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New therapies in Type 1 Diabetes
Carol Huang, MD, PhD Alberta Children’s Hospital University of Calgary
Outline
• History of insulin • Ar=ficial Pancreas • Inhaled insulin • “Smart” insulin
History of Insulin
• 1869 -‐ Paul Langerhans iden4fy “Islets” in the pancreas • 1889 – Oscar Minkowski removed pancreas from dogs to study diges4on, and found the dogs have sugar in their urine – first link of pancreas and diabetes
• 1906/1916 – George Ludwig Zuelzer/Nicolae Paulescu were par4ally successful trea4ng dogs with pancrea4c extract
• 1921 –Ban4ng, Best, kept a diabe4c dog Marjorie alive for the en4re summer by injec4ng her with the pancrea4c extract. Macleod and Collip assisted them to purify fetal calf pancreas
• Jan 11, 1922 – Leonard Thompson received the first insulin injec4on, but impurity caused severe allergic reac4on. Jan 23, second injec4on successfully eliminated glycosuria. First American pa4ent, Elizabeth Hughes Gosse[, daughter of the governor of New York.
• November, 1922 – Eli Lilly and Company was able to make large quan4ty of insulin for sale.
History of Insulin • 1930s-‐1950s – long-‐ac4ng insulin, protamine zinc insulin was used to reduce the number of injec4ons needed
• 1950s – neutral protamine Hagedorn (NPH) became available • 1977 – Arthur Riggs and Keiichi Itakura at City of Hope and Herbert Boyer at Genetech made the first gene4cally engineered human insulin using E. Coli
• 1982 – Genetech and Eli Lilly sold the first commercially available biosynthe=c human insulin (Humulin)
• 1993 – DCCT showed importance of good glycemic control
History of Insulin
• 1996 – Lispro (Humalog) • 2000 – Aspart (Novo Rapid), Glargine (Lantus) • 2004 – Glulisine (Apidra) • 2005 – Detemir (Levemir) • 2006 – Exubera® launched (withdrawn in 2007) • 2008 – Ontario funds insulin pump • 2013 – Alberta funds insulin pump • (2013 – degludec – rejected by FDA) • (Oct 2013 – MannKind filed for inhaled insulin approval with FDA)
• Oct 2013 – FDA approved “ar=ficial pancreas” – MiniMed 530G (Metronic)
Arti:icial Pancreas
BMC Medicine, 2011
Arti:icial Pancreas Objec=ve Insulin-‐delivery modula=on
Reduce severity and/or dura4on of hypoglycemia
Suspension of insulin delivery at hypoglycemia (low glucose suspend)
Hypoglycemia preven4on Pre-‐emp4ve suspension
Control to range Modula4on of insulin delivery outside target range to limit hypo/hyperglycemia
Closed-‐loop system with meal/exercise announcement
Modula4on of insulin delivery aher meals using boluses administered by pa4ent with announcement of these, and exercise to the algorithm
Fully closed-‐loop system Modula4on of insulin delivery when the control algorithm is unaware of meals, exercise, stress and other lifestyle disturbances that affect glucose control; glucagon may be co-‐administered to reduce risk of hypoglycemia
BMC Medicine, 2011
BMC Medicine, 2011
Nocturnal Glucose Control with an Arti:icial pancreas at camp
• Mul4na4onal (Israel, Germany, Slovenia) • Safety and efficacy of an ar4ficial pancreas system for control of night 4me glucose levels in pa4ents (10-‐18 year olds) at diabetes camp
• Compare “closed-‐loop” (i.e. ar4ficial pancreas) vs. “open-‐loop” (i.e. sensor-‐augmented insulin pump)
• Primary goal: • Number of hypoglycemia (sensor glucose value of <3.5mmol/L for at least 10 consecu4ve minutes)
• Time spend with glucose levels <3.3 mmol/L • Average overnight glucose levels
NEJM, 2013
Nocturnal Glucose Control with an Arti:icial pancreas at camp
• Insulin pump (Paradigm Veo, Metronic) • Sensor (Enlite sensor, Medtronic) • Alarms for BG>19.4mmol/L or BG<4.2 mmol/L)
• Glucose meter (Contour) • Finger poke at meals, 2 hrs post meals, bed4me, 3-‐hour intervals throughout the night
• Info on all meals, hypoglycemic episodes, exercise were used to derive personalized sepngs for the ar4ficial pancreas
NEJM, 2013
Nocturnal Glucose Control with an Arti:icial pancreas at camp
• Ar4ficial pancreas and remote monitoring system • MD-‐Logic
• wireless, fully automated closed-‐loop system uses an algorithm based on • fuzzy-‐logic theory (to imitate the line of reasoning of diabetes caregivers) • a learning algorithm and • an alerts module and personalized system sepngs
• Insulin is administered according to the glucose readings in a fully automated manner without informa4on on the size or 4me of meals
• Uses an individual pa4ent’s treatment management • Insulin delivery regimen (i.e. basal insulin plan and insulin correc4on factor) • Insulin pharmacodynamic parameters • Pa4ents physical characteris4cs • CGM readings • Glucometer measurements • Physical ac4vity
è Use all of the above to determine insulin treatment NEJM, 2013
Nocturnal Glucose Control with an Arti:icial pancreas at camp
• Fuzzy-‐logic • Science of reasoning, thinking, and inference that recognizes that not everything is true or false in the real world
• The correctness of any statement becomes a ma[er of degree • Main elements of the fuzzy logic controller:
• Fuzzy sets of mul4ple inputs • Single or mul4ple outputs • Fuzzy rules structured according to the form of • IF (input) – THEN (output) and methods of “fuzzifica4on” and
“defuzzifica4on” to evaluate the fuzzy rule output based on the input
NEJM, 2013
Nocturnal Glucose Control with an Arti:icial pancreas at camp
NEJM, 2013
(3.5)
(3.3)
(7.0) (7.8)
Nocturnal Glucose Control with an Arti:icial pancreas at camp
NEJM, 2013
Nocturnal Glucose Control with an Arti:icial pancreas at camp
NEJM, 2013
Nocturnal Glucose Control with an Arti:icial pancreas at camp
NEJM, 2013
Feasibility of Outpa=ent Fully Integrated Closed-‐Loop Control
First studies of wearable ar4ficial pancreas
Featured Ar=cle:
Boris P. Kovatchev, Ph.D., Eric Renard, M.D., Ph.D., Claudio Cobelli, Ph.D., Howard C. Zisser, M.D., Patrick Keith-‐Hynes, Ph.D., Stacey M. Anderson, M.D., Sue A. Brown, M.D.,
Daniel R. Chernavvsky, M.D., Marc D. Breton, Ph.D., Anne Farret, M.D., Ph.D., Marie-‐Josée Pelle4er, M.D., Jérôme Place, M.S.C., Daniela Bru[omesso, M.D., Ph.D., Simone del Favero, Ph.D., Roberto Visen4n, M.S.C., Alessio Filippi, M.D., Rachele Sco[on, M.D.,
Angelo Avogaro, M.D., Ph.D., Francis J. Doyle III, Ph.D.
Diabetes Care Volume 36: 1851-‐1858
July, 2013
Feasibility of outpatient Fully Integrated Closed-‐Loop Control
• Inexpensive • wearable hardware
• computa=onally capable of running closed-‐loop control algorithms
• Wirelessly connectable to CGM devices and insulin pumps
• Capable of broadband communica=on for remote monitoring and safety supervision
Objectives
• To evaluate the feasibility of a wearable ar=ficial pancreas system, the Diabetes Assistant (DiAs)
• System uses a smart phone as a closed-‐loop control
plaZorm
Kovatchev B. P. et al. Diabetes Care 2013;36:1851-‐1858
Study Designs and Methods
• 20 pa4ents with type 1 diabetes were enrolled at the Universi4es of Padova, Montpellier, and Virginia and at the Sansum Diabetes Research Ins4tute
• U.S. studies were conducted en4rely in an outpa4ent sepng • Studies in Italy and France were hybrid hospital–hotel admissions • A con4nuous glucose monitoring/pump system was placed on the
subject and was connected to DiAs • Pa4ent operated the system via the DiAs user interface in open-‐
loop mode (first 14 h of study), switching to closed-‐loop for the remaining 28 h
• Study personnel monitored remotely via 3G or WiFi connec4on to DiAs and were available onsite for assistance
Kovatchev B. P. et al. Diabetes Care 2013;36:1851-‐1858
Kovatchev B. P. et al. Diabetes Care 2013;36:1851-‐1858
Results
• Total dura=on of proper system communica=on func=oning was 807.5 h (274 h in open-‐loop and 533.5 h in closed-‐loop) • 97.7% of the total possible =me from admission to discharge represented • Predetermined primary end point of 80% system func=onality was exceeded
Kovatchev B. P. et al. Diabetes Care 2013;36:1851-‐1858
Conclusions
• Contemporary smart phones are capable of running outpa=ent closed-‐loop control
• Future steps should include equipping insulin pumps
and sensors with wireless capabili=es, as well as studies focusing on control efficacy and pa=ent-‐oriented clinical outcomes
Kovatchev B. P. et al. Diabetes Care 2013;36:1851-‐1858
Arti:icial pancreas
Factor Desirable improvements
Insulin delivery Faster absorp4on (or intraperitoneal delivery)
Glucose sensing Increased accuracy and reliability; reduced false-‐posi4ve hypoglycemia alarms
Insulin modula4on Adap4ve algorithm
Dual-‐hormone systems Dual-‐chamber pumps
Communica4on between glucose sensor and insulin pump
More reliable connec4vity
Human factors Reduced size of devices
BMC Medicine, 2011
Inhaled Insulin
• Technosphere insulin (IT), or Afrezza • Lung allows faster absorp4on of insulin, reach blood earlier
Journal of Diabetes Science and Technology, 2012
Journal of Diabetes Science and Technology, 2012
Inhaled Insulin
The Lancet, 2010
Inhaled Insulin
• Inhaled insulin at meal 4mes + bed4me insulin glargine (n=216) vs. twice daily premixed biaspart insulin (n=246)
• Inhaled insulin was 4trated to target fas4ng and pre-‐dinner BG of 4.4-‐6.1 mmol/L
• Followed for 52 weeks • Argen4na, Brazil, Canada, Chile, Mexico, Poland, Russia, Spain, UK and USA
• Technosphere insulin (IT, AFREZZA®) reach maximum blood concentra4ons ~14 min and a 4me to maximum effect of 35-‐40 min
The Lancet, 2010
Inhaled Insulin
The Lancet, 2010
Inhaled insulin + Glargine
Biaspart Insulin Difference
Change in HbA1c -‐0.66% -‐0.72% 0.06%
The Lancet, 2010
Inhaled Insulin
Inhaled insulin: less weight gain (0.9kg vs 2.5kg)
The Lancet, 2010
“Smart” insulin
ACSNANO, 2013
ACSNANO, 2013
Ques4ons?