Variations in tissue selectivity amongst insulin secretagogues: a systematic review
Bibliographic record
Abstract
AIM: Insulin secretagogues promote insulin release by binding to sulfonylurea receptors on pancreatic β-cells (SUR1). However, these drugs also bind to receptor isoforms on cardiac myocytes (SUR2A) and vascular smooth muscle (SUR2B). Binding to SUR2A/SUR2B may inhibit ischaemic preconditioning, an endogenous protective mechanism enabling cardiac tissue to survive periods of ischaemia. This study was designed to identify insulin secretagogues that selectively bind to SUR1 when given at therapeutic doses. METHODS: Using accepted systematic review methods, three electronic databases were searched from inception to 13 June 2011. Original studies measuring the half-maximal inhibitory concentration (IC(50)) for an insulin secretagogue on K(ATP) channels using standard electrophysiological techniques were included. Steady-state concentrations (C(SS)) were estimated from the usual oral dose and clearance values for each drug. RESULTS: Data were extracted from 27 studies meeting all inclusion criteria. IC(50) values for SUR1 were below those for SUR2A/SUR2B for all insulin secretagogues and addition of C(SS) values identified three distinct patterns. The C(SS) for gliclazide, glipizide, mitiglinide and nateglinide lie between IC(50) values for SUR1 and SUR2A/SUR2B, suggesting that these drugs bind selectively to pancreatic receptors. The C(SS) for glimepiride and glyburide (glibenclamide) was above IC(50) values for all three isoforms, suggesting these drugs are non-selective. Tolbutamide and repaglinide may have partial pancreatic receptor selectivity because IC(50) values for SUR1 and SUR2A/SUR2B overlapped somewhat, with the C(SS) in the midst of these values. CONCLUSIONS: Insulin secretagogues display different tissue selectivity characteristics at therapeutic doses. This may translate into different levels of cardiovascular risk.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".