A Next-Generation BRAF Inhibitor Overcomes Resistance to BRAF Inhibition in Patients with <i>BRAF</i> -Mutant Cancers Using Pharmacokinetics-Informed Dose Escalation
Bibliographic record
Abstract
RAF inhibitors have transformed treatment for patients with BRAFV600-mutant cancers, but clinical benefit is limited by adaptive induction of ERK signaling, genetic alterations that induce BRAFV600 dimerization, and poor brain penetration. Next-generation pan-RAF dimer inhibitors are limited by a narrow therapeutic index. PF-07799933 (ARRY-440) is a brain-penetrant, selective, pan-mutant BRAF inhibitor. PF-07799933 inhibited signaling in vitro, disrupted endogenous mutant-BRAF:wild-type-CRAF dimers, and spared wild-type ERK signaling. PF-07799933 ± binimetinib inhibited growth of mouse xenograft tumors driven by mutant BRAF that functions as dimers and by BRAFV600E with acquired resistance to current RAF inhibitors. We treated patients with treatment-refractory BRAF-mutant solid tumors in a first-in-human clinical trial (NCT05355701) that utilized a novel, flexible, pharmacokinetics-informed dose escalation design that allowed rapid achievement of PF-07799933 efficacious concentrations. PF-07799933 ± binimetinib was well-tolerated and resulted in multiple confirmed responses, systemically and in the brain, in patients with BRAF-mutant cancer who were refractory to approved RAF inhibitors. Significance: PF-07799933 treatment was associated with antitumor activity against BRAFV600- and non-V600-mutant cancers preclinically and in treatment-refractory patients, and PF-07799933 could be safely combined with a MEK inhibitor. The novel, rapid pharmacokinetics (PK)-informed dose escalation design provides a new paradigm for accelerating the testing of next-generation targeted therapies early in clinical development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".