Bibliographic record
Abstract
In a recent clinical study conducted by Pfizer Inc., pharmaceutical researchers found that the company's new drug Xalkori, originally targeted as a treatment for adult lung cancer, showed great promise against two rare childhood cancers. The drug eradicated the cancer in seven of eight children with a childhood form of lymphoma and in two other children with a lethal form of a nervous-system cancer called neuroblastoma. Xalkori's success is one example of drug repurposing, pharmaceutical research to find new uses for FDA-approved compounds. This innovative approach to developing cost-effective, timely new pharmaceutical therapies is necessary to eliminate the backlog of untreated diseases--biomedical researchers have successfully identified the causes of nearly 4,500 diseases but have created new therapies for only 250 of them. This situation is exacerbated by the time and money required to develop a new drug compound--up to 14 years and upwards of $1 billion to move a drug from discovery to commercialization. The National Institutes of Health (NIH), under the directorship of Dr. Francis S. Collins, recently added its imprimatur to two promising areas of pharmaceutical and biologic research: drug repurposing and drug rescue. The successful repurposing of established drugs to treat other ailments includes the statin class of cholesterol reducing drugs, with Lipitor now repurposed to prevent strokes. Drug rescue is defined as research involving abandoned small molecules and biologics that have not been approved by the U.S. Food and Drug Administration (FDA). These rescued molecular compounds are usually abandoned by pharmaceutical companies in the drug discovery or preclinical testing phase, typically because they do not prove effective for the specific use for which they were developed. Some of these compounds may be useful in treating other diseases for which they have not been tested. New analytical tools are making it possible for scientists to identify likely candidates for repurposing or rescuing. In a recent study, a team of Stanford researchers combed through computerized databases to see how 100 diseases altered the activity of thousands of genes, identifying for each disease a genetic signature defined by specific genetic activity patterns. The researchers then subjected 164 drugs to a similar analysis, characterizing each with a genetic signature based on activity patterns in human cell samples treated with the drug. A computational analysis software program developed by the researchers was used to compare drug and disease signatures, resulting in statistical pairings that allowed researchers to infer that a drug might work to successfully treat a particular disease. In 2011, the Stanford researchers reported that they had uncovered potential drug treatments for 53 human diseases ranging from cancers to Crohn's disease and cardiovascular conditions. Abandoned drugs will follow a similar research process to search for new indications. In an April 2011 NIH-pharmaceutical industry roundtable whose findings were published in the report Exploring New Uses for Abandoned and Approved Therapeutics, participants called for increased NIH engagement and expanded partnerships to support drug repurposing and rescue efforts. Collins has responded robustly, establishing the NIH National Center for Translational Sciences (NCATS), which will work to eliminate the bottlenecks in the drug development and commercialization process and accelerate the translation of basic research into new treatments for human diseases and conditions. NCATS is the home for two pilot programs focusing on drug repurposing and rescue. The first program, the NCATS Pharmaceutical Collection, is a publicly accessible database of 3,800 small-molecule compounds approved by regulatory agencies in the United States, Canada, Europe, and Japan, as well as all compounds that have been registered for human clinical trials in the United States. …
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".