KomIT - the new Center of Competence for Innovative Diabetes Therapy

During the next three years KomIT, the new Center of Competence for Innovative Diabetes Therapy, will be established at the German Diabetes Center (DDZ) based on a total funding of about 3.5 Mio Euro. This funding is jointly provided by the Land North Rhine-Westphalia and the European Union. KomIT is coordinated by Professor Michael Roden, the scientific Managing Director of the DDZ.

Unmet medical needs in NAFLD and NASH

NAFLD (non-alcoholic fatty liver disease) comprises a spectrum of conditions starting with adiposity of the liver (fatty liver, steatosis hepatis) and ranging up to NASH (non-alcoholic steatohepatitis). Currently about 30% of the general population and 60-80% of people with type 2 diabetes are considered to be affected by NAFLD [1].

Premature mortality from NAFLD comes from the development of diabetes and cardiovascular disease and progression of the fatty liver to the more irreversible and aggressive NASH which again predisposes for incident liver cirrhosis and hepatocellular carcinoma [2]. Despite the potentially disastrous outcome of NAFLD and an increasingly rich knowledge concerning the underlying molecular pathogenesis no rational drugs and therapies specifically dedicated to the treatment of NAFLD are available yet. 

In this blog we first provide a brief overview on the key pathogenetic drivers in NAFLD. Then we highlight the inherent opportunities and challenges for developers of lifestyle interventions and drugs tackling NAFLD. And last but not least we discuss some key issues related to the design and implementation of clinical trials in NAFLD and NASH.

If you want to learn more on Profil’s general perception of the liver’s role in health and disease you are invited to read our „Liver first? The liver in diabetes and cardiovascular disease“ series of blogs (part I, part II, part III).

Personalized Diabetes Management in Europe

In the year 2018 Profil joined the team creating the iPDM-GO (integrated Personalized Diabetes Management Goes Europe) consortium for implementing integrated Personalized Diabetes Management in Europe. Following a thorough and competitive evaluation the iPDM-GO project at the beginning of 2019 became part of the Innovation portfolio curated by EIT Health. EIT Health is a public-private knowledge and innovation community (KIC) of best-in-class health innovators backed by the European Union. The KICs Innovation ecosystem enables a collaborative multi-stakeholder approach to implementing a more effective diabetes management in Europe.

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.