Marine pharmacology in 2000: Antitumor and cytotoxic compounds
Bibliographic record
Abstract
During 2000, marine antitumor pharmacology research aimed at the discovery of novel antitumor agents was published in 85 peer-reviewed articles. The purpose of this article is to present a structured review of the antitumor and cytotoxic properties of 143 marine natural products, many of them novel compounds that belong to diverse structural classes, including polyketides, terpenes, steroids and peptides. The organisms yielding these bioactive compounds comprised a taxonomically diverse group of marine invertebrate animals, algae, fungi and bacteria. Antitumor pharmacological studies were conducted with 19 marine natural products in a number of experimental and clinical models that defined or further characterized their mechanisms of action. Potentially promising in vitro cytotoxicity data generated with murine and human tumor cell lines were reported for 124 novel marine chemicals with as yet undetermined mechanisms of action. Noteworthy is the fact that marine anticancer research clearly remains a multinational effort, involving researchers from Austria, Australia, Brazil, Canada, England, France, Germany, Greece, Indonesia, Italy, Japan, New Zealand, Russia, Spain, South Korea, Switzerland, Taiwan, the Netherlands and the United States. Finally, this 2000 overview of the marine pharmacology literature highlights the fact that the discovery of novel marine antitumor agents continued at the same high level of research activity as during 1998 and 1999.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".