In the last decades, we have witnessed an increasing use of the so-called “new-generation” simulation environments to support diabetes research. These tools, usually based on mathematical models of glucose metabolism and cohorts of virtual subjects spanning the real population variability, allow the assessment of efficacy and safety of different treatments under several experimental conditions before human testing, with consequent risk reduction and time- and cost-savings.
Originally, diabetes simulators were mainly employed in type 1 diabetes field, especially for preclinical testing of closed-loop insulin delivery control systems (i.e., the artificial pancreas). Later, its use has become common for testing latest developments in glucose sensors (e.g., continuous glucose monitoring) and agent-based medications (e.g., insulin analogs).
Great improvements have been recently achieved in order to improve simulators' reliability, i.e., their ability to well replicate realistic situations. For instance, new models capturing physiological diurnal glucose variability allowed extending simulation length over multiple days and months, enabling a suitable assessment of long-term treatments (e.g., adaptive controllers and long-acting insulins). However, the large heterogeneity of people with diabetes, and consequent the variety of treatments options, requires a constant update of simulation environments, aiming to reproduce as much as possible real-life conditions for different target populations (e.g., type 1 and type 2 diabetes, prediabetes and other stages of disease progression).
In this regard, this Research Topic will focus on the recent advances in diabetes simulators. We welcome original research and review papers focusing on:
· Providing useful insights about how to optimally design an in silico trial;
· Maximizing real-life resembling by including external factors (e.g., physical exercise and stress);
· Optimizing simulation for a specific population of interest;
· Using simulation for therapy optimization;
· Using simulation for artificial pancreas and decision support systems;
· Using simulation for diabetes care education.
In the last decades, we have witnessed an increasing use of the so-called “new-generation” simulation environments to support diabetes research. These tools, usually based on mathematical models of glucose metabolism and cohorts of virtual subjects spanning the real population variability, allow the assessment of efficacy and safety of different treatments under several experimental conditions before human testing, with consequent risk reduction and time- and cost-savings.
Originally, diabetes simulators were mainly employed in type 1 diabetes field, especially for preclinical testing of closed-loop insulin delivery control systems (i.e., the artificial pancreas). Later, its use has become common for testing latest developments in glucose sensors (e.g., continuous glucose monitoring) and agent-based medications (e.g., insulin analogs).
Great improvements have been recently achieved in order to improve simulators' reliability, i.e., their ability to well replicate realistic situations. For instance, new models capturing physiological diurnal glucose variability allowed extending simulation length over multiple days and months, enabling a suitable assessment of long-term treatments (e.g., adaptive controllers and long-acting insulins). However, the large heterogeneity of people with diabetes, and consequent the variety of treatments options, requires a constant update of simulation environments, aiming to reproduce as much as possible real-life conditions for different target populations (e.g., type 1 and type 2 diabetes, prediabetes and other stages of disease progression).
In this regard, this Research Topic will focus on the recent advances in diabetes simulators. We welcome original research and review papers focusing on:
· Providing useful insights about how to optimally design an in silico trial;
· Maximizing real-life resembling by including external factors (e.g., physical exercise and stress);
· Optimizing simulation for a specific population of interest;
· Using simulation for therapy optimization;
· Using simulation for artificial pancreas and decision support systems;
· Using simulation for diabetes care education.
