MétaCan
Menu
Back to cohort

Phase Ib Study of Combination Therapy with MEK Inhibitor Binimetinib and Phosphatidylinositol 3-Kinase Inhibitor Buparlisib in Patients with Advanced Solid Tumors with<i>RAS/RAF</i>Alterations

2019· article· en· W2968495081 on OpenAlexaff
Aditya Bardia, Mrinal M. Gounder, Jordi Rodón, Filip Jankú, Martijn P. Lolkema, Joe Stephenson, Philippe L. Bédard, Martin Schüler, Cristiana Sessa, Patricia LoRusso, Michael Thomas, Heiko Maacke, Helen H. Evans, Yongjian Sun, Daniel Shao-Weng Tan

Bibliographic record

VenueThe Oncologist · 2019
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer InstituteNovartis Pharmaceuticals CorporationNational Center for Advancing Translational SciencesArray BioPharma
KeywordsKRASMedicineNeuroblastoma RAS viral oncogene homologMEK inhibitorInternal medicineOncologyCancerResponse Evaluation Criteria in Solid TumorsAdverse effectToxicityPhases of clinical researchPharmacologyColorectal cancerMAPK/ERK pathwayKinase

Abstract

fetched live from OpenAlex

BACKGROUND: This multicenter, open-label, phase Ib study investigated the safety and efficacy of binimetinib (MEK inhibitor) in combination with buparlisib (phosphatidylinositol 3-kinase [PI3K] inhibitor) in patients with advanced solid tumors with RAS/RAF alterations. MATERIALS AND METHODS: Eighty-nine patients were enrolled in the study. Eligible patients had advanced solid tumors with disease progression after standard therapy and/or for which no standard therapy existed. Evaluable disease was mandatory, per RECIST version 1.1 and Eastern Cooperative Oncology Group performance status 0-2. Binimetinib and buparlisib combinations were explored in patients with KRAS-, NRAS-, or BRAF-mutant advanced solid tumors until the maximum tolerated dose and recommended phase II dose (RP2D) were defined. The expansion phase comprised patients with epidermal growth factor receptor (EGFR)-mutant, advanced non-small cell lung cancer, after progression on an EGFR inhibitor; advanced RAS- or BRAF-mutant ovarian cancer; or advanced non-small cell lung cancer with KRAS mutation. RESULTS: At data cutoff, 32/89 patients discontinued treatment because of adverse events. RP2D for continuous dosing was buparlisib 80 mg once daily/binimetinib 45 mg twice daily. The toxicity profile of the combination resulted in a lower dose intensity than anticipated. Six (12.0%) patients with RAS/BRAF-mutant ovarian cancer achieved a partial response. Pharmacokinetics of binimetinib were not altered by buparlisib. Pharmacodynamic analyses revealed downregulation of pERK and pS6 in tumor biopsies. CONCLUSION: Although dual inhibition of MEK and the PI3K pathways showed promising activity in RAS/BRAF ovarian cancer, continuous dosing resulted in intolerable toxicities beyond the dose-limiting toxicity monitoring period. Alternative schedules such as pulsatile dosing may be advantageous when combining therapies. IMPLICATIONS FOR PRACTICE: Because dysregulation of the mitogen-activated protein kinase (MAPK) and the phosphatidylinositol 3-kinase (PI3K) pathways are both frequently involved in resistance to current targeted therapies, dual inhibition of both pathways may be required to overcome resistance mechanisms to single-agent tyrosine kinase inhibitors or to treat cancers with driver mutations that cannot be directly targeted. A study investigating the safety and efficacy of combination binimetinib (MEK inhibitor) and buparlisib (PI3K inhibitor) in patients harboring alterations in the RAS/RAF pathway was conducted. The results may inform the design of future combination therapy trials in patients with tumors harboring mutations in the PI3K and MAPK pathways.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.010
GPT teacher head0.316
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations86
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe OncologistSame topicLung Cancer Treatments and MutationsFrench-language works237,207