Pharmacogenomic analysis of ATP-sensitive potassium channels coexpressing the common type 2 diabetes risk variants E23K and S1369A
Bibliographic record
Abstract
OBJECTIVES: The common ATP-sensitive potassium (KATP) channel variants E23K and S1369A, found in the KCNJ11 and ABCC8 genes, respectively, form a haplotype that is associated with an increased risk for type 2 diabetes. Our previous studies showed that KATP channel inhibition by the A-site sulfonylurea gliclazide was increased in the K23/A1369 haplotype. Therefore, we studied the pharmacogenomics of seven clinically used sulfonylureas and glinides to determine their structure-activity relationships in KATP channels containing either the E23/S1369 nonrisk or K23/A1369 risk haplotypes. RESEARCH DESIGN AND METHODS: The patch-clamp technique was used to determine sulfonylurea and glinide inhibition of recombinant human KATP channels containing either the E23/S1369 or the K23/A1369 haplotype. RESULTS: KATP channels containing the K23/A1369 risk haplotype were significantly less sensitive to inhibition by tolbutamide, chlorpropamide, and glimepiride (IC50 values for K23/A1369 vs. E23/S1369=1.15 vs. 0.71 μmol/l; 4.19 vs. 3.04 μmol/l; 4.38 vs. 2.41 nmol/l, respectively). In contrast, KATP channels containing the K23/A1369 haplotype were significantly more sensitive to inhibition by mitiglinide (IC50=9.73 vs. 28.19 nmol/l for K23/A1369 vs. E23/S1369) and gliclazide. Nateglinide, glipizide, and glibenclamide showed similar inhibitory profiles in KATP channels containing either haplotype. CONCLUSION: Our results demonstrate that the ring-fused pyrrole moiety in several A-site drugs likely underlies the observed inhibitory potency of these drugs on KATP channels containing the K23/A1369 risk haplotype. It may therefore be possible to tailor existing therapy or design novel drugs that display an increased efficacy in type 2 diabetes patients homozygous for these common KATP channel haplotypes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".