Closed-Loop Experiences in Europe

Professeur Eric Renard

Centre Hospitalier Universitaire & Université de Montpellier, INSERM CIC 1001, France

e-renard@chu-montpellier.fr

Presenter Disclosure

Eric Renard, MD, PhD, FRCP Edin

Consultant: A. Menarini Diagnostics, Abbott, Eli-Lilly, Johnson &

Johnson, Medtronic, Novo-Nordisk, Roche Diagnostics, Sanofi-

Aventis.

Research Support: Abbott, DexCom Inc., Insulet Inc.

Normal Range of Glucose Control

Time of day

Normal glucose

tolerance

(n = 16)

Me

an

glu

co

se

mg

/dl

(mm

ol/l)

0

100

200

300

400

6:00 10:00 14:00 18:00 22:00 2:00 6:00

(22.2)

(16.7)

(11.1)

(5.6)

140 (7.8)

= administration of meal

Polonsky KS et al. N Engl J Med 1988; 318:1231-9.

70 (3.9)

Near-Euglycemia

Target: 70-180 mg/dl

Tight Glucose Control

Target: 80-140 mg/dl

No

Hypoglycemia

The Closed-Loop Insulin

Delivery Concept

5

6

7

8

9

0 2 4 6 8 10 12 14 16 18 20 22 240.9

HbA1c < 6.5%

1.4

Continuous

Glucose

Monitoring

Control

Algorithm

Insulin

Delivery

Closed-Loop

mmol/l g/l

Bedside Artificial Pancreas :

A ‘miracle’ in the 1970s in US and Europe

Pfeiffer et al, 1974

Albisser et al, 1977

Mirouze et al, 1977

Ambulatory Artificial Pancreas:

How should insulin be delivered?

Hovorka R. Diabet Med 2006;23:1–12.Steil GM, et al. Adv Drug Deliv Rev 2004;56:12–44.

Linking Insulin Pump and Glucose:

LTSS Study (2000-2007)

IV glucose sensing

+ IP insulin delivery

+ PID algorithm

Analysis of Insulin Levels Following IP Insulin Bolus from

Implantable Pumps

-60 0 60 120 1800

10

20

30

40

50

Insulin ( U/ml)

BioexponentialModel fit

R2=0.79 0.04, τ1=49.2 10.9 min, τ2=14.8 3.0 min

0.0

2.5

5.0

7.5

10.0

Time (min)

Insu

lin (

U/m

l)

Basal R

ate (U/h) / B

olus (U)

Time constants

1 2 TTP0

25

50

75

1=49.3 10.9 min

2=14.8 3.0 min

TTP=23.3 3.1 min

Tim

e (m

in)

Intraperitoneal deliveryclearance

0

10

20

30

40

50

60

70

80

CL=38.0 11.7 ml/min/kg

Cle

aran

ce (m

l/min

/kg)

Renard E, et al. Personal data.

Long-term Accuracy of

Glucose Sensing with LTSS

Renard E, et al. Personal data.

What Kind of Algorithm ?

Feedback Control (PID)

Well adapted for closed-loop

control using reactive systems

Insulin delivery is modulated

according to :

The difference between

current glucose level and

target glucose level

(proportional component)

The time during which current

glucose level is different from

target glucose level (integral

component)

The variation of blood glucose

on short-term (derivative

component)

Insulin infusion Plasma Glucose

Glucose sensor

0 60 120 1800

1

2

3

4

TIME (min)

NILUSNI

(U/h)

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

0 . 0

2 . 5

5 . 0

3 0 T h uA u g 2 0 0 1

3 A M 6 A M

CLOS E D LOOP # 3 P a tien t A X M --- S /N 7037

Gl

uc

os

e

(m

g/

dL

)

In

su

li

n

As

sa

y

(u

U/

mL

)

In

su

li

n

(U

ni

ts

)

Renard E, et al, Diabetes Metab 2006;32:497–502.

Closed-loop glucose control using

LTSS overnight (12 AM – 6 AM)

1 5 g 7 5 g 7 5 g 1 0 g

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

0 . 0

2 . 5

5 . 0

1 2 P M2 9 W e d A u g 2 0 0 1

3 P M 6 P M 9 P M 3 0 T h u

CLOS E D LOOP # 3 P a tien t A X M --- S /N 7037

Gl

uc

os

e

(m

g/

dL

)I

ns

ul

in

A

ss

ay

(

uU

/m

L)

In

su

li

n

(U

ni

ts

)

Closed-loop glucose control using

LTSS between noon and midnight

Renard E, et al, Diabetes Metab 2006;32:497–502.

Steil GM, et al. Adv Drug Deliv Rev 2004;56:12–44.

Better PK with IP insulin is offset by

internal delay in IV glucose sensor

Steil GM, et al. Adv Drug Deliv Rev 2004;56:12–44.

What About Better PK with IP insulin and

Shorter Sensing Delay with SC Glucose

Sensor ?

Linking Insulin Pump and Glucose:

HyPID Study (2007-2008)

SC glucose sensing

+ IP insulin delivery

+ PID algorithm

HyPID study: Night-time and post-

prandial control of blood glucose

Renard E, et al. Diabetes Care 2010;33:121–7.

0 60 120 1800

20

40

60

80

100

TIME (min)

INS

UL

IN (

U/m

l)

Derivative component of PID

algorithm can hardly mimic

physiology

Linking Insulin Pump and Glucose:

JDRF-sponsored Studies (2008-2010)

SC glucose sensing

+ SC insulin delivery

+ MPC algorithm

OmniPod®, Insulet Inc.

STS®, DexCom

Model Predictive

Control (MPC)

Well adapted for semi-

closed-loop control

using poorly reactive

systems

Insulin delivery is modulated

according to:

observed relationships

between amount of

delivered insulin and

associated blood

glucose variations

+/- physiological model

What Kind of Algorithm ?

The Cambridge Experience of Closed-

Loop Experience at Night-Time in Type 1

Diabetic Children and Adolescents:

Minimized Time Spent in Hypoglycaemia

R. Hovorka et al, Lancet 2010

Subcutaneous Glucose Sensing, Algorithm

Management, and Subcutaneous Insulin Delivery

Study

Participant

PC running

Model Predictive Control

OmniPod Insulin

Management System

time reference BGFrequent YSI provides

reference BG

Attending

Physician

UCSB/Sansum APS ©

Artificial Pancreas System

Multi-national UVA/Padova/Montpellier Study

(2008-2009, n = 24)

B. Kovatchev, C. Cobelli, E. Renard et al,

J Diabetes Sci Technol 2010

Multi-National Study of Subcutaneous Model-

Predictive Closed-Loop Control in Type 1 Diabetes

Five-Fold Reduction in nocturnal hypoglycemia

with better overall glucose control within target range

Overnight % time within the

target range of 70-140 mg/dl

Nocturnal hypoglycemic

episodes (BG<70 mg/dl)

6478

23

5

B. Kovatchev, C. Cobelli, E. Renard, et al. J Diabetes Sci Technol 2010

J Diabetes Sci Technol, 2009

Safety Supervision Module (SSM)

• detects imminent hypoglycemia

• attenuates insulin delivery

• intercepts boluses potentially

requiring additional carbohydrate

• mitigates hyperglycemia by

automatically injecting hourly

correction boluses calculated

using the patient’s correction

factor

Investigational Trial Assessing SSM

(2009-2010)

Trial performed at CIC INSERM 1001, Montpellier, France

0

1

2

3

4

5

6

7

Total Trial Exercise Night-time

Results: Time spent out of safe range (%)

% time < 70 mg/dl

0

5

10

15

20

25

30

35

Total Trial Exercise Night-time

% time > 180 mg/dl

Twice less time spent in hypo- and in hyperglycemia

during closed-loop

Renard E, et al. EASD 2010

International Artificial Pancreas

Multi-Modular Approach: MPC2R

RCM

• computes the optimal insulin

infusion to control glucose

• takes into account the trade-off

between reaching the target as

fast as possible and using less

insulin as possible

• works using information of the

individual conventional therapy

Hardware Interface

Insulin Pump CGM

CGM &insulin data

Output to pump & CGM

18:00 20:00 22:00 00:00 02:00 04:00 06:00 08:0050

70

100

150

180

200

250

300

Pla

sma

glu

cose

[m

g/d

l]

Standard CSII Closed loop system

EX

16:00

dinner snack

Results:

SSM + RCM Trial Primary End Points

(2010)

Mean + SD

Renard E, et al. ADA 2011

50

55

60

65

70

75

80

85

90

95

Total Trial Day-time Night-time

% time b/w 70 & 180 mg/dl

40

45

50

55

60

65

70

75

80

Total Trial Day-time Night-time

% time b/w 80 & 140 mg/dl

Significantly improved time in Target

Ranges during closed-loop

Results:

SSM + RCM Trial Primary End Points

P < 0.05

Standard CSII Closed loop system

Results: Mean BG and BG Variability

70

90

110

130

150

170

Total Trial Night-time

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

Total Trial Day-time Night-time

Mean BG (mg/dl) Glucose Risk Index

Significantly lower glucose

excursions at night-time

P < 0.05P = 0.005 P < 0.05

Significantly lower average

glucose levels

Standard CSII Closed loop system

Project FP7 n 247138

WP leader 3.1: Clinical validation of the AP systems

The European Project AP@home (2010-2014)

Bringing the Artificial Pancreas @ home:

Testing of one-port systems

Comparison of two Artificial pancreas

systems in T1D

(CAT Trial, n=48, 6 EU centres)

Comparison of two Artificial pancreas

systems in T1D

(CAT Trial, n=48, 6 EU centres)

CAT Trial in Progress

CAT Trial - MTP-05 - Global

80

5060

0

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

340

360

380

400

16:00 18:00 20:00 22:00 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00

Time

Glu

co

se

(m

g/d

L)

YSI CAM YSI iAP YSI OL Hypo limit Hyper limit Meal carbs

Closed-Loop Experiences:

A New World in Diabetes Care