pathway to artificial pancreas systems revisited: moving … · 2015. 5. 12. · regulation of...

Pathway to Artificial PancreasSystems Revisited MovingDownstreamDiabetes Care 2015381036ndash1043 | DOI 102337dc15-0364

Artificial pancreas (AP) systems a long-sought quest to replicate mechanicallyislet physiology that is lost in diabetes are reaching the clinic and the potential ofautomating insulin delivery is about to be realized Significant progress has beenmade and the safety and feasibility of AP systems have been demonstrated inthe clinical research center and more recently in outpatient ldquoreal-worldrdquo environ-ments An iterative road map to AP system development has guided AP researchsince 2009 but progress in the field indicates that it needs updating While it isnow clear that AP systems are technically feasible it remains much less certainthat they will be widely adopted by clinicians and patients Ultimately the truesuccess of AP systems will be defined by successful integration into the diabeteshealth care system and by the ultimate metric improved diabetes outcomes

An electromechanical approach to improve glycemic control and quality of life forpeople with diabetesdan artificial pancreas (AP) (or an automated insulin-deliverysystem or bionic pancreas)dhas been a long-sought technological goal of diabetesresearchers (1) However a number of significant challenges needed to be over-come to deliver an AP system to people with diabetes The past 10 years have seenmany of these challenges addressed and recent studies have demonstrated com-pelling safety and efficacy of the prototype systems (2) Technical feasibility is only astep toward declaring victory Research and development efforts will continue toimprove upon first-generation AP systems But it is clear that resources will need tobe deployed to address clinical adoption challengesdincluding device usability andreimbursementResearch and development efforts over the past 10 years have addressed a num-

ber of the critical issues facing AP development and recent studies again signal amove to a new phase and a call for a new road map to AP systems A number ofgroups have demonstrated proof of concept with a variety of algorithmic ap-proaches and closed-loop strategies and the data are compelling In a Bench toClinic narrative Cefalu and Tamborlane (3) dive much deeper than this Perspectiveintends into the ldquohowrdquo of AP systems They note quite astutely that ldquoit is not thejourney but the destination that mattersrdquo (3)It may come as a surprise to many clinicians but a small and growing group of lay

ldquodo-it-yourselfrdquo technically savvy people with diabetes or loved ones of people withdiabetes has been using semiautomated closed-loop systems at home for well over2 years with impressive results (4ndash6) These systems similar to systems beingstudied in academic studies combine off-the-shelf insulin pumps and continuousglucose monitors with control algorithms (computer software that interprets glu-cose information and drives the dosing of insulin) powered by cell phone devicesThe results from academia and these anecdotal reports are harbingers of a

JDRF New York NY

Correspondingauthor AaronKowalski akowalskijdrforg

Received 18 February 2015 and accepted 27March 2015

copy 2015 by the American Diabetes AssociationReaders may use this article as long as the workis properly cited the use is educational and notfor profit and the work is not altered

See accompanying articles pp 968971 979 989 997 1008 1016 and1030

Aaron Kowalski

1036 Diabetes Care Volume 38 June 2015

TYPE1DIABETES

ATACROSSROADS

technological revolution in diabetes andsignal that AP system availability is nolonger a matter of ldquoifrdquo but rather a mat-ter of ldquowhenrdquoThe ultimate metric for success of AP

systems will be improved outcomesfor people with diabetes The tech-nical feasibility demonstrated to dateraises a series of very important ques-tions that this Perspective will attemptto answer or at least provide fodderfor debate AP systems will provide aninteresting case study in the impor-tance of a field having line of sight be-yond the research laboratory andthrough to all of the stages of a com-mercial development pathway fromregulatory approval to reimburse-ment to clinical adoption For AP sys-tems to improve diabetes outcomesthey will need to be designed to be im-pactful across a diverse group of peoplewith diabetes and will need to be ac-cessible AP system accessibility will bedriven by the value perceived by twoother crucial stakeholdersdhealthcare providers and payers The path-way to the development of an AP hasbecome much more complex than theroad map that was created in 2009and a new road map that addressespostresearch considerations needs tobe drawn

KEY QUESTION WHAT IS AN AP

Answer There Is No SingularAPmdashTechnologies Will Evolve toBecome More AutomatedThis may seem like an obvious questionbut the literature has demonstrated sig-nificant inconsistency in this definitionand this inconsistency has led to confu-sion This Perspective will focus on thenearest-term AP systems The core ele-ments of these AP systems will be aninsulin infusion pump a continuousglucose monitor a control algorithmand rapid-acting insulin analogs (insome cases there may be a handheldcontrol device) Reports in the litera-ture use a wide variety of terminology(artificial pancreas bionic pancreasclosed loop automated insulin deliv-ery device and treat-to-range system[7ndash11]) because there is no nor willthere ever be a singular AP Ratherthese systems will evolve over time toincrease in automation increase in so-phistication and increase in their abil-ity to normalize blood glucose levels In

the near term these ldquoAP systemsrdquo willreduce hypoglycemia (low-glucose andpredictive low-glucose managementsystems) through the reduction or ces-sation of insulin delivery will begin toautomatically dose insulin to targetranges (hybrid closed-loop systemshyperglycemiahypoglycemia-minimizingsystems and semiautomated insulin de-livery systems) and eventually will dosehormones in addition to insulin such asglucagon andor amylin (bionic pancreasdual-hormone AP and multihormoneAP)

In 2006 JDRF launched an initiativeintended to accelerate progress towardAP systems (12) At that time manyquestions existed regarding the techni-cal feasibility and safety of automatedinsulin delivery These questions con-tinue (13) A road map was publishedthat intended to describe how an evolu-tionary process of system developmentcould lead to the commercialization ofclinically meaningful systems that ad-dressed unmet needs in the manage-ment of diabetes (Fig 1) (14) Thisroad map intended to address thesequestions and importantly createclearly defined ldquotarget product profilesrdquothat could guide research funding andcommercial development based uponthe state of technology at the timeThis road map intended to shift the fo-cus from replication of islet functionwith a machine to iterative improve-ments that addressed unmet clinicalneeds through increasing automationof insulin delivery

It should be noted that this road mapincludes both systems that reducestopinsulin delivery due to hypoglycemia orimpending hypoglycemia (Fig 1 boxes1 and 2) as well as systems that auto-mate the delivery of insulin (Fig 1boxes 3ndash6) All of these systems weredescribed as AP systems the criteria be-ing the automation of the control of in-sulin delivery from an insulin pumpThat said there is certainly a significantstep from reducing insulin delivery toincreasing insulin delivery (Fig 1box 3) and many consider the firstAP systems to begin at this point ThisPerspective will discuss hypoglycemia-minimizing systems but will use theterm AP systems and synonyms to de-scribe and discuss future systems thatdose insulin and other hormonesautomatically

KEY QUESTIONS DO WE NEED APSYSTEMS IS THERE AN UNMETNEED ARE DIABETES OUTCOMESSUBOPTIMAL BECAUSE TOOLS ARELACKING OR BECAUSE OF LACK OFCOMPLIANCE WITH TODAYrsquoSTHERAPIES

Answers Yes to AllThe unmet medical need in diabetes isstriking Despite extensive knowledge ofthe damage of hyperglycemia and thepassage of 23 years since the DiabetesControl and Complications Trial (DCCT)(15) glycemic control levels in the USremain suboptimal Current clinical evi-dence including data from the T1DExchange registry paints a picture thatis full of opportunity for significantimprovement across all diabetes out-comes measured The data on importantdiabetes outcomes paints a soberingpicture

A1C Levels

In the US A1C levels remain elevatedwith20 of children and young adultsand 40 of adults 25 years of agemeeting A1C targets (16)

Hypoglycemia

Hypoglycemia has been defined (17) andmeasured in a number of different waysHowever the data remain consistentthat hypoglycemia is frequent by allmeasures and that it remains the limitingbarrier to tight glycemic control (18ndash20)

Diabetic Ketoacidosis

Diabetic ketoacidosis is a life-threateningand costly acute complication Therates remain elevated in the US andglobally (1921)

Time in Range

Time in range is an intuitive metric forglycemic control that captures hyper-hypo- and normoglycemia in one sim-ple view This metric has only been pos-sible to capture since the launch ofcontinuous glucose monitoring (CGM)devices A challenge in measuring timein target is the definition of the targetrange or the ldquonormalrdquo range The defi-nition of the range has varied in the lit-erature (ie 70ndash105 70ndash120 70ndash180mgdL) (22ndash24) Whatever the targetrange that is used people with diabetesare far from achieving normoglycemia

Patient-Reported Outcomes

The impact of diabetes remains signifi-cant on patients beyond suboptimal gly-cemic control People with diabetes still

carediabetesjournalsorg Kowalski 1037

suffer from significantly elevated levelsof anxiety depression and other psy-chosocial issues due to a number of rea-sons including the high burden thatdiabetes management places on the pa-tient (2526)The obvious question is why Why

are glycemic control goals not beingachieved With crystal-clear evidenceof the morbidity associated with hyper-glycemia and hypoglycemia why areonly a fraction of people with diabetesachieving recommended glycemic andmetabolic goals There are many rea-sons that vary from individual to indi-vidual but it is clear that the toolstoday do not easily allow for the nor-malization of glycemia for patients lack-ing b-cell function Self-monitoring ofblood glucose levels (27) continuoussubcutaneous insulin infusion (28)

CGM (18) and now low-glucose sus-pend (LGS) pumps (29) have all beendemonstrated to significantly improveglycemic control either by reducingA1C or hypoglycemia levels Howeverwide glucose excursions above and be-low the target range persist in almost allpatients and attempts to achievetighter and tighter glycemic controltake more and more effort with dimin-ishing returns It is striking that childrenin the JDRF CGM trial who were inten-sively managed met with clinic staffregularly wore CGM devices mostlywore insulin infusion pumps andfinger-stick tested seven times a dayspent greater than 10 h a day with sugarlevels above 180 mgdL and the adultsspent more than 6 h a day with sugarlevels above 180mgdL (18) It would bevery difficult to argue that this was a

noncompliant patient populationClearly todayrsquos tools have helped im-prove glucose control and in somecases reduce some of the burden of di-abetes management However thesedata also clearly support that compli-ance is not the only barrier to optimalglycemic control

KEY QUESTIONS ARE AP SYSTEMSTECHNICALLY FEASIBLE TODAYCAN AN AP SYSTEM REPLICATETHE FUNCTION OF THE ISLET

Answers Yes and NoThere remains a debate in the literatureand at diabetes conferences regardingthe technical feasibility of AP systemsgiven the state of the technology todayThere are strong believers (14) and otherswho have questioned if a machine canever replicate the sophistication of the

Figure 1mdash2009 AP roadmap A target-product profile-based approach to envision iterative steps of increasing automation of insulin delivery initiallytoward a more physiological multihormone delivery eventually

1038 Artificial Pancreas Systems Progress Diabetes Care Volume 38 June 2015

islet (13) Unfortunately much of this de-bate stems from a misframing of the ar-gument For example those who believethat it will not be possible for todayrsquostechnologies to replicate the complexregulation of islet hormone secretionsare probably correct It is highly unlikelythat we will normalize blood glucose lev-els though subcutaneous replacement ofinsulin alone (or even with insulin andglucagon) However a significant reduc-tion of glycemic burden both hyperglyce-mic and hypoglycemic is possible withtodayrsquos technology and as technologyimproves and insulin delivery andor ki-netics become more physiological thebulk of glycemic excursions may beavoided Therefore we should not becomparing AP systems to physiologicalislet function rather we should be ad-dressing unmet needs in current diabe-tes management that can be solvedwith technical solutions Expectationsetting will be very important as AP sys-tems become commercialized andreach clinics First-generation AP sys-tems will not restore euglycemia andwill not be fully automated but theywill significantly improve glycemia andreduce diabetesmanagement burden inmany patientsAP systems are technically feasible to-

day Table 1 shows the six-step roadmap and references of representativestudies demonstrating safety and effi-cacy It is clear that AP systems outper-form todayrsquos standard of care significantlywhen benchmarked across a variety ofdiabetes outcome metrics Larger stud-ies are needed to gather A1C changesversus a randomized comparator butthe data to date is compelling and con-vincing AP systems work

KEY QUESTION MUST AP SYSTEMSUSE GLUCAGON

Answer Glucagon Is Not Essential butMay Provide Additional BenefitsAwidely debated question in the field ofAP research is whether the use of glu-cagon is necessary to build a safe sys-tem Again the data are convincing thatinsulin-alonendashbased systems will im-prove glycemic control reduce hyper-glycemia andor hypoglycemia riskand reduce some aspects of diabetesmanagement burden (Table 1) Theshort answer to this question is there-fore nodglucagon will not be essentialfor AP systems to reach the market

Table

1mdashAPro

admapin

2015

pro

ofofco

nce

ptoffive

ofsix

stepsofite

rative

lymore

sophistica

tedAPsy

stemshasbeendemonstra

tedin

outpatie

nttria

ls

Step1

23

45

6

Nam

eLG

SPred

ictiveLG

S(PLG

S)Hypoglycem

iahyperglycem

icminim

izer(HHM)

Hybrid

closed

loop(HCL)

Fully

automated

insulin

delivery

Multih

orm

one(M

H)

Synonym

sTh

reshold

suspen

d(TS)

Predictive

low-glu

cose

managem

entsystem(PLG

M)

Treat-to-ran

gesystem

(TTR)

Treat-to-target

system(TTT)

Fully

closed

loop(FC

L)Insulin

-glucago

nsystem

bionicpan

creas

Descrip

tion

Insulin

shutsoffu

poncro

ssing

preset

thresh

old

such

as70

mgdL

andresum

esafter

2h

Insulin

shutsofforis

attenuated

uponpred

iction

ofim

pen

dinghypoglycem

iaandresu

mes

delivery

when

hypoglycem

iarisk

isgone

Sameas

step2butwith

automatic

insulin

dosin

gto

reduce

hyperglycem

iaexp

osure

Does

nottarget

euglycem

iarather

the

minim

izationoftim

espen

tabove

acertain

thresh

old

ie180mgd

L

Algo

rithmaim

sforeu

glycemic

targetnotran

gebutrelies

onmealtim

einsulin

bolus

Fully

automated

insulin

delivery

with

minim

alhuman

interactio

n

Fully

automated

multih

orm

oneapproach

insulin

plusglu

cagon

amylin

orother

horm

onesan

alogs

2015statu

sCommercialized

globally

Regu

latory

approvalo

utsid

eUSco

mmercial

availability

inAustralia

Incommerciald

evelopmen

tIn

commerciald

evelopmen

tProofofconcep

tIn

commerciald

evelopmen

t

Exampleof

supportin

gdata

Red

uctio

nin

hypoglycem

iared

uctio

nin

severehypoglycem

iamain

tenan

ceofA

1C(2943)

Red

uctio

nin

severeand

moderate

hypoglycem

ia(2044)

Red

uctio

nin

timespen

thyperglycem

icand

hypoglycem

icand

increased

timein

targetran

geinoutpatien

tsettin

gs(45)

Red

uctio

nin

timespen

thyperglycem

icand

hypoglycem

icand

increased

timein

targetran

geinoutpatien

tsettin

gs(2446)

Red

uctio

nin

hyperglycem

iaandhypoglycem

iaand

increase

intim

ein

targetin

aninpatien

tsettin

gs(4246)

Red

uctio

nin

hyperglycem

iaandhypoglycem

iaand

increase

timein

targetin

outpatien

tsettin

g(9)


That said results from insulinglucagonstudies have been outstanding and havegenerated enthusiasm in the field(93031) and in the popular press (32)Conceptually an insulinglucagon ap-proach is appealing In the nativenon-diabetic islet the cross talk betweena- and b-cells and the liver contributesto the intricate balance between gluco-neogenesis and glycogenolysis and themaintenance of exquisitely tight glu-cose regulation within a very narrowrange (33) A logical conclusion mightbe that AP systems would also benefitfrom such a bihormonal approachHowever more research will be neededto address significant questions regard-ing the consequences of glucagon infu-sion set failure and failure of the liver torespond to glucagon Bakhtiani et al(10) found that glucagon failed to pre-vent hypoglycemia and that these fail-ures occurred more frequently whenglucagon is delivered while glucose isfalling rapidly at a lower glucosethreshold and when there are high lev-els of insulin on board El Youssef et al(34) demonstrated that glucagon failedto prevent hypoglycemia 20 of thetime in their initial studies Pragmaticissues also remain such as the needfor a soluble pumpable glucagonand a dual-chambered pump and cost(35) In the coming years it will beimportant to define the incrementalvalue of glucagon and to define strat-egies to avoid glucagon ldquofailurerdquo TheHelmsley Charitable Trust NationalInstitute of Diabetes and Digestiveand Kidney Diseases JDRF and theindustry have invested significantresources to accelerate solutions tothese challenges

Hormones Beyond Glucagon

Another multihormone approach thathas received less attention is the com-bination of insulin and amylin Con-ceptually this approach is appealingThe loss of b-cell function leads to theobvious loss of insulin production butalso amylin production as well (36)Amylin plays an important role physi-ologically by suppressing glucagonproduction contributing to regulationof gastric emptying and impacting sa-tiety Amylin replacement throughmultiple daily injections of the amylinanalog pramlintide has achievedlimited uptake in the clinic (16)

Pilot studies of multihormone AP sys-tems using insulin and pramlintidehave demonstrated impressive results(37) The same pragmatic issues existfor pramlintide as for glucagon andthis remains an area of continuedinvestigation

KEY QUESTIONS WHERE DO WEGO FROM HERE WHAT APSYSTEMS WILL REACH THE CLINICAND WHEN

Answer AP Systems MustDemonstrate Value to PatientsProviders and Payers to BeSuccessful Value Will Be Defined byMore Than A1C ChangesAP systems must reach people with di-abetes and improve outcomes To do sothey must receive US Food and DrugAdministration (FDA)regulatory bodyapproval be commercialized be reim-bursable and be adopted by providersand patients In 2012 the FDA issuedfinal guidance that provided a pathwayfor manufacturers to commercialize APsystems (38) opening the door forcommercial development AP systemsto reach the market A major challengeto the translation of novel diabetestherapies into practice has been thatthe success of the therapy was judgedwith very narrow metrics that wereheavily weighted to A1C On the otherhand patients clinicians insurancecompanies and government agenciesweigh the benefits of new therapiesby many other factors Clearer descrip-tions of these factors may help inform

the evolution and ultimate successof AP systems and future diabetesinterventions

Diabetes Scorecard

Ultimately success of a novel technol-ogy should bemeasured by the improve-ment in outcomes across the populationwith type 1 diabetes (T1D) which canonly be achieved if clinically effectivetherapies are covered by payers pre-scribed by physicians and used by peoplewith T1D who could benefit Therapieswell positioned for success will improveglycemic outcomes and reduce diseasemanagement burden at a cost consistentwith the benefit provided by the therapyIn other words therapies must provide agood value for people with diabetes butalso for the health care professionalstreating their diabetes and for the payerscovering treatment each of which hassomewhat different perspectives onwhat constitutes value

JDRF is developing a ldquoT1D Scorecardrdquoa tool (or tools) that will provide a frame-work for evaluating the value of newdiabetes technologiestherapies thatis framed by the key attributes thatare important to each stakeholder(Fig 2) While Fig 2 is not an exhaustivelist it highlights attributes that eachstakeholder may weigh when eval-uating new technologies JDRF looksforward to working with other stake-holders to define a set of clinical out-comes by which to judge T1D therapiesthat is broader than A1C and to identifymeasures of disease managementburden

Figure 2mdashFor new technologies to succeed they must demonstrate value to key stakeholdersAn examplenonexhaustive list of areas of importance is shown


KEY QUESTION WHERE WILL THEAP FIELD HEAD IN THE NEXT 10YEARS

Answer Automated Insulin DeliverySystems Have a Clear Path to theMarket and the Focus Will Likely Shiftto Reduction in Burden and Cost andMultihormone Systems Need toOvercome Pragmatic Challenges toReach the Market and ThenDemonstrate Improved GlycemicBenefit and Burden Reduction toDrive Adoption (Compared WithAutomated Insulin-Alone Systems)In light of the progress that has beenmade over the past 6 years the six-step pathway for iteratively more ad-vanced AP systems that was proposedin 2009 needs updating This iterativepathway aimed to address technical lim-itations in glucose sensing and insulindelivery while positively impacting im-portant diabetes outcomes The datasupport that todayrsquos technologies areready to automate some degree of insulindelivery In fact for a small population ofpeople they already are Boxes 1 and 2 inFig 1 are technically complete and thedata support that they are safe and effi-cacious Therefore in 2015 the road maplooks different and the obstacles havechanged Certainly there are furthertechnical advances that will allow formore sophisticated systems But whereshould the focus be for that evolution

Bifurcation in PathwayFigure 3 proposes a new AP pathwaydone that is bifurcated Today the

pathway has evolved Encouraginglytwo of the six steps have been techni-cally completed In 2010 Medtronicreleased the first threshold-suspendsystem in Europe and in 2014 in theUS Such a system was a logical and im-portant first step Prior to its release andto date for all other insulin infusionpumps insulin infusion continued evenin the face of profound hypoglycemiaand even while CGM alarms signal suchlows Cessation of insulin delivery duringthis time while simple held the poten-tial to reduce hypoglycemic exposureand potentially profound hypoglycemiaevents Studies of this system have vali-dated this hypothesis (2939) A morepowerful approach to hypoglycemia re-duction will be predictive suspension ofinsulin delivery with impending hypo-glycemia This approach improves glyce-mic outcomes (reduced hypoglycemiaexposure potentially reduced meanblood glucose vs threshold-suspend sys-tem due to autoresumption of basal in-sulin delivery and the interestingpotentialrestorationof some counterregulation andhypoglycemia awareness) and provides re-duction of burden (fewer alarms reducedfear of hypoglycemia) It is also expectedto be comparable in cost to threshold-suspend systems

A Split

Whereas boxes 3ndash5 in Fig 1 focus onreduction of hyperglycemia through aniterative increase in automation and box6 focuses on multihormone approachestoday a more logical presentation of

potential pathways is a split into twoparallel avenues One avenue will en-compass automated insulin-alone deliv-ery (AID) systems and another will besystems that incorporate another hor-mone or hormones

AID

Of these two pathways AID systems arelikely to reach the clinic first Many stud-ies have demonstrated safety and effi-cacy and the technical barriers are lowThe first systems to reach the clinic willlikely be ldquohybridrdquo treat-to-range ortreat-to-target systems that requiremealtime bolusing and then provide au-tomated functionality that drives glu-cose levels back to a near-normal level(100ndash140 mgdL in studies to date) dur-ing the rest of the day and night Thecontrol algorithms that provide the au-tomation of insulin delivery also providethe framework from moving seamlesslyfrom systems that solely reducestopinsulin delivery to those that add auto-mated increases in insulin delivery aswell (40) The focus of future AID systemdevelopment will be across the threecategories of the Diabetes ScorecardGlycemic Outcomes The main barrier tofurther improvements in glycemic out-comes (beyond a hypoglycemia hypergly-cemic minimizer or hybrid AP systems)and to further automation of the systemis the delay in the absorption and actionof subcutaneously injected insulin Othertechnological improvements such asself-learning algorithms integrationof accelerometers and better bolus

Figure 3mdashRevised AP roadmap AP systemdevelopment can be condensed from six steps to three and has bifurcated into automated insulin deliveryapproaches utilizing solely insulin (AID) and multihormone approaches (MH) which may utilize insulin and glucagon insulin and amylin or insulinand other glucose-modulating agents


calculators may help incrementally im-prove glycemic outcomes and minimizeuser actions but a more rapid insulinprofile will be required to truly ap-proach euglycemia and eliminate userprandial dosing in an AID systemDisease Management Burden The morelikely area of significant return on in-vestment will be in the development ofsystems that reduce the burdens and bar-riers to CGM and insulin pump adoptionThese will likely include calibration-freeCGM smaller CGM transmitters smallerldquotetheredrdquo and ldquopatchrdquo pumps cellphone integration and cloud-baseddata analyticsCostValue The cost of AID systemsshould reflect the technologyrsquos ability toimpact glycemic outcomes and diseasemanagement burden High-value systemswill be appealing to all three key diabetesconstituencies Dual-hormone systemswill likely require more time to reachthe clinic (vs AID systems) as practicaland research questions are addressedWhen compared with AID systems anovel specialized pump will be necessaryand a specialized dual-lumen or modifiedinfusion set as well as the additional hor-mone will be needed These additionalcostswill need to be quantified and consid-ered against the potential for improvedglycemic control with a dual-hormone ap-proach To date there exists very limiteddata comparing dual-hormone to an in-sulin-alone AP system (31) Further stud-ies comparing best-in-class insulin-aloneapproaches to best-in-class multihor-mone systems should be a top priorityto clearly define the pros and cons ofeach approach Reduction of hypoglyce-mia particularly severe hypoglycemiarepresents a potentially significant ben-efit to glucagoninsulin AP approachesWhile potentially more expensive theymay demonstrate additional value to thepeople with diabetes health care profes-sionals and payers and data supportingthis argument will be very important

SUMMARY

For the past half century AP technologieshave been the ldquoholy grailrdquo of diabetestreatment However the sophisticatedglucose regulation provided by the isletand the related metabolic physiology aredifficult to replicate with a machine Theevolution of portable small easy-to-use and efficacious insulin infusionpumps continuous glucose monitors and

control algorithms over the past de-cade has allowed for proof-of-conceptapproachesdAP systemsdthat whilenot perfect replications of islet biologymay provide significant value The first au-tomated insulin delivery systems that auto-matically reduce hypoglycemia exposureare already commercialized and are beingused in clinical practice AP systems that be-gin the dosing of some insulin automaticallyare expected in the 2017 time frame inthe US as reported from the JP MorganHealthcare Conference in January 2015

This progress has raised new ques-tions and areas of focus Success of APsystemswill bedefinedbybetter diabetesoutcomes Better diabetes outcomes willinclude more than improved A1C (41)Broader understanding of the importantglycemic outcomes in particular patientsegments is needed Furthermore tech-nologies will only be adopted and betteroutcomes achieved if they provide goodvaluedimproved glycemic outcomes andreduced burden at a cost consistent withthe benefit provided by the technologyIdeally next-generation diabetes thera-peutics AP systems and beyond will im-prove glycemic outcomes reduce burdenand provide value beyond todayrsquos thera-pies and approaches Improvement onany of these three scores for all of thethree key stakeholdersdpatients healthcare professionals and payersdwill por-tend well for novel technologies Thera-pies that do not provide good value toall three stakeholders will face challengesto being widely adopted

Acknowledgments The author thanks hiscolleagues at JDRF particularly Dr RichardInsel Cynthia Rice Jessica Roth CampbellHutton and Dr Vincent Crabtree for theirthoughtful suggestions He also thanks Dr RoyBeck John Lum the JDRF Artificial PancreasConsortium and David Panzirer of the HelmsleyTrust for countless hours studying discussingand debating the future of AP systems andthanks the diabetes device manufacturers whohave supported AP research over the past 10years and are working to deliver commercial APsystems to people with diabetesDuality of Interest No potential conflicts ofinterest relevant to this article were reported

References1 Schatz H Pfeiffer EF The endocrine pancreasFrom the isolated islet to the ldquoartificial pancreasrdquo(authorrsquos transl)MMWMunchMedWochenschr1976118485ndash492 [in German]2 Skyler JS T1DM in 2014 progress towards abionic pancreas Nat Rev Endocrinol 20151175ndash76

3 Cefalu WT Tamborlane WV The artificialpancreas are we there yet Diabetes Care2014371182ndash11834 Hurley D Diabetes patients are hacking theirway toward a bionic pancreas [article online]2014 Available from httpwwwwiredcom201412diabetes-patients-hacking-together-diy-bionic-pancreases Accessed 24 December20145 Kimball S Meet the bigfoot family and theirhomemade closed loop system [Internet] 2015Available from httpwwwhealthlinecomdiabetesminebigfoot-family-their-diabetes-and-homemade-closed-loop-system1 Accessed4 March 20156 danamlewis DIYPS [Internet] Availablefrom diypsorg Accessed 7 February 20157 Peyser T Dassau E Breton M Skyler JS Theartificial pancreas current status and futureprospects in the management of diabetes AnnN Y Acad Sci 20141311102ndash1238 Lee JJ Dassau E Zisser H Doyle FJ 3rd Designand in silico evaluation of an intraperitoneal-subcutaneous (IP-SC) artificial pancreas Com-put Chem Eng 201470180ndash1889 Russell SJ El-Khatib FH Sinha M et al Out-patient glycemic control with a bionic pancreas intype 1 diabetes N Engl J Med 2014371313ndash32510 Bakhtiani PA El Youssef J Duell AK et alFactors affecting the success of glucagon deliv-ered during an automated closed-loop systemin type 1 diabetes J Diabetes Complications20152993ndash9811 Brown SA Kovatchev BP Breton MD et alMultinight ldquobedsiderdquo closed-loop control for pa-tients with type 1 diabetes Diabetes TechnolTher 201517203ndash20912 Kowalski A Lum JW Juvenile diabetes re-search foundation artificial pancreas consortiumupdate J Diabetes Sci Tech 200931224ndash122613 Winikoff J Drexler A Who needs an artifi-cial pancreas J Diabetes 20135254ndash25714 Kowalski AJ Can we really close the loopand how soon Accelerating the availability ofan artificial pancreas a roadmap to betterdiabetes outcomes Diabetes Technol Ther200911(Suppl 1)S113ndashS11915 The Diabetes Control and ComplicationsTrial Research Group The effect of intensivetreatment of diabetes on the developmentand progression of long-term complicationsininsulin-dependent diabetes mellitus N Engl JMed 1993329977ndash98616 Beck RW Tamborlane WV BergenstalRM Miller KM DuBose SN Hall CA T1D Ex-change Clinic Network The T1D Exchangeclinic registry J Clin Endocrinol Metab 2012974383ndash438917 Seaquist ER Anderson J Childs B et al Hy-poglycemia and diabetes a report of a work-group of the American Diabetes Associationand the Endocrine Society Diabetes Care2013361384ndash139518 Tamborlane WV Beck RW Bode BW et alJuvenile Diabetes Research Foundation Contin-uous Glucose Monitoring Study Group Contin-uous glucose monitoring and intensivetreatment of type 1 diabetes N Engl J Med20083591464ndash147619 Cengiz E Xing D Wong JC et al T1D Ex-change Clinic Network Severe hypoglycemiaand diabetic ketoacidosis among youth with


type 1 diabetes in the T1D Exchange clinic reg-istry Pediatr Diabetes 201314447ndash45420 Maahs DM Calhoun P Buckingham BAet al In Home Closed Loop Study Group A ran-domized trial of a home system to reduce noc-turnal hypoglycemia in type 1 diabetesDiabetes Care 2014371885ndash189121 Livingstone SJ Levin D Looker HC et alScottish Diabetes Research Network epidemiol-ogy group Scottish Renal Registry Estimatedlife expectancy in a Scottish cohort with type 1diabetes 2008-2010 JAMA 201531337ndash4422 Bode BW Schwartz S Stubbs HA Block JEGlycemic characteristics in continuously moni-tored patients with type 1 and type 2 diabetesnormative values Diabetes Care 2005282361ndash236623 Fox LA Beck RW Xing D Juvenile DiabetesResearch Foundation Continuous Glucose Mon-itoring Study Group Variation of interstitial glu-cose measurements assessed by continuousglucose monitors in healthy nondiabetic indi-viduals Diabetes Care 2010331297ndash129924 Leelarathna L Dellweg S Mader JK et alAPhome Consortium Day and night homeclosed-loop insulin delivery in adults with type1 diabetes three-center randomized crossoverstudy Diabetes Care 2014371931ndash193725 Northam EA Lin A Finch S Werther GACameron FJ Psychosocial well-being and func-tional outcomes in youth with type 1 diabetes12 years after disease onset Diabetes Care2010331430ndash143726 Ducat L Rubenstein A Philipson LHAnderson BJ A review of the mental health is-sues of diabetes conference Diabetes Care201538333ndash33827 Strowig SM Raskin P Improved glycemiccontrol in intensively treated type 1 diabeticpatients using blood glucose meters with stor-age capability and computer-assisted analysesDiabetes Care 1998211694ndash169828 John P Insulin pumps Diabetes TechnolTher 201416(Suppl 1)S17ndashS2229 Ly TT Nicholas JA Retterath A Lim EMDavis EA Jones TW Effect of sensor-augmented

insulin pump therapy and automated insulinsuspension vs standard insulin pump therapyon hypoglycemia in patients with type 1 diabe-tes a randomized clinical trial JAMA 20133101240ndash124730 Jacobs PG El Youssef J Castle J et al Auto-mated control of an adaptive bihormonal dual-sensor artificial pancreas and evaluation duringinpatient studies IEEE Trans Biomed Eng 2014612569ndash258131 Haidar A Legault L Messier V Mitre TMLeroux C Rabasa-Lhoret R Comparison of dual-hormone artificial pancreas single-hormone ar-tificial pancreas and conventional insulin pumptherapy for glycaemic control in patients withtype 1 diabetes an open-label randomised con-trolled crossover trial Lancet Diabetes Endocri-nol 2015317ndash2632 Alexandra S 29 January 2015 The next bestthing to a cure for diabetes Time Magazine44ndash4633 Ramnanan CJ Edgerton DS Kraft GCherrington AD Physiologic action of glucagonon liver glucose metabolism Diabetes ObesMetab 201113(Suppl 1)118ndash12534 El Youssef J Castle JR Branigan DL et al Acontrolled study of the effectiveness of anadaptive closed-loop algorithm to minimizecorticosteroid-induced stress hyperglycemia intype 1 diabetes J Diabetes Sci Tech 201151312ndash132635 Rylander D Jr Glucagon in the artificial pan-creas supply and marketing challenges J Dia-betes Sci Tech 2015952ndash5536 Edelman S Maier H Wilhelm K Pramlintidein the treatment of diabetes mellitus BioDrugs200822375ndash38637 Weinzimer SA Sherr JL Cengiz E et al Ef-fect of pramlintide on prandial glycemic excur-sions during closed-loop control in adolescentsand young adults with type 1 diabetes DiabetesCare 2012351994ndash199938 US Food and Drug Administration Guidancefor industry and food and drug administration staffthe content of investigational device exemption(IDE) and premarket approval (PMA) applications

for artificial pancreas device systems [Internet] 2012Available from httpwwwfdagovdownloadsMedicalDevicesDeviceRegulationandGuidanceGuidanceDocumentsUCM259305pdf Accessed9 November 201239 Agrawal P Zhong A Welsh JB Shah RKaufman FR Retrospective analysis of thereal-world use of the threshold suspend featureof sensor-augmented insulin pumps DiabetesTechnol Ther 22 January 2015 [Epub ahead ofprint]40 Breton M Farret A Bruttomesso D et alInternational Artificial Pancreas Study GroupFully integrated artificial pancreas in type 1 di-abetes modular closed-loop glucose controlmaintains near normoglycemia Diabetes2012612230ndash223741 Kowalski AJ Dutta S Itrsquos time to move fromthe A1c to better metrics for diabetes controlDiabetes Technol Ther 201315194ndash19642 PhillipM Battelino T Atlas E et al Nocturnalglucose control with an artificial pancreas ata diabetes camp N Engl J Med 2013368824ndash83343 Bergenstal RM Klonoff DC Garg SK et alASPIRE In-Home Study Group Threshold-basedinsulin-pump interruption for reduction ofhypoglycemia N Engl J Med 2013369224ndash23244 Buckingham BA Cameron F Calhoun Pet al Outpatient safety assessment of an in-home predictive low-glucose suspend systemwith type 1 diabetes subjects at elevated riskof nocturnal hypoglycemia Diabetes TechnolTher 201315622ndash62745 Kovatchev BP Renard E Cobelli C et alSafety of outpatient closed-loop control firstrandomized crossover trials of a wearable arti-ficial pancreas Diabetes Care 2014371789ndash179646 Weinzimer SA Steil GM Swan KL Dziura JKurtz N Tamborlane WV Fully automatedclosed-loop insulin delivery versus semiauto-mated hybrid control in pediatric patients withtype 1 diabetes using an artificial pancreas Di-abetes Care 200831934ndash939


technological revolution in diabetes andsignal that AP system availability is nolonger a matter of ldquoifrdquo but rather a mat-ter of ldquowhenrdquoThe ultimate metric for success of AP

systems will be improved outcomesfor people with diabetes The tech-nical feasibility demonstrated to dateraises a series of very important ques-tions that this Perspective will attemptto answer or at least provide fodderfor debate AP systems will provide aninteresting case study in the impor-tance of a field having line of sight be-yond the research laboratory andthrough to all of the stages of a com-mercial development pathway fromregulatory approval to reimburse-ment to clinical adoption For AP sys-tems to improve diabetes outcomesthey will need to be designed to be im-pactful across a diverse group of peoplewith diabetes and will need to be ac-cessible AP system accessibility will bedriven by the value perceived by twoother crucial stakeholdersdhealthcare providers and payers The path-way to the development of an AP hasbecome much more complex than theroad map that was created in 2009and a new road map that addressespostresearch considerations needs tobe drawn

KEY QUESTION WHAT IS AN AP

Answer There Is No SingularAPmdashTechnologies Will Evolve toBecome More AutomatedThis may seem like an obvious questionbut the literature has demonstrated sig-nificant inconsistency in this definitionand this inconsistency has led to confu-sion This Perspective will focus on thenearest-term AP systems The core ele-ments of these AP systems will be aninsulin infusion pump a continuousglucose monitor a control algorithmand rapid-acting insulin analogs (insome cases there may be a handheldcontrol device) Reports in the litera-ture use a wide variety of terminology(artificial pancreas bionic pancreasclosed loop automated insulin deliv-ery device and treat-to-range system[7ndash11]) because there is no nor willthere ever be a singular AP Ratherthese systems will evolve over time toincrease in automation increase in so-phistication and increase in their abil-ity to normalize blood glucose levels In

the near term these ldquoAP systemsrdquo willreduce hypoglycemia (low-glucose andpredictive low-glucose managementsystems) through the reduction or ces-sation of insulin delivery will begin toautomatically dose insulin to targetranges (hybrid closed-loop systemshyperglycemiahypoglycemia-minimizingsystems and semiautomated insulin de-livery systems) and eventually will dosehormones in addition to insulin such asglucagon andor amylin (bionic pancreasdual-hormone AP and multihormoneAP)

In 2006 JDRF launched an initiativeintended to accelerate progress towardAP systems (12) At that time manyquestions existed regarding the techni-cal feasibility and safety of automatedinsulin delivery These questions con-tinue (13) A road map was publishedthat intended to describe how an evolu-tionary process of system developmentcould lead to the commercialization ofclinically meaningful systems that ad-dressed unmet needs in the manage-ment of diabetes (Fig 1) (14) Thisroad map intended to address thesequestions and importantly createclearly defined ldquotarget product profilesrdquothat could guide research funding andcommercial development based uponthe state of technology at the timeThis road map intended to shift the fo-cus from replication of islet functionwith a machine to iterative improve-ments that addressed unmet clinicalneeds through increasing automationof insulin delivery

It should be noted that this road mapincludes both systems that reducestopinsulin delivery due to hypoglycemia orimpending hypoglycemia (Fig 1 boxes1 and 2) as well as systems that auto-mate the delivery of insulin (Fig 1boxes 3ndash6) All of these systems weredescribed as AP systems the criteria be-ing the automation of the control of in-sulin delivery from an insulin pumpThat said there is certainly a significantstep from reducing insulin delivery toincreasing insulin delivery (Fig 1box 3) and many consider the firstAP systems to begin at this point ThisPerspective will discuss hypoglycemia-minimizing systems but will use theterm AP systems and synonyms to de-scribe and discuss future systems thatdose insulin and other hormonesautomatically

KEY QUESTIONS DO WE NEED APSYSTEMS IS THERE AN UNMETNEED ARE DIABETES OUTCOMESSUBOPTIMAL BECAUSE TOOLS ARELACKING OR BECAUSE OF LACK OFCOMPLIANCE WITH TODAYrsquoSTHERAPIES

Answers Yes to AllThe unmet medical need in diabetes isstriking Despite extensive knowledge ofthe damage of hyperglycemia and thepassage of 23 years since the DiabetesControl and Complications Trial (DCCT)(15) glycemic control levels in the USremain suboptimal Current clinical evi-dence including data from the T1DExchange registry paints a picture thatis full of opportunity for significantimprovement across all diabetes out-comes measured The data on importantdiabetes outcomes paints a soberingpicture

A1C Levels

In the US A1C levels remain elevatedwith20 of children and young adultsand 40 of adults 25 years of agemeeting A1C targets (16)

Hypoglycemia

Hypoglycemia has been defined (17) andmeasured in a number of different waysHowever the data remain consistentthat hypoglycemia is frequent by allmeasures and that it remains the limitingbarrier to tight glycemic control (18ndash20)

Diabetic Ketoacidosis

Diabetic ketoacidosis is a life-threateningand costly acute complication Therates remain elevated in the US andglobally (1921)

Time in Range

Time in range is an intuitive metric forglycemic control that captures hyper-hypo- and normoglycemia in one sim-ple view This metric has only been pos-sible to capture since the launch ofcontinuous glucose monitoring (CGM)devices A challenge in measuring timein target is the definition of the targetrange or the ldquonormalrdquo range The defi-nition of the range has varied in the lit-erature (ie 70ndash105 70ndash120 70ndash180mgdL) (22ndash24) Whatever the targetrange that is used people with diabetesare far from achieving normoglycemia

Patient-Reported Outcomes

The impact of diabetes remains signifi-cant on patients beyond suboptimal gly-cemic control People with diabetes still














Table

1mdashAPro

admapin

2015

pro

ofofco

nce

ptoffive

ofsix

stepsofite

rative

lymore

sophistica

tedAPsy


tedin

outpatie

nttria

ls

Step1

23

45

6

Nam

eLG

SPred

ictiveLG

S(PLG

S)Hypoglycem

iahyperglycem

icminim

izer(HHM)

Hybrid

closed

loop(HCL)

Fully

automated

insulin

delivery

Multih

orm

one(M

H)

Synonym

sTh

reshold

suspen

d(TS)

Predictive

low-glu

cose

managem

entsystem(PLG

M)

Treat-to-ran

gesystem

(TTR)

Treat-to-target

system(TTT)

Fully

closed

loop(FC

L)Insulin

-glucago

nsystem

bionicpan

creas

Descrip

tion

Insulin

shutsoffu

poncro

ssing

preset

thresh

old

such

as70

mgdL

andresum

esafter

2h

Insulin

shutsofforis

attenuated

uponpred

iction

ofim

pen

dinghypoglycem

iaandresu

mes

delivery

when

hypoglycem

iarisk

isgone

Sameas

step2butwith

automatic

insulin

dosin

gto

reduce

hyperglycem

iaexp

osure

Does

nottarget

euglycem

iarather

the

minim

izationoftim

espen

tabove

acertain

thresh

old

ie180mgd

L

Algo

rithmaim

sforeu

glycemic

targetnotran

gebutrelies

onmealtim

einsulin

bolus

Fully

automated

insulin

delivery

with

minim

alhuman

interactio

n

Fully

automated

multih

orm

oneapproach

insulin

plusglu

cagon

amylin

orother

horm

onesan

alogs

2015statu

sCommercialized

globally

Regu

latory

approvalo

utsid

eUSco

mmercial

availability

inAustralia

Incommerciald

evelopmen

tIn

commerciald

evelopmen

tProofofconcep

tIn

commerciald

evelopmen

t

Exampleof

supportin

gdata

Red

uctio

nin

hypoglycem

iared

uctio

nin

severehypoglycem

iamain

tenan

ceofA

1C(2943)

Red

uctio

nin

severeand

moderate

hypoglycem

ia(2044)

Red

uctio

nin

timespen

thyperglycem

icand

hypoglycem

icand

increased

timein

targetran

geinoutpatien

tsettin

gs(45)

Red

uctio

nin

timespen

thyperglycem

icand

hypoglycem

icand

increased

timein

targetran

geinoutpatien

tsettin

gs(2446)

Red

uctio

nin

hyperglycem

iaandhypoglycem

iaand

increase

intim

ein

targetin

aninpatien

tsettin

gs(4246)

Red

uctio

nin

hyperglycem

iaandhypoglycem

iaand

increase

timein

targetin

outpatien

tsettin

g(9)









Diabetes Scorecard









A Split



AID





SUMMARY
























Table

1mdashAPro

admapin

2015

pro

ofofco

nce

ptoffive

ofsix

stepsofite

rative

lymore

sophistica

tedAPsy


tedin

outpatie

nttria

ls

Step1

23

45

6

Nam

eLG

SPred

ictiveLG

S(PLG

S)Hypoglycem

iahyperglycem

icminim

izer(HHM)

Hybrid

closed

loop(HCL)

Fully

automated

insulin

delivery

Multih

orm

one(M

H)

Synonym

sTh

reshold

suspen

d(TS)

Predictive

low-glu

cose

managem

entsystem(PLG

M)

Treat-to-ran

gesystem

(TTR)

Treat-to-target

system(TTT)

Fully

closed

loop(FC

L)Insulin

-glucago

nsystem

bionicpan

creas

Descrip

tion

Insulin

shutsoffu

poncro

ssing

preset

thresh

old

such

as70

mgdL

andresum

esafter

2h

Insulin

shutsofforis

attenuated

uponpred

iction

ofim

pen

dinghypoglycem

iaandresu

mes

delivery

when

hypoglycem

iarisk

isgone

Sameas

step2butwith

automatic

insulin

dosin

gto

reduce

hyperglycem

iaexp

osure

Does

nottarget

euglycem

iarather

the

minim

izationoftim

espen

tabove

acertain

thresh

old

ie180mgd

L

Algo

rithmaim

sforeu

glycemic

targetnotran

gebutrelies

onmealtim

einsulin

bolus

Fully

automated

insulin

delivery

with

minim

alhuman

interactio

n

Fully

automated

multih

orm

oneapproach

insulin

plusglu

cagon

amylin

orother

horm

onesan

alogs

2015statu

sCommercialized

globally

Regu

latory

approvalo

utsid

eUSco

mmercial

availability

inAustralia

Incommerciald

evelopmen

tIn

commerciald

evelopmen

tProofofconcep

tIn

commerciald

evelopmen

t

Exampleof

supportin

gdata

Red

uctio

nin

hypoglycem

iared

uctio

nin

severehypoglycem

iamain

tenan

ceofA

1C(2943)

Red

uctio

nin

severeand

moderate

hypoglycem

ia(2044)

Red

uctio

nin

timespen

thyperglycem

icand

hypoglycem

icand

increased

timein

targetran

geinoutpatien

tsettin

gs(45)

Red

uctio

nin

timespen

thyperglycem

icand

hypoglycem

icand

increased

timein

targetran

geinoutpatien

tsettin

gs(2446)

Red

uctio

nin

hyperglycem

iaandhypoglycem

iaand

increase

intim

ein

targetin

aninpatien

tsettin

gs(4246)

Red

uctio

nin

hyperglycem

iaandhypoglycem

iaand

increase

timein

targetin

outpatien

tsettin

g(9)









Diabetes Scorecard









A Split



AID





SUMMARY



















Diabetes Scorecard









A Split



AID





SUMMARY
















A Split



AID





SUMMARY













SUMMARY










