Increasing the Biological Stability Profile of a New Chemical Entity, UPEI-104, and Potential Use as a Neuroprotectant Against Reperfusion-Injury
Bibliographic record
Abstract
Previous work in our laboratory demonstrated the utility of synthetic combinations of two naturally occurring, biologically active compounds. In particular, we combined two known anti-oxidant compounds, lipoic acid and apocynin, covalently linked via an ester bond (named UPEI-100). In an animal model of ischemia-reperfusion injury (tMCAO), UPEI-100 was shown to produce equivalent neuroprotection compared to each parent compound, but at a 100-fold lower dose. However, it was determined that UPEI-100 was undetectable in any tissue samples almost immediately following intravenous injection. Therefore, the present investigation was done to determine if biological stability of UPEI-100 could be improved by replacing the ester bond with a more bio cleavage-resistant bond, an ether bond (named UPEI-104). We then compared the stability of UPEI-104 to the original parent compound UPEI-100 in human plasma as well as liver microsomes. Our results demonstrated that both UPEI-100 and UPEI-104 could be detected in human plasma for over 120 min; however, only UPEI-104 was detectable for an average of 7 min following incubation with human liver microsomes. This increased stability did not affect the biological activity of UPEI-104 as measured using our tMCAO model. Our results suggest that combining compounds using an ether bond can improve stability while maintaining biological activity.
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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) | 0.001 | 0.000 |
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".