Novel small molecule drugs approved by FDA in 2024 for cancer therapy
Bibliographic record
Abstract
Cancer remains a leading cause of mortality each year and is increasingly emerging as a significant threat to public health due to its rising incidence rates Various types of cancers, prioritized for treatment, are managed through distinct therapeutic approaches. Small molecule-based drugs, which target various receptors in cancer cells, rank among the leading therapeutic options alongside immunotherapeutics and cell-based therapies. As extensive research is going on small molecules designed for different cancer related diseases, and based on new molecules studies deposited, the FDA has approved 50 new molecular drugs for different cancers. Among these, six novel molecules have been approved for oncological applications. The approved drugs include: Tovorafenib for pediatric low-grade glioma, Vorasidenib for Grade 2 astrocytoma, Lazertinib for non-small cell lung cancer, Inavolisib for HR+/HER2-breast cancer, Revumenib for R/R acute leukemia, Ensartinib for ALK-positive non-small cell lung cancer. This note delves in the development, structural properties and applications of the anticancer drugs recently approved by the FDA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".