How to Pick a Neuroprotective Drug in Stroke Without Losing Your Mind?
Bibliographic record
Abstract
All human clinical trials evaluating neuroprotective therapeutics in cerebral ischemia have failed, casting a pall over the field which has not recovered. Numerous methodological issues in the performance of these trials were identified, with the result that current trials are now subject to higher degrees of rigor and transparency. Advances in re-canalization technologies now offer the hope that adjunctive neuroprotection can improve patient outcome. The evaluation of neuroprotection in preclinical animal models has also suffered from methodological issues, which has also been addressed, resulting in an improved performance of studies. This leaves the question of how to actually pick the most appropriate neuroprotective therapy for translation. Given the current limitations in resources, and the numerous strategies that have been proposed to take advantage of clinical and preclinical methodological improvements, we suggest that in vitro studies involving subjecting the most sensitive cells-neurons-to oxygen-glucose deprivation (OGD) can be used to resolve among the many possibilities. Specifically, a large body of evidence shows that successive increases in OGD durations (spanning the lethal/supra-lethal continuum) require increasingly 'strong' drugs and combinations to adequately protect neurons (criteria not met in clinical trials). Notably, as the OGD duration is lengthened, NMDA receptor (NMDAR) antagonists of increasing potency and dose are required to match this increasing severity. Under supra-lethal OGD conditions, cocktails composed of anti-excitotoxic antagonists with maximal potency and dose are required to achieve neuroprotection. We propose that this approach can serve as a strategy-a neuroprotective framework-to prioritize among the many possibilities that exist for neuroprotective therapeutics for translation. Specifically, utilize the OGD continuum to compare within-, between- and outside-classes of drugs, first alone and then in combinations, to identify the most efficacious drugs ('head-to-head' competitions to identify the 'last man standing'). While the current state of knowledge strongly suggests that anti-excitotoxic approaches are required, this framework allows the integration of testing established and new therapeutics alike. This framework should include new technologies such as multi-electrode arrays (MEAs), which allow the evaluation of adverse effects of drugs alone, as well as if a drug truly provides functional neuroprotection, and not just survival. The neuroprotective framework provides a comprehensive strategy to eliminate ineffectual treatments, leaving only those modalities with the highest therapeutic index to be prioritized for translation.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".