Drug-Nutraceutical Co-Crystal and Salts for Making New and Improved Bi-Functional Analgesics
Bibliographic record
Abstract
The discovery and development of effective analgesics is greatly lagging behind the steadily rising prevalence of chronic pain. Currently prescribed analgesics for chronic pain are lacking in efficacy mainly due to their narrowly-targeted mechanism of action. Driving neuronal hyperexcitability that underlies symptoms of chronic pain are multiple non-neuronal processes, among which are tissue hypoxia and oxidative stress. Here we demonstrate the design, synthesis, and activity of new multi-component bi-functional analgesic crystalline solids, co-crystals, and salts, based on pairing of vasodilatory anti-hypoxic drugs pentoxifylline, clonidine and linsidomine with antioxidant nutraceuticals protocatechuic acid, α-lipoic acid, and caffeic acid. After validation, chemical and structural characterization of these novel salts and co-crystals, topical formulations of the products were tested in a rat model of complex regional pain syndrome. Analgesic effects achieved with the salts and co-crystal exceeded the efficacy and/or potency of constituent compounds indicating that more effective, advanced analgesics can readily be developed by careful pairing of compounds that simultaneously target multiple neural and non-neural processes driving chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".