A phase Ib combination study of RO4929097, a gamma-secretase inhibitor, and temsirolimus in patients with advanced solid tumors
Bibliographic record
Abstract
BACKGROUND: To determine the recommended phase II dose (RP2D) and assess the safety, pharmacokinetics (PKs) and pharmacodynamics of RO4929097in combination with temsirolimus. METHODS: Escalating doses of RO4929097 and temsirolimus were administered at three dose levels. Patients received once daily oral RO4929097 on a 3 days on/4 days off schedule every week, and weekly intravenous temsirolimus. Blood samples were collected for PK analysis. Archival tissue specimens were collected for Notch pathway biomarker analysis and genotyping of frequent oncogenic mutations. RESULTS: Seventeen patients with refractory advanced solid tumors were enrolled in three dose levels (DLs): DL1 (RO4929097 10 mg; Temsirolimus 25 mg), DL2 (RO4929097 20 mg; Temsirolimus 25 mg), and DL3 (RO4929097 20 mg; Temsirolimus 37.5 mg). The most common toxicities related to the study drug combination included: fatigue (82 %; grade 3 6 %), mucositis, (71 %; grade 3 6 %), neutropenia (59 %; grade 3 12 %), anemia (59 %; grade 3 0 %), and hypertriglyceridemia (59 %; grade 3 0 %). Two dose-limiting toxicities, grade 3 rash and grade 3 mucositis, were observed in the same patient in the first dose level prompting dose expansion. Eleven patients (73 %) had stable disease as their best response. Co-administration of RO4929097 was associated with increased clearance and reduced exposure to temsirolimus, suggestive of drug-drug interaction via CYP3A4 induction. No correlation between the expression of Notch pathway biomarkers or genotype and time to progression was noted. CONCLUSIONS: RO4929097 can be safely combined with temsirolimus in patients with advanced solid tumors. The RP2D was established at 20 mg of RO4929097 combined with 37.5 mg of temsirolimus.
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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 | 0.000 |
| 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".