The artificial pancreas (AP), known as closed-loop control of blood glucose in diabetes,
is a system combining a glucose sensor, a control algorithm, and an insulin infusion
device. AP developments can be traced back 50 years to when the possibility for external
blood glucose regulation was established by studies in individuals with type 1 diabetes
using intravenous glucose measurement and infusion of insulin and glucose. After the
pioneering work by Kadish (1) in 1964, expectations for effectively closing the loop
were inspired by the nearly simultaneous work of five teams reporting closed-loop
control results between 1974 and 1978: Albisser et al. (2), Pfeiffer et al. (3), Mirouze
et al. (4), Kraegen et al. (5), and Shichiri et al. (6). In 1977, one of these realizations
(3) resulted in the first commercial device—the Biostator (7; Fig. 1), followed by
another inpatient system, the Nikkiso STG-22 Blood Glucose Controller, now in use
in Japan (8).
FIG. 1.
The Biostator (courtesy of William Clarke, University of Virginia).
Although the intravenous route of glucose sensing and insulin infusion is unsuitable
for outpatient use, these devices proved the feasibility of external glucose control
and stimulated further technology development. Figure 2 presents key milestones in
the timeline of AP progress.
FIG. 2.
Key milestones in the timeline of AP progress. EU, Europe; IP, intraperitoneal; NIH,
National Institutes of Health; SC, subcutaneous.
In 1979, landmark studies by Pickup et al. (9) and Tamborlane et al. (10) showed that
the subcutaneous route was feasible for continuous insulin delivery. Three years later,
Shichiri et al. (11) tested a prototype of a wearable AP, which was further developed
in subsequent studies (12,13). In the late 1980s, an implantable system was introduced
using intravenous glucose sensing and intraperitoneal insulin infusion (14). This
technology was further developed, leading to clinical trials and long-term use (15,16);
however, its clinical application remained limited because of the extensive surgical
procedures needed for sensor and pump implantation.
In all early intravenous and intraperitoneal AP systems, the closed-loop control algorithms
belonged to a class known as proportional-derivative controllers, which used blood
glucose values and blood glucose rate of change in a relatively straightforward calculation
of insulin dose. However, as it is explained later in this article, proportional-derivative
control and its enhanced version, proportional-integral-derivative control, have inherent
limitations that hinder their use in subcutaneous systems because of unavoidable time
lags in subcutaneous glucose sensing and insulin action. Newer controllers, known
as model-predictive control (MPC), avoid these limitations by using a mathematical
model of the metabolic system of the person being controlled in their calculations.
Many of these MPC algorithms are based on another 1979 milestone, the Minimal Model
of Glucose Kinetics (17). Thus, since the early years of AP development, glucose sensing
and insulin delivery technologies were accompanied by computer modeling and simulation
(18–22). Review of methods for glucose control prior to 2000 can be found in two concurrent
papers (23,24).
The new wave—subcutaneous AP—developed after minimally invasive subcutaneous glucose
sensing was commercially introduced in 1999 by the MiniMed continuous glucose monitoring
(CGM) system. This set off an accelerating academic and industrial effort focused
on the development of a subcutaneous-subcutaneous system (Fig. 2). The MiniMed (later,
Medtronic) closed-loop project was the first to provide evidence for the feasibility
of the subcutaneous-subcutaneous route for fully automated blood glucose control in
type 1 diabetic patients (25). The 3-year ADICOL project funded by the European Commission
showed the feasibility of using advanced MPC strategies to close the loop (26). In
September 2006, the Juvenile Diabetes Research Foundation International (JDRF) initiated
the Artificial Pancreas Project and funded a consortium of centers to carry out closed-loop
control research. So far, encouraging results have been reported by several centers
(27–31). Two notable achievements were the acceptance by the Food and Drug Administration
of the University of Virginia–University of Padova type 1 diabetes simulator as a
substitute to animal trials in the preclinical testing of closed-loop control strategies
(32), and the design by a team from the University of California Santa Barbara and
the Sansum Diabetes Research Institute of a communication platform allowing the automated
transfer of data between CGM, control algorithm, and insulin pump (33). Following
these developments, JDRF initiated a now-ongoing multicenter, multinational trial
in adults and adolescents with type 1 diabetes. Additional momentum was brought by
the U.S. National Institutes of Health (NIH) funding several AP projects in 2009 and
by the European Commission launching the AP@Home project in 2010, which involves seven
universities and five companies throughout Europe. In the wake of these rapid developments,
this article aims to identify critical problems in AP development and to outline possible
solutions and a pathway toward the clinical acceptance of ambulatory closed-loop control.
Limitations of current glucose sensors
CGM technology was introduced 10 years ago, initially as a method for retrospective
review of glucose profiles (34–36). Shortly after, real-time devices came about, providing
online glucose readings (37). These first devices had limited performance, particularly
in the hypoglycemic range (36,38,39). Since then, significant progress has been made
toward versatile and reliable CGM; a number of studies have documented its benefits
(40–42) and charted guidelines for its clinical use (43,44). Because CGM data are
the input to the AP control algorithm, understanding of the physical, biochemical,
and mathematical principles and limitations of this technology is critical (45,46).
First, most of the commercial subcutaneous CGM devices measure glucose concentration
in a different than blood compartment—the interstitium. However, during rapidly changing
conditions, e.g., after a meal or during a hypoglycemic episode, interstitial glucose
and blood glucose can be markedly different (47–49). Thus, CGM devices require calibration
using one or more daily blood glucose samples. The influence on CGM accuracy of the
number and timing of calibration points was assessed by several studies (50,51). In
particular, the DirecNet Study Group (52) analyzed changes in accuracy by modifying
the calibration retrospectively and showed that calibrating during periods of relative
glucose stability significantly improves CGM accuracy. Modern calibration procedures
were suggested, based on mathematical models of interstitial glucose kinetics (53,54).
Second, time lag exists because of blood-to-interstitial glucose transport and the
sensor processing time (instrument delay). Because such a time lag could greatly influence
the accuracy of CGM (55,56), a number of studies were dedicated to its investigation
(57–61). In most studies CGM readings lagged blood glucose (most of the time) by 4–10
min, regardless of the direction of blood glucose change, but the formulation of the
push-pull phenomenon brought arguments for a more complex relationship than a constant
time lag (60). For the purpose of closed-loop control, mitigation of the time lag
was suggested based on near-term glucose forecast methods (51,61).
Third, errors from transient loss of sensitivity, and random noise confound CGM data
(62–64). Thus, filtering, denoising, and artifact rejection in CGM data are important
for closed-loop control. Algorithms performing these tasks are available in commercial
CGM devices (65–67). The precise tuning of filter parameters in an automatic manner
is, however, a difficult problem. Advanced methods that can be used to resolve this
challenge have been recently reported (50,53,68–70).
Despite of their inherent limitations, CGM devices produce rich frequently-sampled
data sets (e.g., every 5–10 min) allowing them to serve as AP-enabling technology
(43,45). First steps from simple monitoring to control have already been taken; modern
CGM devices display trends and blood glucose rate of change and are capable of alerting
the patient about upcoming hypo- or hyperglycemia (71–73). Studies of the utility
of such alerts have been initiated (73–75), and the next logical step—prevention of
hypoglycemia via shutoff of the insulin pump—has been taken (76).
Limitations of insulin delivery
Because of its pharmacokinetic and pharmacodynamic advantages, the intravenous route
of insulin delivery has been tested for ambulatory use with implantable devices from
the 1970s to the early 1990s (77,78). However, despite their effectiveness, the limitations
caused by recurrent catheter complications due to blood clotting stopped the development
of this route of insulin delivery (79). An alternative approach to staying close to
physiology is to use intraperitoneal insulin delivery, e.g., insulin infusion via
the portal venous system (80–83). In view of closed-loop glucose control, the intraperitoneal
infusion route has several intriguing characteristics: reproducibility of insulin
absorption combined with quick time to peak and return to baseline, close-to-physiological
peripheral plasma insulin levels, and restoration of glucagon response to hypoglycemia
and exercise (84–86). Although the experience of implantable programmable insulin
pumps from the 1990s has highlighted their benefits including sustained improvement
in mean blood glucose and reductions in glucose variability and severe hypoglycemia
(87–90), the clinical use of these devices has been limited because of insulin aggregation
issues (91), increased production of anti-insulin antibodies, which impair insulin
action in some patients (92,93), and the cost associated with this technology. Still,
the development of less invasive and cheaper implantable ports for intraperitoneal
insulin delivery (e.g., DiaPort; Roche Diagnostics, Mannheim, Germany) may extend
this option of insulin infusion in view of an AP (94).
Since the late 1990s, continuous subcutaneous insulin infusion (CSII) has become an
accepted mode of insulin pump therapy (95). Improvements in safety, miniaturization,
refined tuning of insulin pumps allowing for fine adjustments of basal rate, the recently
introduced “patch pumps” (96), and new insulin analogs (97,98), all lead to improved
patient comfort and better glucose control. The key issue of subcutaneous insulin
delivery remains the delay in action caused by the time needed for subcutaneous absorption,
resulting in late insulin peaks up to 120 min after the injection of a subcutaneous
bolus of regular insulin (95). Moreover, with subcutaneous insulin delivery the lost
physiological role of the liver in modulating peripheral insulin levels results in
higher peripheral insulinemia (82). Whether such a reduced hepatic insulinization
impairs the control of hepatic glucose output significantly is still unclear. In terms
of safety, altered absorption at the subcutaneous delivery site has been pointed out
as a risk for insulin underdelivery—a major issue observed with CSII, which may result
in ketoacidosis if undetected and not corrected in time (77,99). This phenomenon has
been shown to occur even more rapidly with the use of fast-acting analogs (100).
In an AP setting, CSII combined with subcutaneous glucose sensing has been shown to
be effective for “out-of-meal” periods, keeping blood glucose in the normal range
in the postabsorptive state (26,45). However, fully automated closed-loop control
has not been so successful in addressing insulin needs at meals (25,26). Indeed, the
rapid rise of postmeal glucose is difficult to avert because of the inherent delays
in subcutaneous insulin absorption and action (101). As a result, all AP trials reported
to date show a significant postprandial glucose peak above the normal range. Moreover,
delayed insulin action while postprandial blood glucose decreases may result in secondary
glucose lows a few hours after the meal (26).
Need for “smart” control algorithms
Despite important developments in sensor and pump technology, the AP must cope with
the delays and inaccuracies in both glucose sensing and insulin delivery described
in the previous sections. This is particularly difficult when a system disturbance,
e.g., a meal, occurs and triggers a rapid glucose rise that is substantially faster
than the time needed for insulin absorption and action (Fig. 3).
FIG. 3.
Block diagram of closed-loop glucose control. Three major delays are indicated: insulin
absorption (regular and ultrafast insulin), insulin action on peripheral tissues and
on the liver, and sensing in the interstitium.
The problem is that with inherent delays, any attempt to speed up the responsiveness
of the closed loop may result in unstable system behavior and system oscillation.
Thus, a sound controller design must consider a relatively slow response, giving time
for the delays to wear off before the next control action. However, a slow response
cannot provide good attenuation of postprandial glucose peaks. Hence the principal
AP control dilemma: find a trade-off between slow-pace regulation well suited to mild
control actions applicable to quasi-steady state (e.g., overnight), and postprandial
regulation calling for prompt and energetic corrections (102,103).
Historically, this problem was clearly demonstrated by the first closed-loop experiments
that used PID algorithms. Because PID is purely reactive, responding to changes in
glucose concentration after they have occurred, it suffers most from the problems
described above. In particular, to avoid hypoglycemia after a meal, one has to design
a moderately aggressive controller; however, such a “cautious” design would not react
promptly and effectively to meals. To improve PID performance, one possibility is
to add a feed-forward action (a regular premeal bolus), which helps with meal compensation
as demonstrated in clinical studies (27). To mitigate hypoglycemic events, an insulin
negative feedback on insulin delivery rate has also been introduced (104).
The new wave of control designs, MPC, is based on prediction of glucose dynamics using
a model of the patient metabolic system and, as a result, appears better suited for
mitigation of time delays due to subcutaneous glucose sensing and insulin infusion.
In addition, MPC is a better platform for incorporation of predictions of the effects
of meals and for introduction of constraints on insulin delivery rate and glucose
values that safeguard against insulin overdose or extreme blood glucose fluctuations.
In some sense, an MPC algorithm works as a chess strategy (Fig. 4). On the basis of
past game (glucose) history, a several-moves-ahead strategy (insulin infusion rate)
is planned, but only the first move (e.g., the next 15-min insulin infusion) is implemented;
after the response of the opponent, the strategy is reassessed, but only the second
move (the 30-min insulin infusion rate) is implemented, and so on. In reality glucose
prediction may be different from the actual glucose measurement or an unexpected event
may happen; with this strategy these events are taken into account in the next plan.
FIG. 4.
A: The concept of MPC. At each step, future glucose levels are predicted and insulin
delivery strategy is mapped several steps ahead. Then, the first insulin delivery
step is implemented, and the situation is reassessed with new glucose data. The process
is very similar to a chess game in which several moves are planned ahead, and after
the implementation of the first move the position is reassessed given the response
of the opponent. B: The critical stage of the famous chess game between Leonid Stein
(white) and Lajos Portisch (black), Stockholm, 1962 (courtesy of Leon Fahri, University
of Virginia).
Several successful clinical trials using MPC were recently published. Kovatchev et
al. (31) reported a study in which the aggressiveness of the controller was individualized
using patient parameters such as body weight, carbohydrate ratio, and insulin basal
rate. Hovorka et al. (28) used a multimodel MPC approach, deciding in real time which
patient model is best fitting the data in hand, while dual-hormone strategies adding
glucagon administration to insulin delivery to avoid hypoglycemia were used by El-Khatib
et al. (30) and Castle et al. (105).
Additional difficulties that the control algorithm must face arise from coping with
inter- and intrapatient variability. Fortunately, MPC allows for relatively straightforward
individualization using patient-specific model parameters (106). Given the difficulty
of identifying accurate individual models, a customizable controller has been proposed,
individually tuned through a “control aggressiveness” parameter calculated from a
few routine biometric and clinical data of each individual (107). In addition, MPC
can have “learning” capabilities; it has been shown that a class of algorithms (known
as run-to-run control) can “learn” specifics of patients’ daily routine (e.g., timing
of meals) and then optimize the response to a subsequent meal using this information
(108,109), or account for circadian fluctuation in insulin resistance, such as the
dawn phenomenon observed in some people (110).
Finally, an MPC system can also have certain feed-forward capabilities, i.e., the
ability to use a combination of feed-forward (e.g., patient-initiated) and feedback
(controller-initiated) insulin delivery that can partially solve the dilemma posed
by the need for trade-off between slow-pace regulation in quasi-steady state and prompt
correction of meals. Associated with such a feed-forward action is a nominal glucose
profile, which represents the expected consequence of the conventional patient-initiated
therapy. The algorithm bases its actions on the difference between the sensor signal
and this nominal profile. If the difference is zero, no closed-loop correction is
applied and the patient is subject to the conventional therapy alone. In practice
the difference will always be nonzero, thus a feed-forward action would also prompt
small-size feedback corrections adapting to unpredicted events, disturbances and changes
in patient’s dynamics. Clinical results obtained by this type of control strategy
are reported by Kovatchev et al. (31).
A step forward: in silico experiments replacing animal trials
The future development of AP will be greatly accelerated by using mathematical modeling
and computer simulation. A number of simulation models have been proposed in the last
4 decades and used to assess the performance of control algorithms and insulin infusion
routes (111–117). However, all these models are “average,” meaning that they are only
able to simulate average population dynamics but not the interindividual variability.
The average-model approach is not sufficient for realistic in silico experimentation
with control scenarios. For this purpose, it is necessary to have a simulator equipped
with a cohort of in silico subjects that spans sufficiently well the observed interperson
variability of key metabolic parameters in the type 1 diabetic population. The knowledge
on intersubject variability is indeed crucial to the design of robust controllers,
providing valuable information about their safety and limitations.
Building on the large scale meal model developed in the healthy state (116,117), we
have developed a type 1 diabetes simulator that, thanks to its ability to realistically
describe intersubject variability, has been accepted by the Food and Drug Administration
as a substitute of preclinical animal trials for certain insulin treatments (32).
In this simulator, a virtual human is described as a combination of several glucose
and insulin subsystems. To permit in silico experiments using CGM, the model includes
subcutaneous glucose transport and sensor errors. In summary, the model consists of
13 differential equations and 35 parameters for each subject (116,117). The simulator
is equipped with 100 virtual adults, 100 adolescents, and 100 children, spanning the
variability of type 1 diabetic population observed in vivo. Key “biometric” characteristics
of these virtual subjects are presented by Kovatchev et al. (32). Figure 5 illustrates
the overall design of the simulation model.
FIG. 5.
Principal component of the type 1 diabetes simulator: a model of the glucose-insulin
system, a model of the sensor, a model of the insulin pump and subcutaneous insulin
kinetics, and the controller to be tested.
With this technology, any meal and insulin delivery scenario can be pilot-tested very
efficiently in silico, prior to its clinical application. Because in silico experiments
produce results at a fraction of time and cost of animal trials, this simulator in
now adopted by the JDRF Artificial Pancreas Consortium and by others as a primary
test bed for new closed-loop control algorithms. The capabilities of the simulator
are now being expanded by incorporating intraday variability of key fluxes (e.g.,
glucose production) and signals (e.g., insulin sensitivity [118]), and by including
hypoglycemia counterregulation (119).
Modular Architecture for Sequential AP Development
Today’s technological advancements open the possibility for ambulatory AP. To account
for the multitude of available possibilities, academic, and industrial developments,
we have introduced the concept of modular approach to AP design, which allows technologies
developed by different entities to be seamlessly integrated in a functional hierarchical
system that can be sequentially deployed in clinical and ambulatory studies. Figure
6 presents an outline of this modular-architecture concept.
FIG. 6.
Modular architecture for sequential AP development.
The key advantage of modular AP architecture is the possibility for sequential development,
clinical testing, and ambulatory acceptance of elements (modules) of the closed-loop
system. In engineering terms, we suggest that the AP should have separate interacting
components responsible for prevention of hypoglycemia, postprandial insulin correction
boluses, basal rate control, and administration of premeal boluses (120). In this
scheme, control modules receive information from system state estimation modules that
are responsible for tracking glucose fluctuations and the amount of active insulin
at any point in time. This structure is dictated by the natural separation of the
computational elements of a closed-loop control system into algorithms estimating
the state of the person and algorithms actuating control. The control layers work
on different time scales. At the bottom, the fastest layer is in charge of safety
requirements. Possible algorithms include pump shutoff (76), insulin on board, and
smooth attenuation of the insulin pump (121). Immediately above, there is the real-time
control layer deciding insulin delivery based on latest CGM data, previous insulin
delivery, and meal information. Typical algorithms are either PID, MPC, or the recently
introduced Zone MPC controller (122). The top layer (offline control tuning) is in
charge of tuning the real-time control layer using clinical parameters and historical
data. Each layer processes available information (CGM and patient inputs) in order
to take decisions that are passed to a lower layer. If necessary, commands from an
upper layer can be overridden; a typical example is the safety layer canceling insulin
delivery suggested by the real-time control module.
With modular architecture in place, various increasingly complex configurations of
an AP system become possible. For example, a relatively simple control system responsible
only for nighttime basal rate regulation has been successfully tested as a first step
to AP (31), followed by control of risk for hypoglycemia associated with exercise
(123). More recently, a new modular control-to-range algorithm has been introduced
as an adjunct to, not a replacement of, standard basal-bolus pump therapy, intervening
only if corrections of hypo- or hyperglycemia are needed (120). This control strategy
resulted in reduced average glucose without increasing patients’ risk for hypoglycemia
(124).
Further, the progression of AP would entail moving the system to outpatient setting.
This critical step would require specific elements, such as server and communication
tools for remote monitoring but also for remote intervention. Remote monitoring would
be particularly important for the first ambulatory clinical trials that would require
continuous “intelligent” observation of AP first deployed outside of the hospital.
From there, step-by-step move to home use would follow module-by-module approach under
remote monitoring. Finally, fully automated closed-loop is expected to deliver safe
and efficacious glucose control at home for a prolonged period of time. To cope with
the changing environmental conditions and with the physiological/behavioral changes
of the patient, the ambulatory AP will have to adapt to the changes in an individual’s
biobehavioral parameters over time. Possible methods to cope with changing reality
include individual controller calibration strategies and run-to-run control algorithms
(107–110), as well as behavioral analysis and profiling of patient lifestyle (125).
Many of these advanced methods are now on the drawing board. Table 1 summarizes the
principal components of a closed-loop system, including the CGM, the insulin infusion
device, the control algorithm and the associated human factors, and lists the areas
that need further development before the ambulatory AP becomes a reality.
TABLE 1
Components of the AP system and the improvements needed before the ambulatory AP enters
mainstream use
Components
Desirable improvements
Glucose sensing
Reliability (minimize missing data)
Error (reduce noise and drift, improve calibration)
Durability and wearability
Long-term implantable and noninvasive technologies
Insulin delivery
Insulin pharmacokinetics and pharmacodynamics
Reliability of infusion (infusion sets)
Durability and wearability
Alternative routes (intraperitoneal, intradermal, inhaled)
Control algorithm
Model prediction (improve horizon and accuracy)
Individualization (prescription of a control algorithm)
Automated meal and exercise recognition and control
Real-time adaptation to patient physiology and behavior
Platform
Communication between devices (first step to integration)
Remote monitoring, alerts, and telecommunication
Integration of pump, sensor, and control devices
Integration of sensing and insulin delivery sites
Human factors
Device user interface (typically graphical user interface)
Hazard identification and task prioritization
Hazard mitigation and unexpected event control
Human factors validation testing