Type I inhibitors of tropomyosin receptor kinase (Trk): a 2020–2022 patent update
Bibliographic record
Abstract
Introduction Trk inhibitors are significant in the realm of personalized medicine as they target specific genetic alterations, such as NTRK gene fusions, leading to improved treatment outcomes for cancer patients. By tailoring the treatment to the genetic characteristics of the tumor rather than the tumor type, Trk inhibitors offer the potential for more effective and precise therapies, resulting in enhanced response rates and prolonged survival for patients with NTRK fusion-positive cancers.Areas covered Patents covering type I inhibitors targeting the Trk family are discussed, building upon our prior review series on Trk inhibitors. Relevant patents were identified through the Web of Science database, Google, and Google Patents.Expert opinion The field of Trk inhibitors has evolved significantly, as reflected in the current patent literature, which emphasizes the selective structural refinement of clinical champions. Efforts now concentrate on enhancing efficacy against on-target resistance mechanisms, with modifications made to improve potency, reduce toxicity, and enhance pharmacokinetics. Combination therapies show potential to address off-target resistance mechanisms and improve treatment outcomes. Challenges remain in accurately diagnosing NTRK gene alterations and integrating screening into routine clinical practice. Trk inhibitors have surpassed their conventional role of inhibition and are now seeing new applications in radiopharmaceutical development and as molecular targeting agents.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".