Close the loop in diabetes care

Artificial pancreas systems are medical products that use algorithms informed by continuous glucose monitoring (CGM) data of a given patient, thereby regulating the rates of a continuous subcutaneous insulin infusion through an insulin pump. In this way, artificial pancreas systems are taking over control of the patient’s blood glucose levels.

Background

Diabetes is a multidimensional challenge for global societies. Despite the availability of drugs and technology many patients don’t reach their treatment goals.  According to the UK National Diabetes Audit data 2016-2017 only 30% of people with type 1 diabetes and 67% of people with type 2 diabetes achieved a HbA1c target of ⩽58 mmol/l (7.5%). When considering also blood pressure and cholesterol targets, these figures dropped to 19% and 41% respectively.

The good news is that diabetes leaves a huge room for Innovation. Risk factors are modifyable, type 2 diabetes is potentially reversible and the role of the patient’s self-management is of outstanding importance. Diabetes could serve as a paradigm for cracking down treatment inertia and narrowing the gap between the efficacy of investigational medicinal products seen in well controlled clinical trials and the lower than expected effectiveness of drugs observed in real-world chronic care (E2E gap) [1].

The glucose clamp is a method for the determination of pharmacokinetic and pharmacodynamic (PK/PD) effects of anti-diabetic drugs (e.g. insulin) where the blood glucose (BG) concentration lowering effect is antagonized by variable glucose infusion rates (GIR).

The first idea [1] for a glucose-responsive insulin (GRI, commonly referred to as smart insulin) was pitched almost 40 years ago, but to our knowledge only one compound has since undergone clinical evaluation. It seems a little surprising that it takes such a long time to develop a functioning GRI because its concept is actually relatively simple: At normal glucose concentrations, small amounts of insulin are released to keep blood glucose (BG) fairly constant. In response to rising glucose concentrations, for example after a meal, more insulin is released to limit the glucose rise and to return glucose concentrations back to normal. This closed-loop insulin release should limit BG variability and because insulin is only released when it is needed, it should also reduce the risk of over- and underdosing insulin. Two key elements are needed for a GRI: (1) A component that can ‘sense’ glucose and (2) a component that can respond and control the release of insulin. The concept may be simple, suggested solutions are complex and diverse [2].

The annual meeting of the European Association for the Study of Diabetes (EASD) took place this year in Berlin, Germany. The present text offers a selection of topics relevant for the field of type 2 diabetes mellitus (T2DM) discussed during that meeting.

Profil will be present at the 18th Annual Diabetes Technology Meeting with two posters and a presentation as invited speaker. The Annual DTM will take place from November 8 to November 10, 2018, in North Bethesda, Maryland. We are looking forward to this exciting event.

The far majority of clinical trials in diabetes exclude patients with active retinal disease, as interventions that lower glucose rapidly can temporarily worsen retinopathy. This was originally shown in type 1 diabetes [1] but more recently also in type 2 diabetes [2, 3]. Screening for diabetic retinopathy before inclusion in a clinical trial is relatively cumbersome, also because it often involves a separate visit to an ophthalmologist. The use of a fundus camera with offline interpretation by an ophthalmologist has gained widespread use in clinical practice, but not so much in the field of clinical trials.

In this blog I will briefly describe four recent studies on artificial intelligence approaches to automate the interpretation of retinal images. I will conclude with an outlook on how this may facilitate the screening of potential trial participants for diabetic retinopathy. But first a brief introduction to deep learning, the methodology applied in all these papers.

Berlin, Germany, 1-5 October 2018

Last week our team joined an exciting and busy 54^th EASD Annual Meeting in Berlin. We would like to give you a brief overview about those orals and posters presenting results from clinical trials Profil had been involved in.

Highlighting the role of open and trans-sectoral collaboration in the advancement of artificial pancreas solutions

Profil has recently published a peer-reviewed article on the topic of “Artificial Pancreas Systems for People With Type 2 Diabetes: Conception and Design of the European CLOSE Project ” in the Journal of Diabetes Science and Technology. Profil is the organisation coordinating CLOSE. The article is authored by CLOSE industry and academic partners, representatives of the EIT Health public-private partnership management boards, and key opinion leaders in the field.

The way forward to closed-loop metabolic control in diabetes care?

The challenge

Diabetes represents a huge and multidimensional challenge. Despite the availability of numerous treatment options, many patents still fail to reach their treatment goals. Administration of  the right amount of insulin at the right time still poses a great challenge for the self-management of many people with diabetes.

Accordingly there is a huge need to implement new innovative products and services improving both the effectiveness of diabetes care and the quality of life for people with diabetes. Particularly user-centered products and sercvices co-created with stakeholders including people with diabetes may have a high potential to increase treatment adherence thereby reducing the enormous pressure on healthcare systems.