Indoleamine 2,3 Dioxygenase as a Potential Therapeutic Target in Huntington’s Disease
Bibliographic record
Abstract
Within the past decade, there has been increasing interest in the role of tryptophan (Trp) metabolites and the kynurenine pathway (KP) in diseases of the brain such as Huntington's disease (HD). Evidence is accumulating to suggest that this pathway is imbalanced in neurologic disease states. The KP diverges into two branches that can lead to production of either neuroprotective or neurotoxic metabolites. In one branch, kynurenine (Kyn) produced as a result of tryptophan (Trp) catabolism is further metabolized to neurotoxic metabolites such as 3-hydroxykunurenine (3-HK) and quinolinic acid (QA). In the other branch, Kyn is converted to the neuroprotective metabolite kynurenic acid (KA). The enzyme Indoleamine 2,3 dioxygenase (IDO1) catalyzes the conversion of Trp into Kyn, the first and rate-limiting enzymatic step of the KP. This reaction takes place throughout the body in multiple cell types as a required step in the degradation of the essential amino acid Trp. Studies of IDO1 in brain have focused primarily on a potential role in depression, immune tolerance associated with brain tumours, and multiple sclerosis; however the role of this enzyme in neurodegenerative disease has garnered significant attention in recent years. This review will provide a summary of the current understanding of the role of IDO1 in Huntington's disease and will assess this enzyme as a potential therapeutic target for HD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".