An overview of the role of selpercatinib and pralsetinib in RET-fusion-positive non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
OBJECTIVE: Selpercatinib and pralsetinib are new targeted therapies used to treat patients with non-small cell lung cancer (NSCLC) due to RET gene rearrangements. The objective of this article is to review selpercatinib and pralsetinib in the context of RET-fusion-positive NSCLC. DATA SOURCES: The pivotal LIBRETTO-001 and ARROW trials were evaluated regarding the use of selpercatinib and pralsetinib as treatment for RET-fusion-positive NSCLC. Comparative studies, review articles and current studies on selpercatinib and pralsetinib in RET-fusion-positive NSCLC were searched on pubmed.org and scholar.google.com using "selpercatinib," "pralsetinib," and "NSCLC" as keywords. Product monographs were searched on google.ca and uptodate.com using the keywords "selpercatinib," "pralsetinib," and/or "monograph." DATA SUMMARY: Selpercatinib and pralsetinib are orally administered highly selective RET inhibitors approved by the FDA following the accelerated approvals granted due to the pivotal LIBRETTO-001 and ARROW trials which evaluated selpercatinib and pralsetinib, respectively. Both drugs have shown efficacy for brain metastases and are primarily metabolized by CYP3A4 through hepatic metabolism. The most common grade 3 or 4 adverse effects of selpercatinib were hypertension, increased ALT level, and increased AST level while for pralsetinib, it was neutropenia, hypertension, and anemia. The safety profile shows similarities in severity and tolerability but additional monitoring for QT prolongation in patients on selpercatinib is recommended, compared to the risks of interstitial lung disease or pneumonitis for patients on pralsetinib. CONCLUSIONS: Overall, the increased use of selpercatinib and pralsetinib has led to the implementation of these drugs in the clinical practice of healthcare professionals such as pharmacists.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".