Automated insulin delivery for type 1 diabetes Garry M. Steil a and Mohammed F. Saad b

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1 Automated insulin delivery for type 1 diabetes Garry M. Steil a and Mohammed F. Saad b Purpose of review Automated insulin delivery using the subcutaneous site for both glucose sensing and insulin delivery, while not commercially available, is starting to be implemented in the research environment. Here, we review issues with glucose sensing and insulin delivery algorithms that need to be resolved in order to achieve a closed-loop system. Recent findings Glucose sensing technology is improving. New studies demonstrating minimal delay in the subcutaneous interstitial fluid glucose response are reviewed, together with early studies indicating longer delay. The pharmacokinetics/ pharmacodynamics of subcutaneous insulin, which pose a significant obstacle to closing the delivery loop, are also reviewed, together with approaches being used to develop algorithms to overcome the delay in insulin absorption from this site. Closed-loop data using the Medtronic MiniMed external Physiologic Insulin Delivery system are presented. Summary Initial closed-loop data in both animals and humans suggest that a completely automated insulin delivery system is attainable, although substantial research remains to be conducted prior to such a system being available for home patient use. Keywords continuous subcutaneous insulin infusion, continuous glucose monitoring system, external Physiologic Insulin Delivery (epid), insulin secretion, minimal model Curr Opin Endocrinol Diabetes 13: ß 2006 Lippincott Williams & Wilkins. a Medtronic MiniMed, Northridge, California and b Stony Brook School of Medicine, Stony Brook, New York, USA Correspondence to Dr Garry Steil, Division of Research and Development, Medtronic MiniMed, Devonshire Street, Northridge, CA 91325, USA Tel: ; fax: ; garry.steil@medtronic.com Current Opinion in Endocrinology & Diabetes 2006, 13: Abbreviations epid ISF MPC external Physiologic Insulin Delivery interstitial fluid model predictive control ß 2006 Lippincott Williams & Wilkins Introduction The desire and demand for an automated artificial pancreas have existed almost from the time insulin was first discovered in Insulin pumps capable of delivering insulin subcutaneously have been in use since the early 1980s, systems that can continuously sense subcutaneous glucose have been available since the late 1990s (Medtronic MiniMed continuous glucose monitoring system), and algorithms for calculating the insulin delivery rate have been studied for more than 40 years [1]. One may reasonably ask: what remains to be done? What remains is still substantial. It is virtually impossible to consider a completely automated closed-loop insulin delivery system being used by patients at home without adequate studies being performed to address a wide range of clinical concerns. Starting with the sensor, it is well known that subcutaneous interstitial fluid (ISF) poses unique challenges when used as a surrogate for blood glucose estimation. These include putative delays in the response due to glucose equilibration between blood and subcutaneous ISF, and the challenges related to calibrating the sensor following insertion. Insulin delivery is likely to be either subcutaneous using an external insulin pump, or intraperitoneal using an implantable pump. Both delivery sites introduce additional delays in insulin kinetics not seen with intravenous delivery. Fast-acting insulin analogues have reduced the subcutaneous absorption time, but the kinetics are still delayed. The ability of any closed-loop algorithm to cope with these delays has not been adequately demonstrated. Ambulatory closed-loop systems are unlikely to have glucagon available to correct hypoglycemia, as no pump-stable formulation exists for this hormone. Finally, variability in insulin requirement related to diurnal variations in insulin sensitivity or endogenous glucose production needs to be addressed, as does controller stability during periods of intense exercise, and the responses to various meal types. The subcutaneous glucose sensing site Use of the subcutaneous ISF for glucose sensing has been the subject of intense focus for many years, with the primary concern being the putative differences between blood and ISF glucose concentrations. The basic problem is that the capillary presents a barrier to glucose entry into the subcutaneous ISF space, leading to a complex dynamic relation involving both glucose diffusion and glucose uptake (Fig. 1) [2]. This barrier is thought to result in insulin-induced changes in the plasma-to-isf 205

2 206 Diabetes and the endocrine pancreas Figure 1 Two-compartment model of capillary blood glucose (G 1 ) and interstitial fluid (ISF) glucose (G 2 ) Figure 2 Plasma insulin concentrations following an intravenous (IV), intraperitoneal (IP) or subcutaneous (SC) bolus of insulin at 0 min Sensor Insulin (µu/ml) 300 IV IP SC Capillary K G 1 G K 2 K Fat/ muscle cell Time (min) Interstitial fluid Glucose (closed circles) moves from the capillary space to the ISF space by diffusion (rate determined by K 12 and K 21 ), and is cleared from the ISF space by uptake into insulin-sensitive tissues (fat or muscle) via a process that may be enhanced by insulin (triangles) binding to its receptor (recessed triangle on cell membrane). Figure adapted with permission from Rebrin et al. [2]. glucose gradient [2,3], different response times for falling and rising glucose signals [4], protracted recovery times following hypoglycemia [5,6], and the possibility that ISF glucose can fall in advance of plasma glucose [7,8]. While each of the aforementioned possibilities is theoretically possible based on a glucose distribution model (Fig. 1), we have shown the delay in ISF glucose to be no more than 6 8 min during hyperglycemic [9] or hypoglycemic [10 ] clamps, and that there is no difference in delay during a fall compared with a rise in glucose. While our studies [9,10 ] were conducted in non-diabetic individuals, the delay estimates are consistent with those reported by Boyne et al. [11] in individuals with type 1 diabetes mellitus. A 6 10-min delay is unlikely to pose a substantial barrier in the development of a closed-loop insulin delivery system. This is not to say that other significant issues do not exist with glucose sensors. In particular, sensors that exist today require an initial calibration in vivo following insertion, and can require recalibration during use (typically two per day for the Medtronic MiniMed continuous glucose monitoring system [12]; and four over 3 days for the Therasense Freestyle [13]). This process cannot be completely error-free, as the calibration is performed against home glucose meters, which are themselves a source of error [14 17]. Site of insulin delivery The applicability of the subcutaneous site for insulin delivery has been greatly enhanced by the development of rapidly absorbed insulin analogues. Subcutaneous insulin kinetics (Fig. 2) [18,19,20 ] are still substantially The intravenous response is shown with an assumed half-life of 10 min, subcutaneous data (open circles) are adapted from Mudaliar et al. [18], and intraperitoneal data (solid circle) are adapted from Kelley et al. [19]. Dashed and chain dashed lines represent fits to a two-compartment insulin clearance model (see [20 ]). delayed, however, with peak plasma insulin levels not being achieved for min and requiring a total of 3 6 h to fully dissipate [18]. Intraperitoneal insulin kinetics are faster (peak value of 40 min [19]), but this is still far slower than the kinetics of intravenous insulin, which has a half life of 5 10 min. System variability All systems have variability, and the glucose response to exogenous insulin is no exception. Even in healthy individuals insulin sensitivity varies both day-to-day [21] and throughout the day [22]. Diurnal variance can result from either a change in insulin sensitivity per se,ora change in endogenous glucose production [23], both of which may be mediated by changes in free fatty acids [24]. For an individual with type 1 diabetes, this results in varying basal insulin requirements throughout the day that are reflected in pump basal profiles. Insulin requirements for meals of identical carbohydrate content can also vary depending on the type of carbohydrate [25,26] and past exercise [27,28]. Ultimately, the list of potential factors influencing insulin requirement is long, and the question is: can a closed-loop insulin delivery algorithm automatically adjust to all these varying needs? Closed-loop insulin delivery algorithms Approaches to developing the closed-loop algorithm have traditionally relied on control theory developed by engineers for any number of different systems (e.g. aerospace or process control). A historical review of closed-loop insulin delivery is likely to provide a complete overview of the past 40 years of research into control theory per se. While such a review is beyond the scope of the present paper (recent reviews are available [29,30]), we make note of what would be considered today to be a state-ofthe-art control approach known as model predictive

3 Automated insulin delivery for type 1 diabetes Steil and Saad 207 control (MPC) [31]. In MPC, a mathematical model of the subject s glucose response is derived, and the difference between the measured glucose and the modelpredicted values is corrected by calculating a sequence of insulin delivery rates looking n samples into the future the number of samples into the future being termed the control horizon. The insulin delivery rates are then optimized based on the difference in the modelpredicted glucose response m-points into the future, in which m is termed the prediction horizon (m and n need not be the same). The algorithm is of particular interest in that it may be able to compensate for delays in glucose sensing (Fig. 1) and insulin appearance (Fig. 2). Successful implementation of an MPC strategy depends largely on the ability of the glucose model to accurately predict future glucose values. Much work has been done in the past decade to develop and validate such models. We have recently compared the description of insulin action in the more common models and found systematic differences among them [32 ]. Nonetheless, these differences could potentially be resolved based on the large amount of data now available from continuous glucose monitoring systems; various existing models have already shown promise in predicting future glucose values [33,34], and MPC has been shown to provide good results in a simulated environment [31,35 ]. MPC represents just one of numerous closed-loop control methods developed primarily from an engineering control theory approach. While the potential for automated insulin delivery algorithms to benefit from the large body of control systems research cannot be overestimated, a strong argument can also be made for understanding the mechanisms by which the b-cell maintains tight glucose control. The b-cell argument has dominated in the development of the Medtronic MiniMed external Physiologic Insulin Delivery (epid) system [1]. The main points are that the b-cell adapts its secretory response to the individual s underlying insulin sensitivity [36 38], and that it adjusts the ratio of first- to second-phase insulin to compensate for a delay in insulin action [39]. In an effort to emulate the b-cell as a basis for closed-loop insulin delivery, we have extended existing mathematical models of the b-cell secretory response that use two phases [40 48] to include a third phase. The existing models are deeply rooted in the monumental work of Gerald Grodsky [49 51], and typically describe the b-cell response to glucose as the sum of two components which variously react proportionally to changes in glucose, have a delayed reaction to glucose, or react to the rate-ofchange of glucose. In our model, all three terms are included (Fig. 3) [52]. We have compared the ability of the three-phase model to compensate for changes in basal insulin requirement with that of the two-phase model proposed by Cobelli and colleagues [40,41]. In our simulations [52], the three-phase model was able to adjust insulin delivery to compensate for changes in insulin sensitivity or endogenous glucose production, whereas the two-phase model was not. The b-cell algorithm has been implemented on a laptop computer modified to receive the glucose sensor signal at 1-min intervals and to transmit insulin delivery commands to the Medtronic-MiniMed insulin pump (Fig. 4). The epid device is labelled for investigation use only and is not commercially available. Nonetheless, research studies have been performed in both animals (diabetic canine) and humans. An example response (Fig. 5), obtained in the canine diabetic model, illustrates the three-phase response during a meal. For these data, the desired glucose set point was 120 mg/dl and the initial glucose concentration was 100 mg/dl. Basal insulin delivery is determined by the slow component (Fig. 3c). Once the meal begins, the rate-of-change component (Fig. 3d) results in a rapid rise in insulin delivery, which is accompanied by more proportionalphase insulin (Fig. 3b) as glucose rises above a set-point. One of the challenges of controlling this response is to recreate the normal diurnal variations in insulin secretion and glucose levels seen in healthy individuals [53]. Two points are particularly relevant in discussing the epid algorithm. The first is that the b-cell model is identical to a classical proportional integral derivative controller [54]. In that system, proportional refers to a control response in proportion to the difference between a measured variable and its desired value, for example, providing gas in proportion to the difference between the speed and a target set-point in an automobile cruise system. Note that, by definition, the proportional component has no output when the system being controlled is at target, that is, no gas in the automobile example when the car is at target speed, and no insulin delivery in the artificial b-cell when glucose is at target concentration. Integral refers to a component of the controller that increments from its most recent value (initial condition if the controller is just turned on) in response to the measured variable being above or below target (mathematically an integral for continuous control). In the cruise control example, the gas would increment up when the car climbs a hill. While starting up the hill the car will temporarily slow down, but will re-achieve the target speed as more gas is given by the integral component. This example is analogous to the situation in which a patient s insulin delivery needs to be increased following a decrease in insulin sensitivity. The integral component holds its last value when the system is at target and is the only non-zero component when a system is stable. Finally, derivative refers to the rate-of-change. The derivative component stabilizes the closed-loop system, in that it always counteracts change (gives gas when the car slows

4 208 Diabetes and the endocrine pancreas Figure 3 Plasma insulin response obtained by the b-cell during a hyperglycemic glucose clamp (a) Plasma insulin concentration (circles) and model fit (solid line through data) of the three-phase b-cell model to the plasma insulin response (data adapted from Steil et al. [52]). Schematic representation of the epid algorithm showing simulated proportional (b; blue), integral (c; green) and derivative (d; magenta) phases, together with their sum (e). Mathematical formulae are shown in (b d), with K P, K I and K D defining the relative magnitude of the proportional (P), integral (I) and derivative (D) phases respectively; G and G b indicating the measured glucose and desired basal glucose concentrations respectively; and P con, I con and D con indicating the proportional, integral and derivative control phases, respectively. Figure 4 Closed-loop insulin delivery developmental system The system is composed of a subcutaneous glucose sensor and a radio-frequency transmitter (a) transmitting subcutaneous glucose sensor values to a laptop computer (c) which calibrates and performs insulin delivery calculations at 1-min intervals, and transmits the rate to an external insulin delivery pump (b) in a sequence of small (0.1 U) boluses [sensor, pump and catheter all from Medtronic MiniMed; glucose transmitter and receiving unit (attached to laptop) are investigation devices only].

5 Automated insulin delivery for type 1 diabetes Steil and Saad 209 Figure 5 Plasma glucose and insulin delivery during a meal controlled by the epid system (a) Plasma glucose ( ) concentration obtained with the epid algorithm (Fig. 3) and system (Fig. 4). Glucose values were obtained every 1 min (solid line), with the meal administered at 06:00 hours. Target glucose was 120 mg/dl. (b) Three-phase insulin delivery based on the model of Fig. 3. down, or insulin when glucose goes up). That the b-cell response has many similarities to a classical control response should not be surprising, as the b-cell largely serves as the body s closed-loop insulin delivery system. The second point is that the b-cell delivers insulin directly into the portal vein, whereas an artificial system is likely to use a peripheral or peritoneal route. Thus, it can be expected that the algorithm will need to be adjusted for differences in insulin pharmacokinetics and dynamics. With regard to the latter, insulin secreted into the portal vein is thought to have a rapid direct effect to suppress hepatic glucose output [55 57] and alter other metabolites [58]. The effect on hepatic glucose output is also mediated by indirect effects, however the so-called single gateway hypothesis that has been extensively studied by Bergman and colleagues [59 65]. In either case, differences exist in the pharmacokinetics of insulin delivered portally compared with peripherally (Fig. 2), and these differences will need to be taken into consideration. To date, the only change in the profile used in the Medtronic MiniMed epid system is a change in the relative proportions of the three phases. For example, in the meal response of Fig. 5, first-phase insulin has been increased approximately 50% compared with that shown in Fig. 3, and the rate of rise in the second phase has been reduced by approximately half (specifically, the ratio K D /K P in Fig. 3 was increased 50%, and the ratio K P /K I was decreased 50%). A more optimal adjustment of the algorithm can likely only be made by modelling the effect of insulin on glucose metabolism. Thus, while MPC strategies can be used as a means of calculating insulin delivery per se, the same model can also be used to formulate optimal adjustments in the relative contributions of the proportional, integral and derivative terms to compensate for known differences in insulin kinetics (Fig. 2) or for known effects of portal insulin delivery (if any). Model-based optimization of the epid algorithm has been investigated using historical canine data [20 ], but has yet to be applied to studies with human subjects. Adding characteristics of the b-cell accounting for diurnal variation in insulin secretion, the effect of insulin concentration to suppress insulin secretion, and the effect of incretins (see [1] for a review) may further aid the optimization process. While this is the present strategy embraced by Medtronic, it must be stated that the use of a b-cell model as a basis for the closed-loop algorithm, or the contention that the b- cell can be described in control theory terms, has not been universally accepted [66 ]. Conclusion While work remains to be done to validate aspects of the epid system, we have used the model as a basis for closedloop insulin delivery in humans, based on subcutaneous glucose sensing with subcutaneous insulin delivery [67], and intravenous glucose sensing with intraperitoneal insulin delivery [68]. Preliminary results from our clinical

6 210 Diabetes and the endocrine pancreas trials using this model/algorithm are promising [67,68], suggesting that a completely closed-loop system will be attainable in the near future. Steps towards automating open-loop pump therapy are ongoing and can be expected to be of increasing benefit for continuous glucose measurement and metabolic modelling. Achieving near-normal glycemic profiles with an automated system will decrease the risk of diabetic complications and improve the patient s quality of life. Acknowledgements This work was partially funded by National Institutes of Health grant RO1-DK64567 to G.M.S. References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest Additional references related to this topic can also be found in the Current World Literature section in this issue (pp ). 1 Steil GM, Panteleon AE, Rebrin K. 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Good companion reference to consider with [65 ], which also utilizes a model to aid closed-loop algorithm development, and [31 ], which looks at the limitations of the modelling approach. 36 Bergman RN, Phillips LS, Cobelli C. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and betacell glucose sensitivity from the response to intravenous glucose. J Clin Invest 1981; 68: Bergman RN, Ader M, Huecking K, et al. Accurate assessment of beta-cell function: the hyperbolic correction. Diabetes 2002; 51 (Suppl 1):S212 S220.

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Continuous glucose monitoring and closed-loop systems. Diabet Med 2006; 23:1 12. This paper details historical achievements by numerous groups. Arguments are presented that minute-to-minute glucose measurement will not be needed, that achieving true physiologic glucose control will require glucagon, and that the b-cell first phase does not occur under physiologic conditions. The arguments presented provide a timely and balanced alternative to the arguments for using the b-cell model that are presented in the present review and in [1]. 67 Steil GM, Rebrin K, Hariri F, et al. Continuous automated closed-loop insulin delivery based on subcutaneous glucose sensing and external insulin pump. Diabetes 2004; Suppl. 53 (abstract A3). 68 Renard E, Panteleon AE, Leong P, et al. Efficacy of a closed-loop control of blood glucose based on an implantable intravenous sensor and intraperitoneal insulin pump. Diabetes 2004; 53:A114.