Development and Optimization of a Target Engagement Model of BrainIDO Inhibition for Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Indoleamine 2,3-dioxygenase (IDO1) inhibition is a promising target as an Alzheimer's disease (AD) Disease-modifying therapy capable of downregulating immunopathic neuroinflammatory processes. METHODS: To aid in the development of IDO inhibitors as potential AD therapeutics, we optimized a lipopolysaccharide (LPS) based mouse model of brain IDO1 inhibition by examining the dosedependent and time-course of the brain kynurenine:tryptophan (K:T) ratio to LPS via intraperitoneal dosing. RESULTS: We determined the optimal LPS dose to increase IDO1 activity in the brain, and the ideal time point to quantify the brain K:T ratio after LPS administration. We then used a brain penetrant tool compound, EOS200271, to validate the model, determine the optimal dosing profile and found that a complete rescue of the K:T ratio was possible with the tool compound. CONCLUSION: This LPS-based model of IDO1 target engagement is a useful tool that can be used in the development of brain penetrant IDO1 inhibitors for AD. A limitation of the present study is the lack of quantification of potential clinically relevant biomarkers in this model, which could be addressed in future studies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".