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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".