Moving Toward Artificial Pancreas -...

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Moving Toward Artificial Pancreas Moshe Phillip Institute for Endocrinology and Diabetes National Center for Childhood Diabetes Diabetes Technology Center Schneider Children’s Medical Center of Israel AACE May 2017 Texas, USA

Transcript of Moving Toward Artificial Pancreas -...

Moving Toward Artificial Pancreas

Moshe PhillipInstitute for Endocrinology and DiabetesNational Center for Childhood DiabetesDiabetes Technology CenterSchneider Children’s Medical Center of Israel

AACE May 2017Texas, USA

Disclosure Information

• Grants/research support: Medtronic, Novo Nordisk, Roche, Eli Lilly, Merck, Sanofi,

Bristol Myers-Squibb, Kamada, Lexicon and Pfizer.

• Honoraria or consultation fees: Sanofi, Medtronic, Novo Nordisk, Eli Lilly and

Pfizer. Advisory board : Sanofi, Medtronic, AstraZeneca, Eli Lilly and Pfizer.

• Stock shareholder: NG Solutions Ltd., DreaMed-Diabetes Ltd., Nutriteen

Professionals Ltd.

Closed Loop = Artificial pancreases

model predictive control (MPC)proportional-integralderivative control (PID)

fuzzy logic control (FL)

Steil GM, Rebrin K, Darwin C, Hariri F, Saad MF. Diabetes 55:3344-50, 2006

Cobelli C et al. Diabetes 2011;60:2672-82

Atlas E, Nimri R, Miller S, Grunberg EA, Phillip M.Diabetes Care. 2010;33:1072-6

Controller Design

Fuzzy Logic Applications

Medicine

• Digital image processing as a diagnostic tool

• Evaluation of cardiac functions, ECG analysis

• Warning system in Intensive care unit

Other

• Air conditioning, washing machines ,mono-rails, elevators

• Video game engines

• Special effects

What is Fuzzy –Logic ?

The Fuzzy –Logic Washing Machine

The degree of dirt

The type of dirt

The color

The required duration of washing time:

The weight

very dirty

If…..

oily

white

heavy

&

&

&

Then….

2 hours

Current BG is: ______ 𝒎𝒈/𝒅𝒍180

If:

AND

Past trend is: ______ 𝒎𝒈/𝒅𝒍

𝒎𝒊𝒏𝒖𝒕𝒆

-1.5

Future BG is: _____𝒎𝒈/𝒅𝒍

𝒎𝒊𝒏𝒖𝒕𝒆?

AND

Then

Goal: to reach a BG level of 90-110mg/dl

The Fuzzy-Logic Controller- The Physician’s Perspective

• Deal with approximate reasoning rather than precise

• Simple way to arrive at a definite conclusion based upon vague, ambiguous and imprecise data

• Aim to solve problems by mimicking how a physician would make decisions, only much faster

• Making it easier to implement a non linear system then in conventional control

• Tests show superiority of Fuzzy Logic controller over conventional controllers handling disturbances (meal, stress)

• It has the ability to learn and adapt

Fuzzy –Logic Approach & Decision Making

Emulate the line of Reasoning of Diabetes Caregivers

Grant P, Medical Engineering & Physics, 2007Pagliaro L, Intern Emerg Med, 2007

IF the Queen covers less squares than does the opponents AND the opponents Queen is closer to the center of the board than mine THEN capture opponents Queen using same or lesser-valued piece, if possible

May, 1997

Future NIH pivotal trials

Advances in Artificial Pancreas

BostonCambridgeVirginiaIsrael& IDC

February 7th, 2017

Cambridge Group - MPC Artificial Pancreas

Day and night closed-loop in young people with type 1 diabetes

Closed loop platform (FlorenceM system)

Advances in Artificial Pancreas

Main Protocol: N=240 , 6-month RCT, ratio 2:1 to Closed-Loop Control vs. Sensor-augmented pump therapy, 8 sites US & 3 EU

Major Eligibility Criteria: Type 1 diabetes > 1 y, Use of pump > 6 mon, Age ≥14 y

Outcomes:Primary outcome: Reduction of time below 70 mg/dL & non-inferiority for time above 180 mg/dL

Secondary Outcomes: HbA1c, technology acceptance

The System – a wireless mobile AP:Sensor: G5, DexcomInsulin Pump: t:AP ,Tandem or Accu-Chek Spirit Combo ,Roche

inControl AP residing on a smart phone and inControl Cloud remote monitoring & alert

The International Diabetes Closed-Loop (iDCL) Trial

Virginia Group - MPC Artificial Pancreas

Advances in Artificial Pancreas

Boston Group - MPC Artificial Pancreas

Late 2017/Early 2018

Mid 2018

Advances in Artificial Pancreas

Advanced HCL – MD-Logic & Medtronic AP

A Crossover Study Comparing Two Automated Insulin Delivery System Algorithms (PID vs. PID + Fuzzy Logic) in Adolescents & Young Adults with T1D

7 Clinical Sites: 4 US (IDC-

Minneapolis, Yale - New Haven,

Joslin- Boston, U of FL- Gainesville)

& 3 Europe (Schneider Children’s -

Israel, Hannover - Germany,

Ljubljana – Slovenia)

Primary Outcome: The advanced HCL (PID +Fuzzy Logic algorithm) will significantly reduce the time spent >180mg/dL during the day (7AM - 11PM) compared to a 670G system using a PID algorithm

Advances in Artificial Pancreas

Closed-Loop Research Sites (Academic)

Dual Hormone Single Hormone

Boston

How You Turn A Dream Into Reality??

Turning A Dream Into A Reality

MD-Logic Way of Bolusescan be combined to other closed-loop systems

April 2015

System Integration

Medtronic Received FDA Approval For the World’s First Hybrid Closed-Loop System For People With Type 1 Diabetes

DreaMed Received CE Marking For the World’s First Closed-Loop Algorithm For People With Type 1 Diabetes

February 2015

The First Step was already Done….

Road to Closed Loop productsPATH TO THE ARTIFICIAL PANCREAS

Pat

h t

o C

lose

d L

oo

p

Threshold SuspendMiniMed® 630G

Predictive SuspendMiniMed® 640G

Hybrid Closed Loop

Towards PersonalizedClosed Loop

Automatically doses insulin with minimal mealtime and exercise inputs from the patient

Suspends delivery when the system predicts a low is approachingSuspends delivery

when a low occurs

Improving interface & meal announcement: small, medium, large meal bolus settings and set meal insulin delivery buttons

Pattern recognition

Additional sensor inputs: Activity, food, heart rate, sleep, free fatty acids

Detecting sensor or infusion set failure

Advanced HybridClosed Loop

Combining PID with Fuzzy in collaboration with DreaMed

PID for insulin delivery & MPC for safety

Suspension protocol based on actual values

Suspension protocol w/ predictive algorithm

*Investigational only. Not approved and not commercially available

jamanetwork.com

Available at jama.com and on The JAMA Network Reader at mobile.jamanetwork.com

Bergenstal RM, Garg S, Weinzimer SA, et al.

Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients With Type 1 Diabetes

Published online September 15, 2016

Results

Results

MODAL DAY SENSOR GLUCOSE (SG) TRACINGS

Median and IQR of SG values throughout day and night.

Gray band and dotted line: run-in phase.

Pink band and solid line: study phase.

All Subjects

Adolescents

Adults

Bergenstal et al. JAMA 316:1407, 2016., EASD 2016, Oral

Noon MidnightMidnight

The future of diabetes management: Automated therapyFOR OPTIMIZED GLYCEMIC CONTROL

SAFETY SHIELD

* Investigational Device

2006

2007

2008

2009

2010

2011

2012

From the Idea to Regulatory Approved Closed-loop System

2003 - The Motivation & Idea

2006 - The DTC Center

2008 – The MD-Logic Controller

2009 - The DREAM Consortium

Diabetes wiREless Artificial Pancreas ConsortiuM

The DREAM way

DREAM 1 DREAM 3Camp

DREAM 44 Nights

DREAM2

Join Study

DREAM 46-14 weeks

DREAM 5Weekend

DREAM 52 weeks

Preclinical Swine Studies

Day & Night

Outpatient-Free livingPre-Clinical Feasibility & Inpatient Transitional

3 Days 2 weeks14 weeks4 nights 6 weeks1 nights

Learning Camp

1Atlas E et al. Diabetes Care, 20102Miller S et al. D. Technol Ther, 2011

3Nimri R et al. D. Technol Ther , 20124Nimri R et al. Pediatr Diabetes, 2013

7Nimri R et al. Diabetes Care, 20148Nimri R et al. Diabetes Obesity & Metab, 2016

2006 2008 2009 2010 2011 2012 2013 2014 2015 2016

Remote Monitoring

Evening & OvernightDay & Night

5Phillip M et al. NEJM, 20136Nimri R et al. Pediatric Diabetes, 2014

2011

2012

2013

2014

2015

2016

2017

First Outpatient Study at Diabetes Camp

Israel: 9-11 Oct 2011

Slovenia: 26-28 Nov 2011

Germany: 28-30 Jan 2012

Results: Glucose Control Over TimeG

luco

se [

mg

/dl]

Time [hh:mm]

MD-Logic Nights Control Nights

P<0.0001

IQR CGMMedian CGM

Median [IQR] Capillary Glucose

MD-Logic Overnight Studies

Site Duration No. Sign

*Inpatient 1 night 12

*Camp 1 night 56

*Home 4 nights 75

Home 6 weeks 22

*Home 3 days^ 47

Mean

• Age 10-55y• A1C 7-9.7%• *Multicenter• ^Only night

Studies Features

ITT (intension To Treat) analysis*Significant overall p-Value(using the Comprehensive meta analysis software)

.-80 -60 -40 -20 0 20

.

Favor Close-loop

Favor SAP

-20 0 20 40 60

Average BG * % within 70-180 mg/dl *

Favor SAP

Favor Closed-loop

.

-10 -8 -6 -4 -2 0 2 4Favor

Close-loopFavor

SAP

.

-60 -40 -20 0 20

.

Favor Close-loop

Favor SAP

Mean difference

% Below 60 mg/dl % Above 180 mg/dl *

closed-loopclosed-loop SAP SAP

MD-Logic Pooled Analysis of Free Living Overnight Studies

# of Nights

N=1033 N=976

Mean Overnight Glucose Levels

[mg/dl]

Histogram of Mean Overnight Glucose Levels, PP

139 (118,161)

1 Nimri R et al, Pediatric Diabetes 15: 20142 Nimri R et al, Diabetes Care 37: 20143 Nimri R et al, Diabetes Obesity & Metab 2016Unpublished data 3 months study

SAPCL

152 (120,190)

Factors predicting Closed-Loop Success

Rs -A Spearman's rank-order correlation was run to determine the relationship between 84 students' hA1C and percentage of readings between 70-140 mg/dl under CL treatment.PM - The Mann-Whitney U test was used to compare differences in time within 70-140 between male and female.

% of readings within 70-180 [mg/dl]

% of readings below 60 [mg/dl]

A1c (%) Age (years) Gender

6 7 8 9 10

20

40

60

80

100

20

40

60

80

100

20

40

60

80

100

010 20 30 40 50 60

6 7 8 9 10 10 20 30 40 50 600

1

2

3

4

0

1

2

3

4

0

1

2

3

4

Male Female

Male Female

2015

2016

2017

2018

2019

2020

2021

Glucositter CE-mark

Solid clinical evidence for safety & efficacy of the closed-loop system to become an integral part of diabetes management

Establishing

Quality system & Risk Management assurance (ISO 14971)Continuous maintenance

Hyperglycemia is Risky

The Challenges The MD-Logic Features

Enhanced Bolus Approach• Basal Bolus• Control to range & control to target• Interplay of basal & bolus• Continuous Corrections

Event Driven• Correction vs. Meal correction bolus is

feasible• Two engine of fuzzy logic

Learning & Adaptation• Personalized

Safety Features• Insulin on board• Safety layers

for insulin delivery

System “disturbances”• Post-Prandial glucose excursions

• Main factor to determine A1c• Limitation of current insulin • Announced meal “Errors”• Unannounced meal

• Physical Activity, Stress..• Unannounced exercise

Within & between days variability

“unexpected glucose responses”Home 24/7 challenges

Exercise

Postprandial Glucose Excursion

Basal - Bolus Approach

Duration No. Sign

4 nights 75

6 weeks 22

3 days^ 47

Mean

• Age 10-55y• A1C 7-9.7%• *Multicenter• ^Only night

• ITT (intension To Treat) analysis• *Significant overall p-Value

(using the Comprehensive meta analysis software)

Home Studies Features

.

Basal Difference Bolus Difference*

-5 -4 -3 -2 -1 0 1 2 3 4 5Favor SAP

Favor Close-loop

Favor SAP

Favor Close-loop

-5 -4 -3 -2 -1 0 1 2 3 4 5

. .

PMD-LogicControl

N.S10.5 ± 4.510.7 ± 5.74 night (N=75)

N.S10.1 ± 4.09.4 ± 3.36 weeks overnight (N=22)

N.S10.2± 3.311± 3.03 days, the overnight (N=47)

Total Insulin Dose [Units] 23:00 – 07:00 (Average ± STD)

Insulin

Glucose

Insulin absorption

Insulin action

Insulin Kinetics

Amount

Glycemic Index

Fat

Insulin-to-Carb ratio

Meal Content

Glucose level & trend

Insulin sensitivity

Insulin on board

Meal Time

Glucometer

Sensor interstitium/ plasma

Sensing Errors

Event Driven Treatment for Closed Loop Meal Challenge

Postprandial Glucose Excursion

*Atlas E et al, Diabetes Care. 2010 ;33:1072-6

Example of MD-Logic Full Closed-Loop Control

Glu

cose

(m

g/d

l)In

sulin

(U)

Time

Time

Meal

Detection(~20 min) *

Insulin

Absorption & Action(~60-100 min)

Default Basal

Legend:

CGM

YSI

Basal

Bolus

Sensor Delay

(~7 min)

Event Driven Treatment Makes the Difference

Meal Bolus

Correction Bolus

Hour of Day

No Bolus

Basal Λ

Bolus

SensorΛ

YSI Λ

Manual Meal Bolus

Λ Median [IQR]

70% BolusReduced – Bolus 70%138.2±39.4 mg/dl*

Glucose(mg/dl)

Normal Bolus130.5±35 mg/dl*

Unannounced175.4±42.7 mg/dl*

Over – Bolus 120%110.6±32.7 mg/dl*

*Mean ± SD

Glucose(mg/dl)

Basal(U/h)

Bolus(U)

Basal(U/h)

Bolus(U)

Event Driven Treatment Makes the Difference

N= 10

Learning & Adaptation - Controller Effort

Example 3

Example 1

Example 2

Data from 3 months overnight study

% of insulin dosing relative to open loop

15y, start A1c 9% (74.8 mmol/mol)

24y, start A1c 7.4% (57.4 mmol/mol)

45y, start A1c 7.1% (54.1 mmol/mol)

73 (44,89)% *

67 (33,95)% *

65 (43,87)% *

Since the beginning of the study (evening & night) *Time within range 70-180 mg/dl

0

10

20

30

40

50

60

70

21/5/13 26/5/13 31/5/13 5/6/13 10/6/13 15/6/13 20/6/13 25/6/13 30/6/13 5/7/13 10/7/13 15/7/13

D.A

L.L

S.T

S.S

Y.Y

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Weeks of Study

Modified CorrectionFactor

Learning & Adaptation Data from the 6 weeks study

Closed Loop Challenges- Exercise

Inpatient Study

The PHYSI-DREAM study

Camp study

Home studies

Overnight glucose control during and after physical activity with

closed-loop system -Gluco Sitter TM in youth with type 1 diabetes

The PHYSI-DREAM study

Two-arms, crossover, open-label, randomized, in-hospital study Single site –Slovenia - Klemen Dovč. MD & Prof. Tadej Battelino. MD

Closed Loop Challenges- Exercise

• Primary Endpoint : % of readings < 60 mg/dl during and following afternoon exercise (till 7:00 AM next day)

• Secondary Endpoints: % of readings < 70, >250 and

within 70-180 mg/dl

Inclusion Criteria N=20 , Mean ± SD

9 females

Age: 10 - 17 years 14.2±2 years

T1D > 1 year 8.3±3.2 years

CSII > 3 months 7.4±3.2 years

BMI 5° - 95°p.le 21.10±2.78 kg/m2

HbA1c < 9%(75 mmol/mol) 7.7±0.6 % (60.0±6.8 mmol/mol)

Insulin dose - 0.8±0.2 units/kg/day

VO2max 43.3±9.3 ml/kg/minPulse 186.6 ±10.2 /min

Study Population

The PHYSI-DREAM Study

Closed Loop Challenges- Exercise The PHYSI-DREAM Study

Over-the Weekend (60h)

Day & NightMD-Logic Control

N=47* Median (IQR)

Age (years) 16.1(13.2,18.5)

Gender (M/F) 18/29

Body Mass Index -SD Score 0.2 (-0.2,0.8)

A1c % (mmol/l) 7.6 (7,8.1), 59.6 (53,65)

Diabetes duration (years) 9.4 (5,12.7)

Pump therapy duration (years) 5.4 (3.1,9.4)

Daily insulin dose (units/kg) 0.8 (0.7, 0.9)

Study PopulationTwo Sites – Germany & Israel

* including Pilot study

Primary Endpoint • Time within 70-180 mg/dl

Main Inclusion

• Type 1 Diabetes > 1y• 12 – 65 years old• 6.5 ≤ HbA1c ≤ 10%• Pump > 3 months• CGM use

Randomized, cross-over MD-Logic vs. SAP

Day & Night Real-Life Challenge

Over the Weekend MD-Logic Study ITT Results [60h]

0

20

40

60

80

0

20

40

60

80

P=N.S P=0.02 P=0.02

% Within 70-180 mg/dl % > 180 mg/dl% < 60 mg/dl

69.4

51.9

0.50.4

0

40

80

120

160

200

144

Glucose Levels [mg/dl]

SAP MD-Logic

• ITT analysis, (N=47) • Median (IQR)

% o

f Ti

me

163

0

20

40

60

80

25.3

42.2

P*ControlMD-LogicN=47 (Median & IQR)

0.13109.6 (96,129)118.4 (99.8, 152.7)Total Insulin Dose [Units]

0.000250.4 (44.5,68.8)44.5(36.3,68)Total Basal Insulin [Units]

0.00158 (44.2,69.4)69.2 (60,90)Total Bolus Dose [Units]

Day & Night MD-Logic Control

P=0.03

0 1 2 3 4 5

% of Readings

P<0.01

P=0.02

P<0.01

MD-Logic SAP

23:00-07:00

07:00-23:00

0 20 40 60 80 100

Within 70-180 [mg/dl]

NS

NS

NS

0 20 40 60

Above 180 [mg/dl]

P<0.01

P=0.02

P<0.01

Day & Night MD-Logic Control - PP results

Below 60 [mg/dl]

N=29

2016

2017

2018

2019

2020

2021

2022

Advanced Closed Loop with Automated Correction Bolus

CampJune 2016

Inpatient 36 HDec 2016

The Next Step on the Closed Loop Path

Feasibility study to evaluate Hybrid-Logic Closed Loop System

Time In Day

Glu

cose

Val

ue

[mg/

dl]

MD- Logic Boluses [Units]

Basal PID Rate [Units/Hour]

Unannounced Lunch - 95 gr Carb Meal

14 years, A1c 7.8%

Insu

lin

FDA Approved

What's Next ?

Goal : commercial availability in 2020

NIH funded study

• cross-over study

• 124 patients from 7 centers (USA&EU)

• 670 G vs. 690 G

Thank you for your attention !

THE DREAM TEAM