The landmark trials DCCT/EDIC trials show how important good glycaemic control is for the prevention of late complications in type 1 diabetes (T1D). According to the American Diabetes Association (ADA) an HbA1c target value of less than 7% should be targeted throughout all age groups to reduce the risk of late complications unless safety issues for individual patients or patients groups indicate higher HbA1c goals. However, most patients have difficulties to reach this target value despite improvements in diabetes therapy options during the last years. Especially the age group between 15 and 25 years have significantly higher HbA1c levels as analysis of data of various countries show [1]. The analysis of an US database, the so-called T1D Exchange registry which contains data of more than 35000 patients with T1D, indicate that glycaemic control in adolescents and young adults seems to get even worse in the past years as from 2012 until 2018 the average HbA1c has increased, particularly in the younger age groups [2].

The euglycemic, hyperinsulinemic glucose clamp is the gold standard for the determination of pharmacokinetic and pharmacodynamic (PK/PD) effects of (new) anti-diabetic drugs, in particular insulins. In a typical glucose clamp experiment, a drug-induced decline in blood glucose (BG) concentrations is prevented by infusing glucose with a variable glucose infusion rates (GIR) to keep blood glucose concentrations (BG) as closely as possible to a pre-defined target level. 

From fitness trackers and health apps to sophisticated sensors and software that support clinical decision, wearables are reshaping the delivery of health care. The use of wearables allows patients to keep track and take control of their own health and can offer primary care professionals with day-to-day insights of patients’ health conditions, allowing early diagnosis and interventions to take place even outside of traditional care facilities. Wearables allow the collection of real-world data in everyday life settings and can be especially useful to enable a precision medicine approach and optimize treatment of chronic diseases [1].

How machine learning methods are changing diabetes research and diabetes care

When voice assistants tell us what the weather will be like, when self-driving cars steer through dense city traffic, or when computers sort our digital photo collections by the faces of our families and friends, whenever computers take over these tasks that would have required human intelligence only a few years back, artificial intelligence (AI) is the underlying technology.

Non-invasive Continuous Glucose Monitoring (niCGM) is the holy grail of glucose monitoring. But the quest has been arduous so far; research carried out over the past 35 years has not resulted in in a device with durable availability on the market [1, accessed March 23, 2020].

Two devices have briefly been commercially available. The first, called GlucoWatch Biographer, has been for sale from 2002 to 2007. Poor local tolerability and low accuracy were the limiting factors. The device applied a low voltage current to obtain interstitial fluid transcutaneously. Skin irritation, probably inherent to this technique, together with insufficient accuracy, resulted in withdrawal from the market and dissolvement of the manufacturer [2].

In recent years there has been rapid growth in the field of medical and health technology. Not only the number of players in this sector has increased over time, but the type of products and services including those that integrate artificial intelligence (AI)-powered components has changed too. These novel health technologies face new challenges in terms of development, validation, implementation, usability and adoption. Innovators can face hurdles not only to obtain regulatory approval but also to achieve sustainable adoption to meet stakeholders’ expectations, as they often require substantial evidence of impact and value before deciding to invest on a novel product. This has implications for the route that companies including Contract Research Organizations (CROs) need to navigate to bring an innovative idea to market.

In Europe chronic diseases account for 86% of deaths and 77% of disease burden, thereby creating a tremendous challenge on societies. At the same time digitisation is bringing huge technological and cultural opportunities. In healthcare the usage of data-driven forecasts on individual and population health as by integration of artificial intelligence (AI)-enabled algorithms has the potential to revolutionise health protection and chronic care provision while securing the sustainability of healthcare systems.

Lipohypertrophy (LH) is a common side effect of insulin therapy in patients with diabetes mellitus. The prevalence of lipohypertrophy is high with cross-sectional studies reporting up to 64% of patients being affected, with higher numbers in type 1 diabetes. Predisposing factors for the development of lipohypertrophy include duration of insulin treatment, needle reuse frequency, BMI and incorrect injection and site rotation techniques. Particularly the latter seem to be of major importance as re-education of patients in proper injection site rotation with avoidance of LH tissue was reported to improve glycaemic control [1].

Type 1 diabetes is a T-cell mediated autoimmune disease. For more than 40 years, researchers have tried to intervene in the autoimmune process to halt or perhaps even reverse the slow destruction of insulin producing beta cells [1]. This has proven an elusive goal, but very recently FDA granted a breakthrough therapy designation to teplizumab, an anti-CD3 monoclonal antibody  [2]. The drug modifies CD8+ T lymphocytes, which are thought to be the key effector cells that kill beta cells.

Waiving Bridging studies under certain circumstances for biosimilar applications?

A bridging study is a study performed in a new region to provide pharmacodynamic or clinical data on efficacy, safety, dosage and dose regimen that will allow extrapolation of foreign clinical data to the population in the new region [1]. However, in most cases bridging studies between an original product versus a so called foreign reference or a local reference product generate costs without providing any notable benefit for the specific patient, nor notable scientific output.