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Record W4411325981 · doi:10.1002/hon.70094_443

443 | A PHASE 1 STUDY OF PRT2527, A SELECTIVE CDK9 INHIBITOR, AS MONOTHERAPY AND IN COMBINATION WITH ZANUBRUTINIB IN RELAPSED/REFRACTORY LYMPHOID MALIGNANCIES: UPDATED ANALYSIS

2025· article· en· W4411325981 on OpenAlexaff
Wojiech Jurczak, Clémentine Sarkozy, Katharine L. Lewis, Geoffrey Shouse, Eliza A. Hawkes, Woo Seob Kim, Monica Tani, François Lemonnier, Sarit Assouline, F. Morschhauser, Bruce D. Cheson, Catherine Diefenbach, Gareth P. Gregory, T. Munir, Petra Langerbeins, Constantine S. Tam, G. Musuraca, C. A. Portell, Anastasios Stathis, Y. R., Joana M. Xavier, Mohammed A. Osman, Siminder Atwal, J. Huang, P. L. Zinzani

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsMcGill UniversityJewish General Hospital
FundersBeiGeneGilead SciencesMerck KGaARegeneron PharmaceuticalsPharmacyclicsGenentechCelgeneAstraZenecaEli Lilly and CompanyBristol-Myers SquibbTG TherapeuticsAmgen
KeywordsRefractory (planetary science)MedicineOncologyInternal medicineCancer researchBiology

Abstract

fetched live from OpenAlex

Introduction: CDK9, a key transcription elongation regulator, is a potential target in transcriptionally addicted cancers dependent on oncogenic drivers with short half-lives. The BTK inhibitor zanubrutinib (zanu) upregulates BCL2-modifying factor, a proapoptotic molecule inhibited by BCL2, BCLXL, and BCLW. CDK9 and BTK inhibition may synergistically enhance apoptotic priming and shift dependency toward the CDK9 targets. Methods: Phase 1 study of PRT2527, a highly selective potent CDK9 inhibitor, as monotherapy (mono) and in combination (combo) with zanu in patients (pts) with select relapsed/refractory (R/R) hematologic malignancies (NCT05665530). Pts must be R/R to or ineligible for standard-of-care therapy. PRT2527 administered intravenously as a once-weekly infusion in escalating doses guided by BOIN based on dose-limiting toxicities (DLTs) in cycle 1; zanu administered orally. Results: As of January 31, 2025, 56 pts with lymphoma (20 DLBCL-NOS, 4 HGBL, 2 Richter syndrome, 20 TCL, 6 MCL, and 4 CLL/SLL) were enrolled and treated with PRT2527. Median age was 64 (range, 27–94) years, 59% were male, and median prior lines of therapy was 3 (range, 1–7). Thirty-five pts were treated with PRT2527 mono (9 mg/m2, n = 6; 15 mg/m2, n = 5; 18 mg/m2, n = 18; 24 mg/m2, n = 6) and 21 with PRT2527 + zanu combo (9 mg/m2, n = 6; 15 mg/m2, n = 7, 18 mg/m2, n = 8). Median duration of study treatment was 6 weeks (range, 1.0–39.0) for mono and 13 weeks (range, 4.4–45.0) for combo. Ten (18%) pts remained on study (mono, n = 3; combo, n = 7), and 46 (82%) discontinued treatment mainly due to progressive disease (66%) and adverse events (AEs; 9%). Most frequent (> 20%) any-grade treatment-emergent AEs (TEAEs) were neutropenia (46%), nausea (38%), and anemia (21%) in all pts. Most frequent grade ≥ 3 TEAEs were neutropenia (43%), anemia (11%), thrombocytopenia (9%), and febrile neutropenia, sepsis, and pneumonia (5% each). Twenty-five (45%) pts had a TEAE leading to PRT2527 dose hold: 14 (25%) due to neutropenia, with 13 resolved with growth factor, and 1 due to febrile neutropenia. One DLT of grade 3 tumor lysis syndrome (TLS) occurred in a pt with CTCL (mycosis fungoides) with 24 mg/m2 mono. Responses shown in Table. Whole blood expression of MCL1 and MYC showed target engagement at all doses. At 18 mg/m2 (n = 13), a median decrease of 74% in MCL1 and 88% in MYC mRNA was achieved at end of infusion. At 6 h, MCL1 and MYC levels remained suppressed with median inhibition of 20% and 34%, respectively. Conclusions: PRT2527 had an acceptable safety profile with activity observed in both mono and in combo with zanu, including CR in 2 pts relapsed after CAR-T and responses in CLL/SLL post BTK. Most common TEAE was neutropenia, manageable with growth factor support. One DLT (grade 3 TLS) was observed in mono (24 mg/m2) and no DLTs in combo. Data supports further evaluation of PRT2527 at 18 mg/m2 (recommended phase 2 dose) as mono in PTCL and in combo with zanu in aggressive B-cell lymphoma. Research funding declaration: Prelude Therapeutics Incorporated Keywords: non-Hodgkin; molecular targeted therapies; molecular targeted therapies Potential sources of conflict of interest: W. Jurczak Consultant or advisory role: Abbvie/Genentech, AstraZeneca, BeiGene, Janssen-Cillag, Lilly, and Takeda Other remuneration: Research Funding: Abbvie/Genentech, AstraZeneca, BeiGene, Janssen- Cillag, Lilly, Merck, MSD, Prelude Therapeutics, Roche, and Takeda C. Sarkozy Consultant or advisory role: Janssen, BeiGene, Roche, Bristol Myers Squibb, and MSD Honoraria: AstraZeneca, BeiGene, and AbbVie Educational grants: Roche, Gilead Other remuneration: Research Funding: Roche K. L. Lewis Consultant or advisory role: AstraZeneca, Roche, Merck/MSD, and AbbVie Honoraria: Janssen, Roche, AstraZeneca, and Gilead/Kite Other remuneration: Patents/Royalties: Loxo/Lilly, AstraZeneca; Advisory Board: AbbVie, Merck/MSD, and IQVIA; Trial Steering Committee: Loxo/Lilly G. Shouse Consultant or advisory role: BeiGene, Kite Pharma Honoraria: BeiGene, Kite Pharma Other remuneration: Speakers Bureau: BeiGene, Kite Pharma, and ADC Therapeutics E. A. Hawkes Consultant or advisory role: Roche, Merck Sharp & Dohme, AstraZeneca, Gilead, Antengene, Novartis, Regeneron, Janssen, Specialised Therapeutics, and Sobi Educational grants: AstraZeneca Other remuneration: Research Funding: Roche, AstraZeneca, Merck KGaA, Bristol Myers Squibb, TG Therapeutics and Merck; Speakers Bureau: Regeneron; Expert Testimony: Specialised Therapeutics W. S. Kim Other remuneration: Grant/Research Support: Sanofi, BeiGene, Boryong, Roche, Kyowa-Kirin, and Donga M. Tani Consultant or advisory role: Roche, Lilly, BeiGene, and AstraZeneca Honoraria: Sobi, AbbVie Educational grants: Janssen-Cilag F. Lemonnier Consultant or advisory role: Bristol Myers Squibb, Kiowa Educational grants: BeiGene, Kite, and AbbVie S. E. Assouline Consultant or advisory role: Roche, AstraZeneca, Novartis, Gilead, and BeiGene Honoraria: Roche, AstraZeneca, Norvatis, Gilead, AbbVie, and Beigene Other remuneration: Research Funding: Novartis; Speakers Bureau: Roche, Novartis F. Morschhauser Consultant or advisory role: Roche, Bristol Myers Squibb, and Gilead Honoraria: Takeda, Roche, and Chugai Other remuneration: Participation on a Data Safety Monitoring Board or Advisory Board: Gilead, Bristol Myers Squibb, and AbbVie B. D. Cheson Employment or leadership position: American Oncology Network Consultant or advisory role: AbbVie, AstraZeneca, Regeneron, Calyx, and Imaging Endpoints Honoraria: BeiGene, Lilly Other remuneration: Leadership: Symbio Pharmaceuticals; Research Funding: Lilly, BeiGene, Genentech, and Prelude; Speakers Bureau: Lilly C. S. Diefenbach Employment or leadership position: NYU Grossman School of Medicine/Perlmutter Cancer Center at NYU Langone Health Consultant or advisory role: Bristol Myers Squibb, Celgene, Genentech/Roche, Genmab, I MAB, Incyte, Merck, MorphoSys, Seattle Genetics Stock ownership: Gilead Sciences, OverT Therapeutics Other remuneration: Membership on a Board or Advisory Committee: AstraZeneca; Research Funding: Bristol Myers Squibb, FATE Therapeutics, Genentech/Roche, Incyte, MEI Pharma, Merck, Millenium, Seattle Genetics G. P. Gregory Consultant or advisory role: Roche/Genentech, Merck, Gilead Kite, Amgen, and Prelude Therapeutics Educational grants: Novartis Other remuneration: Research Funding: Merck, BeiGene, AbbVie; Speakers Bureau: Roche/Genentech, Gilead Kite T. Munir Consultant or advisory role: Abbvie, Lilly, AstraZeneca, Johnson and Johnson, and Novartis Honoraria: Johnson and Johnson, AstraZeneca, BeiGene, Lilly, and AbbVie Other remuneration: Research Funding: Johnson and Johsnon, AbbVie; Speakers Bureau: Johnson and Johnson, AstraZeneca, BeiGene, Lilliy, Sobi, and Novartis P. Langerbeins Consultant or advisory role: Janssen, AbbVie, AstraZeneca, and BeiGene Honoraria: Janssen, AbbVie, AstraZeneca, and BeiGene Educational grants: Janssen, AbbVie, AstraZeneca, and BeiGene Other remuneration: Research Funding: Janssen-Cilag C. S. Tam Honoraria: Janssen, AbbVie, BeiGene, and AstraZeneca Other remuneration: Research Funding: AbbVie, Janssen G. Musuraca Consultant or advisory role: Janssen, Roche, AbbVie, Genmab, Incyte, Takeda, BeiGene, AstraZeneca, SOBI, and Lilly Educational grants: Janssen, AbbVie, AstraZeneca Other remuneration: Speakers Bureau: Genmab, Incyte, Lilly, and AbbVie; Expert Testimony: Genmab, Incyte, Lilly, and AbbVie C. A. Portell Consultant or advisory role: AstraZeneca, BeiGene, Jansen, Merck, and Genentech Honoraria: Pfizer Other remuneration: Research Funding: AbbVie, Genentech, Prelude, Lilly, AstraZeneca, Pfizer, Acerta, and BeiGene A. Stathis Consultant or advisory role: Debiopharm, Janssen, AstraZeneca, Incyte, Eli Lilly, Novartis, Roche, and Loxo Oncology Educational grants: Incyte, AstraZeneca Other remuneration: Research Funding: Abbvie, ADC Therapeutics, Amgen, Astra Zeneca, Bayer, BMS, Cellestia, Debiopharm, Incyte, Loxo Oncology, Merck MSD, Novartis, Pfizer, Philogen, Prelude Therapeutics, and Roche J. Xavier Employment or leadership position: Prelude Therapeutics Consultant or advisory role: Sanofi, Pfizer, and Genentech Stock ownership: Prelude Therapeutics Other remuneration: Research Funding: Genentech, Sanofi; Patents, Royalities: Incanthera Ltd M. Osman Employment or leadership position: Prelude Therapeutics Stock ownership: Prelude Therapeutics, AstraZeneca S. K. Atwal Employment or leadership position: Prelude Therapeutics Stock ownership: Prelude Therapeutics, BeiGene J. Huang Employment or leadership position: Prelude Therapeutics, Protara Stock ownership: Prelude Therapeutics, BeiGene Other remuneration: Patents, Royalties: Prelude Therapeutics P. L. Zinzani Honoraria: Kyowa Kirin, Roche, AbbVie, BeiGene, Bristol Myers Squibb, Gilead, Novartis, Incyte, and Sobi Other remuneration: Speakers Bureau: Kyowa Kirin, Roche, AbbVie, BeiGene, Bristol Myers Squibb, Gilead, Novartis, Incyte, and Sobi

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.366
Teacher spread0.346 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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