Translational cognitive biomarkers for preclinical drug testing in neurodegenerative diseases
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) are characterized by the accumulation of misfolded proteins, including amyloid-β, tau, and alpha-synuclein (a-Syn). Despite extensive research, no effective disease-modifying therapies exist. A major barrier to drug development is the poor translatability of animal models and the lack of biomarkers with predictive power for clinical efficacy. Higher-order cognitive deficits, such as impaired reversal learning, are observed in patients with synucleinopathies, suggesting that cognitive biomarkers could be valuable in preclinical drug testing. This study evaluates whether touchscreen-based cognitive testing enhances the predictive validity of preclinical drug testing. We assessed PU-AD, an HSP90 epichaperome disruptor targeting the abnormal chaperone network implicated in protein misfolding disorders, and a mouse version of Cinpanemab, an anti-a-Syn antibody that recently failed in clinical trials, despite initial positive results in animal models. METHOD: Hemizygous M83 transgenic mice were intracerebrally injected with preformed fibrils (PFFs) to model synucleinopathy-related neurodegeneration. Mice underwent cognitive assessment using the Pairwise Visual Discrimination and Reversal (PVD-R) touchscreen task, a highly translational measure of cognitive flexibility sensitive to a-Syn toxicity. PU-AD and Cinpanemab were administered intraperitoneally post-PFF injection. Drug doses matched previous animal model studies. Motor performance was assessed with grip strength, rotarod, and wire hang tests. MRI was used to detect brain atrophy. Immunohistochemical and biochemical analyses evaluated a-Syn pathology and neuroinflammation. RESULT: M83/PFF-injected mice showed significant reversal learning deficits earlier than motor deficits. Pharmacokinetic analysis confirmed PU-AD reached brain concentrations comparable to those in other models. PU-AD treatment rescued cognitive and motor impairments, suggesting broad neuroprotective effects. In contrast, Cinpanemab-treated mice showed no improvements in cognition and preliminary data suggest no improvements in motor symptoms. Ongoing experiments are evaluating pathology and brain atrophy. CONCLUSION: The lack of Cinpanemab efficacy in our pre-clinical testing pipeline aligns with clinical trial results. The effectiveness of PU-AD in reducing both cognitive and motor impairments suggests it may have broad therapeutic potential in synucleinopathies. We suggest that touchscreen-based cognitive testing enhances the predictive validity of preclinical drug evaluation.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".