The Emory‐Sage‐SGC‐JAX TREAT‐AD Center: Target Enabling Packages for Alzheimer’s Disease Emerging Targets
Bibliographic record
Abstract
Abstract Background There is an urgent need for new therapeutic and diagnostic targets for Alzheimer’s disease (AD). Dementia afflicts roughly 55 million individuals worldwide, and the prevalence is increasing with longer lifespans and the absence of preventive therapies. Given the demonstrated heterogeneity of Alzheimer’s disease in biological and genetic components, it is critical to identify new therapeutic approaches. The Emory‐Sage‐SGC‐JAX TREAT‐AD Center is generating and openly distributing validated experimental tools necessary to test target predictions generated through sequence‐based characterization of human disease state. We believe that these tools and reagents, including chemical and biological probes that target the multifaceted dysregulation in the brains of AD patients, will advance the discovery of potential drug targets for AD. Method Our Center has focused on developing a robust target prioritization and target enabling package (TEP) development pipeline, where we not only produced, characterized, and validated key enabling reagents for dozens of potential AD drug targets for which there were few reagents, but also developed new approaches to expedite target prioritization and organization, antibody characterization, and chemical probe discovery. The current target portfolio was assembled by evaluating prioritized understudied proteins by the AMP‐AD consortium and additional NIA‐supported AD consortia. Nominated targets were evaluated by calculating an unbiased AD risk score and then mapped to 19 biological domains (BDs) that describe and codify the different processes that are dysregulated in AD and prioritized based on multiple lines of evidence for overall AD‐risk. Targets that meet criteria for development are evaluated to identify a set of experimental reagents necessary for hypothesis testing. Result We have prioritized more than 50 understudied targets for TEP development. For each understudied target, a TEP includes expression constructs, purified protein and methods, validated knockout cell lines, and antibody validation. For a subset of tractable targets additional TEP components include assay development, crystal structures, screening, and probe development. Advanced targets include SYK, DDX1, SDC4, ARHGEF2, and MDK. Conclusion All data, protocols, reagent sets, and chemical tools will be made widely available on the AD Knowledge Portal with no intellectual property claims. For more information see www.treatad.org .
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".