A phase I study of LOXO-292, a highly selective RET inhibitor, in pediatric patients with <i>RET</i>-altered cancers.
Bibliographic record
Abstract
TPS10066 Background: Genomic alterations in the RET kinase, including gene fusions and activating point mutations, are implicated in the pathogenesis of lung, thyroid, sarcoma and other cancers in both chidren and adults. Currently available multikinase inhibitors with anti-RET activity are non-selective and may be associated with less favorable toxicity profile. LOXO-292 is a novel, highly selective, ATP-competitive small molecule RET inhibitor. LOXO-292 has preclinical nanomolar potency against diverse RET alterations (e.g. fusions, activating mutations and anticipated acquired resistance mutations) and anti-tumor activity in the brain. LOXO-292 has demonstrated clinical activity in adult patients with RET-alterated solid tumors. Methods: LIBRETTO-121 (EudraCT 2019-000212-28) is an ongoing multicenter phase 1/2 dose escalation multicenter trial in patients 6 months-21 years of age with advanced, RET-altered solid and CNS tumors. Dose escalation follows a rolling 6 design starting at the equivalent of the adult recommended phase 2 dose. Enrollment began on 12 Feb 2019 and is ongoing. Key eligibility criteria include: solid or CNS tumor with a documented RET gene alteration refractory to standard therapy; age 6 months to 21 years of age; and adequate bone marrow, liver and kidney function. LOXO-292 is administered orally BID for continuous 28-day cycles. Both capsule and liquid suspension dosage forms are available. The primary objective of the phase 1 portion of the study is to determine safety and dose limiting toxicities. Key secondary objectives include characterization of pharmacokinetic properties, identification of the MTD and initial characterization of the anti-tumor activity of LOXO-292. Archival tissue will be used to further characterize molecular abnormalities. Clinical trial information: 2019-000212-28.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".