Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes

Abstract

Reusing published models saves time; time to be used for informing decisions in drug development. In antihyperglycemic drug development, several published HbA1c models are available but selecting the appropriate model for a particular purpose is challenging. This study aims at helping selection by investigating four HbA1c models, specifically the ability to identify drug effects (shape, site of action, and power) and simulation properties. All models could identify glucose effect nonlinearities, although for detecting the site of action, a mechanistic glucose model was needed. Power was highest for models using mean plasma glucose to drive HbA1c formation. Insulin contribution to power varied greatly depending on the drug target; it was beneficial only if the drug target was insulin secretion. All investigated models showed good simulation properties. However, extrapolation with the mechanistic model beyond 12 weeks resulted in drug effect overprediction. This investigation aids drug development in decisions regarding model choice if reusing published HbA1c models.

Optimization of trial duration to predict long‐term HbA1c change with therapy: A pharmacometrics simulation‐based evaluation

“…Wellhagen et al reported similar results. 13 The second-worst model was FFH 2 , which showed misprediction in particular for liraglutide, although smaller than FFH SS .…”

“…Inclusion criteria for baseline HbA1c and FPG were 7.5%–9.8% (58.5–83.6 mmol/mol; HbA1c (mmol/mol) = 10.93 ⋅ HbA1c (%) – 23.5 mmol/mol) 15 and 7.0–13.3 mmol/L, 13 respectively. In addition, patients with glucose <4.5 mmol/L were excluded.…”

“…Wellhagen et al 13 compared four of the aforementioned models (ADOPT, IGRH, FHH, and FFH ss ), looking at power, prognostic, and extrapolation predictive performance in relation to hypothetical drug effects. The authors of that work concluded that the best model depends on the intended use and the hypothetical drug effect.…”

Lessons learned from IDeAl — 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

“… If fast computations of power curves are needed from a non-linear mixed effects model, we recommend using the parametric power estimation algorithm as implemented in the stochastic simulation and estimation tool of PsN (potentially with a type-I correction based on the “randtest” tool in PsN) [ 17 , 20 , 21 ]. The simulation methods described above can be utilized to investigate the effects of using different, smaller, more parsimonious models to evaluate data from complicated biological systems prior to running a clinical study [ 28 , 29 ]. We recommend the use of Sampling Importance Resampling to characterize the uncertainty of non-linear mixed effects model parameter estimates in small sample size studies.…”

“…The simulation methods described above can be utilized to investigate the effects of using different, smaller, more parsimonious models to evaluate data from complicated biological systems prior to running a clinical study [ 28 , 29 ].…”