Topical Drug Delivery in the Treatment of Chronic Pain: A Review
Bibliographic record
Abstract
Chronic inflammatory or neuropathic pain are chronic syndromes poorly treated by commonly recommended systemic pharmacological therapies, partly due to dose-limiting side effects or adverse events.The use of topical therapeutics for chronic pain is growing and benefits from the reduced potential for adverse effects and the ability to target directly peripheral pathological processes.The current review identifies and describes the limitations of various commonly prescribed systemic pharmacological therapies for chronic inflammatory and neuropathic pain.It also justifies increased research to develop topical therapeutics for chronic pain, mainly localized inflammatory and neuropathic pain.The review discusses the various classes of topical treatments used for chronic pain, including agents that block sensory inputs; provide mechanism-based therapeutics; activate inhibitory systems; include combinations that produce multimodal therapeutic effects; and are targeted to mucosal tissues.It can be argued that current topical therapeutics for chronic pain rely too heavily on local anesthetics and capsaicinoids.More research is needed on multimodal topical therapies and/or targeted at the peripheral sources of pathology.Novel topical therapeutic development would also benefit from further research on topical co-drugs, drug-drug salts, cocrystal and hydrates, and ionic liquids.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".