Phase Ib Pharmacodynamic Study of the MNK Inhibitor Tomivosertib (eFT508) Combined With Paclitaxel in Patients With Refractory Metastatic Breast Cancer
Bibliographic record
Abstract
PURPOSE: Preclinical data motivate clinical evaluation of inhibitors of MAPK-interacting kinases 1 and 2 (MNK1/2). We conducted a phase 1b clinical trial to study target engagement and safety of tomivosertib, a MNK1/2 inhibitor, alone and in combination with paclitaxel. PATIENTS AND METHODS: Eligible patients had metastatic breast cancer resistant to standard-of-care treatments. Biopsies were obtained at baseline and during treatment with tomivosertib, and then tomivosertib was continued with the addition of paclitaxel until disease progression or toxicity. Serum drug levels were measured, and pharmacodynamic endpoints included IHC, proteomics, translatomics, and imaging mass cytometry. RESULTS: Tomivosertib alone and in combination with paclitaxel was well tolerated. There was no pharmacokinetic interaction between the drugs. We observed a clear reduction in phosphorylation of eIF4E at S209, a major substrate of MNK1/2, and identified tomivosertib-induced perturbations in the proteome, translatome, and cellular populations of biopsied metastatic breast cancer tissue. CONCLUSIONS: We conclude that tomivosertib effectively inhibits MNK1/2 activity in metastatic breast cancer tissue and that it can safely be combined with paclitaxel in future phase II studies. We demonstrate feasibility of using proteomic profiles, translatomic profiles, and spatial distribution of immune cell infiltrates for clinical pharmacodynamic studies.
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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 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".