P1‐076: Development of Bio‐Conjugates for the Treatment of Excitotoxicity During Alzheimer's Disease
Bibliographic record
Abstract
Glutamate, as the major excitatory neurotransmitter in the central nervous system is involved in many aspects of normal brain function including learning, memory and behavior (Campos-Peña et al., 2014, Hynd et al., 2004). During Alzheimer’s disease (AD), the high levels of glutamate, induces excitotoxicity and leads to neuronal death and loss of cognitive function. Some studies have suggested that reducing blood levels of glutamate could induce efflux from the brain to the blood which will lead to the decrease in it's cerebral concentrations (Boyko et al., 2014, Ruban et al., 2014). Our hypothesis is that the use of enzyme-polymer bio-conjugates could be interesting for the treatment of AD by reducing blood glutamate concentration. The glutamate dehydrogenase (GDH) via its catalytic activity, will consume the excess glutamate and the biocompatible polymer selected, which is polyethylene glycol (PEG), will increase the duration of circulatory half-life of the GDH. We propose to synthesize bio-conjugates GDH-PEG, to validate the maintenance of enzymatic activity and to check their therapeutic efficacy. For this purpose, we (1) Conjugate PEG on the surface of the GDH (by using 2 ratios of PEG/GDH), (2) Validate the reaction by visualization of our bio conjugates after Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) and by separating them with Size Exclusion Chromatography (SEC), (3) Characterize the number of PEG per GDH by NMR and finally (4) Evaluate the enzymatic activity before and after bio-conjugation. Our results demonstrate that the use of different Ratio allows us to have variable number of PEG grafted on the surface of our enzyme. After PEGylation we showed that the GDH activity is maintained. We can conclude that the PEGylation of the GDH was optimized with the conservation of the enzymatic activity. Currently we are planning tests in vitro and in vivo in streptozotocine rat’s models, which are well known animal model for sporadic AD, to evaluate the effectiveness of the elimination of the excess toxic glutamate from the cerebrospinal fluid.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".