  Around a third of type 2 diabetes patients (T2D) are escalated to basal
insulin injections. Basal insulin dose is titrated to achieve a tight glycemic
target without undue hypoglycemic risk. In the standard of care (SoC),
titration is based on intermittent fasting blood glucose (FBG) measurements.
Lack of adherence and the day-to-day variabilities in FBG measurements are
limiting factors to the existing insulin titration procedure. We propose an
adaptive receding horizon control strategy where a glucose-insulin fasting
model is identified and used to predict the optimal basal insulin dose. This
algorithm is evaluated in \textit{in-silico} experiments using the new UVA
virtual lab (UVlab) and a set of T2D avatars matched to clinical data
(NCT01336023). Compared to SoC, we show that this control strategy can achieve
the same glucose targets faster (as soon as week 8) and safer (increased
hypoglycemia protection and robustness to missing FBG measurements).
Specifically, when insulin is titrated daily, a time-in-range (TIR, 70--180
mg/dL) of 71.4$\pm$20.0\% can be achieved at week eight and maintained at week
52 (72.6$\pm$19.6%) without an increased hypoglycemia risk as measured by time
under 70 mg/dL (TBR, week 8: 1.3$\pm$1.9% and week 52: 1.2$\pm$1.9%), when
compared to the SoC (TIR at week 8: 59.3$\pm$28.0% and week:52 72.1$\pm$22.3%,
TBR at week 8: 0.5$\pm$1.3% and week 52: 2.8$\pm$3.4%). Such an approach can
potentially reduce treatment inertia and prescription complexity, resulting in
improved glycemic outcomes for T2D using basal insulin injections.
