Population Pharmacokinetic– Pharmacodynamic Analysis to Characterize the Effect of Empagliflozin on Renal Glucose Threshold in Patients With Type 1 Diabetes Mellitus
Bibliographic record
Abstract
Abstract Sodium glucose cotransporter 2 inhibitors increase urinary glucose excretion (UGE) by lowering the renal threshold for glucose (RTG). We aimed to quantify the effect of the sodium glucose cotransporter inhibitor empagliflozin on renal glucose reabsorption in patients with type 1 diabetes mellitus (T1DM) using a mechanistic population pharmacokinetic–pharmacodynamic (PK‐PD) model and to compare results with analyses in patients with type 2 diabetes mellitus (T2DM). The PK‐PD model was developed using data from a randomized phase 2 study in which patients with T1DM received oral once‐daily empagliflozin 2.5 mg, empagliflozin 10 mg, empagliflozin 25 mg, or placebo as an adjunct to insulin. The model assumed that UGE was dependent on plasma glucose and renal function and that empagliflozin lowered RTG. The final model was evaluated using visual predictive checks and found to be consistent with observed data. Calculated RTG with placebo was 181 mg/dL, and with empagliflozin (steady state) 1 mg and 2.5 mg was 53.4 mg/dL and 12.5 mg/dL, respectively. Empagliflozin 10 mg and 25 mg yielded negative RTG values, implying RTG was reduced to a negligible value. Although estimated PK‐PD parameters were generally comparable between patients with T1DM and patients with T2DM, slight differences were evident, leading to lower RTG and higher UGE in patients with T1DM compared with patients with T2DM. In conclusion, the model provided a reasonable description of UGE in response to administration of empagliflozin and placebo in patients with T1DM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".