Successful Pregnancy and Healthy Baby Outcome in a Patient With Tyrosine Kinase Inhibitor–Refractory ALK-Positive NSCLC and Central Nervous System Metastasis Treated With Lorlatinib With Maternal-Fetal Pharmacokinetics and Child Milestone Development Correlations
Bibliographic record
Abstract
INTRODUCTION: Lorlatinib is the preferred first-line treatment for advanced ALK+ (ALK+) NSCLC on the basis of the 5-year CROWN update. However, the effects of lorlatinib on the fetus and neonate remain unknown. METHODS: We report the first case of a patient with metastatic ALK+ NSCLC who became pregnant during treatment with lorlatinib. A literature review was performed to identify all previously reported pregnancies of ALK+ NSCLC. The collection, determination, and analysis of lorlatinib pharmacokinetics and mass spectroscopy imaging of lorlatinib are both performed. RESULTS: A 33-year-old pregnant woman with a 7-year history of ALK+ metastatic NSCLC was admitted to the hospital with brain metastases recurrence at 20 weeks of gestation. She self-discontinued lorlatinib at 4 weeks of gestation after long-term disease control on a reduced dose. According to the patient's preference, low-dose lorlatinib was reintroduced at 20 weeks, with successful tumor control and normal fetal growth. At 20-month follow-up postpartum, the mother maintained intracranial and systemic remission, and no congenital abnormalities were observed in the baby. Pharmacokinetic analyses and mass spectroscopy imaging peridelivery confirmed the placental transfer of lorlatinib. CONCLUSIONS: The case highlights both the potential safety and safety concerns with the use of lorlatinib during pregnancy, along with the unique nature of central nervous system-dominant ALK+ NSCLC, and the potential clinical utility of dose-reduced lorlatinib.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".