MétaCan
Menu
← Back to cohort

Initial results from a first-in-human, phase I study of immunomodulatory aryl hydrocarbon receptor (AhR) inhibitor BAY2416964 in patients with advanced solid tumors.

2023· article· en· W4379283554 on OpenAlexaff
Ecaterina E. Dumbrava, Michael Cecchini, Jon Zugazagoitia, Juanita Lopez, Dirk Jäger, Marc Oliva, Sebastian Ochsenreither, Valentina Gambardella, Ki Y. Chung, Federico Longo, Albiruni Ryan Abdul Razak, Martin Wermke, T.R. Jeffry Evans, Natalie Cook, Maxime Chénard-Poirier, Radost Pencheva, David Schaer, T. J. Wagener, Kyriakos P. Papadopoulos

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversité LavalPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineAdverse effectInternal medicinePharmacodynamicsCancerResponse Evaluation Criteria in Solid TumorsLung cancerOncologyNauseaPancreatic cancerToxicityChillsTolerabilityPhases of clinical researchGastroenterologyPharmacokinetics

Abstract

fetched live from OpenAlex

2502 Background: AhR activation is involved in tumor growth, immunomodulation, and resistance to immune checkpoint inhibitors. BAY2416964 is a novel, potent, oral AhR inhibitor (AhRi) that antagonizes AhR ligand-induced immunosuppressive effects, resulting in enhanced proinflammatory activity of antigen-presenting cells and T cells and reduced activity of immunosuppressive myeloid cells. Methods: A first-in-human, Phase I clinical trial of AhRi BAY2416964 (NCT04069026) is evaluating its safety, pharmacokinetics, pharmacodynamics, recommended Phase II dose, and anti-tumor activity per RECIST v1.1 and iRECIST. BAY2416964 was administered orally in patients with advanced solid tumors in a dose-escalation cohort using a modified toxicity probability interval (mTPI) design. The initial expansion cohorts enrolled patients with non-small-cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC). Results: As of November 4, 2022, 72 patients had been treated with BAY2416964: 39 patients in dose escalation and 33 patients in the initial dose expansion treated with 500 mg twice daily (25 NSCLC, 8 HNSCC). The most common tumor types enrolled in dose escalation were colorectal cancer ( n= 12), breast cancer ( n= 6), and pancreatic cancer ( n= 4). Median age was 61 years (range 35-80). 51/72 (70.8%) patients had received ≥3 lines of therapy (including 16 [22.2%] who had received ≥6 lines) and 47/72 (65.3%) had received immune checkpoint inhibitors. Drug-related treatment-emergent adverse events (TEAEs) of all grades reported in ≥10% of patients were nausea (13.9%; 1.4% grade 3) and fatigue (11.1%; 1.4% grade 3). Most drug-related TEAEs were grade 1 or 2; 9 (12.5%) patients experienced drug-related grade 3 TEAEs and no patients experienced drug-related grade ≥4 TEAEs. No dose-limiting toxicities were observed. Two patients in dose expansion discontinued treatment due to drug-related TEAEs. Plasma exposure to BAY2416964 increased according to dose and food intake. Analysis of biomarkers demonstrated evidence of target engagement and an increase in immune activation at the doses tested. Of 67 patients evaluable for response by RECIST, 22 (32.8%) had stable disease per RECIST v1.1, including 1 with thymoma in dose escalation achieving an iRECIST partial response. Conclusions: BAY2416964 was well tolerated across all dose levels and regimens tested. Initial evaluation of biomarkers shows BAY2416964 inhibits AhR and modulates immune functions. Encouraging preliminary anti-tumor activity was observed in heavily pretreated patients. The disease-specific dose-expansion part of this study is ongoing. The observed manageable safety profile also supports combination therapies. Clinical trial information: NCT04069026 .

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)0.003
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.055
GPT teacher head0.431
Teacher spread0.376 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicCancer Immunotherapy and Biomarkers→French-language works237,207→