The Emory‐Sage‐SGC TREAT‐AD Center: Tool and probe development for emerging targets in Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease is the most common form of dementia and there is currently no effective therapy for either treatment or prevention. With repeated failure in DRUG DEVELOPMENT across industry and demonstrated heterogeneity in biological and genetic components of the disease, there is a need to evaluate a broad range of therapeutic hypotheses for target prioritization. The Emory‐Sage‐SGC TREAT‐AD Center (https://treatad.org) 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 The Emory‐Sage‐SGC TREAT‐AD Center uses integrated computational approaches to identify target predictions from a set of prioritized therapeutic hypotheses. Emerging AD targets derived from systems biology studies within the Accelerating Medicines Partnership in AD (AMP‐AD) consortium and additional NIA‐supported AD consortia were mapped to 15 biological domains (BDs) and prioritized based on an unbiased bioinformatic assessment across multiple lines of evidence for overall AD‐risk. Targets are then evaluated to identify a set of experimental reagents necessary for hypothesis testing, termed a “target enablement package (TEP). Result Within these 15 biological domains, we prioritized more than 30 targets for target enabling package (TEP) development. TEPs include expression constructs, knockout cell lines, assays, antibody validation, and crystal structures. All reagents are developed to meet established quality criteria. For a subset of tractable targets (MSN, SYK, SFRP1, SDC4 and CAPN2), chemical probe development is underway to provide tools to test these therapeutic hypotheses in cellular and animal systems. TREAT‐AD investigators place all data, knowledge, reagents, and tools into the open domain with no intellectual property claims. Conclusion The open drug discovery approach of TREAT‐AD is aimed to de‐risk potential AD therapeutics to catalyze robust and independent evaluation of a diverse portfolio of promising yet untested AD therapeutic hypotheses. All data, protocols, reagent sets, and chemical tools will be made widely available on the AD Knowledge Portal. For more information see www.treatad.org.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".