Clinical Research in Medical Devices: Revisiting Innovative Design — Fifteen Years On

In September 2011, we published a perspective on the fundamental design challenges in medical device clinical trials — a field that was, at the time, struggling with questions of comparability, control selection, and the gap between trial outcomes and real-world clinical practice. Fifteen years later, those questions remain. But the tools available to answer them have changed dramatically.

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What We Argued in 2011

The core observation in our original article was straightforward but important: medical device clinical trials are structurally different from drug trials in ways that conventional trial design cannot adequately address. There is no perfect control. The comparator often serves a different patient subset than the device under investigation. And outcomes — because they are so dependent on operator skill, patient selection, and procedural context — frequently fail to translate into the real-world clinical practice they are supposed to inform.

We used the stent-versus-CABG debate as the clearest illustration of this problem. Landmark trials including FREEDOM and MAIN-COMPARE had generated substantial data — and substantial controversy — without resolving the fundamental question of which intervention was superior, largely because the populations they studied were not truly comparable. SYNTAX stood apart, we argued, because its innovative design — the development of a structured scoring system that quantified lesion complexity before randomization — created a framework for comparison that was more clinically meaningful than simple randomization across heterogeneous patient groups.

Our proposed solutions in 2011 centered on three ideas: rethinking study hypotheses toward more device-specific, measurable endpoints; designing studies that included rather than excluded real-world patient complexity; and using scoring systems and multi-arm compound designs to make comparisons more honest.

Those ideas have aged well. But the field has moved so far beyond them that revisiting the original article requires more than an update — it requires a reckoning with how profoundly the design landscape has shifted.

Key Takeaways Medical device trials face unique challenges that differ fundamentally from pharmaceutical studies. Adaptive, Bayesian, and platform trial designs have transformed modern medical device research. Real-world evidence (RWE) is now central to both pre-market and post-market device evaluation. EU MDR has significantly increased clinical evidence expectations for device manufacturers. Digital technologies and decentralized trial models have expanded what can be measured in device studies. Clinical evidence generation is now a lifecycle activity rather than a one-time regulatory requirement.

What Has Changed — and What Hasn't

The Core Problem Persists

The fundamental challenge we identified in 2011 — the absence of a perfect control in medical device trials — has not been solved. It has been better managed, more creatively approached, and more honestly acknowledged in regulatory frameworks. But it has not gone away.

The reasons are structural. Medical devices are operator-dependent in ways that drugs are not. A stent deployed by an experienced interventional cardiologist at a high-volume center will perform differently from the same device deployed by a less experienced operator at a lower-volume center — and no randomization scheme can fully account for this. A diagnostic imaging device's performance depends on how images are acquired, how they are read, and what clinical decisions flow from the interpretation. These operator and system dependencies mean that the "effect of the device" cannot be cleanly isolated from the effect of the human context in which the device is used.

This is why the design innovations we highlighted in 2011 — structured scoring, compound multi-arm designs, hypothesis reframing — remain relevant. They were responses to a fundamental structural challenge, not temporary workarounds for a problem that would eventually be solved. What has changed is the sophistication and range of the toolkit available to address that challenge.

Adaptive and Bayesian Designs Have Moved from Theory to Practice

In 2011, adaptive trial designs — which allow pre-specified modifications to sample size, endpoints, or treatment arms during a trial based on interim data — were an emerging methodology with limited regulatory acceptance for device trials. Bayesian designs, which formally incorporate prior evidence into the analysis of new data, were primarily an academic discussion.

Both have now achieved mainstream regulatory acceptance. The US FDA's guidance on adaptive designs for medical devices has evolved considerably, and adaptive designs are now used routinely in cardiovascular, orthopedic, and diagnostic device development. The value proposition is particularly compelling for medical devices because the rapid pace of technological iteration means that a traditional trial with a five-year enrollment and follow-up period may be evaluating a device that has already been superseded by the time results are available. Adaptive designs that can modify the study based on accumulating data — stopping early for clear benefit or futility, or adjusting sample size based on observed variability — substantially reduce this risk.

Bayesian designs address a different but related problem. For devices with limited pre-trial human data — novel implants, new diagnostic modalities, devices for rare conditions — traditional frequentist sample size calculations produce impractically large enrollment requirements. Bayesian frameworks allow prior evidence — from bench testing, animal studies, early human experience, or related devices — to be formally incorporated into the analysis, reducing the number of patients needed to reach a statistically credible conclusion. The FDA's Bayesian guidance for medical devices, now well-established, has made this approach a practical tool rather than a theoretical one.

Platform Trials Have Emerged as a Response to Rapid Innovation

Perhaps the most significant design innovation since 2011 that directly addresses the problems we identified is the platform trial — a master protocol under which multiple device iterations or treatment strategies can be evaluated simultaneously, with shared infrastructure, shared controls, and the ability to add or remove arms as the evidence evolves.

The platform trial model was developed primarily in oncology drug development, but its logic applies directly to medical device research. In a field where devices are continuously iterated — where a coronary stent may go through multiple generations of polymer coating, strut thickness, and drug elution profile within the span of a single five-year trial — a platform design that can evaluate successive iterations against a shared control arm is far more efficient and scientifically coherent than conducting a separate randomized trial for each iteration.

This addresses one of the limitations we noted in 2011: that study designs excluding real-world patient complexity made results less generalizable. Platform trials, by running continuously and accommodating evolving patient populations and device iterations, generate evidence that is inherently more contemporaneous and more applicable to current clinical practice than traditional trials that freeze their design at initiation.

Real-World Evidence Has Gone from Aspiration to Regulatory Currency

In 2011, we noted that the outcomes of device trials often failed to match day-to-day clinical observations — the SPIRIT and Endeavor trial groups being examples where controlled trial results and real-world practice diverged. The solution we implied was better trial design. The solution that has actually emerged, equally if not more powerfully, is the formal integration of real-world evidence into the regulatory evaluation of medical devices.

The US FDA's Real-World Evidence Program, the EU MDR's post-market clinical follow-up requirements, and the increasing use of registries and electronic health records as data sources for regulatory submissions represent a fundamental shift in how device evidence is structured throughout a product's lifecycle. Real-world evidence is no longer a supplement to clinical trial data — it is, in many cases, the primary source of post-market evidence that regulators require, and increasingly, it is being accepted as a component of pre-market evidence packages for devices in well-characterized indication spaces.

This has direct implications for device trial design. Hybrid designs that combine a randomized controlled trial for pre-market approval with a pre-specified real-world evidence generation program for post-market surveillance are now a recognized and increasingly standard approach. The randomized trial answers the regulatory question at approval; the real-world program answers the questions that the trial cannot — long-term durability, performance in broader and more complex patient populations, comparative effectiveness against devices that emerged after the trial was designed.

For device companies and CROs, this means that clinical evidence strategy can no longer stop at the point of regulatory approval. It is a lifecycle activity — and designing the trial to integrate seamlessly with the post-market evidence program, rather than treating them as separate undertakings, is now a marker of sophisticated clinical development.

The EU MDR Has Raised the Bar Fundamentally

In 2011, regulatory frameworks for medical devices varied enormously across jurisdictions. India's CDSCO device regulation was nascent. The EU's Medical Device Directive, though established, was interpreted inconsistently across notified bodies. The US FDA's 510(k) pathway allowed many devices to reach market on the basis of substantial equivalence to predicate devices with limited clinical evidence.

The EU MDR, which came into force in 2021 after a transition period, has changed this picture significantly — and its effects are reverberating globally. The MDR requires substantially more robust clinical evidence for CE marking than its predecessor, eliminates many of the equivalence pathways that previously allowed devices to rely on historical data, mandates post-market clinical follow-up as a condition of continued market access, and requires periodic safety update reports that maintain the clinical evidence file throughout the device's commercial life.

The practical consequence is that device companies seeking EU market access now need clinical programs that are, in many respects, as rigorously designed and executed as pharmaceutical trial programs. This is a direct response to the gaps we identified in 2011 — the lack of robust controls, the operator dependency, the failure to include real-world patient populations — but it imposes those requirements as regulatory obligations rather than as design aspirations.

For Indian device manufacturers and CROs, the MDR also changes the global competitive landscape. Indian companies with serious EU market ambitions need clinical programs designed to EU MDR standards from the outset — which requires both regulatory expertise in the EU framework and clinical research infrastructure capable of executing studies to those standards.

Digital Integration Has Transformed What Is Measurable

The most transformative development since 2011 — one we could not have anticipated in its full scope — is the integration of digital technologies into the clinical research process itself, and into the devices being studied.

Connected devices — implants with remote monitoring capability, wearables that continuously measure physiological parameters, digital therapeutics that generate their own performance data — have fundamentally changed what can be measured in a device trial. Outcomes that previously required clinic visits and subjective patient reporting can now be captured continuously, objectively, and remotely. Patient-reported outcomes, which we identified in 2011 as an important but underdeveloped endpoint category for device trials, can now be collected via validated electronic instruments that reduce recall bias and improve data completeness.

Decentralized trial models — which allow trial visits, data collection, and even intervention administration to occur outside the traditional clinical site — are now operationally viable in ways they were not in 2011. For device trials involving implantable or wearable technologies, this is particularly significant: devices that generate continuous data streams allow the trial to capture the full performance profile of the device in real-world conditions rather than at isolated clinic visit timepoints.

AI-assisted data analysis is enabling the detection of device performance patterns and safety signals at scales and speeds that were not previously achievable. For devices that generate large volumes of operational data — imaging systems, cardiac monitors, respiratory devices — the ability to analyze that data systematically and in near-real-time represents a fundamental improvement in the quality of safety surveillance and performance monitoring.

The Enduring Lessons from 2011

Against the backdrop of everything that has changed, the core insights from the original 2011 article remain sound — and in some ways, the intervening fifteen years have validated them more thoroughly than we could have expected.

Hypothesis design determines study utility. The observation that vague composite endpoints — event-free survival, proportion of patients free from events — are poorly suited to evaluating device-specific performance has been reinforced by fifteen years of regulatory experience. Modern device guidance documents from the FDA, EMA, and notified bodies under EU MDR all emphasize device-specific, clinically meaningful endpoints that are tied directly to the device's mechanism of action and intended performance. The SYNTAX scoring approach we highlighted as innovative in 2011 is now standard practice in complex cardiovascular intervention research.

Real-world generalizability requires deliberate design. Our 2011 argument that excluding complex patient subsets systematically undermines the applicability of trial results has become regulatory orthodoxy. The FDA's guidance on device trials, EU MDR requirements, and the CDSCO's evolving framework all emphasize the inclusion of representative patient populations and the pre-specification of subgroup analyses that allow the evidence to speak to the full range of patients who will use the device in clinical practice.

Multiple control strategies remain necessary. The creative use of historical controls, external control arms, and compound multi-arm designs that we described in 2011 as innovative has become a recognized and accepted feature of the modern device trial landscape — particularly as Bayesian frameworks have made the formal incorporation of prior evidence methodologically credible and regulatorily acceptable.

Conclusion: A More Sophisticated Toolkit for Persistent Challenges

Fifteen years on, the fundamental challenges of medical device clinical research — operator dependency, the absence of perfect controls, the gap between trial populations and real-world patients, the rapid pace of technological iteration — remain as relevant as they were in 2011. What has changed is the sophistication and diversity of the design toolkit available to address them.

Adaptive designs, platform trials, Bayesian frameworks, real-world evidence integration, digital data capture, and decentralized trial models have collectively transformed what is possible in device clinical research. The regulatory frameworks — particularly EU MDR and the FDA's evolving device guidance — have raised the evidentiary bar and made rigorous clinical evidence a commercial necessity rather than a scientific aspiration.

For organizations conducting medical device clinical research, the implication is clear: the era of minimal, late-stage, pre-market clinical evaluation is over. Clinical evidence is now a lifecycle activity, from feasibility through post-market surveillance, and the study designs that will generate the most commercially and scientifically valuable evidence are those that integrate the full range of modern methodological tools from the earliest stages of program planning.

At Genelife Clinical Research, we have been engaged in medical device clinical research since our earliest years — and the evolution of this field over the past fifteen years has shaped both our capabilities and our approach. We work with device companies to design and execute clinical programs that generate evidence meeting the standards of CDSCO, US FDA, and EU MDR — evidence that is rigorous, generalizable, and built to sustain regulatory scrutiny throughout the product lifecycle.

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This article updates Genelife's original September 2011 perspective on medical device clinical trial design, authored by Dr. Ashish Indani, Head of Clinical Operations, Genelife Clinical Research.

Visit genelifecr.com to learn more about Genelife's medical device clinical research capabilities.

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Drug–Device Combination Products: Revisiting the Challenges, Thirteen Years On

In June 2012, we published a perspective on the challenges of drug–device combination products — a field that was then considered promising but operationally complex. Thirteen years later, the landscape has changed so fundamentally that the original question — whether the benefits justify the challenges — deserves a thorough revisit.

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Where We Started

In 2012, our assessment was cautiously optimistic. The core argument was straightforward: merging pharmaceutical and medical device disciplines offered real clinical and commercial advantages, but the regulatory, engineering, and quality challenges were substantial — particularly around the question of whether drug and device manufacturing could realistically operate within a shared facility. 

Our conclusion then was that "when weighing risks and challenges against benefits, the homeostasis shifts towards the merger." The manufacturing model we favored was two distinct facilities — separate but coordinated — as the most viable approach for managing divergent regulatory and engineering requirements.

That conclusion still holds. But almost everything around it has changed.

The products themselves are more sophisticated. The regulatory frameworks governing them are more demanding, more harmonized, and more globally interconnected. The manufacturing and quality expectations have risen substantially. And an entirely new dimension — digital health and connected devices — has introduced a category of complexity that did not meaningfully exist in 2012.

What follows is an honest reassessment of where the field stands today, what has evolved, and what it means for organizations navigating this space.

The Products Have Changed

In 2012, the primary examples of drug–device combination products were relatively straightforward: drug-eluting stents, prefilled syringes, inhalers, nebulizers, some orthopedic implants. The integration of drug and device was largely mechanical — a physical delivery system for a pharmaceutical agent.

Today, the category has expanded into territory that was largely conceptual in 2012. Wearable injectors that deliver large-volume biologics subcutaneously over hours. Smart inhalers that track adherence in real time and transmit data to healthcare providers. Autoinjectors with electronic feedback mechanisms. Implantable devices with drug-eluting components designed for controlled, sustained release over months or years.

The clinical ambition of combination products has grown alongside their technical sophistication. These are no longer primarily cost-reduction or convenience plays — they are integral to the therapeutic strategy in oncology, autoimmune disease, diabetes, rare disorders, and chronic pain management. The device is not incidental to the drug; in many cases, it is inseparable from the drug's clinical effectiveness.

This expansion of scope has amplified every challenge we identified in 2012 — and created several new ones.

Regulatory Complexity: More Demanding, More Harmonized, More Consequential

In 2012, the regulatory landscape for combination products was fragmented and, in many jurisdictions, still developing. The US FDA had frameworks in place, but global harmonization was limited. In India, combination product regulation was nascent.

The past decade has brought significant change on both fronts.

Primary Mode of Action (PMOA) determination has become a foundational regulatory question — and a contentious one. Whether a combination product is primarily regulated as a drug or as a device determines the regulatory pathway, the applicable quality standards, and the agency or directorate that leads the review. For products where the drug and device components interact dynamically, this determination is rarely straightforward. Getting it wrong has compounding consequences throughout the product lifecycle.

Human factors engineering (HFE) and usability studies are now mandatory requirements, not optional considerations. Regulators — including the US FDA and EMA — expect sponsors to demonstrate not just that a combination product is safe and efficacious, but that it can be used correctly by its intended users under realistic conditions. This means structured formative and summative usability studies, root cause analysis of use errors, and design iteration based on user feedback. For self-administration devices like autoinjectors and prefilled pens — where the consequence of a use error may be a missed dose of a biologic or an accidental needle stick — this is a genuinely demanding evidentiary standard.

Cybersecurity has emerged as an entirely new regulatory dimension. For digitally integrated combination products — connected inhalers, wearable drug delivery systems, implantable devices with software components — regulators now expect cybersecurity risk assessments, secure design practices, and post-market surveillance for software vulnerabilities. The US FDA's guidance on cybersecurity in medical devices has evolved considerably, and the EU MDR has introduced requirements that push in the same direction. In 2012, this was not a consideration. Today, it is a regulatory necessity for a growing proportion of combination products.

Post-market surveillance expectations have intensified. Both the EU MDR and the FDA's evolving frameworks require proactive, systematic post-market surveillance — not just passive adverse event reporting. Real-world evidence from clinical practice is increasingly expected to feed back into benefit-risk assessments over the product lifecycle. This places ongoing operational demands on manufacturers that extend well beyond the approval milestone.

In India, the regulatory framework for combination products has been slower to formalize, but the direction of travel is clear: increasing alignment with US FDA and EU MDR principles, greater scrutiny at the approval stage, and growing expectations around post-market data generation.

Quality Management: The Hybrid QMS Challenge

In 2012, we identified quality management as one of the areas of greatest stress in a combined drug–device organization. The challenge was structural: pharmaceutical quality systems (GMP) and medical device quality systems (ISO 13485, then the FDA's Quality System Regulation) were designed independently, for different product types, with different documentation philosophies and different approaches to validation and design control.

That structural challenge has not gone away. If anything, it has intensified.

The FDA's transition from the legacy Quality System Regulation (21 CFR Part 820) to alignment with ISO 13485 has moved the regulatory goalposts for device manufacturers — and created additional complexity for organizations that must simultaneously maintain GMP compliance for their pharmaceutical components. These two quality systems are not designed to be merged; they must be operated in parallel, with deliberate integration at the points where drug and device development and manufacturing intersect.

The practical implications are significant. Design controls — a device QMS requirement — must be applied to the device component of a combination product, including design history files, design verification and validation, and risk management under ISO 14971. Process validation — a GMP requirement — must be applied to the pharmaceutical component. Where the two components interact, both sets of requirements apply simultaneously. Managing this without gaps, overlaps, or contradictions requires quality professionals who are genuinely fluent in both domains — a capability that remains in short supply.

Modern practice has responded with digital QMS platforms that can manage documentation, change control, and compliance tracking across both frameworks from a single system. These platforms reduce the risk of version control errors and compliance gaps, but they do not eliminate the underlying complexity of operating under two regulatory paradigms simultaneously.

Engineering and Manufacturing: The Two-Facility Model Validated

Our 2012 recommendation — that operating drug and device manufacturing in two distinct facilities was the more viable model — has been validated by industry practice over the intervening decade. The "two-facility" or "segregated but integrated" model is now the accepted standard.

The reasons have not changed: sterilization requirements conflict (devices typically require terminal sterilization; pharmaceuticals may degrade under the same conditions), environmental controls differ (cleanroom classifications, HVAC requirements, contamination control protocols), and the regulatory audit burden of a fully integrated facility is operationally very difficult to manage.

What has changed is the scale and sophistication of outsourcing as a solution to these challenges. The growth of specialized CDMOs (Contract Development and Manufacturing Organizations) with combination product expertise has given sponsors an alternative to building integrated capabilities in-house. A CDMO with established GMP pharmaceutical manufacturing and ISO 13485 device assembly capabilities can manage the integration challenges on behalf of a sponsor — including the interface between the two facilities, the supply chain coordination, and the quality management alignment.

This model introduces its own complexities — multi-party quality agreements, supply chain risk management, technology transfer — but for many sponsors, particularly those developing combination products as part of a broader portfolio rather than as a core manufacturing competency, it is a more pragmatic path than building fully integrated in-house capabilities.

Material compatibility remains one of the most technically demanding aspects of combination product development. Drug–polymer interactions, leachables and extractables from device components, the stability of pharmaceutical agents in contact with device materials over extended periods — these require systematic study from early development, not as an afterthought. Regulatory submissions increasingly expect comprehensive extractables and leachables data, and gaps in this area have caused approval delays and post-market complications.

Clinical Development: No Longer the Easier Path

In 2012, we characterized clinical development as one of the segments "eased" by the drug–device merger — the argument being that clinical evaluation could leverage the established pharmaceutical trial infrastructure.

That characterization no longer holds. Clinical development for combination products has become one of the most demanding aspects of the regulatory pathway.

The core challenge is that a combination product must demonstrate the performance of both components — and their interaction — within a single clinical program. Efficacy and safety of the pharmaceutical agent must be established. The performance of the device component must be demonstrated under real-world conditions of use. And the interaction between the two — whether the device affects drug delivery, pharmacokinetics, or user behavior in ways that influence clinical outcomes — must be characterized.

Usability and human factors data, once collected outside the clinical trial, are now expected to be integrated into the clinical evidence package. For self-administration devices, this means studying not just whether the product works, but whether patients — with varying levels of health literacy, dexterity, and prior device experience — can use it correctly in their own homes.

Real-world evidence generation post-approval is increasingly a regulatory expectation, not just a commercial aspiration. For combination products with extrapolated indications, new patient populations, or novel delivery mechanisms, post-marketing studies that capture device performance and patient outcomes in routine clinical practice are becoming part of the approval conditions.

The Digital Dimension: What 2012 Could Not Anticipate

Perhaps the most significant development since 2012 is the emergence of digitally integrated combination products as a distinct and rapidly growing category.

Connected inhalers that measure dose delivery and adherence. Wearable patch injectors controlled via smartphone applications. Implantable drug delivery systems with remote monitoring capabilities. Combination products that generate continuous streams of patient data, transmit it wirelessly, and incorporate it into treatment algorithms — sometimes in real time.

These products introduce regulatory, engineering, and quality challenges that have no precedent in the traditional combination product framework. Software as a Medical Device (SaMD) requires regulatory classification and validation in its own right. Cybersecurity is a lifecycle concern, not a one-time approval requirement — software updates, patch management, and vulnerability monitoring must continue throughout the product's commercial life. Data privacy and interoperability with healthcare information systems introduce regulatory and legal dimensions that neither pharmaceutical nor medical device regulations were designed to address.

The regulatory frameworks are catching up, but the gap between product innovation and regulatory guidance remains significant. Organizations developing digitally integrated combination products are, in many cases, navigating terrain for which clear precedent does not yet exist — which demands both regulatory sophistication and a proactive engagement strategy with regulators.

The Strategic Case: Stronger Than Ever

Despite the intensification of every challenge we identified in 2012, the strategic case for combination products has not weakened. If anything, it has strengthened.

The commercial logic is compelling. In crowded therapeutic markets — where biologic drugs face biosimilar competition and differentiation is increasingly difficult to achieve on pharmacology alone — the delivery system has become a genuine source of competitive advantage. A drug that is easier to self-administer, more reliably delivered, better tolerated, and digitally connected to the prescriber's monitoring system is a meaningfully better product, not just a marginally more convenient one.

Regulatory exclusivity associated with combination product innovation provides periods of market protection that are difficult to replicate through formulation changes alone. And the patient adherence benefits of well-designed combination products translate into better outcomes data — which increasingly matters for payer access and reimbursement decisions.

The business case for combination products — product differentiation, lifecycle extension, market access, patient outcomes — is as strong today as it was in 2012. The complexity of execution has grown proportionately. The organizations that succeed are those that plan for that complexity from the earliest stages of development, engage the right expertise across regulatory, engineering, quality, and clinical domains, and treat the integration challenges as a core part of their development strategy rather than an operational afterthought.

Conclusion: The Same Verdict, A More Complex World

In 2012, we concluded that the balance of risks and benefits favored integration in drug–device combination products. Thirteen years later, that verdict stands — but with a clearer understanding of what it actually requires to execute successfully.

The regulatory burden is heavier. The quality management demands are more complex. The engineering challenges have multiplied with the emergence of digital health integration. The clinical evidence requirements have expanded. The post-market obligations are more substantial.

And yet the products are more clinically powerful, the commercial opportunity is larger, and the patient impact — in therapeutic areas where biologic delivery, adherence, and real-time monitoring can make a genuine difference to outcomes — is more significant than ever.

For sponsors navigating this space, the lesson of the past decade is clear: the complexity of combination product development is not a reason to avoid it. It is a reason to approach it with the right expertise, the right partners, and a regulatory and operational strategy that is integrated into the program from day one.

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Genelife Clinical Research supports combination product development across regulatory strategy, clinical trial execution, quality management coordination, and post-marketing surveillance. To learn more, visit genelifecr.com.

This article is an update to our original 2012 perspective by our ex colluge Dr. Ashish Indani, Head of Clinical Operations, Genelife Clinical Research.(The Challenges with Drug/Device Combination Products; 2012)

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