...Kinexum will also be participating in the program of several conferences this coming quarter, including the Diabetes Technology Meeting in Bethesda in November and the Cardiovascular Clinical Trialists Forum in Washington, DC in December. Let us know if you will be at one of these conferences—would hope to see you!
 
Happy reading, and wishing you a healthy, productive fall season!

Cheers,

Thomas
 

...Any new technology in totality can be considered equally an assistant and a challenger. New technologies may improve the delivery of more patient-centered and highly personalized care; in parallel, they may present various challenges to the adoption and integration into real-world clinical practice.⁶ Although digital solutions have considerable potential to modify the diabetes ecosystem, many barriers and challenges persist.³ The ability or willingness to incorporate technology into one's everyday life is the ultimate test of success.⁷

2) INTRODUCTION – DIABETES TECHNOLOGY
Diabetes technology has continually evolved over the years to improve the quality of life and ease of care for affected patients.⁸ Diabetes technology is the term used to describe the hardware, devices, and software that people with diabetes use to help manage blood glucose levels, stave off diabetes complications, reduce the burden of living with diabetes, and improve quality of life.⁹
 
Despite significant therapeutic advancements, a person with diabetes routinely experiences physiological, cognitive, pragmatic, and psychological burdens.¹⁰ Many fail to achieve their glycemic goals due to multiple factors, including delays in intensification of treatment regimens, resistance to changes in lifestyle, lack of patient education resources, inadequate treatment regimens, and poor adherence to treatment.¹⁰ Acceptability, usability, device burden and benefits, visibility, and smooth integration into everyday living are all important factors that must be accounted for when considering the introduction of new technology.⁷ 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.⁴⁴

3) INTRODUCTION – CLOSED-LOOP DELIVERY STRATEGIES AND APDS
In the past four decades, advances in closed-loop delivery strategies and overall development of diabetes technologies have progressed remarkably through continuous glucose monitoring (CGM), real-time continuous glucose monitoring (rtCGM), mathematical modeling of the human metabolic system, and control algorithms driving closed-loop control systems known as the “artificial pancreas” (AP).¹² The convergence of iterative advances of existing technologies, the emergence of novel technologies, digital health approaches, computational capabilities, advanced data techniques, algorithmic design, and miniaturization, and the ability to generate and harness large-scale data have enabled new pathways for the discovery, development, and deployment of wearable Artificial Pancreas Device Systems (APDS). On the other hand, APDS as medical devices are considered adoptive of progressive sensor technologies, innovative pump technologies, intelligent decision support systems, health informatics, software platforms intended for medical purposes, and cybersecurity approaches.
 
APDS and hybrid closed-loop control systems are poised to revolutionize diabetes management, and numerous improvements are on the horizon that could more fully automate APDS technology and make the devices more straightforward and more user-friendly.¹³ However, the ultimate metric for success of APDS will be improved outcomes for people with diabetes. APDS accessibility will be driven by the value perceived by patients and two other crucial stakeholders—health care providers and payers.¹⁴
 
Large-scale clinical trials with more extended follow-up periods are needed to fully clarify the glycemic benefit that comes from the implementation of closed-loop systems and to investigate its effect on glucose variability, hypoglycemia risk, HbA1c levels, and acute and chronic diabetes complications.¹³ Economic analyses are necessary to assess the cost-effectiveness of APDS in the management of type 1 diabetes. Also, data privacy, cybersecurity, and privacy need to be considered and incorporated in the design and product development process. Adoption of APDS across diverse end users, as well as usability and patient experience, will demand relevant evidence to show that they are safe, clinically effective, affordable, and meet the expectation of all stakeholders. 

4) ANTICIPATED ADVANCES AND FUTURE DEVELOPMENTS

a) Insulin-alone delivery systems to multi-hormone APDS
The current technology is around insulin-alone delivery systems and allows closed-loop insulin delivery in response to glucose values and trends. However, at this point in time, users still need to check glucose values at least twice daily to calibrate the CGM device used in conjunction with the closed-loop system. Additionally, currently available systems require users to manually give bolus insulin for meal times by entering in carbohydrates and the current glucose value from either CGM or a fingerstick value.⁸ These hybrid closed-loop systems are relatively new to the market, with multiple new systems anticipated in the coming years. 
 
In the near term, APDS will reduce hypoglycemia through the optimization of insulin delivery and will automatically dose insulin to target ranges via hybrid closed-loop systems, hyperglycemia/hypoglycemia-minimizing systems, and semiautomated insulin delivery systems. As these systems continue to evolve, more closed-loop systems will emerge onto the market. This evolutionary path is anticipated to develop automated insulin-alone delivery (AID) systemsand eventually dose additional hormones, such as glucagon and/or amylin (dual-hormone AP, and multi-hormone APDS).¹⁵ Bihormonal therapy with glucagon and insulin has long been considered ideal for mimicking the endogenous functioning pancreas. The emergence of a bihormonal closed-loop system as a standard of care is expected.⁸ However, development of soluble pumpable glucagon, dual-chamber pumps, and dual-lumen catheters, as well as development of multi-hormone algorithms, is considered one of the main challenges for the dual-hormone approach.¹⁶

b) Advances in control techniques and algorithms
Several new control algorithms, modifications of existing control algorithms, and development of new modeling approaches are reported continuously.^17–23 Comprehensive assessment of the dynamics of glycaemic fluctuations is crucial for providing accurate and complete information to the patient, physician, automated decision-support, and to the APDS control component.²⁴ Advances in modeling human metabolic rate, new modeling systems, and platforms have opened novel avenues for exploring the developmental trajectory, physiology, biology, and pathology of the human pancreas.^25-27 Data mining-based intelligent decision support systems are embedded with concepts like data mining, neural networks, deep learning, and evolutionary algorithms.⁵ The Juvenile Diabetes Research Foundation (JDRF) aims to identify the areas of algorithm enhancements through big-data analysis to build or improve mathematical constructs and relationships that may be incorporated into next-generation artificial pancreas algorithms and possibly even personalized algorithms.²⁷
 
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.¹⁶ Future algorithms may also incorporate physiological time delays and mechanical delays in the system and estimate model parameters with competent statistical methods. On the other hand, machine learning algorithms that continually evolve are often called “adaptive” or “continuously learning” algorithms and can learn from new user data presented to the algorithm through real-world use. 
 
Future developments may also incorporate:

1. Algorithms for multi-signal & multi-sensor systems;
2. Advances in multi-hormonal & counter-regulatory hormone algorithms;
3. Algorithm self-learning capabilities & integration of auxiliary sensors;
4. Big data & AI-augmented algorithms for performance, hormonal delivery, and safety;
5. Advances in mathematical modeling of glucose homeostasis and human metabolic rate;
6. Advances in euglycemia modeling and simulation models;
7. Precision-tailored algorithms for personalization; and
8. Improvement in control algorithm verifications methods & approaches.

c) Advances in pump technologies
Over the last 50 years, insulin pumps have continued to advance and improve with more precision and convenience than ever before. However, most conventional pumps, in general, are bulky, intrusive, and expensive.²⁸ Relatively user-friendly patch pumps have emerged on the market, offering flexible insulin delivery options. The patch-pump platform systems provide several advantages over conventional insulin pump delivery systems, including being free of tubing, operating discreetly under clothing, and being small and lightweight.^29,30 This nonmechanical pumping technology allows for accurate and precise delivery of minimal amounts of exogenous pancreatic hormones, including concentrated insulin.²⁸ Multiple new patch pump systems are anticipated in the coming years. For example, French pharmaceutical company Sanofi, Swiss medical technology company Sensile Medical, and Alphabet's life sciences arm Verily joined forces to develop a connected insulin patch pump. Together, the three companies plan to create and commercialize a new generation of "all-in-one" pre-filled insulin patch pumps for patients with type 2 diabetes.³¹ Multiple collaborations are ongoing to make currently available CGM devices compatible with other independent insulin pumps, with the goal of developing closed-loop and suspend before low systems in the near future.⁸
 
Future work may involve miniaturization and approaches to improve pump performance, accuracy, reliability, safety, and accessibility. Future developments may also incorporate:

1. Patch pump technologies & microfluidic systems;
2. Smarter pump & infusion set technologies;
3. Embedding microelectromechanical systems (MEMS);
4. Advanced multi-hormonal delivery pump systems; and
5. AI-augmented pump/patch pump technologies.

d) Advances in sensor technologies
The CGM functional component represents the sensing arm of APDS and performs continuous or repeated measuring of the patient’s blood glucose levels.³² CGM data serve as the conditional input for insulin-delivery automation devices.¹⁶ The advent and progress of ambulatory glucose sensor technology have enabled rtCGM and its integration with insulin therapy. Minimally invasive rtCGM has become the standard of care for type 1 diabetes and includes factory-calibrated subcutaneous glucose monitoring and long-term implantable glucose sensing.³³ Although different techniques for subcutaneous glucose measurement were introduced, only electrochemical transcutaneous CGM systems are currently available to patients.
 
CSII and rtCGM use in adults were associated with dermatological complications.³⁴ The most common reasons for stopping insulin pump use were body image with wearing the device, discomfort, cost, and trust.³⁵ As these products continue to develop, sensors and transmitters will likely become smaller and lower-profile to the skin for ease of wearability. Additionally, new adhesive strategies need to be employed to keep these devices in place as the lifetime of a sensor continues to improve.⁸ Additional limitations for CGM systems include potential inaccuracy of interstitial glucose measurements due to medication interferences, sensor lag, or sensor drift. Limitations for closed-loop systems also include the need for routine monitoring to detect infusion site issues, as well as monitoring to ensure adequate insulin supply in reservoir to avoid abrupt cessation of insulin infusion that would lead to severe hyperglycemia.³⁶
 
Future work may involve miniaturization, advances in glucose-sensing technologies, and approaches to improve sensor performance, accuracy, reliability, and safety. Future developments may also incorporate:

1. Miniaturized, implantable/wearable minimally invasive CGM sensors;
2. Miniaturized multi-sensor platforms;
3. Development of non-glucose biomarkers sensing systems;  
4. AI-augmented glucose-sensing & AI-powered rtCGM devices;
5. Improvement in CGM signal filtering levels & calibration algorithms;
6. Embedding microelectromechanical systems (MEMS); and
7. Novel biocompatible smart implantable biomaterials/ biohybrids matrices/membranes. 

In addition to using CGM data, some APDS may consider measurig other biometric/physiological fluctuations (e.g., galvanic skin response). These are known as multivariable or adaptive systems.³⁷
 
Multiple new CGM systems are anticipated with an extended range, including monitoring pre-diabetics. For example, Nemaura Medical is a medical technology company developing SugarBEAT® as a non-invasive, affordable, and flexible CGM designed to help people with diabetes and pre-diabetics to better manage their glucose levels by spending more time in range.³⁸

e) Interoperability
The modular architecture of APDS consists of various functional components that must be functionally compatible as a medical device and work together as a closed-loop system. As the current diabetes technology ecosystem is heterogeneous, a significant concern in an APDS system is interoperability.³⁹ It is worth mentioning that open-protocol systems, open-source initiatives, and the adoption of real-world data will impact future APDS software and control algorithms developed for closed-loop delivery. CGMs and insulin pumps are reverse-engineered, allowing open-protocol efforts, such as OpenAPS, AndroidAPS, and Tidepool Loop, to display data in innovative ways and even to control automated insulin delivery.⁴⁰ Tidepool Loopis a project that is building and supporting an FDA-regulated version of Loop, which will be available in the iOS App Store, and is intended to work with commercially available insulin pumps and CGMs. Tidepool aims to deliver Tidepool Loop as an FDA-regulated product, broadly available via the iOS App Store, and compatible with multiple, in-warranty pumps and CGMs.⁴¹ JDRF launched its Open-Protocol Automated Insulin Delivery (AID) Systems 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.⁴²

f) Advances in generation and validation of digital biomarkers
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.⁴³ Digital biomarkers are consumer-generated physiological and behavioral measures collected through connected digital tools.⁴⁴ The identification of new digital biomarkers, based on data generated by CGM 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 in which an individualized approach towards HbA1c monitoring can be combined with parameters derived from such devices, including time spent in range, glycemic exposure, glycemic variability, and hypo- and hyperglycemia.³ For example, Eli Lilly is expanding its collaboration with Evidation Health into a multi-year project aimed at developing digital biomarkers.⁴⁵ 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.⁴³ 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.

g) Clinical research environment
The modular software-hardware combination has created new opportunities for patient care and biomedical research, enabling remote monitoring and decentralized clinical trial designs.⁴³
 
Future clinical research may focus on generating gap-filling evidence (clinical or otherwise) to the scientific community and beyond on the use of AP systems in a targeted population and/or identifying barriers in AP system design or implementation.⁴⁶ Application of AI and cognitive computing offer promise in diabetes care.⁴ Published studies suggest that a broad spectrum of market-ready AI approaches are being developed, tested, and deployed today in the prevention, detection, and treatment of diabetes.⁴ Future research may explore the nuances of applying real-time learning and advanced elements of AI into an APDS that could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly.¹¹

h) Practical implementation and adoption
Practical implementation of technology encompasses several related domains of inquiry, including clinical utility and guidance, education, economics and access to technology, benefits/barriers, and “real-world” use.⁴⁷ Real-world outcomes affect how care is delivered, paid for, and administered.⁴⁷ To achieve widespread adoption, a digital health technology for diabetes must overcome five barriers: (1) usability to satisfy people with diabetes, (2) clinical benefit to satisfy clinicians, (3) economic benefit to satisfy payers, (4) security to preserve safety and satisfy product regulators, and (5) data privacy to satisfy legal regulators.⁴⁸ Failure to integrate with leading electronic health records and personal health records can pose issues with long-term adherence of a digital health product.⁴⁸
 
As diabetes technology increases in usefulness, new evaluations of cost-effectiveness will need to be done, and new populations will need to be considered for diabetes technology benefit.⁴⁹ Diabetes devices have a wide variety of uses, and device specifics (e.g., usability, utility, and human factors) will inform appropriate characteristics for candidacy.⁴⁹ For example, integration of CGM technology in clinical practice presents various challenges, from concerns regarding time constraints during office visits to a lack of systematic approach to the interpretation of the data.^6,50 These barriers are magnified as CGM systems are adopted more broadly in primary care networks and health care systems.⁵¹
 
If perceived usefulness and perceived ease of use (e.g., conventionally, benefits, and burden) are central to whether individuals embrace APDS, diabetes clinicians have a crucial responsibility to help set these expectations appropriately.⁵² There is still much to be learned about incorporating APDS into clinical care. Future research will elucidate best practices, including appropriate indication for APDS, patient selection characteristics, and optimal education strategies. All of these, however, will be contingent on provider and patient expectations of the system, setting the course for successful use.⁵² As technology continues to advance, endocrinologists and diabetes providers need to stay current to better guide their patients in optimal use of emerging management tools.⁸ Dedicated guidance, recommendations, and clinical training are needed in this regard, particularly for health care professionals who are not diabetes specialists but interact regularly to manage care of elderly patients with diabetes.⁵³ Optimal design of devices specifically for elderly diabetes patients will enable these devices to be used most effectively. More research in device usability with a gerontological focus is needed.⁵³

i) Diabetic care delivery service models
Emerging digital innovations, such as wireless mobile devices, wearables, interactive online platforms, and electronic data collection tools, exert a transformative power on many domains of human action and interaction.⁵⁴ Patient-level data is essential to understanding and improving health outcomes. Patient-generated health data (PGHD) are health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern.⁵⁵ Electronic patient-generated health data (e-PGHD) is electronically captured, shared, and used PGHD consisting of digital information created outside traditional healthcare contexts.^56,57 Also, remote patient monitoring (RPM) programs and pilots have become more prevalent and efficacious in improving outcomes, adherence, and cost reductions.⁵⁸
 
We will likely see a growth in technology-enabled modern virtual clinics with a continuous remote care model, an effective alternative to in-person clinical care. As these care models evolve, they may incorporate APDS and its components for continuous remote care monitoring into their clinically-proven glucose management approaches. In addition, these emerging care models may incorporate e-PGHD from APDS components, possibly filling gaps in information and providing a more comprehensive picture of ongoing patient health. This would result in potential cost savings and improvements in health care quality and outcomes, care coordination, and patient safety.
 
Steady Health provides a technology-enabled modern clinic for personalized and convenient diabetes care available through a monthly membership.⁵⁹ Steady Health specializes in CGM, use data, and technology. Virta Health is a clinically-proven glucose management method with a continuous remote care model, upending traditional diabetes treatment by providing near real-time and technology-enabled access to medical providers and health coaches.⁶⁰ The Verily-Sanofi joint venture is known as Onduo, the virtual clinic, coaching service, and clinical support for type 2 diabetes management.⁶¹ Lark's Diabetes Management Program (DMP) uses conversational AI and is the fastest-growing chronic disease management platform.⁶² Preliminary data of a pilot study reported a decrease in A1c by 1.1 points, reducing the probability of diabetes-related complications.⁶³ The DarioEngage Platform provides full coverage healthcare solutions that are easily customized to integrate with any clinical programs, covering all facets of prediabetes and diabetes management.⁶⁴
 
Effective use of e-PGHD and incorporating e-PGHD into clinical workfloware anticipated to better manage and engage a population of individuals suffering from diabetes.
 
We will likely see a growth in connected diabetes management platforms. As they evolve, they may incorporate or interface with APDS functional components. The DreaMed diabetes management platform is a unique, cloud-based AI that goes beyond data aggregation and transforms dynamic, real-time patient data into actionable insulin treatment insights.⁶⁵ Glooko Enterprise is one of the largest diabetes data management platforms focused on improving the lives of people with diabetes and their caregivers.⁶⁶ GlucoMe is a comprehensive, connected diabetes care platform that simplifies the way patients, caregivers, and medical professionals manage diabetes.⁶⁷ Rimidi is a cloud-based software platform that enables personalized management of chronic cardiometabolic conditions across populations.⁶⁸ In addition, device manufacturers as well offer various diabetes management systems. Examples include the Accu-Chek 360° diabetes management system⁶⁹ and Dexcom CLARITY^® Diabetes Management Software.⁷⁰
 
These new models of care must be proven to be effective, ethical, convenient, and financially sustainable.¹⁰ As technology continues to advance, endocrinologists and diabetes providers need to stay current to better guide their patients in optimal use of emerging management tools.⁸

j) Reimbursement environment
Cost-effectiveness analyses are important for the expansion of payment for diabetes technologies. Historically, health plans and insurers have moved cautiously in covering APDS and its functional component services. Insurers seem to provide coverage for an APDS when it is determined to be medically necessary and complies with the respective medical necessity criteria and guidelines.^71–75 In addition to the clinical benefits, cost-effectiveness is an important factor in access and utilization of APDS technology. To support adoption, cost-effectiveness should be assessed to allow for reimbursement by various healthcare systems and ensure that adequate infrastructure exists.⁷⁶

k) Regulatory environment
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. In the future, we will likely see a regulatory model that provides more streamlined and efficient regulatory oversight of APDS. To the best extent possible, regulatory agencies shall 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 modular architecture of APDS 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 will continuously evolve to catch up to technological and scientific field advances. Future developments may also incorporate the harmonization of various terminologies and standards required to complement the development of anticipated automated insulin-alone APDS and multi-hormonal APDS. 

l) Diabetes technology business ecosystem
This modular architecture may also require different types of partnerships or business ecosystems and may represent new roles of digital health companies to increase the adoption of advanced technologies and accelerate innovation.
 
Advances in APDS technologies and realizing their full potential are dependent on the structured collaborations among all stakeholders and a streamlined alignment to overcome barriers in widespread implementation. In the future, we will likely see intensive yet essential partnerships among funding bodies, commercial entities, research institutions, regulatory agencies, payers, and not-for-profit organizations. 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.

5) KEY PLAYERS INVOLVED IN APDS RESEARCH, DEVELOPMENT, AND COMMERCIALIZATION

a) Corporations (alphabetically organized):
Please note this list is not exhaustive.

• Abbott Diabetes Care, Inc.
• Agamatrix, Inc.
• Ascensia Diabetes Care Holdings AG
• Beta Bionics, Inc.
• Bigfoot Biomedical, Inc.
• Cellnovo, Ltd.
• Defymed SAS
• Dexcom, Inc.
• Diabetes Neuromathix Pty Ltd.
• DreaMed Diabetes, Ltd.
• Evolving Machine Intelligence Pty Ltd.
• Hoffmann-La Roche, Ltd.
• Insulet, Inc.
• Medtronic Minimed, Inc.
• Pancreum, Inc.
• Senseonics, Inc.
• Seventh Sense Biosystems, Inc.
• SFC Fluidics, Inc.
• Tandem Diabetes Care, Inc.
• Tidepool
• TypeZero Technologies, Inc.
• ViCentra B.V.
• Xeris Pharmaceuticals, Inc.
• Ypsomed AG

b) Research Institutions (alphabetically organized):
Please note this list is not exhaustive.

• Barbara Davis Center, University of Colorado
• Centre hospitalier universitaire de Sherbrooke (CHUS), Quebec
• Diabetes Technology Society (DTS)
• Diabetes wiREless Artificial Pancreas ConsortiuM (DREAM)
• Diablo Clinical Research Walnut Creek, California
• Harvard University, Cambridge
• Icahn School of Medicine at Mount Sinai, New York
• Institut de recherches cliniques de Montréal (IRCM), Montréal
• Joslin Diabetes Center, Boston
• Juvenile Diabetes Cure Alliance (JDCA)
• Juvenile Diabetes Research Foundation (JDRF)
• Massachusetts General Hospital, Boston
• Mayo Clinic Rochester, Minnesota
• McGill University Health Center, Montréal
• Rainier Clinical Research Center Renton, Washington
• Sansum Diabetes Research Institute, California
• Schneider Children's Medical Center Petach-Tikva, Israel
• Sinai Health System Toronto, Ontario
• Stanford University, California
• University of Virginia Center for Diabetes Technology, Virginia
• Yale University School of Medicine, Connecticut 

6) DISCLAIMER
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. This article is not based on the analysis and interpretation of data using meta-analysis or any other validated scientific literature review methodologies. Author personal perceptions and opinions that are guided by review of academic literature and websites (commercial and non-commercial) is the basis of recommendations for future developments. All information was obtained from the public domain. 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. 
 
ABBREVIATIONS
Artificial Intelligence (AI)
Artificial Pancreas (AP)
Artificial Pancreas Device Systems (APDS)
Continuous Glucose Monitoring (CGM)
Electronic Patient-generated Health Data (e-PGHD)
Juvenile Diabetes Research Foundation (JDRF)
Real-time Continuous Glucose Monitoring (rtCGM)

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

 

...Aided by the Soviet army, a band of communists staged a military coup, toppled the autocratic – though relatively benign – president Daoud Khan’s regime, and declared a “democratic socialist” government. Soon after, under the Leninist banner of “proletariat revolution,” they undertook a large-scale brutal Stalin-like campaign to confiscate farmlands and undo the thousand-year old feudalistic traditions in the country. The socialist revolutionary assault on the country was further exacerbated by their perverse mocking of Afghanistan’s deeply held traditions of Islamic beliefs and institutions – all done under the banner of modernizing the society. Soon, an uprising in the countryside began and rapidly gained momentum, swelling into an all-out resistance war against the regime. In response to the growing resistance, and after another round of regime change in 1979 brought on by infighting amongst the communist factions, the Soviets invaded the country in January 1980.
 
The war against the Soviet occupation had begun. It was then that the first of a series of personal tragedies struck my family. Early in March 1980, a day of general strike was called in the central business district in Kabul. My father needed to check on his business office and got into his car early in the evening. Accompanying him were his two boys, then 20 and 18. That was the last I saw my father – or his body. While the details were sketchy and truth hard to know, for no investigation was done, his car had come under Soviet fire. He was instantly killed, and the younger of my two brothers was shot in the chest. The older escaped injuries, only to be taken to jail for violation of nighttime curfew. My father’s body was never found, only weeks later someone pointed to a shallow grave. The younger brother’s life was miraculously saved by the only female surgeon in Afghanistan who had trained in the Soviet Union and happened to be on call at the military hospital where their bodies were dumped. She recognized my father; he was her cousin.
 
The horrific loss of my father, who was the family patriarch, suddenly plunged our lives into chaos. What made matters worse was the inability to mourn his loss, and we feared for the safety and lives of my older brothers. Soviet soldiers were roaming the streets in Kabul and would randomly pick young men who appeared to be of conscription age and deploy them directly to the fronts to fight the resistance. Cousins and extended family who had the means began to escape the country. Those who were fortunate and could manage so did it by plane, but most fled on foot, crossing the border to Pakistan or Iran in search of safety. The world’s attention was focused on Afghanistan when the US and many other western democracies boycotted the Moscow Olympics of 1980. Afghanistan was front page news and the term mujahideen, meaning “freedom fighters,” was introduced into the English lexicon.
 
Tragedy struck our family again when my second oldest brother, then 19 and a medical student, died crossing the border into Pakistan. Again, we never saw his body or held a funeral. According to my oldest brother who was accompanying him, the group they were traveling with was stuffed 20-plus deep in a false compartment of a freight lorry by the people smugglers. The journey from Kabul to the nearest border town in the Northwest province of Pakistan was no more than 150 miles but would take 7 to 8 hours. It was early June, and though morning temperatures were in the 60’s in high elevations around Kabul, they soared to 110-plus degrees in the afternoon in the valleys closer to the border. My brother died of asphyxiation after several people apparently collapsed on him. He was hoping to go to Germany to continue his medical studies. Instead, he was buried by kind strangers along a dirt road. I visited his grave three years later after the rest of us younger siblings and my mother had managed to flee the country. My mother had to learn the news of his death through the youngest of the three brothers, then 17, who was now the family patriarch. She never found the courage to visit his grave.
 
By the autumn of 1980, my oldest brother had managed to come to America under a special refugee status for Afghans fleeing the Soviet war. At 21 and without any other family members, he settled in Jacksonville, Florida, where several other Afghan families had also recently arrived. The rest of us – my mother 50, my sisters 15 and 5, my brother 17, and I, barely 12 at the time – moved to various homes around Kabul for the next 2 years. Late in 1982, one of my dad’s office managers decided he had better save his family’s lives by fleeing the country. We tagged along with him. Under the pretense of a wedding party, we rode straight into the heart of Afghanistan – high into the mountains not far from the location of the since-destroyed giant Buddhas. The 200-mile journey to Pakistan took us weeks – walking on foot, riding in the back of trucks and farm equipment and sleeping in makeshift dirt structures. We were young, and although the fear of being attacked by the Soviets was the motivating force, we were also hopeful. 
 
For 18 months in Pakistan, we waited for the US immigration process to be completed under the family reunification with my brother as the sponsor of our application for refugee resettlement in America. It seemed like an eternity to me then, but now it feels like a fortnight. It was then, at the age of 14, when I learned my first words of English. While we weren’t allowed to attend local schools, my mother managed to hire a teacher to introduce me and my older sister to English in anticipation of coming to America. For three months, one hour every other day, “the master,” as we called him according to tradition in the former British colonies, would patiently teach us simple English language syntax, conjugation, nouns, and verbs. “Repeat after me,” he’d say, “I am sitting cross legged under a shady tree,” with particular emphasis on the “correct” pronunciation of t’s and d’s customary to the Indian subcontinent. The rest we had to learn on our own. By the time we were getting ready to leave for America, I had read and translated into Farsi enough children’s stories (Robin Hood was my favorite) and watched old Chinese movies with English subtitles on Pakistani TV that I could cobble together simple English phrases in writing. Correct pronunciation, however, had to still wait.
 
Landing at JFK airport was definitely the most exciting day of my life to that day. Looking out the airplane window as it approached New York, I tried unsuccessfully to locate the Statue of Liberty. I had seen photos of the Manhattan skyline and simply imagined us also living high up in a skyscraper somewhere in Jacksonville, Florida. Well, it turned out to be a quiet two-story apartment complex in a wooded area 15 miles from downtown Jacksonville with no tall structures in sight. But it had a pool, we had a car, and every morning, a big yellow bus actually drove by to pick me up and take me to school! It would only get better from there. After all, this was the America I dreamed of and now lived in! 
 
My first day in American high school coincided with Halloween. Needless to say, a powerful bolt of cultural shock had struck this poor 16-year-old teenager. But I was focused laser sharp on my studies. How could one not be focused, be appreciative of the safety, security and freedom, and take advantage of the land of opportunity? Just as if having the safety from Communist oppression and freedom of speech weren’t enough, Americans, through their generosity, handed me books, pencil, and paper, and they brought me to school, fed me lunch, and took me back home. I knew immediately then all I had to do was the right thing to succeed.
 
And boundless opportunities I found. Within two and a half years of arrival in the US, I was a freshman at the University of Chicago sitting amongst an elite group of the best and brightest high school graduates. Did I have the academic preparation, scholastic background, and family support of a typical Chicago student? Definitely not. But someone in the admissions office must have understood the challenges I had to overcome to get to that point. I was given a chance to prove myself. This is what we immigrants would call the quintessential Americana.
 
We all owe our success to many generous people we meet in our lives. I was lucky to have met the late Professor Leslie DeGroot, a renowned endocrinologist and expert in thyroid disease soon after I started my premedical studies. Dr. DeGroot had spent a year in Afghanistan, a landlocked country where goiter is endemic, conducting research on dietary deficiency of iodine in the country. By the mid 1980’s, the Soviet-Afghan war was in its peak, causing thousands of civilian casualties on a monthly basis. Dr. DeGroot had become actively engaged in spearheading medical relief efforts to provide care for select wounded civilians brought to the US under a special humanitarian assistance program. The medical staff needed an interpreter; I was able, I was available, and I was eager to get involved. Having learned of my interest in medicine, Dr. DeGroot took me to his lab to meet postdocs under his supervision. This was the beginning of a multi-year guidance, mentorship, and support on his part that culminated in my acceptance to the university’s medical school. In 1994, 10 years after arriving in the States as a political refugee from Afghanistan, I had a medical degree in hand. As I entered clinical medicine, many more mentors helped me along the way, notably Dr. Stan Freeman, an outstanding clinician at Scripps in La Jolla, and Dr. Carl Grunfeld at UCSF. Beyond clinical medicine, I learned clinical investigation, writing and executing research protocols, and conducting sophisticated metabolic studies at the CRC in San Francisco. I felt I had reached a major milestone in my life as an American. 
 
For newly arrived immigrants, the “American Dream” is about success, freedom, and being able to control your own destiny. In the summer of 2001, I accepted my first clinical research dream job at the pharmaceutical giant Bristol-Myers Squibb in New Jersey. I boarded a flight from San Francisco to Newark. As we approached the New York skyline, I instinctively looked out the window, locating the Statue of Liberty. This time I didn’t have to look very hard. She was there holding her torch for me.

Written by Mustafa Noor, MD, Kinexum Clinical Development