Modeling Subcutaneous Absorption of Long-Acting Insulin Glargine in Type 1 Diabetes

Objective: Subcutaneous (sc) administration of long-acting insulin analogs is often employed in multiple daily injection (MDI) therapy of type 1 diabetes (T1D) to cover patient's basal insulin needs. Among these, insulin glargine 100 U/mL (Gla-100) and 300 U/mL (Gla-300) are formulations indicated for once daily sc administration in MDI therapy of T1D. A few semi-mechanistic models of sc absorption of insulin glargine have been proposed in the literature, but were not quantitatively assessed on a large dataset. The aim of this paper is to propose a model of sc absorption of insulin glargine able to describe the data and provide precise model parameters estimates with a clear physiological interpretation. Methods: Three candidate models were identified on a total of 47 and 77 insulin profiles of T1D subjects receiving a single or repeated sc administration of Gla-100 or Gla-300, respectively. Model comparison and selection were performed on the basis of their ability to describe the data and numerical identifiability. Results: The most parsimonious model is linear two-compartment and accounts for the insulin distribution between the two compartments after sc administration through parameter k. Between the two formulations, we report a lower fraction of insulin in the first versus second compartment (k = 86% versus 94% in Gla-100 versus Gla-300, p <; 0.05), a lower dissolution rate from the first to the second compartment (k
_sp
= 0.0013) versus 0.0008 min
^-1
in Gla-100 versus Gla-300, p <; <; 0.001), and a similar rate of insulin absorption from the second compartment to plasma (k
_a
= 0.0018) versus 0.0016 min
^-1
in Gla-100 versus Gla-300, p = NS), in accordance with the mechanisms of insulin glargine protraction. Conclusions: The proposed model is able to both accurately describe plasma insulin data after sc administration and precisely estimate physiologically plausible parameters. Significance: The model can be incorporated in simulation platforms potentially usable for optimizing basal insulin treatment strategies.