Anticancer Agents from Unique Natural Products Sources
Bibliographic record
Abstract
The National Cooperative Natural Products Drug Discovery Group (NCNPDDG) “Anticancer Agents from Unique Natural Products Sources, CA 67786” was first awarded in September 1995. The goal of the project is to discover and develop novel anticancer agents from a variety of natural products sources. The key accomplishments of this NCDDG which will be highlighted in this manuscript include:Development of tools to probe fungi for the production of novel natural products by DNA-based probes. Discovery that the majority of these fungi can produce natural products via nonribosomal peptide synthetases, polyketide synthases, or both – a much larger percentage than current culturing techniques reveal.Identification of the MDR-selective cytotoxic agent austocystin D, and use of a novel yeast deletion strain approach to help identify its molecular target(s).Identification of hemiasterlin and other naturally occurring analogs as potent antimitotic agents with excellent in vivo activity against human solid tumors in mouse models.Development of a total synthesis of hemiasterlin. The utilization of this methodology to provide the first SAR for the hemiasterlin family of antimitotic agents and to identify the synthetic analog HTI-286, which is being examined in clinical trials as an anticancer agent.To provided technology transfer, educational opportunities and compensation to countries of origin for collection and study of their natural product resources. This NCNPDDG program has provided funding to research programs at the University of the Philippines, The University of the South Pacific in the Fiji Islands, Colombo University in Sri Lanka, the Instituto de Quimica de Sao Carlos, Universidade de Sao Paulo, Brazil, and the University of Papua New Guinea.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".