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Impact of lorlatinib dose modifications on adverse event outcomes in the phase 3 CROWN study.

2025· article· en· W4410805349 on OpenAlexaff
Geoffrey Liu, Ernest Nadal, Shobhit Baijal, Alessandra Bulotta, Christina S. Baik, Holger Thurm, Anna Polli, Makoto Nishio

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer Centre
FundersPfizer
KeywordsMedicineAdverse effectPhase (matter)Event (particle physics)Internal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

8590 Background: In an updated analysis of the CROWN study (NCT03052608), after 5 years of follow-up, lorlatinib continued to show superior efficacy over crizotinib in patients with previously untreated advanced ALK + non-small cell lung cancer (NSCLC), with median progression-free survival (PFS) still not reached. A post hoc analysis of CROWN found no impact on PFS or time to intracranial progression with lorlatinib dose reductions within the first 16 weeks. These findings underscore the importance of dose modifications to mitigate toxicity and maintain long-term treatment efficacy. The objective of this analysis was to further characterize lorlatinib dose reductions and their impact on safety and adverse event (AE) outcomes. Methods: The CROWN study is an ongoing, international, open-label, randomized phase 3 trial comparing lorlatinib vs crizotinib in patients with previously untreated advanced ALK + NSCLC. Patients were randomized 1:1 to receive lorlatinib 100 mg once daily (QD; n=149) or crizotinib 250 mg twice daily (n=147). This post hoc analysis used data from the 5-year follow-up to assess time to dose reduction, duration of treatment with reduced dose, and its impact on AEs and outcomes associated with lorlatinib. A genAI tool (12/13/24; Pfizer; GPT-4o) developed the 1st draft; authors assume content responsibility. Results: At 5 years of follow-up, 49 of 149 patients in the lorlatinib arm had ≥1 lorlatinib dose reduction. Treatment is ongoing in 33% of patients who had 1 dose reduction (n=24) and in 20% who had 2 dose reductions (n=25). In patients who had 1 dose reduction to 75 mg QD, median time to dose reduction was 7.1 months (range, 1.7-64.8), and median duration of treatment with the 75-mg dose was 42.2 months (range, 0.2-68.3). In patients who had 2 dose reductions (dose reduced to 75 mg QD and then again to 50 mg QD), median time to second dose reduction was 11.3 months (range, 2.5-56.9), and median duration of treatment with the 50-mg dose was 20.7 months (range, 0.5-61.8). In patients who had 1 or 2 dose reductions, all-cause AEs associated with dose reductions are shown in the table. Of the 30 AEs leading to 1 dose reduction, 27% of events resolved and 13% partially resolved. Of the 59 AEs leading to 2 dose reductions, 46% of events resolved and 5% partially resolved. Conclusions: This post hoc analysis of the CROWN study showed that dose reductions were effective in managing AEs associated with lorlatinib. These findings show the importance of dose modifications to mitigate toxicity and continue lorlatinib treatment for prolonged periods of time in patients with advanced ALK + NSCLC. Clinical trial information: NCT03052608 . AEs associated with dose reductions in >2 patients, n (%) Any grade Grade ≥3 1 dose reduction (n=24) Any 23 (96) 14 (58) Peripheral edema 4 (17) 2 (8) 2 dose reductions (n=25) Any 24 (96) 11 (44) Peripheral edema 6 (24) 0 Blood triglycerides increased 3 (12) 2 (8) Disturbance in attention 3 (12) 0 Generalized edema 3 (12) 1 (4)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.149
GPT teacher head0.618
Teacher spread0.468 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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