Novel serotonergic and non-serotonergic migraine headache therapies
Bibliographic record
Abstract
In the last four years discovery of pharmacotherapeutic treatments for migraine headaches has received much attention. Since the patent literature was last reviewed in 1997 [1], advances have been made in the understanding of mechanism and pathophysiology of migraine. Introduction of sumatriptan to the market has led to acceleration in research efforts towards finding safe and effective treatments for migraine. The importance of this field is evidenced by the number of compounds in clinical trials and by the number of patents filed in recent years. For example, besides sumatriptan, a second generation of three new drugs (naratriptan [2], zolmitriptan [3] and rizatriptan [4]) has entered the marketplace and few others are presently in clinical evaluation. In addition, classical drug design has yielded highly potent and selective ligands to target relevant receptor subtypes in migraine treatment. This article highlights and reviews the research advances published in patent literature between January 1997 through November 2000. The article is supplemented with selected references on design and development of novel agents with which to treat migraine and to study its mechanism and pathophysiology. Emphasis is made on serotonergic agents, namely seratonin (5-hydroxytryptamine, 5-HT) receptor subtype (5-HT1D, 5-HT1F and 5-HT5) agonists, drug combinations (e.g., 5-HT1D agonists with COX-2 inhibitors or NSAIDs), tachykinin receptor (NK1) antagonists and GABAergic agents. Also included are patents describing chemical entities that may be effective in migraine therapy based on their pharmacological actions as anticonvulsants, LTD4 receptor blocker agents and thromboxane inhibitors. By no means has any attempt been made to exhaustively review the literature; but rather, primary references along with citations to latest literature reviews have been included in each section.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".