Targeting the MAPK Signaling Pathway in Cancer: Promising Preclinical Activity with the Novel Selective ERK1/2 Inhibitor BVD-523 (Ulixertinib)
Bibliographic record
Abstract
Abstract Aberrant activation of signaling through the RAS–RAF–MEK–ERK (MAPK) pathway is implicated in numerous cancers, making it an attractive therapeutic target. Although BRAF and MEK-targeted combination therapy has demonstrated significant benefit beyond single-agent options, the majority of patients develop resistance and disease progression after approximately 12 months. Reactivation of ERK signaling is a common driver of resistance in this setting. Here we report the discovery of BVD-523 (ulixertinib), a novel, reversible, ATP-competitive ERK1/2 inhibitor with high potency and ERK1/2 selectivity. In vitro BVD-523 treatment resulted in reduced proliferation and enhanced caspase activity in sensitive cells. Interestingly, BVD-523 inhibited phosphorylation of target substrates despite increased phosphorylation of ERK1/2. In in vivo xenograft studies, BVD-523 showed dose-dependent growth inhibition and tumor regression. BVD-523 yielded synergistic antiproliferative effects in a BRAFV600E-mutant melanoma cell line xenograft model when used in combination with BRAF inhibition. Antitumor activity was also demonstrated in in vitro and in vivo models of acquired resistance to single-agent and combination BRAF/MEK–targeted therapy. On the basis of these promising results, these studies demonstrate BVD-523 holds promise as a treatment for ERK-dependent cancers, including those whose tumors have acquired resistance to other treatments targeting upstream nodes of the MAPK pathway. Assessment of BVD-523 in clinical trials is underway (NCT01781429, NCT02296242, and NCT02608229). Mol Cancer Ther; 16(11); 2351–63. ©2017 AACR.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".