Improving Outcomes of Tyrosine Kinase Inhibitors in Hepatocellular Carcinoma: New Data and Ongoing Trials
Bibliographic record
Abstract
Targeted therapies such as oral tyrosine kinase inhibitors (TKIs) are the main therapeutic strategy effective for advanced hepatocellular carcinoma (HCC). Currently six tyrosine kinase inhibitors for HCC therapy have been approved. The newly approved first-line drug donafenib represent the major milestones in HCC therapeutics in recent years. However, drug resistance in HCC remains challenging due to random mutations in target receptors as well as downstream pathways. TKIs-based combinatorial therapies with immune checkpoint inhibitors such as PD-1/PD-L1 antibodies afford a promising strategy to further clinical application. Recent developments of nanoparticle-based TKI delivery techniques improve drug absorption and bioavailability, enhance efficient targeting delivery, prolonged circulation time, and reduce harmful side effects on normal tissues, which may improve the therapeutic efficacy of the TKIs. In this review, we summarize the milestones and recent progress in clinical trials of TKIs for HCC therapy. We also provide an overview of the novel nanoparticle-based TKI delivery techniques that enable efficient therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".