AI Summary
Researchers at MIT have developed a computational model to predict how the human body will respond to different versions of glucose-responsive insulin (GRI). The model can also compare human responses to those of lab animals used in preclinical testing. The study analyzed a discontinued GRI clinical trial and found that the drug's lack of effectiveness in humans was due to differences in the behavior of a sugar receptor. The model can be used to design improved GRIs.
For diabetes patients who must give themselves frequent insulin injections, the risk of low blood sugar can be life-threatening. A potential solution is a type of engineered insulin that circulates in the body and springs into action only when needed. Researchers working on this type of “glucose-responsive insulin” (GRI) hope that it could be injected less often and help the body maintain normal blood sugar levels for longer periods of time.
To help in the efforts to develop this kind of insulin, MIT engineers have created a computational model that predicts how the human body will respond to different versions of GRIs. Their model is unique in that it can also compare the human response to those of lab animals used for preclinical testing of GRIs.
In a new study, the MIT team used the model to analyze the results of a recent GRI clinical trial that was discontinued because the drug showed little effect in humans. Their analysis found that the drug, which had worked well in animal studies, acted differently in the human body because of differences in the behavior of a sugar receptor that helps to control the drug’s action.
Using this model, researchers could design novel GRIs and obtain better