New Perspectives for Developing Therapeutic Bioconjugates of Metabolite-Depleting Enzymes: Lessons Learned Combating Glutamate Excitotoxicity
Bibliographic record
Abstract
Glutamate, the main excitatory neurotransmitter in the central nervous system, plays an essential role in several cognitive activities such as memorizing and learning. Excessive glutamate release and disturbance of glutamate homeostasis participates in multiple neuronal pathologies including cerebral ischemia (inadequate blood supply), traumatic brain injury (e.g., from a fall or an accident), multiple sclerosis, epilepsy, migraine, fetal hypoxia, or Alzheimer's disease. Attenuating excitotoxicity by, for example, targeting glutamate receptors has proved to be beneficial in animal models but has largely failed in clinical trials because of toxic side effects. New therapeutic concepts have been explored to reduce the excitotoxic effect caused by the excessive glutamate release by using or stimulating glutamate-depleting enzymes in the bloodstream. These enzymes indirectly act upon the brain by depleting glutamate in the bloodstream, which is believed to siphon it out of the brain. Recent studies have shown that bioconjugate approaches applied to such enzymes exacerbate this therapeutic effect but raise additional questions for future research. This Perspective provides an overview of lessons learned by our group when exploring bioconjugate approaches for combatting glutamate excitotoxicity as an illustration of how research on therapeutic bioconjugates is evolving.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".