The exploration of pioglitazone’s potential as a pharmacotherapy option for drug addiction
Bibliographic record
Abstract
Pioglitazone is a selective agonist for peroxisome proliferatoractivated receptor gamma (PPARγ) that is currently used for the treatment of type 2 diabetes mellitus. However, recent evidence suggests that the PPARγ pathway may be a promising novel target for drug addiction therapy. There has been considerable evidence with preclinical models of addiction that support pioglitazone’s therapeutic potential for opioid, alcohol, methamphetamine, and cocaine dependence. Although the precise mechanisms remain unclear, these preclinical studies suggest that pioglitazone blocks the excitation of ventral tegmental area dopamine signaling, which is associated with the addictive properties of abused drugs. Recently, clinical studies have also emerged to investigate the role of pioglitazone for drug addiction in humans. Clinical evidence supports preclinical findings that pioglitazone may indeed be beneficial for the treatment of cocaine dependence. Other clinical evidence suggests that pioglitazone may also be effective for nicotine addiction. Further clinical research is needed to investigate pioglitazone’s effects in opioid, alcohol, and methamphetamine addiction. Pioglitazone also has a favourable and safe profile. These findings suggest that pioglitazone may be a novel treatment option for drug dependence in the future. Due to its status as a medication approved by the Food and Drug Administration, there is a potential for accelerated establishment of pioglitazone as an addiction treatment method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".