Newsletter Summer 2019


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Note from Kinexum CEO


Thomas Seoh

Thomas Seoh
President and CEO
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Dear Friends of Kinexum,

Welcome to the Summer 2019 edition of Kinexions, the Kinexum newsletter! Featured in this issue are:
(1) the second installment of a comprehensive series on artificial pancreas devices, by Prasad Palthur, PhD, of Innoneo Health Technologies (see his first installment here);

(2) the recent revolution in Chinese drug regulation, which could have a global effect on drug development, by George Baeder, SVP of Strategy & Business Development, and Eric Zhang, PhD, VP Regulatory Affairs & Strategy, of Shanghai-based CRO dMed Biopharmaceutical;

(3) some observations of a data monitoring committee biostatistician, by Alan Fisher, DrPH, Kinexum biostatistics expert; 

(4) a synopsis of Kinexum’s impactful March webcast featuring Dr. Ralph DeFronzo on type 2 diabetes prevention, by Jennifer Zhao, Kinexum Associate; and

(5) some recommendations on "traceability" in product development, by Bill Hoover, Medical Device and Quality consultant.





Artificial Pancreas Device Systems (ADPS) – General Product Development and Regulatory Pathways


M Prasad Palthur, PhD

M Prasad Palthur, PhD
Co-founder, VP Design & Development
Innoneo Health Technologies
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This article is a continuation and extension of the article “Artificial pancreas device systems – an evolving approach and research pathway.”

The fourth industrial revolution combines the physical, digital, and biological spaces and is rapidly changing the healthcare industry [1]. The internet-of-things (IoT), wireless medical devices, mobile medical apps, digital health software, and a vast and heterogeneous combination of sensors capable of generating actionable information are transforming the landscape of digital and connected healthcare [2, 3]. Many innovative digital health tools, such as wearable sensors and companion software that provide information, decision support, or even control of a drug delivery system, are being developed.





Experiences as a Data Monitoring Committee Biostatistician


Alan Fisher, DrPH

Alan Fisher, DrPH
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Data monitoring committees (DMCs) monitor the data that accumulate over the course of clinical trials that address major health outcomes. The intent of a DMC is to assure the safety of study subjects and the integrity of the study and study results.

I have had the opportunity to be involved in several DMCs, ranging from Phase II safety studies with about 100 subjects to Phase III and IV efficacy studies with several thousand subjects.





Issues with Product Development Traceability


Bill Hoover
Medical Device and Quality Consultant
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A key element of product development is the concept of “traceability.” Traceability is part of FDA’s guidance for the Quality System Regulations and Design Controls. The FDA has described it as “Traceability among requirements, specifications, identified hazards and mitigations, and verification and validation testing.” The Agency’s guidance documents also describe traceability information to be in the form of a matrix or a table. This very general guidance can be interpreted in many different ways by individual companies.


From Kinexum Founder


Zan Fleming, MD

Zan Fleming, MD
Executive Chairman
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Dear Kinexum friends and family,

Summer is in full swing, with longer days, summer vacations, and quality time with friends and family abound. Kinexum continues to engage in a flurry of activities, including exciting projects with old and new clients, as well as pro bono activity in organizing the Targeting Metabesity 2019 conference to be held in Washington, DC, in October.
On a sad note, I recall the very untimely death of Lana Pauls this past April, which resulted from an accident in her home in Kensington, Maryland. Lana was one of our stellar Kinexum consultants, who brought great experience, energy, and encouragement to the projects that she led. Lana and I went back many years to the time that we worked together in FDA’s Division of Metabolism and Endocrine Drug Products. Lana went on to senior roles in the Agency and joined IQVIA (previously Quintiles) after retiring from FDA.

As a way of knowing better the full measure of each other in our professional lives, we at Kinexum will start a regular feature article in the next edition of Kinexions to highlight the lives of individual colleagues and friends of Kinexum. I know that you will find these stories fascinating.

In approaching this season of rest and re-creation, may we savor the time we have with friends, family, and colleagues.  
To your health and wellbeing! 






How China is Changing the Clinical Development Landscape: Implications for Global Development Strategy


George Baeder

George Baeder
SVP Strategy & BD  
dMed Biopharmaceutical
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Co-author: Eric Zhang, PhD,
VP Regulatory Affairs & Strategy
dMed Biopharmaceutical 
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In less than 24 months, China has radically altered the global clinical development landscape, creating new pathways to commercialization that alter the traditional drug development paradigm. 

Since joining ICH (International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use) as a full regulatory member in June 2017, the country has refocused its regulatory process on rapid approval of innovative therapies and devices, with non-Chinese biopharma firms emerging as the main beneficiaries. 





Webcast Recap: Dr. Ralph DeFronzo on Type 2 Diabetes


Jennifer Zhao
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Ralph DeFronzo, MD, Deputy Director of the Texas Diabetes Institute, spoke on “Prevention of Type 2 Diabetes (T2D): A Rational Approach Based on Its Pathophysiology” in Kinexum’s March public webcast.

A preeminent authority in the field of diabetes and metabolism, Dr. DeFronzo reviewed the research behind the factors that lead to diabetes and the drugs available to treat T2D. 

What leads to diabetes?
As of 2017, about 30 million Americans have diabetes and 84 million Americans have prediabetes [1]. These numbers are expected to increase, along with the cost of treating diabetes. Of the $175 billion cost of treating diabetes in 2007, about one-third of costs were related to cardiovascular disease (CVD)/peripheral vascular disease (PVD), another one-third to microvascular complications (eye, kidney, nerve; all are related to high A1C), and another to other costs (e.g., office visits) [2].

Prediabetes can be diagnosed in three ways. A person has prediabetes if he or she has impaired fasting glucose (IFG) (fasting glucose = 100-125 mg/dL), impaired glucose tolerance (IGT) (2-hour glucose during an oral glucose tolerance test [OGTT] = 140-199 mg/dL), or HbA1c (measure of mean blood glucose over the preceding three months) = 5.7-6.4%. 




Metabesity 2019 Update







Metabesity 2019 Speakers


* Final speaker roster may vary



Metabesity 2019 Sponsors

For more information, visit or contact This email address is being protected from spambots. You need JavaScript enabled to view it. .




Upcoming Webcasts


August Webinar featuring James Valentine, JD, MHS, and Larry Bauer


Kinexum’s August public webcast features James Valentine, JD, MHS, Associate, and Larry Bauer, Senior Regulatory Drug Expert, at Hyman, Phelps, & McNamara, PC. Mr. Valentine and Mr. Bauer will speak on "Catalyzing the Transition from Bench to Bedside: Don’t Wait Until the First-in-patient Study to Partner with Patient Organizations." 

These patient engagement experts will discuss the value of partnering with patient organizations and ways to maximize their involvement. 

Click here to RSVP




New Kinexum Team Members



Alan Fisher, DrPH

Alan Fisher, DrPH

Learn more about Alan


Ramachandra (Ram) Naik, MD

Ramachandra (Ram) Naik, MD
Clinical Development
Learn more about Ram


Michael Zemel, PhD

Michael Zemel, PhD
Preclinical and Clinical Development, Project Management & Regulatory Affairs





Upcoming Conferences

Kinexum executives and leading experts will attend the following conferences. If you are interested in meeting with a Kinexum representative at these conferences, please contact  This email address is being protected from spambots. You need JavaScript enabled to view it. .







Targeting Metabesity 2019







Cardiovascular Clinical Trialists Forum 2019




Recent Publications

International Consensus on Risk Management of Diabetic Ketoacidosis in Patients With Type 1 Diabetes Treated With Sodium–Glucose Cotransporter (SGLT) Inhibitors
Thomas Danne, Satish Garg, Anne L. Peters, John B. Buse, Chantal Mathieu, Jeremy H. Pettus, Charles M. Alexander, Tadej Battelino, F. Javier Ampudia-Blasco, Bruce W. Bode, Bertrand Cariou, Kelly L. Close, Paresh Dandona, Sanjoy Dutta, Ele Ferrannini, Spiros Fourlanos, George Grunberger, Simon R. Heller, Robert R. Henry, Martin J. Kurian, Jake A. Kushner, Tal Oron, Christopher G. Parkin, Thomas R. Pieber, Helena W. Rodbard, Desmond Schatz, Jay S. Skyler, William V. Tamborlane, Koutaro Yokote, and Moshe Phillip
Diabetes Care (June 2019)

Implementation of Basal–Bolus Therapy in Type 2 Diabetes: A Randomized Controlled Trial Comparing Bolus Insulin Delivery Using an Insulin Patch with an Insulin Pen
Richard M. Bergenstal, Mark Peyrot, Darlene M. Dreon, Vanita R. Aroda, Timothy S. Bailey, Ronald L. Brazg, Juan P. Frias, Mary L. Johnson, David C. Klonoff, Davida F. Kruger, Shenaz Ramtoola, Julio Rosenstock, Pierre Serusclat, Ruth S. Weinstock, Ramachandra G. Naik, David M. Shearer, Vivien Zraick, and Brian L. Levy
Diabetes Technology & Therapeutics (May 2019)

Real-World Evidence Should be Used in Regulatory Decisions about New Pharmaceutical and Medical Device Products for Diabetes
David C. Klonoff, Alberto Gutierrez, Alexander Fleming, and David Kerr
Journal of Diabetes Science and Technology (April 2019)

Regulatory Considerations for Early Clinical Development of Drugs for Diabetes, Obesity, Nonalcoholic Steatohepatitis (NASH) and Other Cardiometabolic Disorders
G. Alexander Fleming and Brian E. Harvey
In Translational Research Methods in Diabetes, Obesity, and Nonalcoholic Fatty Liver Disease: A Focus on Early Phase Clinical Drug Development (2nd ed., Springer International Publishing, 2019)
Eds: Andrew J. Krentz, Christian Weyer, and Marcus Hompesch




Continuation of Previous Articles


Note from Kinexum CEO (cont.)

We are attracting stellar speakers for our Targeting Metabesity 2019 conference at the Carnegie Institution for Science in Washington, DC on October 15-16, 2019, including Janet Woodcock and Susan Mayne (CDER and CFSAN Directors at FDA), Richard Hodes (Director of the National Institute on Aging at NIH), and other leaders in geroscience, diabetes, cancer, industry, venture capital and other fields.  Please join this silo-busting conversation on shifting focus from acute to chronic diseases, treatment to prevention, and individual diseases to extension of healthy lifespan. Super Early Bird discount rates are in effect through July 1!
Kinexum attended BIO, ADA and DIA in June. Let’s meet at upcoming conferences such as EASD in Barcelona in September, Metabesity in Washington, DC in October, BioCentury China Healthcare Summit in Shanghai in November, the Cardiovascular Clinical Trialists Forum in Washington, DC in December, or JP Morgan in San Francisco in January!
We commend a couple summer webcasts: on Auto-Immune and Inflammatory Diseases, hosted by our colleagues at Cello Health BioConsulting on Tuesday, August 6; and on early partnering with patient organizations, for our August Kinexum webcast, featuring James Valentine and Larry Bauer of Hyman, Phelps, & McNamara.  
Wishing you a healthy, productive and fun summer!




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Artificial Pancreas Device Systems (ADPS) – General Product Development and Regulatory Pathways (cont.)


As a result of this industrial revolution, the fusion of physical, digital, and biological technologies allow patients to be connected to each other, their caregivers, and clinicians [4].
Data generated online and by digital technologies represent – by the quantity and variety of information – a major potential to modify the way people with diabetes are monitored to identify new digital markers and patterns of risk [5]. In recent years, digital biomarker development has begun integration into translational and clinical research [6]. The development of data science methods and artificial intelligence (AI) adapted for health data has led to the development of ecosystems of digital tools and digital interventions for behavioral changes tailored to patient preferences and characteristics [5, 7]. Additionally, digital health methodologies are well positioned to improve patient identity, patient privacy, study transparency, data sharing, competent informed consent, and the confidentiality and security of humanitarian operations [8].

As an area of therapy with the best market potential and one of the most expensive global diseases, diabetes attracts healthcare players to embrace innovative technologies [9]. Data from the industry, academic, and regulatory communities indicate that there is a recent surge of interest in digital health as a paradigm for treating diabetes and other chronic diseases. Ultimately, digital advancements and the innovation of future technology will lead to more patient-centered and highly personalized resources that, in turn, will improve the delivery of diabetes care and overall quality of life [10]. Although digital solutions have a considerable potential to modify the diabetes ecosystem, many barriers and challenges persist [5].
Given the hyper transformation of technology and business models, the discovery, development, and deployment paths of medical devices, particularly of digital health products, have recently transformed significantly and resulted in the emergence of new product categories like APDS and software-as-a-medical device (SaMD).


Medical device development generally follows a well-established path. The development of a successful medical device requires not only reliable engineering design, but also clinical, regulatory, marketing, and business proficiency. The conventional medical device discovery, development, and deployment framework includes a dynamic representation of the translational science, regulatory science, modern engineering, and therapeutic development process to expedite new therapies for patients. “Regulatory science” integrates the knowledge within and among basic science research, clinical research, clinical medicine, and other specific scientific disciplines, focusing on product development and regulatory decision making [11,12]. “Translational science” is generally the application of the scientific method to address a health need [12]. The “translation” is the process of turning observations in the laboratory, clinic, and community into interventions that improve the health of individuals and induce behavioral changes [13].

However, the contemporary medical device framework of discovery, development, and deployment embraces the inclusion of human-centered design, clinically-inspired engineering, patient-inspired approaches, and human factors engineering. Human factors engineering is the discipline that integrates human physical and psychological characteristics in the design of devices and interactive systems that involve people, tools and technology, and environments to ensure safety, effectiveness, and ease of use [14]. A patient-inspired approach focuses on the clinical problem(s) and needs of the patient. Human-centered design is considered an integral part of both regulatory compliance and commercial success.
On the other hand, medical devices related to digital health consider the inclusion of advanced sensor technologies, artificial intelligence technologies, machine learning technologies, intelligent decision support systems, health informatics, software intended for medical purposes, and cybersecurity approaches. Intelligent decision support systems aid the better diagnosis, therapeutic guidance, and tailored treatment plan [15]. Data mining-based intelligent decision support systems are embedded with concepts like data mining, neural networks, deep learning, and evolutionary algorithms [16]. These advanced technologies facilitate the application of systematic, quantitative, and integrative digital health approaches. 
Additionally, medical devices that aim for automated and closed-loop drug delivery need to consider three aspects of interdisciplinary research: (i) modeling of physiological processes on a whole-body level; (ii) using optimal control theory for designing therapy protocols; and (iii) using simulation and analysis techniques for identification of complex intracellular regulatory mechanisms [17].
Moreover, medical devices related to digital health, such as computer systems, can be vulnerable to security breaches, potentially impacting the safety and effectiveness of the device. The product development approach should better anticipate cybersecurity risks and apply mitigation strategies early in the total product lifecycle of a device and with increased agility throughout the device lifespan as necessary. Examples of strategies include: identifying and preparing for cyber intrusions, reducing medical device vulnerabilities, mitigating potential impacts on patients, and enabling timely restoration of devices and systems [18].
Strategic planning of medical device development and deployment is dependent on a well-conceptualized regulatory strategy. A regulatory-science driven and outcome-oriented regulatory strategy is an essential part of today's medical device early development planning and successful product development. Furthermore, such regulatory strategy can facilitate a central agenda for the overall development plan, align the clinical development plan with business objectives, and assist in defining the value path to market. Furthermore, it can enable the synchronization of technical, nonclinical, and clinical requirements required for registration, as well as preemptively identify challenges and proposed alternative/innovative approaches. 
For brevity, a regulatory strategy is expressed as a formal document that aligns regulatory activities to bring a new or modified product to market with the business strategy for that product. A regulatory plan is a document that describes the specific steps and actions required to successfully meet the regulatory strategy objectives. A comprehensive and systematic approach to regulatory compliance is vital for medical device manufacturers to more accurately plan the resources and time required to achieve compliance, leverage new standards for evidence generation, support continuing development, and gain global market authorizations.


For this article, APDS is used to generalize the context of integrated systems and technologies of electromechanical AP approaches. A hundred years after the discovery of insulin, the technology is entering the stage of fully automated portable APDS that provides real-time, long-term optimal control of diabetes in patients’ natural environments [19].
APDS as a medical device embodies the modular architecture of various functional components. In general, APDS modular architecture, at a minimum, represents the following functional components:

1.              Continuous Glucose Monitoring (CGM) Component

2.              Continuous Subcutaneous Infusion (CSI) Pump Component

3.              Control Algorithm (CA) Component 

4.              Communication Pathway (CP) Component

5.              User Interface (UI) Component

The modular architecture of AP systems on both the hardware and software level allows APDS to be assembled from independent but compatible modules, each performing a specific function [20].

There are several AP systems currently under development in both academic and commercial endeavors. APDS systems in development can be broadly grouped into those using dedicated embedded hardware, those relying on a dedicated locked-down smartphone device, or some combination of the two approaches [21]. Each system offers unique features in the configuration of pumps, glucose sensors, algorithms, single- or dual-hormone delivery functionality, user interface, and data management. Also, various closed-loop APDS systems employ different combinations of hormonal approaches, control algorithms, and glycemic control strategies [22]. This modular architecture may also require different types of partnerships or business ecosystems and may represent new roles for digital health companies to increase the adoption of advanced technologies and accelerate innovation.

The modular architecture of APDS systems allows for accommodation of rapidly changing diabetes device technology and advanced algorithms [23]. However, the modular architecture may also require diverse requirements for performance, software, biocompatibility, sterility, shelf life, electrical safety, magnetic resonance imaging safety, and human factors. This modular architecture positions a complex regulatory scenario for each of the functional components within the APDS system and the APDS system as a whole.

The unique requirements of APDS can have a substantial impact on the size of the clinical development program as well as on the risk. APDS is a more specialized product category – the technology is more complex, and the regulatory environment may be intricate. An early understanding of the registration requirements offers efficiencies that can be realized throughout the development of a novel APDS product. Judicious and well-executed regulatory strategy, as well as proactive and cooperative interactions with regulatory authorities, is often a critical factor for bringing successful and innovative APDS products to market.


a) Medical Devices in General

In general, the product approval process takes place within a structured framework that includes analysis of the target condition and available treatments, assessment of benefits and risks from clinical data, and strategies for managing risks [24].
The Food and Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) supports and fosters medical device innovation as it upholds its mission of ensuring that medical devices are safe and effective. CDRH provides comprehensive regulatory resources that explain many aspects of medical device law, regulation, guidance, and policies encompassing the entire product life cycle [25].
Federal law (FD&C Act, section 513) established the risk-based device classification system for medical devices. Regulatory classes for medical devices are based on the level of control necessary to provide reasonable assurance of its safety and effectiveness. The FDA assigns devices to 3 main regulatory classes: Class I (low to moderate risk; required general controls), Class II (moderate to high risk; required general controls and special controls), and Class III (high risk; required general controls and premarket approval (PMA)) [26].

Device product classifications can be found by searching the Product Classification Database [27]. The database provides the name of the device, classification, and a link to the Code of Federal Regulations (CFR), if any. If there are 510(k)s cleared by FDA and the new device is substantially equivalent to any of those cleared devices, then the sponsor/applicant should submit a premarket submission (510(k)). If the proposed device is a high-risk device and is not substantially equivalent to a Class I, II, or Class III device with a 510(k), then the sponsor/applicant must submit a PMA before marketing in the U.S. 
Class I, II, and III devices intended for human use, for which a PMA is not required, must submit a 510(k) to FDA, unless the device is exempt from the 510(k) requirements of the FD&C Act and does not exceed the limitations of exemptions [28]. A 510(k) is a premarket submission made to FDA to demonstrate that the device to be marketed is at least as safe and effective (i.e., substantially equivalent) to a legally marketed device (21 CFR 807.92(a)(3)) that is not subject to PMA. Substantial equivalence has to be established concerning: intended use, design, energy used or delivered, materials, performance, safety, effectiveness, labeling, biocompatibility, standards, and other applicable characteristics. FDA’s 510(k) submission process webpage provides additional aspects of laws, regulations, guidance, and policies encompassing the entire product life cycle [29].
Premarket approval (PMA) is the FDA process of scientific and regulatory review to evaluate the safety and effectiveness of Class III medical devices. PMA is the most stringent type of device marketing application required by the FDA. The applicant must receive FDA approval of its PMA application before marketing the device. PMA approval is based on a determination by FDA that the PMA contains sufficient valid scientific evidence to assure that the device is safe and effective for its intended use(s). FDA’s PMA webpage provides additional aspects of laws, regulations, guidance, and policies encompassing the entire product life cycle [30].

Some devices that are found to be not substantially equivalent to a cleared Class I, II, or III (not requiring PMA) device may be eligible for the de novo process as a Class I or Class II device. The de novo process provides a pathway to classify novel medical devices for which general controls alone, or general and special controls, provide reasonable assurance of safety and effectiveness for the intended use, but for which there is no legally marketed predicate device [31, 32]. Manufacturers must identify a primary predicate device that is most similar to the device under review with respect to indications for use and technological characteristics. Manufacturers may identify more than one predicate device to help demonstrate substantial equivalence in certain circumstances [32].
Medical devices may have one or more accessories that support, supplement, and/or augment the performance of the parent device. An accessory is a finished device that is intended to support, supplement, and/or augment the performance of one or more parent devices [33]. FDA will classify an accessory based on the risks of the accessory when used as intended and the level of regulatory controls necessary to provide reasonable assurance of the safety and effectiveness of the accessory, notwithstanding the classification of any other device with which such accessory is intended to be used. The guidance document “Medical Device Accessories – Describing Accessories and Classification Pathways” [34] describes the risk- and regulatory control-based framework for the classification of accessories separate from the classification of parent devices and the appropriate processes for submitting an accessory classification request [33]. An accessory classification request is a written request submitted to the FDA under section 513(f)(6) of the FD&C Act.

Digital health technology changes at a rapid pace, and the regulatory landscape for digital health is constantly evolving. The traditional paradigm of medical device regulation does not suit digital health technologies in a way that optimizes and fully leverages their potential impact on healthcare and patients [35].
The FDA is working to align itself with the needs of the digital health industry and recognizes that a new regulatory pathway is needed to enable products to be available to patients faster without compromising safety or effectiveness [10]. The FDA's CDRH has established the Digital Health Program, which seeks to better protect and promote public health and provide continued regulatory clarity by: (a) fostering collaborations and enhancing outreach to digital health customers, and (b) developing and implementing regulatory strategies and policies for digital health technologies [36]. Early FDA publications on digital health contained three important novel initiatives: (a) clarity on the medical software provisions of the 21st Century Cures legislation, (b) launch of a pilot precertification program (called the FDA Pre-Certification for Software), and (c) expansion of the FDA’s digital health expertise by creating a Center of Excellence (CoE) for Digital Health. This new CoE also will create a cybersecurity unit to complement advances in software-based devices and to aid in the review of cyber advances affecting the more traditional hardware and software-based medical devices.
The highly iterative nature of digital health technologies requires a flexible regulatory approach to allow product developers, patients, and regulators to keep pace with the software updates that frequently happen in this space. Having a regulatory framework that enables a rapid cycle of product improvement is integral to ensuring innovation and success for digital health technologies.

As medical devices become more digitally interconnected and interoperable, they can improve the care patients receive and create efficiencies in the health care system. However, medical devices, like computer systems, can be vulnerable to security breaches, potentially impacting the safety and effectiveness of the device [18]. Ensuring that medical devices are safeguarded from cyber intrusions is a shared responsibility across the medical device ecosystem. 
The FDA’s role and commitment to medical device cybersecurity continue to increase in scope and nature, considering the implications of compromised devices across their total product lifecycle [18, 37]. The FDA has been working to stay a step ahead of these changing cybersecurity vulnerabilities. The FDA has issued draft guidance that provides updated recommendations to industry on cybersecurity considerations for device design, labeling, and documentation that the FDA recommends being included in premarket submissions for medical devices with cybersecurity risk. This guidance incorporates new recommendations, including premarket submission of a “cybersecurity bill of materials” (analogous to the ingredient list for a medication), which is a list of commercial and/or off-the-shelf software and hardware components of a device that could be susceptible to vulnerabilities [38].

FDA intends for a future regulatory model to provide more streamlined and efficient regulatory oversight of software-based medical devices [39]. Historically, most software products have been categorized as software in a medical device (SiMD), which operates the device and sensors (e.g., firmware). More recently, they are categorized as software as a medical device (SaMD) solutions [6]. The term “SaMD” is defined by the International Medical Device Regulator’s Forum (IMDRF) as software intended to be used for one or more medical purposes without being part of a hardware medical device [40].

As the FDA is creating a more efficient regulatory process, it also recognizes the inherent risks to software and digital products that may not be as visible as those in other types of products that FDA regulates. The FDA is collaborating with stakeholders in the medical device ecosystem to build the National Evaluation System for health Technology (NEST) to more efficiently generate better evidence for medical device evaluation and regulatory decision-making. The collaborative national evaluation system will link and synthesize data from different sources across the medical device landscape, including clinical registries, electronic health records, and medical billing claims [41].

b) Medical Devices – ADPS

APDS consists of separate components that must be functionally compatible as a medical device, and the components work together as a closed loop system [42]. The modular architecture of APDS systems allows for accommodation of rapidly changing diabetes device technology and advanced algorithms [23]. However, the modular architecture may also require diverse requirements for performance, software, biocompatibility, sterility, shelf life, electrical safety, magnetic resonance imaging safety, and human factors. This modular architecture positions a complex regulatory scenario for each of the functional components within the APDS system and the APDS system as a whole.

Currently, APDS components are authorized for sale as diabetes management devices only in a specific configuration, while others are authorized for use with other compatible devices, which may include automated insulin dosing systems, insulin pumps, blood glucose meters, or other devices used for diabetes management. Some of these diabetes management devices may be reviewed by the FDA as a whole system, or they may be reviewed to be compatible with other FDA-authorized components, such as integrated continuous glucose monitoring (CGM) systems [43]. This is known as interoperability, which allows patients to safely tailor their diabetes management to their individual preferences by choosing devices that are authorized by the FDA to work together. For example, an authorized automated insulin dosing system will include a specific CGM system, a specific insulin pump, and a specific algorithm. These devices are all tested and authorized together as a system. 
Recently, FDA has issued safety communication warning patients and health care professionals of risks associated with the use of unapproved or unauthorized devices for diabetes management, including CGM systems, insulin pumps, and automated insulin dosing systems [44]. Earlier in 2019, FDA received a report of a serious adverse event, in which a patient used an unauthorized device that received electronic signals from an FDA-authorized glucose sensor and converted it to a glucose value using an unauthorized algorithm [43]. In its communication, FDA expressed its concern around the movement known as the do-it-yourself (DIY) approach. The Open Artificial Pancreas System (OpenAPS) movement includes individuals building their own DIY closed-loop systems from commercially available insulin pumps (although sometimes out of warranty), CGM devices, and an open-source algorithm [45]. The algorithms used in DIY APS are currently unregulated and untested in clinical trials. While CGM and insulin pump technology are regulated for use in diabetes care, their use in combination with DIY APS algorithms makes their use off-label [46]. The risks are unknown, but could relate to the algorithm itself, user interaction with the system, general safe pump use, or use of out-of-warranty pumps [46]. Also, when devices that are not intended for use with other devices are combined or when unauthorized devices are used, new risks that have not been properly evaluated by the FDA for safety are introduced. Efficient and effective algorithms are routinely developed and embedded in various formats for closed-loop control of APDS.

A challenge to the evaluation of algorithms is that many are proprietary, patented, or are trade secrets. Structured collaboration between engineers, mathematicians, computer scientists, data scientists, and clinical researchers is required for mathematical modeling, simulation, and formal analysis for the development of efficient algorithms. Future work may involve the achievement of greater sensitivity by factoring specific aspects of body physiology, patient statistics to fine-tune control parameters, algorithm self-learning capabilities, and integration of auxiliary sensors for individualized treatment and treatment adaption over time [47]. FDA recently announced steps to consider a new regulatory framework specifically tailored to promote the development of safe and effective medical devices that use advanced artificial intelligence algorithms [48]. The artificial intelligence technologies granted marketing authorization and cleared by the Agency so far are generally called “locked” algorithms that don’t continually adapt or learn every time the algorithm is used [48]. On the other hand, machine learning algorithms that continually evolve are often called “adaptive” or “continuously learning” algorithms. These algorithms can learn from new user data presented to the algorithm through real-world use.
As the current diabetes technology ecosystem is heterogeneous, a major concern in an APDS system is interoperability [21]. Development of soluble pumpable glucagon, dual-chamber pumps, and dual-lumen catheters, as well as finalization of algorithms, is considered one of the main challenges for the dual-hormone approach [47]. Zealand Pharma is working with Beta Bionics on a next-generation first-in-class dual-hormone APDS containing both insulin and glucagon (dasiglucagon) which – guided by an algorithm – could maintain and control blood glucose levels without the need for patient intervention [49].
The unique requirements of APDS can have a substantial impact on the size of the clinical development program as well as on the risk. To appropriately evaluate the effectiveness, risks, and benefits of an APDS, different kinds of clinical studies may be needed [42]. Studies might be conducted in clinical research centers and/or transitional settings. Transitional settings are similar to clinical research settings because they provide ready access to health care providers and monitoring equipment, but patients have more control over daily activities during the study period. To gain FDA approval to market an APDS, investigators will generally perform an outpatient study. In many cases, patients in the study will use the devices in their homes while under the care of a doctor or nurse [42].
Recent advances in the development of mobile digitally connected technologies have led to the emergence of a new class of biomarkers measured across multiple layers of hardware and software [6]. Digital biomarkers are consumer-generated physiological and behavioral measures collected through connected digital tools [50]. The modular software-hardware combination has created new opportunities for patient care and biomedical research, enabling remote monitoring and decentralized clinical trial designs [6]. The identification of new digital biomarkers, based on data generated by CGMs or other connected devices, is likely to profoundly change clinical practice by moving from an era in which controlling HbA1c is the gold standard to an era where an individualized approach towards HbA1c monitoring can be combined with parameters derived from these devices, including time spent in range, glycemic exposure, glycemic variability, and hypo- and hyperglycemia [5]. For example, Eli Lilly & Co. is expanding its collaboration with Evidation Health into a multi-year project aimed at developing digital biomarkers [51]. However, a systematic approach to assessing the quality and utility of digital biomarkers to ensure an appropriate balance between their safety and effectiveness is needed [6]. Furthermore, verification and validation of digital biomarkers require a uniquely collaborative approach, with engineering, data science, health information technology, and clinical research functions tightly coordinated as integrated multidisciplinary units.
An early understanding of the registration requirements offers efficiencies throughout the development of a novel APDS product. Conceptualizing a regulatory strategy of APDS is dependent on multiple factors: the nature of hardware components, software components, integration of software and hardware to provide reasonable assurance of performance, safety and effectiveness, substantial equivalency to any cleared devices, and intended use. Sponsors and/or applicants are advised to review the Total Product Life Cycle (TPLC) database [52]. It includes information pulled from CDRH databases including PMAs, Premarket Notifications (510(k)), adverse events, and recalls, presenting an integrated record of premarket and postmarket activity for medical devices [52].
Additionally, each of the functional components within an APDS may have a different regulatory device classification and can be different from the complete APDS system. An example is an alternate controller enabled insulin infusion pump (Class II), bi-hormonal control automated insulin dosing device system (Class III), integrated CGM system for non-intensive diabetes management (Class II), and CGM retrospective data analysis software (Class I). Table 1 represents a few examples of device classification of APDS components in the US and their regulation reference. Furthermore, accessories that support, supplement, and/or augment the performance of APDS may have a different level of regulatory control necessary to provide a reasonable assurance of safety and effectiveness of the accessory, notwithstanding the classification of the parent device.

Table 1: Examples of APDS Components and Device Classifications

FDA is helping to advance the development of APDS by prioritizing the review of research protocol studies, providing clear guidelines to industry, setting performance and safety standards, fostering discussions between government and private researchers, co-sponsoring public forums, and finding ways to shorten study and review time [65]. Additionally, the FDA is involved in developing two standards related to the APDS: a CGM standard (CLSI POCT05-A) that discusses performance characteristics, and a standard that discusses characteristics of a feedback control system applied to closed-loop control algorithms, such as the one used in the artificial pancreas (ISO 60601-1-10) [65].
FDA recently authorized the first interoperable insulin pump intended to allow patients to customize treatment through their individual diabetes management devices [66]. Tandem’s Diabetes Care t:Slim X2 insulin pump has interoperable technology (interoperable t:Slim X2) for delivering insulin under the skin for children and adults with diabetes [67]. This new type of insulin pump, referred to as an alternate controller enabled (ACE) infusion pump (or ACE insulin pump) can be used with different components that make up diabetes therapy systems, allowing patients to tailor their diabetes management to their individual device preferences. Because of the interoperability with other diabetes device components, the ACE pump was reviewed through the de novo premarket review pathway, a regulatory pathway for novel, low-to-moderate-risk devices of a new type. 
Because FDA’s ACE insulin pump authorization created a new regulatory classification [63], future ACE insulin pumps will be able to go through the more efficient 510(k) review process, helping to advance this innovative technology [66]. Along with this authorization, the FDA is establishing criteria called special controls, which outline requirements for assuring the accuracy, reliability, cybersecurity, and clinical relevance of ACE infusion pumps, as well as describe the type of studies and data required to demonstrate acceptable pump performance. 
Various prescription-only software medical device for dosing recommendations have also been approved. These products have potential to interface with APDS in the near future. Glucommander is a therapy management cloud-based software solution suite of FDA-cleared proprietary algorithms for intravenous, subcutaneous, and pediatric insulin dosing [68]. Insulia® is a prescription-only software medical device intended for use by healthcare professionals and adult type 2 diabetes patients treated with long-acting insulin analogs as an aid that recommends basal insulin doses based on the treatment plan created by the healthcare provider [69]. Mellitus Health's Insulin Insight software makes precision insulin dosing recommendations in seconds, enabling clinicians to optimize insulin regimens [70].


Although this article attempts to be a comprehensive guide, it is impossible for an article to be an all-inclusive exhaustive guide. This article intends to provide key references and sources of information to conceptualize APDS general product development and regulatory strategies.

a) Regulations, guidance, and information related to the product development and regulatory affairs of APDS and its components

·                Guidance for Industry and FDA Staff: The Content of Investigational Device Exemption (IDE) and Premarket Approval (PMA) Applications for Artificial Pancreas Device Systems [71]

·                FDA Artificial Pancreas Device Systems website [72]

·                American Diabetes Association. Diabetes Technology: Standards of Medical Care in Diabetes–2019 [73]

·                FDA databases: 510(k) Premarket Notification database [74], Premarket Approval (PMA) database [75], and Device Classification under Section 513(f)(2)(de novo) [76]

·                Total Product Life Cycle (TPLC) database­ – presents an integrated record of premarket and postmarket data for medical devices [52]

·                FDA infusion pumps webpage­ – for specific information and regulatory scenarios of infusion pumps, one of the functional components that make an APDS [77]

·                Guidance for Industry and FDA Staff: Infusion Pumps Total Product Life Cycle [78]

·                Ongoing and completed clinical trials of APDS – U.S. National Library of Medicine resource [79]

·                FDA Software as a Medical Device (SaMD) website [80]

·                Guidance for Industry and FDA Staff: Software as a Medical Device (SAMD): Clinical Evaluation [81]

·                Global Approach to Software as a Medical Device Software as a Medical Device [82]

·                Diabetes Technology Society Mobile Platform Controlling a Diabetes Device Security and Safety Standard (DTMoSt). Diabetes Device Security and Safety StandardGuidance for the Use of Mobile Devices in Diabetes Control Contexts [83]

·                Draft Guidance for Industry and FDA Staff: Content of Premarket Submissions for Management of Cybersecurity in Medical Devices [38]

·                Medical Device Cybersecurity Regional Incident Preparedness and Response Playbook [84]

·                Medical Device Data Systems [85]  

·                Artificial Intelligence and Machine Learning in Software as a Medical Device [86]

·                FDA guidance webpage over premarket information – device design and documentation processes [87]

b) Standards related to the development of APDS and its components

Standard Designation Number/Date

Title of Standard

Standard Developing Organization


Standard for Wireless Diabetes Device Security (DTSec) [83]


DTSec Protection Profile Version 2.0 -November 25, 2017

Protection Profile for Connected Diabetes Devices (CDD) [83]


ISO 60601-1-10

Medical electrical equipment – Part 1-10: General requirements for basic safety and essential performance – Collateral standard: Requirements for the development of physiologic closed-loop controllers



Performance Metrics for Continuous Interstitial Glucose Monitoring; Approved Guideline



Software as a Medical Device (SaMD): Key Definitions



Software as a Medical Device (SaMD): Possible Framework for Risk Categorization and Corresponding Considerations



Software as a Medical Device (SaMD): Application of Quality Management System


11073-10425 First edition 2016-06-15

Health informatics - Personal health device communication - Part 10425: Device specialization - Continuous glucose monitor (CGM)


11073-10417 Third edition 2017-04

Health informatics - Personal health device communication - Part 10417: Device specialization - Glucose meter


11073-10419 First edition 2016-06-15

Health informatics - Personal health device communication - Part 10419: Device specialization - Insulin pump


11073-10417 Third edition 2017-04

Health informatics - Personal health device communication - Part 10417: Device specialization - Glucose meter


11073-10419 First edition 2016-06-15

Health informatics - Personal health device communication - Part 10419: Device specialization - Insulin pump


Std 11073-10425-2014 

Health informatics - Personal health device communication Part 10425: Device Specialization - Continuous Glucose Monitor (CGM) 



Principles for medical device security - Risk management



Application of quality management system concepts to medical device data systems


TIR 45:2012

Guidance on the use of AGILE practices in the development of medical device software


82304-1 Edition 1.0 2016-10 

Health software - Part 1: General requirements for product safety


2900-2-1 First Edition 2017

Standard for Safety Software Cybersecurity for Network-Connectable Products Part 2-1: Particular Requirements for Network Connectable Components of Healthcare and Wellness Systems


646 Third edition 1991-12-15

Information technology - IS0 7-bit coded character set for information interchange


TR 80001-2-8 Edition 1.0 2016-05 

Application of risk management for IT-networks incorporating medical devices - Part 2-8: Application guidance - Guidance on standards for establishing the security capabilities identified in IEC TR 80001-2-2


TR 80002-1 Edition 1.0 2009-09

Medical device software - Part 1: Guidance on the application of ISO 14971 to medical device software


15026-2 First edition 2011-02-15

Systems and software engineering - Systems and software assurance - Part 2: Assurance case


2900-1 First Edition 2017

Standard for Safety Standard for Software Cybersecurity Network-Connectable Products Part 1: General Requirements


15026-1 First edition 2013-11-01

Systems and software engineering - Systems and software assurance - Part 1: Concepts and vocabulary


TR 80001-2-2 Edition 1.0 2012-07

Application of risk management for IT Networks incorporating medical devices - Part 2-2: Guidance for the disclosure and communication of medical device security needs risks and controls



Classification of defects in health software


29147 First edition 2014-02-15

Information technology - Security techniques - Vulnerability disclosure



Point-of-Care Connectivity


TR 80001-2-5 Edition 1.0 2014-12

Application of risk management for IT-networks incorporating medical devices - Part 2-5: Application guidance - Guidance on distributed alarm systems


11073-10101 First edition 2004-12-15

Health informatics - Point-of-care medical device communication - Part 10101: Nomenclature


Std 11073-10207-2017

Health informatics - Point-of-care medical device communication Part 10207: Domain Information and Service Model for Service-Oriented Point-of-Care Medical Device Communication


11073-20702 First edition 2018-09

Health informatics - Point-of-care medical device communication - Part 20702: Medical devices communication profile for web services


60812 Edition 3.0 2018-08

Analysis techniques for system reliability - Procedure for failure mode and effects analysis (FMEA)


62304 Edition 1.1 2015-06 consolidated version 

Medical device software - Software life cycle processes


30111 First edition 2013-11-01

Information technology - Security techniques - Vulnerability handling processes


F2761-09 (2013)

Medical Devices and Medical Systems - Essential safety requirements for equipment comprising the patient-centric integrated clinical environment (ICE) - Part 1: General requirements and conceptual model


c) Regulations, guidance, and information related to the product development and regulatory affairs of medical devices

  • Guidance for Industry and FDA Staff: Medical Device Classification Product Codes
  • Guidance for Industry and FDA Staff: Factors to Consider When Making Benefit-Risk Determinations in Medical Device Premarket Approval and De Novo Classifications
  • Guidance for Sponsors, Clinical Investigators, Institutional Review Boards, and FDA Staff: FDA Decisions for Investigational Device Exemption Clinical Investigations
  • Guidance for Industry, Clinical Investigators, Institutional Review Boards, and FDA Staff: Design Considerations for Pivotal Clinical Investigations for Medical Devices
  • Guidance for Industry and FDA Staff: De Novo Classification Process (Evaluation of Automatic Class III Designation)
  • Draft Guidance for Industry and FDA Staff: Benefit-Risk Factors to Consider When Determining Substantial Equivalence in Premarket Notifications [510(k)] with Different Technological Characteristics
  • Guidance for Industry and FDA Staff: Center for Devices and Radiological Health Appeals Processes
  • Guidance for Industry and FDA Staff: Requests for Feedback on Medical Device Submissions: The Pre-Submission Program and Meetings with Food and Drug Administration Staff
  • Proposed Rule: Human Subject Protection; Acceptance of Data From Clinical Studies for Medical Devices
  • Draft Guidance for Industry, FDA Staff, and Third Party Review Organizations: 510(k) Third Party Review Program
  • Guidance for Industry and FDA Staff: Applying Human Factors and Usability Engineering to Medical Devices 
  • Draft Guidance for Industry and FDA Staff: List of Highest Priority Devices for Human Factors Review
  • Guidance for Industry and FDA Staff: Guidance for the Content of Premarket Submissions for Software Contained in Medical Device
  • Final Guidance for Industry and FDA Reviewers: Guidance on Medical Device Patient Labeling
  • Guidance for Industry and FDA Staff: Content of Premarket Submissions for Management of Cybersecurity in Medical Devices 
  • Guidance for Industry and FDA Staff: Postmarket Management of Cybersecurity in Medical Devices [88]
  • Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices
  • Guidance to Industry: Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software
  • FDA Digital Health webpage [36]  
  • Qualification of Medical Device Development Tools Guidance for Industry, Tool Developers, and FDA Staff
  • Prescription Drug-Use-Related Software; Establishment of a Public Docket; Request for Comments [89]

d) Technology agencies and references for open protocol systems

  • Diabetes Technology Society [83]
  • National Evaluation System for health Technology Coordinating Center (NESTcc) [90]
  • JDRF launched its initiative in 2017 with the goal to explore ways to overcome potential challenges in the use and adoption of open protocol systems, notably helping to establish clear financial, regulatory, and legal frameworks [91].
  • OpenAPS code framework is designed to work with interoperable insulin pumps and CGMs from any manufacturer to build and facilitate building DIY closed loop implementation [92].
  • AndroidAPS is an application with a code framework to build a closed-loop system that can communicate with bluetooth-enabled insulin pumps [93].
  • Tidepool Loop is an open source hybrid closed-loop system currently in development for iPhone and Apple Watch [94].


Healthcare is undergoing a massive technological transformation, and it is imperative for the industry to leverage new technologies to create new product approaches and generate, collect, and track novel data. The convergence of new technologies, digital health approaches, computational capabilities, advanced data techniques, algorithmic design, miniaturization, and the ability to generate and harness large-scale data enables new pathways for the discovery, development, and deployment of wearable APDS. The overarching goal of APDS is the development of innovative technologies and systems that enable an integrated, wearable/implantable, and more accurate glucose-regulated closed-loop insulin/pancreatic hormone delivery system to achieve and sustain daily euglycemia management and prevent acute and chronic complications in a personalized fashion, ultimately relieving patients of the burden of diabetes self-management.

APDS demands a more carefully tailored regulatory approach. It is a more specialized product category – the technology is more complex, and the regulatory scenario is generally multifaceted. An APDS consists of separate components that must be functionally compatible as a medical device, and the components must work together as a closed-loop system. The modular architecture of APDS at both the hardware and software level allows APDS to be assembled from independent but compatible modules, each performing a specific function.  Each system can offer unique features in configuration, functionality, user interface, and data management. However, the modular architecture may also require diverse requirements for performance, software, biocompatibility, sterility, shelf life, electrical safety, magnetic resonance imaging safety, and human factors. Guidelines and directives for many emerging technologies are either evolving, or there are no development guidelines available yet. Submission and approval requirements continuously evolve with technological and scientific field advances.
Early engagement of an experienced team of regulatory professionals, regulatory consultants, and compliance consultants is advised to help with compliance and regulatory efforts, including the development of regulatory strategy, planning and development of evidence to include in submissions, and execution of the submission process. The regulatory complexity for conceptualizing a regulatory strategy of APDS is dependent on the nature of hardware components, software components, integration of software and hardware to provide reasonable assurance of performance, safety and effectiveness, substantial equivalency to any of cleared devices, and intended use. The unique requirements of APDS may have a substantial impact on the size of the clinical development program, as well as on the risk assessment. Early and frequent dialogue between the FDA and APDS sponsors addressing critical aspects of study design and submission adequacies has the potential to mitigate several potentially preventable submission deficiencies and reduce delays in the approval process. Furthermore, the modular architecture of various functional components of APDS may require different types of partnerships, business ecosystems, and relationships for product development and regulatory submissions.
The regulation of APDS generally involves competing goals of assuring safety and efficacy while providing rapid adoption of emerging innovative technologies through the investigative and regulatory processes as quickly as possible. The FDA is planning for a future regulatory model to provide more streamlined and efficient regulatory oversight of APDS. To the best extent possible, FDA is helping to advance the development and regulation of APDS by prioritizing the review of research protocol studies, providing clear guidelines to industry, setting performance and safety standards, and fostering stakeholders’ discussions.


The interpretations, conclusions, and recommendations in this article are the author’s personal views and do not necessarily represent those of the organization(s) and committees of the author’s affiliation. The reader must not construe the information of this article as an alternative to regulatory advice from an appropriately qualified regulatory affairs professional/agency. 
Although the author and publisher have made every effort to ensure that the information in this article is reliable, the author and publisher do not assume any responsibility for the accuracy, completeness, topicality, or quality of the information provided. Any liability claims against the author in respect of any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from negligence, accident, or any other cause, including any information which is incorrect or incomplete, are therefore excluded.
Copyright:  The author retains copyright.  This article in its entirety, with its tables/images/mappings/annexures may, however, be reproduced without redaction, with acknowledgment to the author and current publisher, and with the copyright retained by the author. This article may not be used in support of a commercial medical product or investigational product.

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91. SFC Fluidics, Inc. Announces Partnership with JDRF to Develop Patch Pump with Open-Protocol Communication. JDRF. Accessed January 21, 2019.
92. OpenAPS Reference Design – Accessed February 12, 2019.
93. AndroidAPS — AndroidAPS 2.0.0 documentation. Accessed February 12, 2019.
94. Tidepool. About Tidepool Loop. Accessed February 12, 2019.

Written by M Prasad Palthur, PhD, Cofounder & VP, Design & Development, Innoneo Health Technologies, Inc.


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How China is Changing the Clinical Development Landscape: Implications for Global Development Strategy (cont.)


In addition to making the world’s second largest market more accessible virtually overnight, the more profound impact on the industry is the way in which China’s policy revamp and emerging global role point to a new and exciting paradigm for future global clinical development and regulatory strategies.
Previous 20th Century Drug Innovation Model
The previous model of drug innovation stemmed from clear US leadership in drug innovation driven by:

  • Strong public sector funding of US academic research
  • US FDA’s role as the global gold standard for regulatory approval
  • Availability of capital from both public markets and venture capitalists (VCs) 
  • High US drug prices that incentivized companies and investors to focus on US-centric drug development 

Each of the drivers of US pre-eminence has steadily undergone gradual erosion. Stagnant public sector US research funding, globalization of development and regulatory guidelines, diversification of global capital sources, and growing US domestic political pressure to end the US price subsidy for global drug innovation have become familiar themes.  
With 60-70% of drug development cost coming at the clinical stage, an increasingly important nail in the coffin of the old model lies in the competition for trial subjects in the US. Streamlined regulations and deployment of new technologies have not changed the fundamental math of clinical development: today, too many US trials chase too few subjects. This challenge will continue to grow as precision medicine calls for more specific and exclusive inclusion criteria. The inevitable result: slower recruitment, longer trials, and rising direct and indirect costs that, in turn, must be shared across a more narrowly defined base of potential US patients willing or able to pay higher prices.
Drivers of the Emerging Drug Innovation Model 
The rapid-fire changes in China create a new global environment, with an array of new drivers that firms are rapidly integrating in order to build truly global development strategies.
For example, Chinese approval for clinical trials that previously would have taken years now take 60 working days, making it possible to include China in global Phase II/III trials. Moreover, Chinese data will be accepted by US regulators, if it meets global quality standards. For our clients, we increasingly pursue simultaneous pre-IND meetings with both the US FDA and China NMPA to ensure full alignment of the clinical plan across the two leading markets.
Another example is patient recruitment speed. In areas where US trials struggle to recruit patients, including immuno-oncology, NASH, chronic diseases, and a host of orphan indications, China has a wealth of treatment-naive patients concentrated in top urban medical centers with direct costs generally 30% lower than in the US. As a result, recruitment could often be 2-3 times faster. Additionally, using a small handful of sites makes project management and quality assurance simpler while developing deeper relationships with internationally recognized principal investigators (PIs).
As an example, Table 1 shows the number of trials for PD-1/PDL-1s in non-small-cell lung carcinoma (NSCLC) and the number of newly diagnosed patients annually in both the US and China. Not surprisingly, the speed of patient recruitment and direct costs look more attractive in China. The gap is narrowing as both innovative Chinese firms and the more globally-attuned Western biotechs begin to leverage China’s attractions, but the potential impact on the time and cost of trials offers a compelling reason to rethink the balance of sites between the US/EU and China.
Table 1. Comparison of non-small-cell lung carcinoma (NSCLC) trials in US versus China
Comparison of non-small-cell lung carcinoma (NSCLC) trials in US versus China
(Sources: 1) Trial started: 01/01/2013 – 01/04/2019 
4) PhRMA: Biopharmaceutical Industry-Sponsored Clinical Trials: Impact on State Economies, 2015)

The pie chart (Figure 1) shows the distribution of clinical trial sites for 180 oncology studies conducted in China. Nearly 60% of these studies are concentrated in just five sites; hence dozens of high-quality sites have yet to be fully utilized.
Figure 1. Site distribution of 180 oncology studies conducted in China. (Source: Challenges in anticancer drug R&D in China. The Lancet Oncology. 2019; 20: 183-186.)
Site distribution of 180 oncology studies conducted in China

At the same time, Chinese VCs and commercial partners are seeking novel compounds to serve the needs of Chinese patients or to expand their global portfolios. The amount of capital raised by China-focused healthcare funds leapt from less than $4 billion in 2014 to $42.8 billion in 2018. Investments flowing into therapeutics continue to grow rapidly, rising from $2.7 billion in 2017 to $7.6 billion in 2018. This creates opportunities to sell China rights in order to raise non-dilutive capital or – as is increasingly the case – adding Chinese investors to support a more robust and truly global development program.
For an added incentive, innovative drugs – mainly from foreign firms – are being added to the National Reimbursed Drug List (NRDL) for the first time, albeit at steep discounts. The first 30 drugs – mainly oncology treatments already available in China – entered national reimbursement in 2017 with price cuts ranging from 50-70%. Roche, the firm with the largest number of newly listed therapies, saw demand for its drugs grow dramatically, with volume more than making up the difference in top-line sales and profitability. In 2018, 36 innovative drugs were added to the NRDL. A fresh batch of innovative drugs is likely to be announced in Q3 of 2019. 
Mapping the Future
Accelerating development and lowering costs while gaining registration in China’s large and more accessible market provide compelling logic for a more expansive global development model. For companies developing innovative drugs, why not pursue an approach that includes:

  • Simultaneous pre-IND meetings with both the US FDA and Chinese NMPA in order to design a clinical and regulatory strategy that works across both markets?
  • Initiate trials in both countries, with the distribution of subjects designed to maximize speed and reduce cost while delivering the data needed by the US, Chinese, and other key regulators? 

The Critical Challenge
With regulatory barriers falling at the same time that companies face rising pressure to find innovative solutions, biotechs keen to explore China’s global role need to focus on identifying trusted partners who can deliver to global standards and work seamlessly with Western clinical and regulatory teams.
Questions most frequently asked by Western companies include:

  • How do I navigate the Chinese landscape with its myriad of different commercial partners, CROs, and investors?
  • Where are the resources to bridge our lack of understanding of the clinical development terrain and regulatory nuances? Won’t these generate huge potential management distractions?
  • Can I be sure that the quality of data from China will support my global program and meet key milestones?

Definitive answers clearly depend on the specifics of each company’s capabilities and goals. However, here are a few practical suggestions that may offer useful guidelines:

1. Look to partners with deep experience developing innovative drugs in China.  
Only a handful of the long-established Chinese biopharma companies have either clinical or regulatory experience beyond their generic product portfolios. Innovative Chinese biotechs are more attuned to the complexity of innovative medicines, but only a select few have moved the bulk of their assets from the lab to the clinic. Additionally, most CROs – local and global – have built organizations designed to meet the needs of China’s former model, focused on gaining approval for local generics or innovative Western drugs already registered for years in the US and Europe.

2. Find a team you can trust to collaborate smoothly and effectively with your global clinical and regulatory people. 
Besides overcoming obvious challenges such as language and differences in clinical practice, at the heart of this challenge lies your China partner’s understanding of and commitment to global standards. This capability generally can only be found among teams with decades of experience managing clinical development on the ground in China for leading global biopharma companies.

3. Structure the approach and timing to maximize value.  
While transactions for China rights have been rising in value, Chinese investors and licensees drive hard bargains and demand extensive data to support attractive valuations. Consequently, an upfront investment in early clinical development while advancing Chinese registration can pay a high dividend, whether seeking to partner China rights locally or building toward a global transaction. Bridging the financial gap to make that upfront investment has become increasingly easy to achieve.
4. Seek a shared “owner’s mindset.” 
Ask yourself the following questions: Does the commercial partner share your passion for the molecule and its global development, or are they narrowly focused on local commercialization and clinical support for local regulatory approval? Does your CRO’s China team see themselves as a service provider that dutifully checks the boxes, or do they act like true partners who help anticipate challenges and offer innovative solutions that reflect both China’s complexities and those of your global development objectives?
As food for thought, I end with a question that seasoned CEOs of leading US VCs ask: for our potential investment candidates, are they open to leveraging the new global development paradigm, or are they entrenched in the past, destined to “wait and see” while others leap ahead? How you answer this question and act upon it can make all the difference.
About the Authors and dMed Biopharmaceutical 

George Baeder, SVP of Strategy and Business Development of dMed Biopharmaceutical, has lived in Asia for more than 40 years, focused on helping Western biopharma and device firms build successful businesses and leveraging the region’s capabilities for global competitive advantage.
Eric Zhang, PhD, VP of Regulatory Affairs & Strategy of dMed Biopharmaceutical, joined dMed after 10 years at FDA as a clinical pharmacologist reviewer (for neutral therapeutic areas, anti-infectives, ophthalmology, and transplants) and a compliance officer. Previously, he spent 5 years at Pfizer as a principal scientist. Drawing from his extensive history of working in both regulatory agency and the pharmaceuticals industry, Eric provides strategic advice for Chinese companies coming to the US.
dMed is a full-service clinical CRO headquartered in Shanghai, with offices in Beijing, Wuhan, Washington DC, New York, and San Francisco. Founded by Dr. Lingshi Tan, who built Pfizer’s R&D centers in China to more than 1,000 staff, in less than three years dMed has built a team of nearly 400 professionals recruited from top multinational biopharma firms, who bring deep clinical development and regulatory experience built on a foundation of strict compliance with global standards.


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Experiences as a Data Monitoring Committee Biostatistician (cont.)


Virtually all DMCs include physicians with clinical expertise in the medical area being studied and a biostatistician experienced in clinical trials and group sequential techniques and with a clinical understanding of the disease area. The committee may also include a bioethicist and a representative of the patient community.
DMC biostatisticians are generally perceived to primarily interpret the results of interim analyses. However, DMC biostatisticians encompass much more than that. Below, I describe some of my experiences that illustrate the biostatistician’s importance as an integral team member.
In one of my experiences, I was the reporting statistician for a major Phase III trial in which time to death was a primary endpoint. Unlike most current DMC charters in which members are unblinded throughout the course of the study, the DMC charter in this situation specified that the members and the reporting biostatistician be blinded until the DMC voted that it was necessary to break the blind.  
As the reporting statistician, I ran the listings, figures, and summary tables for each interim analysis. For each analysis, I received from the CRO responsible for the data management and randomization an updated randomization file list that displayed whether subjects were randomized to group A or B. One of the quality control procedures I employed was to check the output for each interim report analysis to that of prior reports. In one of the analyses, I saw that some of the previously entered subjects were now assigned to a different treatment group. This blatant error of an inaccurate randomization list was quickly recognized and then rectified. (Unfortunately, and particularly with the advent of IVRS (interactive voice randomization systems), and now IWRS (interactive web-based randomization systems), the occurrence of incorrect randomizations that result in destroying the integrity of the study is unfortunately not that uncommon. This is further exacerbated by errors in on-demand shipping and kit randomization lists.) 
Many members of this DMC were also serving on NIH committees, and I was exposed to best practices. For example, there was an open session, in which the sponsor and key investigators were present, and a closed session that included the reporting statistician and the DMC members. The DMC requested that the pages of the open and closed reports be bates stamped (a technique now replaced by converting files in a report to PDF and merging the files and numbering the pages with Adobe Acrobat). This allowed for an expedient review of the data during the meetings.  
As another example, a major focus for the committee was to ensure data accuracy and timeliness and to adjudicate endpoints on an ongoing basis. I prepared an addendum of any important events, such as deaths and serious adverse events (SAEs) that occurred between closure of the database and the meeting. More importantly, having a DMC in place allowed us to conduct a sample size re-estimation toward the end of the study. It was recognized that the discontinuation rate, one of the assumptions in the sample size calculation for survival analysis techniques, was lower than expected. By taking this into account, and without breaking the blind, the sample size was reduced and the study was terminated earlier than anticipated–simply by modifying this one parameter. 
In other situations, in which I was the DMC voting biostatistician, the reports to the DMC were prepared by a group within the same company, either a pharmaceutical firm or a CRO. This reporting group was also responsible for the final study report. In each situation, this group had to be explained to that the standard data listings, tables, and listings that are in a final clinical study report do not meet the needs of a DMC. A DMC often needs visual output, particularly box and whisker plots for safety data. Data must be current, a need that is met by electronic data capture (EDC) systems, which focus on entering data immediately. Moreover, for DMC reports, data sets need not be CDISC compatible and analysis data sets may not be required, which accelerates the time to produce a DMC report.  
In my experience, the initial set of output prepared by the reporting group often has programming errors and errors in the calculations of statistical summaries. I therefore recommend that templates be prepared before the first report that is presented to the entire DMC, and that these reports include an initial set of data from the study. In some situations, I have been able to work with the reporting group so that output presented to the DMC avoids such issues. 
Another situation I was in reinforced the need to always be skeptical about the accuracy of the data. In reviewing the safety data for a Phase II safety study, it was noted that there was a dramatic dose response relationship for a specific important laboratory parameter. If the data were valid, the concern for the safety of subject participants might have required a recommendation to either halt enrollment or terminate the study, both of which would be major issues for the company. Instead, and particularly since the data were entered at the site by electronic data capture (EDC), we requested copies of the case report forms (CRFs). Interestingly, we uncovered transcription errors at several sites, and these errors were predominantly for subjects of the high-dose group. After correcting these transcription errors, there ended up being no dose response relationship. The DMC also recommended to the sponsor that the data be reread at a central lab.
In these situations, the DMC biostatistician serves, to some extent, as an independent biostatistician to the client. DMC charters typically mandate review of the protocol and focus on the integrity of the study and the study results. The DMC statistician must not be a consultant to the company for the studies in which he/she is on a DMC, as this presents a conflict of interest. In my experience, I have resigned from being a consultant for projects in which I was a DMC member.
In conclusion, I and my colleagues on the DMCs that I have served on recognize the gravity of our assignment. In each situation, the relationship between the client and the DMC has been cordial, and the client has recognized the value added by incorporating a DMC into their study program. We at Kinexum can advise you on how to achieve such a relationship in the development of your projects.

Written by Alan Fisher, DrPH, Biostatistics

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Webcast Recap: Dr. Ralph DeFronzo on Type 2 Diabetes (cont.)

It is important to note that IFG and IGT are separate clinical entities with different prevalence and overlap, as can be shown by results of an OGTT (Figure 1). Even if a person has an A1C between 5.7-6.4% (diagnosis for prediabetes), a diagnosis based on A1C does not indicate whether the person has IFG or IGT. Whereas people with IFG have insulin resistance in the liver and impaired first-phase secretion of insulin...people with IGT have insulin resistance in the muscle and impaired second-phase insulin secretion. Another difference between IGT and IFG is that IGT, not IFG, is associated with increased CVD risk. As a result, people with IGT have the metabolic syndrome including insulin resistance, obesity, hypertension, and dyslipidemia. Because IFG and IGT have different pathophysiologic underpinnings, it is likely that people with either diagnosis will respond differently to different medications.

Shape of oral glucose tolerance test (OGTT) of impaired fasting glucose (IFG) versus impaired glucose tolerance (IGT)
Figure 1. Shape of oral glucose tolerance test (OGTT) of impaired fasting glucose (IFG) versus impaired glucose tolerance (IGT).

However, both IFG and IGT have similarly high risk of progression to T2D. Figure 2 displays results from the Botnia study in Sweden of the 7-8 year incidence rate of conversion rate to diabetes based on glucose tolerance status. The conversion rate for combined glucose intolerance (CGI) is higher than IFG or IGT alone.

Conversion rate to type 2 diabetes (T2D) based on glucose tolerance status
Figure 2. Conversion rate to type 2 diabetes (T2D) based on glucose tolerance status. NGT=normal glucose tolerance, IFG=impaired fasting glucose, IGT=impaired glucose tolerance, CGI=combined glucose intolerance. 

Why should pharmacologic therapy be used to prevent the development of T2D in high-risk individuals?
Although non-pharmacologic therapy (i.e., diet and exercise) is effective in the short term, it is not effective in the long term. In a meta-analysis of 46 randomized, controlled trials in which intensive dietary and lifestyle modification were instituted, the maximum decrease in body mass index (BMI) was 1.9 kg/m2, with weight regain of 0.36 kg/mper year during the maintenance phase [3, 4]. Consequently, all the lost weight was regained in 5 years. Although weight loss should still be encouraged for its immediate benefit, other approaches should be used in tandem for long-term benefit and to reduce A1C to a point that would reduce microvascular (eye, kidney, nerve) complications.
Screening for T2D improves outcomes 
T2D meets the following criteria for whether a disease should be screened [5]:

1.              Disease is serious

2.              Natural history is known (normal glucose tolerance to prediabetes to diabetes)

3.              Preclinical stage of disease is detectable (can be measured by fasting glucose, A1C, or a glucose tolerance test)

4.              Screening is quick, inexpensive, and valid

5.              Effective therapies are available (diet and exercise, pharmacologic therapy)

6.              Early treatment is more effective than late treatment (the greater loss of beta cells in the progression to diabetes increases the need for patients to go on insulin therapy)

In 2012, the US Preventive Service Task Force (USPSTF) recommended screening for abnormal blood glucose (prediabetes) and T2D, especially for adults that were at least 45 years, overweight or obese, and with a first-degree relative with T2D. Additionally, ethnic minority groups, notably blacks and Latino Hispanics, are at increased risk for T2D [6].
What is the evidence that diabetes can be prevented in people with prediabetes?
The US Diabetes Prevention Program (DPP) showed that weight loss is effective in preventing the progression from prediabetes to diabetes, but it cannot be maintained. In people with IGT, three groups were studied for three years:

1.              heavy lifestyle change (reduced weight by 7%, ate a low-fat diet, and exercised for 150 min/week),

2.              850 mg metformin twice a day, and

3.              light lifestyle change (received information on diet and exercise).

There was a fourth group that took troglitazone, but it was not followed up because of side effects. 
The results showed that the metformin group decreased the conversion rate from IGT to T2D by 31%, compared to 58% for diet and exercise [7]. However, after 4 years, weight was regained in the lifestyle group (Figure 3) [8]. Although weight loss was achieved with lifestyle intervention, 40-50% of individuals with IGT still progressed to T2D.

Weight regain in the DPP study
Figure 3. Weight regain in the DPP study. The DPP was terminated after 3.2 years, and the DPP Outcomes Study (DPPOS) was initiated.

Pharmacological therapy works
The figure below shows that a number of studies have utilized pharmacological interventions for T2D prevention (Figure 4). Overall, several pharmacological interventions, including metformin, thiazolidinediones (TZDs), alpha-glucosidase inhibitors (AGIs), and others not shown, have been demonstrated to effectively reduce the risk for diabetes and the conversion from IGT to diabetes. TZDs have been the most effective in preventing the conversion; 18 IGT subjects would need to be treated for one year to prevent the development of one case of T2D [9]. This is likely to be very cost effective since pioglitazone is generic and costs only $5.00 per month.

Examples of studies on pharmacological interventions for T2D prevention
Figure 4. Examples of studies on pharmacological interventions for T2D prevention.

Why not wait until the diagnosis of diabetes is made to start metformin therapy or start an agent that preserves beta cell function?
One reason is the delay made by physicians to intensify therapy when a patient has an A1C greater than 8.0% and is already on a therapy; this is known as physician inertia [10]. Physicians do not readily change treatment in people with diabetes. Another reason is that as time passes for people with IFG, IGT, and T2D, more beta cells (insulin-secreting cells) are lost. People with IFG and IGT have lost ~20% of their beta cell volume compared to people with NGT, and there is further loss in people with T2D [11]. Thus, beta cell failure occurs early in the natural history of T2D and is more severe than previously thought. As a result, pharmacologic intervention should be initiated as early as possible to preserve beta cell function.
Treatment of T2D should be based on pathophysiology
The core characteristics of diabetes can be grouped into the “ominous octet”:

  1. Insulin resistance in liver (increased hepatic glucose production, HGP)
  2. Insulin resistance in muscle (decreased glucose uptake)
  3. Beta cell failure (decreased insulin secretion)
  4. Insulin resistance in fat cell (increased lipolysis)
  5. Decreased incretin (GLP-1 and GIP) effectiveness (affecting insulin production) 
  6. Increased glucagon secretion from islet alpha cells
  7. Neurotransmitter dysfunction in brain (resistance to appetite suppression)
  8. Increased glucose reabsorption by the kidneys (glucosuria)

There is no one drug that can correct all 8 pathophysiologic defects, so a combination of multiple drugs is needed to effectively treat T2D. Furthermore, treating T2D should be based on known pathogenic abnormalities (i.e., beta cell failure), not simply on the reduction in HbA1c. Because people with prediabetes also have insulin resistance in the liver, insulin resistance in muscle, and beta cell failure, treatment must be started early to prevent progressive beta cell failure, improve the insulin resistance and halt the progression to T2D. 
Below are overviews of several classes of drugs used to treat diabetes: 
Sulfonylureas (SUs)
SUs bind to beta cells, and the increase in insulin secretion initially help patients overcome insulin resistance. However, they do not work long-term. Per the UKPDS study, HbA1c decreases initially, but later increases due to progressive beta cell failure [12].
Metformin acts on the liver and inhibits hepatic gluconeogenesis. With metformin treatment, HbA1c decreases initially, but later rises due to the lack of beta cell preservation. Nonetheless, in the UKPDS study, there was a 37% decrease in microvascular complications with metformin treatment [12].
The effect of metformin on cardioprotection remains unclear. In the UKPDS study, metformin monotherapy reduced myocardial infarction (MI), stroke, and diabetes-related death by about 40%. However, when metformin was added to SU treatment, diabetes-related death increased by 39% [12]. Nonetheless, metformin will remain as first-line therapy for T2D because of its efficacy and strong safety profile. 
Thiazolidinediones (TZDs)
TZDs are insulin sensitizers and also have an effect on beta cell function. Unlike SUs and metformin, TZDs have been shown to decrease A1C and maintain that decrease in the long-term. 
Additionally, pioglitazone provides cardioprotection, reverses lipotoxicity, improves NASH/NAFLD, reduces blood pressure, reduces inflammation, corrects diabetes dyslipidemia, and does not cause hypoglycemia [13, 14].
SGLT2 Inhibitors
SGLT2 inhibitors correct a novel renal defect, have durable A1C reduction, reverse glucotoxicity (improve beta cell function and insulin sensitivity), provide cardioprotection and renal protection, reduce blood pressure, cause weight loss, do not cause hypoglycemia, and have a good safety profile [15].
GLP-1 Receptor Agonists 
GLP-1 receptor agonists effectively reduce HbA1cpreserve beta cell function, promote weight loss, correct 6 pathophysiologic defects of T2D, do not cause hypoglycemia, have an excellent safety profile, and provide cardioprotection [16]. Moreover, a single dose of liraglutide has been shown to restore beta cell insulin response to hyperglycemia in T2D patients [17].
Additional Resources
The full recording of the webinar can be found 
here, and slides to Dr. DeFronzo’s presentation can be found here.
About Ralph DeFronzo, MD
Dr. Ralph DeFronzo is a leader in the diabetes field. He is responsible for many of the advances achieved in diabetes over the last 50 years, including developing the concept of insulin resistance, leading the US development of metformin, and discovering a new approach to diabetes treatment that targets glucose reabsorption in the kidneys. His most recent work, along with Dr. Bruno Doiron, has led to a possible cure for diabetes in mice and is being developed for studies in larger animals. 
Dr. DeFronzo's major interests focus on the pathogenesis and treatment of T2D and the central role of insulin resistance in the metabolic-cardiovascular cluster of disorders known collectively as the Insulin Resistance Syndrome. Using the euglycemic insulin clamp technique in combination with radioisotope turnover methodology, limb catheterization, indirect calorimetry, and muscle biopsy, he has helped to define the biochemical and molecular disturbances responsible for insulin resistance in T2D.
Dr. DeFronzo graduated from Yale University with a degree in biology and biochemistry, followed by Harvard Medical School with further studies in endocrinology and nephrology. He holds the Joe R. & Teresa Lozano Long Distinguished Chair in Diabetes in the Long School of Medicine at UT Health San Antonio, where he has been on the faculty since 1988. He has received numerous awards, including the Harold Hamm International Prize for Biomedical Research in Diabetes (2017), the Novartis Award at the Annual Scientific Meeting of the American Diabetes Association (ADA) as the outstanding clinical investigator worldwide (2005), and the Albert Renold Award from the ADA for the training of more than 200 young diabetes investigators (2002). He is the author of 750 publications dating back to 1967.
[1] Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. Updated Mar 6, 2019. Accessed Jun 16, 2019.   
[2] American Diabetes Association. Diabetes Care. 2008;31(6):596-615.
[3] Andersen JW, et al. Am J Clin Nutr. 2001;74(5):579-84.
[4] Dansinger ML, et al. Ann Int Med. 2007;147(1):41-50.
[5] Engelgau MM, et al. Diabetes Care. 2000;23(10):1563-80.
[6] LeFevre ML; U.S. Preventive Services Task Force. Ann Intern Med. 2014;161:587-93.
[7] Diabetes Prevention Program Research Group. N Engl J Med. 2002;346:393-403.
[8] Venditti EM, et al. Int J Obes. 2008;32,1537-1544.
[9] DeFronzo RA, et al. N Engl J Med. 2001;364:1104-15.
[10] Brown JB, et al. Diabetes Care. 2004;27:1535-40.
[11] Butler AE, et al. Diabetes. 2003;52(1):102-10.
[12] UKPDS Group. Lancet. 1998;352(9131):837-53 and 853-865. 
[13] Dormandy JA, et al. Lancet. 2005; 366(9493):1279-89.
[14] Kernan WN, et al. N Engl J Med. 2016;374:1321-31.
[15] Zinman B, et al. N Engl J Med. 2015;373:2117-28.
[16] Triplitt C, DeFronzo RA. Expert Rev Endocrinol Metab. 2006;1(3):329-41.
[17] Chang AM, et al. Diabetes. 2003;52(7):1786-91.

Written by Jennifer Zhao, Associate

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Issues with Product Development Traceability (cont.)

Although traceability appears to be a straightforward concept to implement, it can become a developmental roadblock for companies that do not use this valuable tool properly and with care. Here are some examples of issues that have been encountered: 
1. Viewing traceability as a “checklist” item for submission
Company X viewed traceability as a standalone process to create a document for its 510(k) submission. The company did not start the traceability process until the submission was being assembled by the regulatory group. The entire product development team needed to be devoted to reverse engineer and document the traceability. As a result, the FDA submission was delayed by several months.
Traceability should be a parallel, interactive part of the development process. The value of traceability is greatly diminished if it is performed after development. In order to avoid this pitfall, the type and timing of traceability need to be clearly defined in a company’s product development process.
2. Traceability to user or customer requirements
In another scenario, a product development team spent many weeks tracing customer requirements to system and subsystem requirements. Unfortunately, this involved creating imaginary customer requirements for items, such as electrical safety requirements, required by regulatory agencies or for a proprietary reagent manufacturing process. These imaginary customer requirements were frequently repetitive with the system and subsystem requirements or did not have a logical source.
There is a common misunderstanding in industry that all requirements must trace back to a user or customer requirement or need. In reality, requirements originate from where they are introduced in the development process. These may be business requirements, regulatory requirements, risk mitigation requirements (developed as part of the product’s risk management process), or requirements that meet a manufacturing need. These requirements do not necessarily trace back to customer or user requirements. 
The level or document used for introducing a requirement needs to be logical and appropriate. Additionally, the source and reasoning for the requirement need to be documented. The control and processing of these requirements need to be defined in a company’s requirements-management process.
3. Very large traceability tables
In a third scenario, a product development team spent countless hours creating a single traceability table in Microsoft Excel that related approximately 30 documents as columns and the traced items from each document as rows. As a result, the table contained over 20 thousand rows! Because the table was so large, only item numbers could be listed in each cell; normally, the full text of the items being related is used in traceability tables for ease of understanding. In this case, however, a copy of each of the 30 documents was needed to determine what was being linked in the table. The item number in each document had to be looked up to read the associated text. This extra effort counteracted the usefulness of the traceability document and could have inhibited efficient FDA review. 
Unfortunately, this is a common occurrence, largely due to how FDA guidance documents describe traceability. FDA’s statement of “Traceability among requirements, specifications, identified hazards and mitigations, and verification and validation testing” can be interpreted as the submission of a single document. Furthermore, FDA’s provided examples do not accurately reflect the actual number of documents produced in product development processes.
Traceability is an extremely valuable tool, but it needs to be defined so that it adds to the process, not create massive documentation obstacles. All traceability does not need to be accomplished in a single table; traceability may be documented in a manner that provides the most value at the time. Some examples could be as follows:

  • Traceability between requirements and between the lowest-level input requirements to the design documentation are key needs.
  • The project’s risk management process should require traceability between the identified risks, the risk mitigation requirements, and the validation of the effectiveness of the mitigations. 
  • Tracing the lowest-level input requirements to their respective verification is another key need.

A company’s product development standard operating procedures (SOPs) need to define when and how such traceability tables will be documented and how they are used to provide the appropriate feedback to the development team. These procedures should include how software-based traceability tools are used in the development process.
These tables are also submitted as required to regulatory agencies – the benefit is that the regulatory agencies will have an easier time following the documentation, helping to shorten the approval timeframe.
When creating or re-engineering processes and SOPs, it is not enough to be in compliance. The process also needs to be as efficient as possible to save time, minimize required resources, produce outstanding products, and more. The experts at Kinexum can support your efforts with their depth of knowledge and experiences with multiple development projects, helping you achieve compliance and effectiveness together.

Written by Bill Hoover, Medical Device and Quality Consultant

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