Recent advances in tropomyosin receptor kinase (TRK) inhibitors: a 2023–2024 patent landscape review
Bibliographic record
Abstract
Introduction The rise of tissue-agnostic therapies has revolutionized cancer treatment, with therapies targeting NTRK fusions leading the way. TRK inhibitors like larotrectinib and entrectinib marked a paradigm shift – prioritizing molecular alterations over the tumor’s anatomical location. Acquired resistance remains a significant challenge, with next-generation inhibitors and combination strategies at the forefront of efforts to enhance the clinical efficacy of TRK-targeted therapies.Areas covered This review discusses patents published in 2023 and 2024 covering inhibitors of the TRK family, extending our ongoing review series on TRK inhibitors. Patent searches were conducted using the key word ‘TRK*’ and ‘inhibitor’ in Google Patents database to identify novel TRK-targeting inhibitors and therapeutic strategies.Expert opinion Ongoing advancements in TRK inhibitor development, combination therapies, and precision diagnostics continue to elevate the potential of treatment outcomes. Recent patent filings reflect the expanding promise of TRK inhibitors for NTRK fusion-driven cancers. However, widespread adoption of high-throughput screening remains crucial unlocking their full therapeutic value and delivering truly precision-guided care. Combination therapies with TRK inhibitors are emerging as a key strategy to enhance efficacy and overcome resistance. The FDA’s recent approval of repotrectinib underscores progress in the TRK inhibitor landscape, while highlighting the continued need for innovation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".