TRanslational Initiative to DE‐risk NeuroTherapeutics (TRIDENT) – A revolutionary pre‐clinical approach to maximize the predictive validity of pre‐clinical evaluation
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative diseases such as Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), Dementia with Lewy Bodies (DLB), Parkinson's Disease (PD), and Multiple System Atrophy (MSA), are complex and poorly understood conditions characterized by progressive neuronal degeneration, affecting brain structure, function, and behavior. Cognitive impairment is seen in all these disorders despite heterogeneous clinical presentation, even in primarily motor diseases such as PD, causing hardship for the individuals and their caregivers. Dementia alone presents a growing public health crisis globally. Canada's annual dementia-related healthcare costs are projected to rise from $19.7 billion in 2011 to $92.8 billion by 2031. Additionally, the lack of effective treatments and the high failure rate (92%) of clinical trials for new central nervous system (CNS) therapies underscores the immediate need for effective pre-clinical approaches that can reliably predict success in clinical trials. High failure stems from four key issues: neglect of human-relevant cognitive measures, lack of sex and age factors, reliance on single-investigator studies, and poor reproducibility due to limited collaboration and sociocultural barriers. METHOD: The TRanslational Initiative to DE-risk NeuroTherapeutics (TRIDENT), with its novel platform for pre-clinical evaluation, addresses these critical gaps. TRIDENT integrates three advanced model systems- newly developed human-induced pluripotent stem cells (iPSCs) and organoids, as well as new humanized mouse and marmoset models-into a unified platform to dramatically improve the predictive validity of the pre-clinical to clinical outcomes. TRIDENT leverages the human-relevant touchscreen-based cognitive tasks in mice and marmosets, which are highly translatable to humans. Further, TRIDENT incorporates Sex-Based Analysis (SBA)+ and open science principles across all stages of preclinical evaluation, fostering inclusivity, collaboration, reproducibility, and accessibility. With testing facilities at McGill University, the University of Toronto, and the Center for Addiction and Mental Health, TRIDENT ensures robust multi-site validation of results. Currently, TRIDENT is evaluating compounds for alpha-synucleinopathies and AD using these models to validate the platform. RESULT: TRIDENT offers a revolutionary solution for developing effective therapies by maximizing predictive validity and de-risking the drug discovery process. CONCLUSION: This will transform the current approaches of pre-clinical testing in neurodegenerative disorders, making the process attractive to pharmaceutical and biotechnology industries to find effective cures and therapies.
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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 | 0.084 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".