LGG-25. The first-in-class ERK inhibitor ulixertinib (BVD-523) shows activity in MAPK-driven pediatric low-grade glioma models as single agent and in combination with MEK inhibitors or senolytics
Bibliographic record
Abstract
Abstract Ulixertinib (BVD-523) is a catalytic ERK1/2 inhibitor that showed promising responses in adult patients with mitogen-activated protein kinase (MAPK)-driven solid tumors. Pediatric low-grade gliomas (pLGG) are the most common pediatric brain tumors, with the most frequent driving alterations in the MAPK pathway. The anti-tumor activity of ulixertinib in pLGG and its potential synergism in combination with MEK inhibitors, senolytics, and chemotherapy were investigated in vitro using metabolic activity, MAPK reporter assay and high-content microscopy in pLGG-derived cell lines (DKFZ-BT66 - KIAA:BRAF fusion; BT40 - BRAF V600E mutation and CDKN2A/B deletion). The most clinically relevant combinations were further validated in vivo: 1) in zebrafish embryo models (BT40 and DKFZ-BT66 yolk sac injection) and 2) in NSG mice (BT40 orthotopic PDX) including in vivo pharmacokinetic and -dynamic analyses. Ulixertinib inhibited MAPK pathway activity in all models and reduced cell viability in the BRAF V600E mutated cell line at concentrations in the nanomolar range. In vivo pharmacokinetic and -dynamic analyses showed penetrance of the drug into mouse brain tissue and on-target activity, with concentrations above the in vitro IC50 and reduction of MAPK activity. Ulixertinib treatment slowed tumor growth and significantly increased survival in NSG mice with BT40 xenografts. Ulixertinib showed indications for anti-proliferative synergy in vitro in combination with MEK inhibitors (trametinib, binimetinib) or BH3 mimetics (navitoclax, A-1331852). Combinations with chemotherapy (carboplatin, vinblastine) were at most additive. Indications for synergy with binimetinib and navitoclax were confirmed in the zebrafish embryo models. In the NSG mouse model, the combination of ulixertinib with senolytics induced effects on tumor growth and survival comparable to ulixertinib monotherapy. Ulixertinib shows promising potential as a clinically relevant therapeutic option for the treatment of pLGG to be further investigated in upcoming clinical trials. Potential synergism with MEK inhibitors and BH3 mimetics was noted and warrants further investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".