sustainability initiative.

In reality, it’s so much bigger than that. It’s a commercial opportunity that businesses cannot afford to miss. Businesses will increasingly need to rethink not only the products they develop, but the systems that sit behind them, from materials and manufacturing through to digital services, recovery and entirely new business models.

Solving these challenges requires a different way of innovating

These problems can’t be solved by looking at packaging, engineering or software in isolation. They require organizations to think across the whole system. That’s why bringing together designers, engineers, scientists, software specialists, human factors experts and insight strategists has never been more important.

The biggest opportunity comes before the brief is written

One thing I’ve learned over the years is that many of the biggest project risks are created before development even starts. Too often, organizations arrive with a well-defined product brief when the bigger opportunity might be to rethink the problem entirely. The earlier we can help shape the thinking, align stakeholders and understand user needs, the greater the opportunity to create value – not just reduce risk.

I’ve been fortunate to spend more than two decades working with some of the world’s best consumer businesses. If there’s one thing I’ve learned, it’s that the companies that succeed aren’t necessarily the ones with the biggest R&D budgets or the newest technology. They’re the ones willing to ask better questions early, challenge their own assumptions and see opportunity where others only see complexity. That’s why I’m so optimistic about the next decade of consumer innovation.

Looking ahead

The future of consumer-packaged goods won’t be defined solely by the next successful product launch.

It will be defined by how intelligently businesses create, retain and recover value across an entire product ecosystem. That requires new thinking, new partnerships and a willingness to challenge long-held assumptions. It’s an exciting moment for our industry, and I believe the organisations that embrace that change early will be the ones best placed to thrive.

Connect with CDP

For more on how to accelerate meaningful innovation in consumer packaged goods, from user experience to scalable design, contact Cambridge Design Partnership.

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De-Risking Post-Market Change Through Pilot Production for Drug Delivery Devices

July 17th 2026

Featured in ONdrugDelivery, CDP explores how pilot production drug delivery can be used to de-risk post-market change in drug delivery devices, and why this approach is becoming essential for manufacturers navigating change at scale.

Managing change in drug delivery device manufacturing post launch is inherently challenging. Whether driven by regulatory requirements, issues with an existing on-market device, supply chain disruption, or design evolution, even small modifications can introduce significant risk to product quality, cost, and supply continuity.

These challenges are compounded by the practicalities of generating statistically meaningful data from prototype designs without disrupting established, high-volume manufacturing operations.

This article explores how pilot production, delivered through an integrated design, development and manufacturing capability, can be used to de-risk change. It highlights how such approaches can improve confidence in decision-making while reducing overall time and cost.

What Drives Change in Drug Delivery Devices?

Even the most comprehensive design verification programmes cannot always accurately predict all failure modes that may emerge once a product is deployed at commercial scale. As production increases from hundreds to millions of units, variability in materials, processes, and real-world use conditions can reveal previously unobserved behaviours.

In addition to on-market performance considerations, manufacturers must also respond to external and internal drivers such as:

Regulatory updates
Material obsolescence
Supplier changes
Cost optimisation initiatives

Collectively, these factors make change management an essential element in the lifecycle of successful combination products.

However, implementing changes without disrupting supply presents a significant challenge. The commercial justification often requires data that can only be gained by transitioning from small numbers of lab-built prototypes to thousands of devices produced using representative manufacturing processes.

While guidance such as ISO 20069:2019 (Guidance for assessment and evaluation of changes to drug delivery systems) provides a framework for assessing and documenting changes, it offers limited direction on how exactly to generate the representative data efficiently without impacting validated production lines.

A Common Challenge

Consider an on-market drug delivery device that is manufactured at scale. The production system to make and assemble such a device will be highly optimised. Material handling, in-process quality controls, final packaging, labelling ‒ every step will be designed to reduce variation and ensure quality.

If such a device experienced an issue post-launch, the impact to the manufacturer could be enormous. The decision on how to proceed has significant consequences and could potentially trigger FDA (or other regulatory body) intervention.

This is reflected in the number of recalls and corrections reported by the FDA, underlining that the ability to implement changes to marketed products in a controlled way is a strategic need for manufacturers.

Examples from infusion and syringe-based drug delivery systems help illustrate this challenge. Table 1 presents a selection of recent product recalls and corrections affecting this class of devices. Whilst the specific failure mechanisms differ, the examples highlight recurring challenges associated with fluid handling and sensing functions, such as leak paths and occlusion detection.

Device	Failure mode	Patient risk	Date	Remediation
Cardinal Health, Monoject Luer-lock syringes	Recognition, compatibility and pump performance issues when used with syringe pumps and patient controlled analgesia pumps	Overdose, under dose, delay in therapy and delays in occlusion alarms	March 2024	Recall of specific product
B. Braun Infusomat Space Large Volume Pump	On certain models, occlusion alarm may sound when no occlusion exists	Interrupted or failed delivery of medication or fluids	September 2023	Correction of occlusion pressure sensor
Fresenius Kabi Ivenix Infusion System	Fluid leak that causes damage to the electrical system	Delay or interruption to treatment	March 2023	Urgent device recall letter sent to customers
Eitan Medical Sapphire Infusion Pumps	Failure to detect air in line when running on battery power	Serious injury or death from air embolism risk	September 2023	Recall and customer notification, software update

Design modifications to address issues such as these require large data sets to generate sufficient statistical confidence that the issue has been resolved. Where an issue is not fully understood or occurs on an infrequent basis, this can potentially run to tens of thousands of units.

The requirement for high numbers of finished devices exposes a gap in available manufacturing options:

Volumes too high for conventional prototyping approaches
Volumes too low, and timelines too short, to engage commercial-scale contract manufacturing organisations (CMOs)
Existing production lines may not accommodate the design without significant disruption

This scenario reflects a common industry challenge, where development teams must balance two competing priorities: minimising change to fit within existing manufacturing capabilities vs allowing sufficient design freedom to maximise the likelihood of technical success.

Contract Manufacturing can be Inflexible

In conventional outsourcing models, Contract Manufacturing Organisations (CMOs) are typically optimised either for low-volume engineering support or for stable, high-volume commercial manufacture. Small-scale engineering workshops prioritise flexibility and rapid iteration, whereas production facilities are designed around efficiency, repeatability and validated processes (see Figure 1). Projects that sit between these two models can create significant operational tension.

In the case of drug delivery systems, the challenge is often amplified by the combination of bespoke automation, tight tolerances and sub-assemblies containing both rigid and compliant parts. Even relatively small engineering programmes may require dedicated fixtures, custom tooling, automation development and specialised operator training. These investments can be difficult to justify when production volumes remain limited, and product designs are still evolving.

Furthermore, these engineering builds often involve a high degree of uncertainty. Device configurations may change frequently, process parameters may still be under investigation and build schedules can fluctuate as development priorities evolve. For a CMO operating under conventional production metrics, such variability can disrupt factory planning, reduce equipment utilisation and negatively impact operational efficiency.

There is also an economic challenge. Low-to-medium volume engineering programmes rarely achieve the economies of scale associated with high volume commercial manufacture, yet they may still demand significant engineering oversight and quality infrastructure. As a result, the commercial model can become unattractive for both the client and the manufacturing partner.

Many CMOs now advertise Design as a differentiator ‒ spawning the initialism “Contract Design Manufacturing Organisation” (CDMO) ‒ offering design services as well as more traditional manufacturing services. However, as design is often not a core skill, they can leave clients with a design that only works with their in-house manufacturing approach, or where the IP is no longer with the client.

For these reasons, many organisations may benefit from dedicated pilot production environments operating outside conventional commercial manufacturing structures, providing a more effective route for executing complex flexible manufacturing.

Conventional Prototyping Lacks Rigour

Traditional prototyping is typically focused on evaluating functional concepts and demonstrating technical feasibility. Prototype devices are often produced in small quantities using flexible, low-volume methods that prioritise speed and adaptability over repeatability. These builds are valuable during early-stage development, where the objective is to assess usability, confirm mechanical principles or explore initial design architectures. However, prototypes are rarely manufactured under conditions representative of commercial production. As a result, they may not fully reveal the interactions between product design, automation strategy and manufacturing variability.

Pilot manufacturing occupies a different position within the development pathway. Rather than simply proving that a device can function, pilot manufacturing aims to demonstrate that it can be assembled repeatedly, efficiently and robustly under production-representative conditions. This includes consideration of automation compatibility, process capability, quality inspection and operational throughput. The parts themselves are often manufactured by representative processes, for example injection moulded plastic housings rather than 3D printed parts.

The distinction is important because many challenges associated with drug delivery systems emerge only when products are built at scale. Tolerance accumulation, fixturing behaviour and automation interactions may appear manageable during low-volume prototyping but can become significant risks during industrialisation.

By bridging the gap between prototyping and commercial manufacturing, pilot production enables engineering teams to identify and resolve these issues earlier in development, reducing industrialisation risk and supporting more robust product and process design.

Why Existing Production Lines May Not Accommodate Design Changes

Many manufacturing systems for drug delivery devices rely on bespoke automation, tightly controlled tolerances and carefully sequenced assembly operations, developed specifically for a defined product configuration. Even relatively small design modifications, such as changes to component geometry, material behaviour or assembly orientation, can have cascading effects across the production process.

In automated systems, manufacturing equipment is often programmed around precise assumptions regarding part position, stiffness, insertion forces and component interaction. A seemingly minor design change may therefore require reconfiguration of robotic motion paths, vision system parameters, fixturing, feeding systems or inspection methods. In some cases, the modification can introduce variability that existing automation is simply unable to accommodate reliably.

These challenges are particularly acute in assemblies containing both rigid and compliant parts. Elastomeric tubes, adhesives or soft materials may behave differently during automated handling when adjacent components are modified, creating interactions that only become apparent at production scale. For example, a section of tubing may curl in one direction 98% of the time based on how it is presented. The low occurrence of the alternative behaviour means it may not be observed in small sample sizes, leading to assumptions during automation development that later prove unreliable.

Importantly, commercial manufacturing lines for medical and combination products are usually validated environments operating under strict quality and regulatory controls. Any significant modification to equipment, tooling or process parameters may trigger formal change control activities, revalidation requirements and production downtime. For manufacturers supplying commercial products, this introduces both operational risk and potential supply chain disruption.

As a result, manufacturers are often reluctant to trial experimental designs directly on operational manufacturing lines, particularly where product demand remains high.

Pilot production ‒ a hybrid of prototyping and commercial manufacturing ‒ provides a practical alternative. By replicating critical manufacturing operations outside the commercial environment, it allows design changes to be evaluated under production-representative conditions without interrupting ongoing supply, nor compromising validated manufacturing systems.

Developing a Hybrid Manufacturing Strategy

Replicating the full complexity of the existing high-volume manufacturing system when conducting pilot manufacturing is usually neither practical nor necessary. Instead, a targeted approach can be adopted to balance fidelity with flexibility and cost.

This hybrid strategy, illustrated in Figure 2, involves:

Mapping the device production process flow
Identifying critical-to-quality (CTQ) and critical-to-function (CTF) process steps
Assessing risk and applying suitable mitigations so each of these steps is replicated using appropriate technology. For example:
- A needle insertion step, which requires precise needle alignment (high risk), may require a controllable and repeatable automated system such as a Selective Compliance Articulated Robot Arm (SCARA).
- Non-critical processes (lower risk) could be conducted using manual or semi-automated methods.

As the pilot production system will be operating at a slower rate than a commercial line, a flexible approach to labour organisation and work balancing can manage uncertainties in the new, untested line. Even when simulating the new line with digital tools, the pinch points in production flows may not be known without running the system. The use of adaptable fixturing and work-in-process storage, which allows stations to build up inventories, is recommended to cope with these unknowns.

In addition, it may be possible to redeploy equipment from the existing production lines to maintain process fidelity without incurring unnecessary cost. These valuable pieces of process equipment may be unused spares or repurposed from obsolete lines. However, due care and attention must be observed when bringing them online: they may have different voltages, require repair or maintenance or translation of production documentation from other languages.

Implementation and Outcomes

The resulting pilot production line incorporates a combination of approaches that can be designed and implemented rapidly:

Automated assembly cells for critical processes
Manual and fixture-based operations for non-critical steps
Integrated inspection and functional testing capabilities

To give an example that puts this into context, a system such as this was designed, built and operated within a 12-week timeframe, including both factory acceptance testing (FAT) and site acceptance testing (SAT). This enabled rapid deployment into an engineering production environment while design activities continued in parallel.

In total, more than 14,000 devices were manufactured across seven design variants. These units supported:

Engineering performance evaluation
Accelerated ageing studies
Ongoing engineering verification testing to prove functional performance

The pilot production approach therefore provided both the scale and fidelity required to support robust, data-driven decision-making.

Conclusion

For established drug delivery devices, the pressure to change can be driven by performance data, regulatory evolution, supply chain disruption and the ongoing pursuit of improvement. Yet the tools available to generate the evidence needed to support those changes remain poorly matched to the task. Conventional prototyping lacks manufacturing fidelity; commercial-scale CMOs are structured around stability, not experimentation; and validated production lines cannot easily absorb the uncertainty of iterative design work.

Pilot production addresses this gap directly. By replicating critical manufacturing operations in a flexible, lower-volume environment, development teams can generate statistically meaningful data under production-representative conditions, without putting commercial supply at risk. Crucially, it is not a replacement for formal design transfer, but a means of arriving at that stage with greater confidence and fewer unknowns.

As drug delivery systems become increasingly complex, and as expectations for continuous improvement grow, such approaches are likely to play an increasingly important role.

By bridging the gap between concept and commercial manufacture, pilot production enables organisations to pursue innovation with greater confidence – while maintaining the reliability and supply continuity that patients depend on.

REFERENCES:

“Medical Device Safety Communications Database”. Web Page, US FDA, accessed 8th May 2026.

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For more on how pilot production can de-risk post-launch changes to drug delivery devices, contact Cambridge Design Partnership.

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Sustainable Pharma Packaging Starts with Asking Better Questions

June 15th 2026

Jon Powell

Head of Manufacturing

We were delighted to see AstraZeneca and Deloitte nominated for an MCA Award for their work on sustainable pharmaceutical packaging.

Cambridge Design Partnership supported the project through our materials science and manufacturing teams. It is a strong example of what we see with large pharmaceutical clients: sustainable packaging is a product development challenge, not a side issue about greener materials.

The project focused on moving towards fully recyclable blister packaging. Three requirements shaped the work: recyclability, barrier performance and ease of manufacture, all in a regulated market where drug performance and patient safety matter.

That combination shows why pharmaceutical packaging forces us to ask better questions.

A greener material can still be the wrong answer

The narrow question is, “Can we make this pack recyclable?”

The better question is, “What has to be true for a more sustainable pack to work in the real world?”

That moves clients from material preference to product evidence.

A material cannot be judged by its specification alone, or by whether it is recyclable, bio-based, fiber-based or lower carbon. It has to protect the medicine, run on packaging lines, survive transport and storage, meet regulatory expectations, support credible claims and work in the waste and recycling systems where it is sold.

Sustainable pharma packaging is not a material swap. It is a system design challenge.

Supplier data is not product evidence

The narrow question is, “Is this material more sustainable?”

The better question is, “Will this material still protect the medicine after we process it, seal it, pack it and ship it?”

A supplier may present a laminate, film, coating or fiber-based structure with strong barrier data, valid and given in good faith. But it usually describes the material under laboratory test conditions, not after forming, sealing, printing, sterilization, filling, transit and storage.

Once a material enters a commercial process, barrier performance can fall, seals can become inconsistent, moisture protection can become marginal, and machinability can create scrap.

The supplier is describing the material. The development team has to prove the pack.

The current pack may be over-specified

Patient safety is not negotiable. But that does not mean the current pack should always be copied.

The narrow question is, “Can the new pack match the existing pack?”

The better question is, “What pack performance does this medicine actually need?”

With over 25 years working with leading pharmaceutical companies, you soon learn that packs are often based on specifications set years ago. Some requirements are essential. Others may reflect old material choices, equipment limits, qualification decisions or requirements that have not been reviewed for a long time.

There is also a practical reason legacy formats stay in place. Changing a pharmaceutical pack can create cost, project risk and, in some cases, the need for regulatory approval or updated filings. That risk is real. But it is also why the requirement needs to be clear before change is ruled in or out.

That does not mean organizations should lower standards. It means defining the real requirement: barrier performance, shelf life, safety margins, sterility and sustainability all needs to be considered and understood.

Not every sustainability opportunity is worth pursuing

In many of our projects, the useful starting point is not one problem material. It is the portfolio.

The narrow question is, “Which material should we replace?”

The better question is, “Which change is worth pursuing?”

Which formats create the most material burden? Which markets create the greatest regulatory exposure? Which SKUs use more packaging than the protection need justifies? Which changes affect validation or line performance? Which products should be left alone because the benefit is too small or the risk is too high?

That portfolio view matters because the cost of change is real. Packaging lines are optimized, validated and expensive to alter. Changing equipment, requalifying a process or updating a specification can take months if notyears and require major investment.

A material that cannot run at line speed is not a solution. A pack that improves end-of-life performance but creates stability or validation risk is not a solution.

Recyclable in theory is not enough

The narrow question is, “Is this pack recyclable?”

The better question is, “Will this pack actually be collected, sorted and recycled in the market where it is sold?”

For global brands, a pack may be recyclable in one country, misunderstood in another and incinerated in a third. It may need separation steps patients will not perform, or use coatings, adhesives, inks, labels or mixed components that reduce the value of the recovered stream. It may be too small, contaminated, complex or unfamiliar for the sorting system.

Designing for end-of-life means working backwards from real infrastructure: patient behavior, local collection, sorting, recycler tolerance and the actual route in each market.

If that chain breaks, the intended environmental benefit may never appear.

Waiting for regulation is already too late

The EU Packaging and Packaging Waste Regulation is moving packaging towards clearer requirements for recyclability, labeling, waste management and evidence. Healthcare and contact-sensitive packaging has specific treatment because patient protection matters. But that should not be read as permission to wait.

Pharmaceutical packaging changes can take years. If a change affects barrier properties, stability, sterility, line performance or regulatory filings, the timescale expands quickly.

The narrow question is, “What does regulation require next year?”

The better question is, “What packaging choices are we making now that will still be in market when regulation, infrastructure and procurement expectations have moved on?”

Leadership starts with the better question

It’s great to see this our Astra Zeneca and Deloitte collaboration project recognized with a nomination but it is equally important to recognize that the best consultancy projects begin with the client challenge. Real progress starts when companies identify the challenges that need solving and ask the right questions. AstraZeneca has consistently done that on sustainability, creating the impetus for work like this and driving the search for practical solutions.

This work on blister packs is just one element of AstraZeneca’s wider sustainability program. The company has set a goal of 50% waste circularity by 2030 and is already applying circular thinking across the business: from liquid helium reuse to silica waste reduction and its Turbuhaler take-back scheme in Sweden.

That is leadership in pharmaceutical sustainability.

At the heart of CDP’s approach: how to turn sustainability ambition into real products.

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For more on how to accelerate meaningful innovation in sustainable pharmaceutical packaging, contact Cambridge Design Partnership.

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Circularity Isn’t a Sustainability Story. It’s a Business Model Reset.

June 10th 2026

James Harmer

Head of Consumer Packaged Goods (CPG)

Why the next growth opportunity in consumer technology may already be sitting in customers’ homes.

As I prepare for the European Innovation Summit, one of five flagship events within Bucharest Tech Week, bringing together more than 350 CEOs, C-suite executives, entrepreneurs, technology leaders, and innovation practitioners from leading organizations across Europe, my message to consumer technology companies is deliberately simple:

Don’t start your circularity strategy by asking whether a product can be recycled. Start by asking where value is being lost.

That may sound like a sustainability question. It isn’t. It’s a commercial one.

For years, circularity has been discussed through the lens of responsibility, compliance, and environmental impact. But some companies we work with are gaining new advantages because they are not asking us how to minimize waste. They are asking us how to retain value. Increasingly, these turn out to be the same question.

The Old Model is Broken

For the past three decades, many of our consumer technology clients have operated on a remarkably consistent formula: build a product, sell it, upgrade it, and replace it. Each innovation cycle delivered incremental improvements—better screens, faster processors, more sensors, greater connectivity, and lower material and production costs—and together, those increments produced extraordinary growth.

It also concentrated value into a single moment: the initial sale.

Once a product left the factory, most companies had limited visibility into, or influence over, what happened next. When it failed, components reached their end of life, became obsolete, or the product was replaced, much of its remaining economic value effectively disappeared from the system and became complex waste.

That model worked in a world of abundant materials, predictable supply chains, and relatively low replacement friction. But as we know, those conditions are changing…

The world generated a record 62 billion kilograms of electronic waste in 2022, according to figures highlighted by the World Economic Forum. Yet only 22% was formally collected and recycled through environmentally sound processes.

That statistic is often framed as a waste challenge. But at CDP, we work with our clients to elevate it into a value challenge. Every discarded device still contains materials, components, manufacturing effort, logistics investment, and embedded energy that we allow to exit the system. Why?

That statistic is often framed as a waste challenge. But at CDP, we work with our clients to elevate it to a value challenge. Every discarded device still contains materials, components, manufacturing effort, logistics investment and embedded energy that we allow to exit the system. Why?

A New Model for Value Retention

Circularity is often treated as a new concept, but in many ways it is a return to older economic logic.

There was a time when televisions, washing machines, and other appliances were routinely repaired, refurbished, and reused (Radio Rentals, for those who remember). Products were maintained because it made economic sense to do so. Value was recovered, not discarded.

As products became cheaper, more reliable, and easier to replace, ownership replaced recovery. Repair networks contracted, refurbishment ecosystems weakened, and disposal became the default end state.

For a period, that made sense—and now the underlying economics are shifting again.

Material security is becoming a strategic concern. Supply chain resilience and energy costs continue to influence manufacturing decisions. At the same time, regulation is beginning to move beyond recycling toward more responsible product lifecycles.

European ecodesign requirements for smartphones, feature phones, cordless phones, and tablets came into force in June 2025. The EU’s Right to Repair framework is also being implemented across member states. These are often positioned as compliance requirements, but they are better understood as market signals.

All of this is supported by the work of the Ellen MacArthur Foundation, which advocates for more responsible practices in the development of products and packaging, while also raising awareness across business sectors of the significant economic loss that occurs when circular systems design is not implemented.

They point toward a future where value retention is not optional; it is a competitive advantage for those that harness the economic potential of doing it well and scaling the model effectively.

Connected Doesn’t Mean Circular

One of the defining advantages of modern consumer technology is visibility. Connected devices, software platforms, sensors, and AI now allow companies to understand how products are performing in real time.

In theory, this creates an unprecedented opportunity: knowing where a product is, how it is being used, when it needs maintenance, and when it is approaching failure.

But the digital layer only supports circularity when it enables action—maintenance, repair, upgrade, authentication, return, refurbishment, resale, parts recovery, and material capture. In fact, a world of always-on, AI-enabled physical products acting autonomously at human direction could end up creating more unsustainable practices due to increasing energy consumption demands.

The objective of the consumer tech and healthcare projects we’re working on today is not product connectivity for its own sake. It is extending the useful, productive, and economic life of products for as long as possible, which requires a different design mindset from the outset.

Circular Design is Not a One-Size-Fits-All Solution

A common misconception is that circularity requires a single operating model applied universally across all products. In reality, the opposite is true.

Different product categories carry different economics. A premium smartphone behaves very differently from a low-cost accessory. A gaming controller has different failure modes from a wearable. A connected appliance sits somewhere else entirely.

Product-as-a-service may be powerful in some categories and unnecessary in others. Not every product should be “servitized,” and forcing that model can create friction rather than value.

Even simple products can be designed more intelligently. A kettle does not need to become a subscription service to benefit from circular thinking. It can be designed for easier repair, longer component life, and more efficient material recovery at end of use.

The first question we often ask is not “what is the circular model?” but “what is the right model for maximizing value retention?”

Which products contain enough residual value to justify refurbishment? Which fail due to predictable, replaceable components? Which become obsolete because of batteries, software, or modular constraints? Which should be harvested for parts? Which should move directly into material recovery?

These are not sustainability decisions. They are portfolio decisions, sitting at the intersection of engineering, design, and commercial strategy.

The Return Loops is Where Value is Won or Lost

Even the most elegantly designed circular product fails if it does not come back into the system effectively.

The return loop is where value is either preserved or destroyed.

Once products are returned, they must be identified, assessed, and routed quickly to their highest-value next use. Some will be refurbished and resold. Some will become warranty replacements or service stock. Some will be broken down for components. Others will move into material recovery streams.

Timing is critical. Slow return loops erode value as products sit idle, components age, visibility is lost, and economics deteriorate. Efficient return loops do the opposite: they preserve optionality, and optionality is where value lives.

The real challenge is not simply recovering products. It is making the right decisions about them quickly enough that value does not decay in the meantime.

Philips has shown that circularity scales when product design, operational capability, and commercial incentives align, turning returned products into new revenue streams and generating 24% of revenue from circular products and services in 2024.

Philips is not alone. Companies including Apple, Cisco, Dell Technologies, HP, Schneider Electric, and Michelin have all invested in circular business models built around repair, refurbishment, remanufacturing, product take-back, and lifecycle extension. While the models differ by category, the principle remains the same: keeping products, components, and materials working at their highest value for longer, rather than relying solely on the next new sale.

The next generation of market leaders may not be the companies that sell the most products. They may be the companies that recover the most value from the products they’ve already sold.

The Next Competitive Advantage

For a long time, sustainability was treated as a cost center. Circularity is now beginning to look like something else entirely: a growth strategy.

The companies that lead the next phase of consumer technology will not simply be those with the strongest sustainability narratives. They will be the ones that identify where value is disappearing and redesign their products, services, and operations to keep that value in motion.

Sometimes that means repair. Sometimes upgrade. Sometimes refurbishment, resale, parts harvesting, or material recovery. The mechanism is less important than the outcome: keeping value circulating for longer. Because circularity is not really about recycling; it is about recovering value that would otherwise be lost.

At Cambridge Design Partnership, we see this challenge at the intersection of product design, software, systems engineering, service design, and business model innovation. The companies that succeed will not treat these disciplines separately.

They will integrate them.

And in doing so, they may discover that one of the biggest growth opportunities in consumer technology is not the next product they sell, but the value they have already created—and have yet to recover.

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From Impossible to Feasible: Using Theoretical, Synthetic and Tissue Models to Accelerate Novel Drug Delivery Development

May 18th 2026

Dr Karla Sanchez

Head of CNS Technology

Frances Pencliffe

Operations Lead – Drug Delivery

Introduction

Platform devices are often the preferred starting point for combination product development, and for good reason. A well-characterised platform offers a proven architecture, defined performance boundaries, and a clear path to market. However, an increasing number of therapies in development are pushing into territories where there is currently no existing platform to serve their needs.

Novel biologics are extending the boundaries of volumes and viscosities, cell and gene therapies are leading to ever-more tissue-targeted deliveries, suspensions and co-delivered (or sequentially delivered) therapies are among those demanding entirely new thinking about how, where, and whether a drug can be delivered. These formulations can all be gathered under the umbrella term “specialty delivery” and are united by a common challenge – where there is no existing platform device able to deliver the formulation, a delivery system becomes a core part of formulation development and testing. Where traditional developments are often able to rely on vial-and-syringe delivery to enable early clinical data to be gathered, these specialty delivery developments (especially those requiring tissue-targeted delivery) require more sophisticated devices to be available earlier in the therapy or formulation’s life cycle.

For these developments, feasibility work looks very different to what we expect when adopting a platform. Rather than asking “Can this platform device deliver our formulation?”, the question becomes “Is it possible to deliver this formulation at all?”, which could mean the difference between a few weeks of benchtop testing and months or even years of exploratory research. Answering this new feasibility question efficiently requires a structured, pragmatic approach that balances scientific rigour with the realities of early-stage development timelines and budgets.

The framework described in this article has been developed through more than a decade of feasibility work on some of the most challenging drug delivery developments we’ve encountered; projects where the answer wasn’t obvious, and where finding it efficiently made the difference between a development moving forward and stalling.

This article explores how theoretical, synthetic and tissue models can be combined to de-risk novel drug delivery developments, and how to deploy them intelligently to turn an apparently impossible challenge into a feasible one. While the worked example we’ll draw on throughout – a novel device for drug delivery directly to the brain – sits at the more complex end of the spectrum, the principles apply equally to any development where an existing platform doesn’t fit, or where it isn’t yet clear whether a device is a good match for the therapy. The approach scales to the necessary level of complexity.

What Does Feasibility Really Mean?

Feasibility for specialty delivery means different things depending on where you sit on the development complexity spectrum. At one end, it might mean confirming that an existing device can deliver a formulation that sits just outside its stated performance envelope, e.g., pushing the viscosity boundary and testing whether a longer injection time is still acceptable to users.

At the other end, feasibility might mean starting from scratch with a therapy that has never been delivered before, to a target site that has never been accessed in this way, using a device that doesn’t yet exist. Here, the risks are profound, and the unknowns are numerous, spanning therapy efficacy, navigation and targeting (including positional accuracy), tissue tolerability, usability, and many more.

The first challenge is therefore to understand where your development sits on this spectrum, which can be done by identifying the key risks to be addressed. This starts by understanding what the needs of the formulation are as compared to existing formulations on the market or in clinic. If, for example, you are targeting subcutaneous delivery, chances are navigating the device to the right area is not going to be a huge risk. But if you’re targeting a specific structure of the brain or a nerve within the nasal cavity, suddenly how the formulation is delivered to the right location is an unknown factor that’s critical to the efficacy of the therapy. The same principles apply for other aspects – are you targeting a new user group? Or a new use environment? Each area where you’re looking to push a boundary is a potential risk to be investigated during feasibility work.

The Model Landscape: A Brief Orientation

Once the key risks have been identified, the next step is to select the right tools -often models- to investigate them. In the context of drug delivery feasibility, models broadly fall into three categories.

In silico models – computational and mathematical approaches – range from simple physics-based calculations through to complex simulations such as computational fluid dynamics (CFD) or finite element analysis (FEA). At their best, they are fast, flexible, and low cost to iterate, making them well suited to early-stage exploration. Their limitation is that they are only as good as the inputs you give them, and at feasibility stage those inputs are often hugely uncertain.

In vitro models – bench-based physical testing – provide empirical data that computational models simply cannot. Synthetic tissue analogues, flow rigs, and bench prototypes all fall into this category. They offer a tangible way to test device and formulation behaviour under controlled conditions and are generally more accessible and affordable than animal or human studies.

Ex vivo models – testing in excised biological tissue – offer real tissue behaviour without the complexity and cost of full animal studies. For early feasibility work involving novel delivery routes or target tissues, ex vivo models are particularly valuable: they can provide rapid, biologically relevant data to characterise tissue properties and validate computational predictions at a stage where in vivo work would be premature.

In practice, the most effective feasibility programmes don’t rely on a single model type. The real skill is in knowing which combination to use, and in what order – and that starts with asking the right questions.

Framing the Right Questions: A Worked Example

Having the right models available is only half the challenge. The bigger risk in early feasibility work is asking the wrong questions of them, reaching for high-fidelity simulation before the problem is properly understood, or pursuing a level of detail that isn’t yet warranted. A multiphysics model of tissue mechanics or a full physiologically-based pharmacokinetic (PBPK) simulation might ultimately be the right tools but deploying them before the fundamental questions have been framed correctly is an expensive way to generate false confidence.

A more effective approach starts not with the model, but with the decision that needs to be made. At each stage of feasibility, it is worth asking: what do we need to demonstrate right now? What is stopping us from moving forward? Once that decision is clear, the next step is to identify the key drivers – what are the dependencies, and do we have reliable data for them? Only then can a model, or set of models, be properly selected, and the guiding principle should always be to choose the simplest approach that can answer the question with the necessary degree of confidence.

To illustrate how this plays out in practice, consider the development of a novel device for delivering drug directly to a target structure within the brain, a scenario that was once thought to sit firmly at the impossible end of the feasibility spectrum. There are many questions that could be addressed during feasibility, but we’ve selected three unrelated questions that have been posed to us in the past as examples of our approach to feasibility:

What is the delivery force?
Will the device achieve the required formulation distribution in tissue?
How will the device influence the clinical effect?

1. What is the delivery force?

The initial instinct may be to model the delivery force in full using a high-fidelity simulation incorporating device mechanics, friction, fluid dynamics, and tissue backpressure simultaneously. However, at this early stage, information about the conditions of delivery e.g. tissue properties, fluid interactions, variability (patient, user, device); may not be fully known. As such, reframing the question reveals a more useful starting point: the real need wasn’t to predict the exact force profile, but to understand whether the force required to deliver the drug would exceed what a typical user could reasonably apply. With that decision in mind, a more pragmatic model strategy emerges; ex vivo tissue characterisation to measure backpressure and understand fluid-tissue interactions, early bench testing with a simple prototype to assess injection force, and a low-order physics-based mathematical model to combine these inputs. Fast to generate, easy to iterate, and sufficient to answer the question that actually mattered at this stage.

2. Will the device achieve the required formulation distribution in the tissue?

The initial expectation might be a complex computational fluid dynamics (CFD) and fluid-structure interaction (FSI) simulation with a nonlinear, anisotropic tissue model and multiphase flow, an approach that would be computationally expensive and heavily dependent on tissue property inputs that aren’t yet fully known. Reframing shifts the question to something more tractable: can the device achieve the required distribution area (or volume) in tissue? This opens up a staged model strategy; simplified flow and porous media models for rapid screening, followed by testing in brain tissue analogues developed from published academic literature, allowing findings to be benchmarked against existing research. Targeted CFD is then reserved for where refinement is genuinely needed. Lower cost, easier to verify, and designed to work even when input data is uncertain.

3. How will the device influence the clinical effect?

Building a full PBPK model from preclinical data is an understandable ambition, but difficult at feasibility stage, where the biological inputs required are often unavailable or unreliable. Reframing the question from predicting clinical efficacy to “can the device achieve sufficient therapy exposure for target absorption?” enables a more practical, modular approach. Mechanistic models (which describe system behaviour) can be used to characterise device-to-delivery behaviour. Simplified transport models and targeted experiments address delivery-to-distribution as described above. And for distribution-to-exposure, findings can be bridged to existing pharmacokinetic and pharmacodynamic (PK/PD) models (the mathematical frameworks that describe how a drug moves through and acts on the body) making it straightforward to hand off to specialist teams when the time comes. This keeps the focus on the decision at hand and avoids reliance on uncertain biology.

In each case, the pattern is the same: resist the pull towards complexity, reframe the question to be asked around the decisions to be made, and choose the simplest model that can answer it. Models don’t just answer questions, used well, they help reveal the questions worth asking in the first place.

Conclusion

Even the most complex drug delivery challenges can be broken down into manageable pieces. The key is phase-appropriate pragmatism; understanding where your development sits on the feasibility spectrum, selecting models that are fit for the question rather than fit for the complexity, and knowing when you have enough information to make the next decision.

The worked example above illustrates how this plays out in practice. What was once considered an impossible development became feasible through the efficient combination of analytical, synthetic and tissue models, each chosen not for its sophistication, but for its ability to answer a specific, well-framed question at the right stage of the programme. It is an approach we have refined across many such projects, and one that we continue to apply wherever a development pushes beyond the boundaries of what existing platforms and precedent can answer.

To accelerate innovation, we need prompt decision making. Obtaining feasibility answers in weeks rather than years allows redirection of resource toward where it’s most needed (e.g. reframing of therapy or administration route), ensuring effective treatments reach patients faster. The framework works precisely because it is designed to find the answers that matter efficiently.

The tools are available. The challenge, and our experience, is in deploying them wisely.

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For more on how to accelerate novel and targeted drug delivery feasibility using computational, synthetic, and tissue models to de-risk combination product development, contact Cambridge Design Partnership.

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From Pilot to Portfolio: Scaling Circular Packaging

April 10th 2026

James Harmer

Head of Consumer Packaged Goods (CPG)

Adrian Segens

Senior Sustainability Leader

We have seen plenty of circular packaging pilots that work in isolation.

A new design that’s more recyclable. An increase in recycled content. A workable deposit return trial that performs well in-store. A positive refill system experiment with a strong story behind it.

Then they stall

Not because the intent was wrong, but because pilots sit outside the full operating system and true commercial pressures. They are rightly protected from the cost, infrastructure, and commercial realities to test and learn consumer behavior, but are often ill-equipped to adapt for scale.

That is why packaging Extended Producer Responsibility (EPR) matters, as this is a scale-centric challenge.

It shifts packaging from a waste topic to a design and business topic. The Organisation for Economic Co-operation and Development (OECD), describes EPR as a policy approach that makes producers responsible through the post-consumer stage, while also generating funding and information for collection, sorting, and recycling systems. And the policy context is no longer theoretical. In the EU, the PPWR entered into force on February 11, 2025, and generally applies from August 12, 2026. In the UK, obligated producers must register, report packaging data, and pay fees. Australia is reforming packaging regulation to align packaging with circular economy principles. Ontario completed its transition to full producer responsibility on January 1, 2026. Canada expects packaging EPR for packaging in most, if not all, provinces and territories by 2030.

So the question is no longer whether circular packaging should be scaled.

The more useful question is this: will compliance effort be treated as a cost of doing business, or used as a lens for sharper portfolio choices?

Because as EPR becomes a reality, companies are forced to define things that pilots can leave vague or don’t answer. Which end-of-life pathway is realistic in each market? How likely is collection and effective sorting in normal conditions? Where is packaging complexity creating cost without improving recovery? Those are not paperwork questions. They are design questions, procurement questions, and portfolio questions. This is why EPR is better understood as a portfolio lens than a pilot trigger. Pilots still matter. They are often essential for testing formats, claims, and consumer participation models. But pilots alone do not tell you how a portfolio performs across geographies, channels, suppliers, materials, and recovery systems. That wider view is where scale is won or lost.

Pilots often succeed because they benefit from exceptional conditions. One geography. One retail partner. One highly engaged consumer group. One supplier willing to stretch. One team willing to intervene when reality gets messy. In some cases, even supportive national policy environments, such as France’s emerging regulatory push on reuse and refill under its circular economy legislation, can effectively act as a scaled, semi-controlled test bed.

Portfolios operate under normal conditions. They carry multiple markets, multiple channels, multiple suppliers, competing cost pressures, and uneven infrastructure. At that scale, the test is not whether a packaging idea worked once. The test is whether it still works when it becomes business as usual.

EPR also brings consumer behavior into focus. Packaging systems only work when people can participate in them. If organizations say they are consumer-centered, this is where that claim has to show up. Legislation should be used not just to meet regulatory requirements, but to design packaging experiences that are intuitive, low-friction, and aligned with everyday behavior. Disposal instructions need to be clear. Return and refill participation needs to feel intuitive. Sorting needs to work in ordinary households, not just in ideal conditions. Get this right, and you improve more than recovery. You reduce contamination, lower fee exposure, and strengthen the overall product experience. Regulations will then not only encourage circularity, but they create a purposeful moment of action and innovation for companies to strengthen brand trust, delivering tangible value to consumers as well as the business. In other words, EPR can turn circularity from a pilot activity into an operating model that also improves the consumers’ experience, if companies use the opportunity.

Circularity has always been a system design challenge, and EPR is accelerating this advancement. The task is not simply to improve one pack in isolation. It is to understand how material choice, format, infrastructure compatibility, consumer participation, evidence burden, fee exposure, and end market reality interact. That is a different level of discipline, and it tends to expose weaknesses quickly.

A portfolio view allows better questions. Which formats create the highest compliance and cost exposure? Which packs have the weakest real-world recovery pathway? Which material choices add complexity without improving the outcome? Where can harmonization reduce cost and improve recyclability? Which claims are robust, and which are vulnerable? Where could redesign create both environmental gain and economic value?

The strongest companies will not treat EPR as a layer of administration added to yesterday’s packaging choices. They will use it to redesign how those choices are made. In practice, that means defining end-of-life pathways in operational terms, separating what can be standardized globally from what must be adapted locally, evaluating packs with a balanced scorecard rather than a single metric, testing behavior honestly, building the evidence plan early, and staging change across the portfolio where learning is fastest and risk is lowest.

Handled tactically, EPR will bring short-term pain with few long-term gains. Handled strategically, it should shape and accelerate the decisions you ultimately need to make to protect your future.

As part of a strategy, it can become a source of commercial advantage. Not because regulation is inherently beneficial to producers. It is not. But because it can force the level of scrutiny, many organizations have postponed. That scrutiny can lead to fewer problematic formats, better alignment between design and infrastructure, lower material intensity, stronger claims, smarter use of recycled content, and clearer investment cases for reuse, refill, or redesign where those moves are genuinely viable.

The companies most likely to create value from packaging EPR will be the ones that use that pressure to review the portfolio properly and scale the changes that actually work.

At Cambridge Design Partnership, we help teams translate regulatory changes to practical design and engineering action. That means identifying where recovery pathways are weak, where behavioral assumptions are unrealistic, where evidence requirements need to shape the brief earlier, and where material and format decisions are creating hidden risk. Typically, that means combining circular diagnostic work, sustainability screening, Sustainability Clean sheeting, human-centered design, engineering validation, and regulatory readiness into a single decision process.

It’s worth asking one final question. Are you only preparing to comply, or are you using this moment to reshape the portfolio for a more circular and commercially resilient future?

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Sustainable pharmaceutical packaging without compromising safety or usability

February 24th 2026

Adrian Segens

Senior Sustainability Leader

Jon Powell

Head of Manufacturing

Steve Augustyn

Senior Consultant Mechanical Engineer

When people talk about “sustainable packaging,” they often picture quick material swaps and bold recyclability claims. But in pharmaceuticals, it’s rarely that simple.

Pharma packaging is a safety-critical system. It protects sensitive formulations, supports regulatory compliance, and helps patients take the right medicine in the right way, every time.

That’s why packaging teams are under a different kind of pressure: they are being asked to reduce environmental impact while holding the line on performance, patient safety, and supply resilience.

At Cambridge Design Partnership (CDP), we work with pharma and healthcare teams to make that trade space manageable. The goal isn’t sustainability as a side project. It’s packaging decisions that are evidence-led, patient-centered, and durable under regulatory scrutiny.

The structural tension at the heart of pharmaceutical packaging

In practice, pharmaceutical packaging exists inside tight constraints that are in place for good reason:

Validated moisture, oxygen, and light barriers (often with narrow stability margins)
Strict control of chemical interactions and leachables across materials, inks, adhesives, and coatings
Tamper evidence, traceability, and serialization requirements
Repeatable, audited manufacturing processes with controlled change management
Global regulatory alignment, long shelf-life assurance, long qualification cycles, and post-approval variation burden

However, here is another non-negotiable that is often underweighted in sustainability conversations: patient usability.

In effect, packaging is the interface between medicine and the person using it. It must enable patients to identify the correct drug clearly, complete any necessary inspection (for example, tamper evidence, integrity, or visual checks, where relevant), and access the drug product reliably. If a sustainability change makes a pack harder to open, harder to read, or easier to confuse, it creates a risk that overwhelms the environmental benefit.

As a result, progress is rarely about a single material substitution. Sustainable outcomes come from system decisions – barrier, labeling, usability, manufacturing, logistics, and end-of-life considered together.

Why the pressure is now unavoidable

1. Regulation is becoming a market access issue.

In Europe, the PPWR (Packaging and Packaging Waste Regulation) is now the anchor regime: it entered into force in February 2025 and will apply from August 2026, with recyclability tightening through 2030 and a formal review horizon in 2035 that is explicitly relevant to certain pharma pack exemptions. Here, the key challenge is timing: regulatory clocks move faster than pharma packaging platforms can change.

2. Stakeholder expectations are rising.

At the same time, payers, providers, investors, and patients increasingly expect credible action. Packaging is visible, measurable, and easy to compare – so it’s becoming a practical test of seriousness, not a marketing footnote.

3. The business case is shifting from “nice to have” to “must manage”.

Consequently, packaging decisions now touch cost, resilience, and speed to market: material exposure, waste fees, supply fragility, and late-stage redesign risk. In most cases, getting ahead of change is usually cheaper than reacting when options are already locked.

What we see in real programs

A few patterns show up repeatedly when teams try to move from intent to execution.

The biggest wins aren’t always in the primary pack.
In many cases, primary packaging can be the hardest part of the system to change quickly. By contrast, secondary and tertiary packaging (such as cartons, leaflets, protective elements, and shipping formats) often provide faster, lower-risk opportunities – especially when you design them to reduce total material use, improve transport efficiency, and avoid formats that create sorting and recycling problems.

“Recyclable” is not the same as “safe, compliant, and used correctly.”
For pharma, the right question is usually: What is the lowest-impact design that still delivers stability, compliance, and patient usability? That framing prevents false optimization.

Late redesign is the hidden cost.
When sustainability is added after packaging architecture decisions are made, you end up negotiating against a nearly fixed design. That’s when cost and time blow out – and when risk rises.

A practical framework for executive decision-making

If you’re leading packaging strategy, the most useful step is to turn sustainability into a structured decision process rather than a series of ad hoc requests. Here’s a framework we use with teams to keep work focused and defensible.

1. Define your non-negotiables up front

Before exploring options, align on what cannot be compromised:
Patient safety and correct use
Readability and differentiation (right medicine, strength, dose, expiration)
Access and openability under real-world conditions
Barrier performance and shelf-life confidence
Tamper evidence and traceability requirements
Validated manufacturing performance and supply resilience

This avoids “optimizing” a pack into something that fails in the field.

2. Establish a credible baseline, quickly

You don’t need a year-long study to find direction. A focused baseline – material flows, key pack components, manufacturing yield sensitivity, logistics assumptions, and end-of-life reality – usually reveals where the impact sits and where it doesn’t.

This is where we often apply lifecycle thinking and our Sustainability Cleansheet method: Quantify the big cost and environmental impact drivers early so you don’t spend months improving the wrong thing.

3. Build a short list of options and stress-test the tradeoffs

For each option, teams should be able to answer clearly:

What changes physically? (materials, structure, labels, coatings, inks, adhesives)
What risks move? (stability margin, E&L, usability, line performance, supply continuity)
What improves? (impact reduction, cost, simplification, waste reduction, data/traceability)
What evidence is needed? (bench tests, line trials, stability, human factors validation)

The aim is not perfect certainty. It’s the early elimination of weak options and disciplined focus on the few options that can scale.

4. Pilot to reduce uncertainty, not to signal virtue

In pharma, pilots only matter if they answer hard questions: manufacturability, patient behavior, stability confidence, and real end-of-life outcomes (not just theoretical recyclability).

We design pilots to generate decision-grade evidence, so teams can commit without gambling.

5. Use “smart print” technologies thoughtfully

Many teams want digital capability – traceability, anti-counterfeit protection, patient guidance, or better sorting instructions – without turning packaging into electronics.

That’s where smart print technologies can help: Printed features (from advanced QR codes and variable data to printed conductive inks and thin printed circuits) can deliver “DPP-style” benefits – linking the pack to verified product data, instructions, and chain-of-custody information – without adding bulky components.

But they still require end-of-life thinking. Even small amounts of conductive ink or functional layers can affect recycling behavior and material recovery if they’re used indiscriminately. The practical approach is:

Keep digital features as light as possible (often secondary packaging is the right home)
Avoid designs that contaminate or complicate recycling streams
Choose materials and inks with recovery pathways, where available
Be explicit about the end-of-life intent, not just the in-use feature set

Smart features can support compliance and patient outcomes – but only if they’re designed as part of the packaging system, not bolted on.

6. Build a roadmap that matches pharma timelines

Packaging change in pharma is slow by design: qualification, validation, supplier readiness, and stability programs all take time. That’s exactly why the gap between product development cycles and regulatory timelines matters. The right roadmap staggers effort:

Near term: Secondary and tertiary improvements and material reduction
Mid term: Architecture changes where stability risk is manageable
Long term: Platform shifts and primary packaging strategies aligned to the next regulatory horizon

How CDP helps

Clients bring us in when they need momentum without compromising on safety. What makes CDP different is the way we connect the disciplines that usually sit apart:

Packaging engineering and materials science
Human-centered design and real usability validation
Manufacturing realism (line performance, transfer to manufacture, scale-up)
Lifecycle thinking and decision-grade impact analysis
Digital and data capability to support traceability and future information needs

The result is packaging strategy that holds up: Lower-impact solutions that are still manufacturable, compliant, and usable – built on evidence rather than hope.

The opportunity

Sustainable pharmaceutical packaging isn’t about copying approaches from consumer goods. It’s about designing within the constraints that matter – stability, safety, usability, and supply assurance – while still making real progress on impact.

If you’re responsible for packaging strategy and you’re facing tighter timelines, rising expectations, and harder tradeoffs, we can help you move faster with confidence.

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For more on how to accelerate meaningful innovation in sustainable pharmaceutical packaging, contact Cambridge Design Partnership.

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Why so few smokers have transitioned to Reduced Risk Products (and what the industry can do about it)

January 20th 2026

Ben Illidge

Head of THR

Simon Rucker

This article summarises our recent analysis of why so few smokers have transitioned to reduced-risk products (RRPs). More importantly, it moves beyond diagnosis to set out a clear strategic roadmap for what must now change if RRPs are to become a credible alternative rather than a solution that has yet to live up to expectations.

Where Are We Today?

Market Reality

Despite over a decade of innovation and major investment, global RRP (heated tobacco, vapes, and nicotine pouches) adoption remains low (c. 2.5% of 1.3B smokers). Most smokers continue to favor cigarettes.
Even in mature markets (e.g., Japan, Italy), sole use of RRPs rarely exceeds 15% – well below expectations. Dual-use (RRP + combustible) dominates, further diluting harm-reduction objectives.
Governments are increasingly sceptical of RRPs due to youth uptake concerns. Disposable vapes face bans; nicotine pouches and e-liquids are under intense scrutiny.
Despite resilient industry share prices, investors are increasingly demanding proof of value and scalability for RRPs, not just good intentions.
Cigarettes offer clear barriers to entry (brands, supply chains, regulation). RRPs lack comparable moats, leaving Big Tobacco exposed to nimble competitors and margin erosion.

Root Problem

There remains a fundamental misunderstanding of smoker psychology: RRPs aim to reduce harm, but fail to replicate the emotional, sensory, and ritualistic satisfaction of smoking and its convenience.
Most smokers don’t want a new product – they want their existing experience made safer (and more socially acceptable).
Device-centric solutions miss the emotional core of smoking, disrupt familiar rituals, and add friction via charging, cleaning, and unintuitive tech interfaces.
More recently, innovation has focused on fixing RRP friction rather than delivering a superior smoking alternative.

Systemic Missteps

Device-centric innovation ceded control to Chinese OEMs and fractured the consumer understanding of inhalable categories (heated tobacco, vapes).
An IP arms race between Big Tobacco players limited consumer choice and reduced overall appeal of RRPs.
Industry underestimated the impact illicit products and poorly enforced trading standards would have on regulatory sentiment.

So, What Can The Industry Do About It?

The first order of business is to develop a new ‘Strategic Roadmap’.

Big Tobacco needs to reclaim RRP product leadership by returning to what the industry knows best: FMCG agility, consumer insight, and emotionally resonant brands.

Firstly this means thinking less like Samsung and more like Starbucks: going back to focusing on creating emotionally satisfying alternatives in key smoking ‘moments’, not one-size-fits-all functionally safer gadgets.

Secondly this means remembering that smokers crave familiarity in terms of ‘taste’, satisfying nicotine delivery, hand-to-mouth gestures, and ritual. Big Tobacco should concentrate on replicating ‘the cigarette experience’, not reinventing it.

Thirdly, Big Tobacco needs to grasp the nettle and decide whether it’s going to leverage the trust and value of established tobacco brands to make RRPs more relevant or try and create new brands from scratch that are unlikely to have any meaning to smokers.

The next thing Big Tobacco should do is get very clear on the key technical & development JTBDs.

The first is that to innovate a superior inhalable product that delivers FMC-like nicotine, ‘taste’, and delivery profile, new technologies are required that overcome the fundamental issues of heated product physics (thermodynamics / thermokinetics) and heater control. This can be done.

The second is how to make suitable consumables at the scale, speed, and cost required – ideally with enhanced ESG. Again, this is not rocket science.

The third is to work out how to deliver the whole experience in a product that is familiar and reassuring to smokers. This means designing intuitive, non-device-centric RRPs that fit seamlessly into a smoker’s life, mimic the rituals they value, and offer meaningful, low-friction, FMCG-like accessibility.

This is not just about traditional ‘look and feel’ considerations but also, crucially, which additional design elements (product semiotics and brand cues) are required to deliver a truly appealing consumer offer that can serve as a genuine alternative to cigarettes.

To achieve a step change in the rate and overall number of smokers transitioning to RRPs requires truly disruptive ideas that are also brilliantly executed and presented.

And Finally

Big Tobacco needs to get on with it! The window to regain RRP leadership is closing. Big Tobacco is losing the RRP argument with regulators. Additionally, Chinese OEMs are poised to take over. For industry examples outside tobacco, look at Green Technology, BEVs, Rare Earths, and AI.

In the increasingly fast-moving RRP categories sector, hesitation is a liability. If you are a senior leader serious about maintaining or regaining your competitive edge and avoiding costly regulatory missteps, you must act quickly.

Let’s schedule a high-priority discussion to ensure your next move is your best move.

Receive further news from our THR team at CDP

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For more on how to accelerate meaningful innovation in tobacco harm reduction, contact Cambridge Design Partnership.

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Prioritize Innovation and Commercial Viability in Surgical Robotics with Intelligent Architecture

Functional density: why intelligent architecture definition matters

Intelligent architecture

Conclusion

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CDP advances surgical robotic technology

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Establish the Test Matrix and Reporting Metrics

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James Harmer: Leading CPG Into Its Next Growth Phase

One of the biggest misconceptions I see is that circularity is positioned as simply another sustainability initiative.

Solving these challenges requires a different way of innovating

The biggest opportunity comes before the brief is written

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De-Risking Post-Market Change Through Pilot Production for Drug Delivery Devices

What Drives Change in Drug Delivery Devices?

A Common Challenge

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Conventional Prototyping Lacks Rigour

Why Existing Production Lines May Not Accommodate Design Changes

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Implementation and Outcomes

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REFERENCES:

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Sustainable Pharma Packaging Starts with Asking Better Questions

A greener material can still be the wrong answer

Supplier data is not product evidence

The current pack may be over-specified

Not every sustainability opportunity is worth pursuing

Recyclable in theory is not enough

Waiting for regulation is already too late

Leadership starts with the better question

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Circularity Isn’t a Sustainability Story. It’s a Business Model Reset.

Why the next growth opportunity in consumer technology may already be sitting in customers’ homes.

The Old Model is Broken

A New Model for Value Retention

Connected Doesn’t Mean Circular

Circular Design is Not a One-Size-Fits-All Solution

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The Next Competitive Advantage

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From Impossible to Feasible: Using Theoretical, Synthetic and Tissue Models to Accelerate Novel Drug Delivery Development

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The Model Landscape: A Brief Orientation

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1. What is the delivery force?

2. Will the device achieve the required formulation distribution in the tissue?

3. How will the device influence the clinical effect?

Conclusion

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Sustainable pharmaceutical packaging without compromising safety or usability

The structural tension at the heart of pharmaceutical packaging

Why the pressure is now unavoidable

1. Regulation is becoming a market access issue.

2. Stakeholder expectations are rising.

3. The business case is shifting from “nice to have” to “must manage”.

What we see in real programs

A practical framework for executive decision-making

How CDP helps

The opportunity

Connect with CDP