118-LB: The Antipsychotic D2 Receptor Antagonist Diphenylbutylpiperidines Improve Glycemia in Experimental Obesity by Inhibiting Succinyl-CoA—3-Ketoacid CoA transferase
Bibliographic record
Abstract
Purpose: Despite significant progress in understanding the pathogenesis of type 2 diabetes (T2D) , it remains difficult to manage, hence, new therapeutic options are required. We previously observed that elevated skeletal muscle succinyl CoA:3-ketoacid CoA transferase (SCOT) activity, the rate-limiting enzyme of ketone (KB) oxidation, contributes to obesity-induced hyperglycemia. Moreover, we identified that the antipsychotic agent, pimozide, is a SCOT inhibitor with glucose-lowering actions. In silico molecular modeling determined that a wide range of DPBPs can theoretically inhibit SCOT activity, therefore, we determined whether the DPBP drug class could be repurposed for the treatment of T2D. Methods: 8-week-old male wild-type and muscle-specific/brain-specific SCOT knockout (SCOTMuscleKO, SCOTBrainKO) mice were subjected to experimental obesity via consumption of a high fat, high sugar diet for 12-weeks. Lean control mice received a low fat, low sugar diet. At 8-weeks, lean and obese mice were treated with DPBPs (penfluridol, fluspirilene, pimozide (10mg/kg)) once every 2 days via oral gavage for 14-days, following which circulating KB levels and glucose homeostasis were assessed. To rule out a contribution of the canonical actions of DPBPs as dopamine 2 (D2) receptor antagonists to DPBP-mediated glucose-lowering, obese mice were treated with the structurally unrelated D2 receptor antagonist, lurasidone (10 mg/kg) . Results: All tested DPBPs improved glucose homeostasis in obese mice through a mechanism dependent on the inhibition of both brain and muscle SCOT activity. Treatment with lurasidone failed to improve glycemia in obese mice, thus consistent with a SCOT-dependent mechanism of action. Conclusions: Our findings suggest all DPBPs have glucose-lowering actions and therefore may have clinical utility in being repurposed for the treatment of T2D. Disclosure S. Tabatabaei dakhili: None. C. A. Velazquez: None. P. A. Crawford: Advisory Panel; Abbott Diabetes, Johnson & Johnson Global Services. M. Glover: None. R. Al batran: None. J. R. Ussher: None. R. Abou farraj: None. A. A. Greenwell: None. C. T. Saed: None. K. Yang: None. K. Gopal: None. J. S. F. Chan: None. C. Lee: None. F. Eaton: None. Funding Canadian Institutes of HealthResearch
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".