A phase I dose-finding, pharmacokinetics and genotyping study of olaparib and lurbinectedin in patients with advanced solid tumors
Bibliographic record
Abstract
Abstract The poly (ADP-Ribose) polymerase (PARP) inhibitor olaparib has shown antitumor activity in patients with ovarian or breast cancer with or without BRCA1/2 mutations. Lurbinectedin is an ecteinascidin that generates DNA double-strand breaks. We hypothesized that the combination of olaparib and lurbinectedin maximizes the DNA damage increasing the efficacy. A 3 + 3 dose-escalation study examined olaparib tablets with lurbinectedin every 21 days. The purpose of this phase I study is to determine the dose-limiting toxicities (DLTs) of the combination, to investigate the maximum tolerated dose (MTD), the recommended phase II dose (RP2D), efficacy, pharmacokinetics, in addition to genotyping and translational studies. In total, 20 patients with ovarian and endometrial cancers were included. The most common adverse events were asthenia, nausea, vomiting, constipation, abdominal pain, neutropenia, anemia. DLT grade 4 neutropenia was observed in two patients in dose level (DL) 5, DL4 was defined as the MTD, and the RP2D was lurbinectedin 1.5 mg/m2 + olaparib 250 mg twice a day (BID). Mutational analysis revealed a median of 2 mutations/case, 53% of patients with mutations in the homologous recombination (HR) pathway. None of the patients reached a complete or partial response; however, 60% of stable disease was achieved. In conclusion, olaparib in combination with lurbinectedin was well tolerated with a disease control rate of 60%. These results deserve further evaluation of the combination in a phase II trial.
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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 | 0.001 |
| 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) | 0.001 | 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".