Pazopanib: an orally administered multi-targeted tyrosine kinase inhibitor for locally advanced or metastatic renal cell carcinoma.
Bibliographic record
Abstract
INTRODUCTION: Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults, responsible for approximately 90% of all kidney cancers. Prior to 2005, treatment options for patients with locally advanced and metastatic disease were limited. After the approval of sorafenib by the US Food and Drug Administration (FDA), other tyrosine kinase inhibitors (TKI) have been successively used for treating patients with advanced RCC. Pazopanib is the newest, orally bioavailable, and multi-targeted TKI, and is considered a first-line treatment option for certain patients. This review summarizes updated clinical studies, mechanism of action, and pharmacokinetics of pazopanib. MATERIALS AND METHODS: Published English language literatures and data information on pazopanib for treating advanced RCC available as of March 2011 were identified and summarized. RESULTS: In phase II and III randomized clinical trials, pazopanib treatment resulted in considerably longer progression-free survival in patients with advanced RCC compared to placebo, with an acceptable side-effect profile. In addition, there are a few ongoing pazopanib studies including comparison to other TKIs, use for patients who have failed prior cytokine therapy, and combination with other therapeutic agents. CONCLUSIONS: Pazopanib has been used in the United States, Europe and Canada for treating patients with advanced RCC. Currently, it is being used in good or intermediate risk RCC and shows survival benefit with acceptable adverse effects. Pazopanib is a new treatment option and needs further evaluation, particularly on its effect relative to other TKIs as well as its use in combination with other agents.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".