The Antipsychotic Dopamine 2 Receptor Antagonist Diphenylbutylpiperidines Improve Glycemia in Experimental Obesity by Inhibiting Succinyl-CoA:3-Ketoacid CoA Transferase
Bibliographic record
Abstract
Despite significant progress in understanding the pathogenesis of type 2 diabetes (T2D), the condition remains difficult to manage. Hence, new therapeutic options targeting unique mechanisms of action are required. We have previously observed that elevated skeletal muscle succinyl CoA:3-ketoacid CoA transferase (SCOT) activity, the rate-limiting enzyme of ketone oxidation, contributes to the hyperglycemia characterizing obesity and T2D. Moreover, we identified that the typical antipsychotic agent pimozide is a SCOT inhibitor that can alleviate obesity-induced hyperglycemia. We now extend those observations here, using computer-assisted in silico modeling and in vivo pharmacology studies that highlight SCOT as a noncanonical target shared among the diphenylbutylpiperidine (DPBP) drug class, which includes penfluridol and fluspirilene. All three DPBPs tested (pimozide, penfluridol, and fluspirilene) improved glycemia in obese mice. While the canonical target of the DPBPs is the dopamine 2 receptor, studies in obese mice demonstrated that acute or chronic treatment with a structurally unrelated antipsychotic dopamine 2 receptor antagonist, lurasidone, was devoid of glucose-lowering actions. We further observed that the DPBPs improved glycemia in a SCOT-dependent manner in skeletal muscle, suggesting that this older class of antipsychotic agents may have utility in being repurposed for the treatment of T2D.
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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 | 0.001 |
| 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".