A privileged dual action Alzheimer's disease therapeutic platform targeting immunopathic and proteopathic mechanisms: (<i>E</i>)-3-styrylindoles as inhibitors of indoleamine 2,3-dioxygenase-mediated tryptophan metabolism and β-amyloid aggregation
Bibliographic record
Abstract
The design of potent indoleamine 2,3-dioxygenase 1 (IDO1) enzyme inhibitors targeting immunopathic neuroinflammation has emerged as an area of interest for the treatment of Alzheimer's disease (AD); additionally, recent findings on the clinical benefits of antibodies preventing β-amyloid (Aβ) aggregation have renewed efforts to discover small molecule anti-aggregants targeting proteopathic protein misfolding. Exploiting an endogenous tryptophan-like scaffold, we describe the design and synthesis of small-molecule inhibitors of both immunopathic and proteopathic processes, thus presenting the possibility of single therapeutics acting simultaneously on multiple AD pathogeneses. Specifically, investigations on compounds that inhibit both IDO1 (in human recombinant enzyme, transfected HEK293 cells, and interferon-γ stimulated human microglia assays) and Aβ aggregation (in thioflavin-T and biotinylated-Aβ oligomeric assays) are presented. Five compounds have been identified with high potency against both targets, identifying ( E)-3-styryl indoles as useful tool compounds for developing Alzheimer's therapeutics. Brain penetration of these compounds via passive diffusion or active transport was predicted using Blood-Brain Barrier Score and Brain Exposure Efficiency Score calculations, respectively; the effects of efflux (pgp, BCRP), and influx (OCT1, OCT2) transporters were similarly predicted. Structure–activity relationships were rationalised with molecular docking and molecular dynamics simulations, which also provide insights for future lead compound optimisation.
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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 | 0.000 |
| 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".