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Record W4411410596 · doi:10.1016/j.ard.2025.06.1258

ABS0448 ZASOCITINIB (TAK-279), AN ORAL, ALLOSTERIC, SELECTIVE TYK2 INHIBITOR, IN MODERATE-TO-SEVERE PLAQUE PSORIASIS: EFFICACY ANALYSIS BY BASELINE CHARACTERISTICS FROM A RANDOMISED PHASE 2B TRIAL

2025· article· en· W4411410596 on OpenAlexaff
Nada Elbuluk, M. Gooderham, John Blau, Weidong Zhang, J. Uy, Warren Winkelman, M. Lebwohl

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsSKiN Health
Fundersnot available
KeywordsMedicinePlaque psoriasisPsoriasisInternal medicineRandomized controlled trialClinical trialPharmacologyDermatology

Abstract

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Background: Zasocitinib (TAK-279) is a highly selective and potent, oral, allosteric tyrosine kinase 2 (TYK2) inhibitor. In a phase 2b trial of moderate-to-severe plaque psoriasis, the primary endpoint (psoriasis area and severity index [PASI] 75 response at Week 12) was met with zasocitinib 5, 15 and 30 mg once daily; 33% of patients receiving zasocitinib 30 mg achieved PASI 100 [1]. Objectives: To evaluate the influence of baseline characteristics on zasocitinib efficacy in patients with plaque psoriasis in the phase 2b trial. Methods: This was a phase 2b, randomised, multicentre, double-blind, placebo-controlled, multiple-dose study (NCT04999839). Post hoc analyses were performed on Week 12 PASI 75/90/100 responses, Physician Global Assessment (PGA) scores of clear (0) or almost clear (1), and Dermatology Life Quality Index (DLQI) scores stratified by weight, sex, age, race, disease duration, prior biologics use and baseline PASI. The treatment difference between zasocitinib groups and placebo was calculated using the Mantel–Haenszel method, and p values were calculated using a Cochran–Mantel–Haenszel test with prior biologic treatment included as a stratification factor; treatment difference estimates stratified by prior biologic treatment, in which treatment difference estimates were unadjusted and chi-square test was used to calculate p values. For DLQI, a mixed model for repeated measures was used to calculate the least square mean, with treatment, visit, treatment-by-visit interaction and prior biologic treatment (not included for the subgroup analysis by prior biologic treatment) as fixed effects and baseline score as the covariate. Results: Overall, 259 patients were included in this study. PASI 75 response rates with zasocitinib were greater than placebo regardless of weight (≤ 90 kg: placebo, 8.8%; 15 mg, 70.4%; 30 mg, 69.7%; > 90 kg: placebo, 0%; 15 mg, 65.4%; 30 mg, 63.2%, each p < 0.001), sex (male: placebo, 0%; 15 mg, 61.8%; 30 mg, 72.7%, each p < 0.001; female: placebo, 14.3%; 15 mg, 78.9% [ p < 0.001]; 30 mg, 57.9% [ p < 0.01]), age (≤ 40 years: placebo, 0%; 15 mg, 55.6% [ p < 0.01]; 30 mg, 62.5% [ p < 0.001]; > 40 years: placebo, 7.3%; 15 mg, 74.3%; 30 mg, 69.4%, each p < 0.001), race (White: placebo, 6.8%; 15 mg, 67.4%; 30 mg, 64.3%, each p < 0.001; non-White: placebo, 0%; 15 mg, 71.4% [ p < 0.05]; 30 mg, 80.0% [ p < 0.005], disease duration (≤ 10 years: placebo, 3.6%; 15 mg, 47.4%; 30 mg, 58.8%; >10 years: placebo, 8.3%; 15 mg, 79.4%; 30 mg, 71.4%, each p < 0.001), prior biologics (yes: placebo, 0%; 15 mg, 100% [ p < 0.001]; 30 mg, 37.5% [ p = 0.055, not significant]; no: placebo, 6.8%; 15 mg, 61.4%; 30 mg, 72.7%, each p < 0.001) and baseline PASI (≤ 16: placebo, 10.7%; 15 mg, 57.9%; 30 mg, 60.7%; > 16: placebo, 0%; 15 mg, 93.3%; 30 mg, 75.0%, each p < 0.001; Figure 1). PASI 90, PASI 100, PGA 0/1 and DLQI were also significantly improved with zasocitinib treatment versus placebo in almost all subgroups. Conclusion: Treatment with zasocitinib 15 mg or 30 mg demonstrated consistent improvements in PASI 75, PASI 90, PASI 100, PGA 0/1 and DLQI at Week 12 versus placebo in patients with moderate-to-severe plaque psoriasis, regardless of baseline weight, sex, age, disease duration, prior biologic use and PASI. Phase 3 trials (NCT06088043 and NCT06108544) are ongoing to investigate the efficacy and safety of zasocitinib in larger patient groups. REFERENCES: [1] Armstrong A, et al. JAMA Dermatol 2024;160:1066–74. Acknowledgements: This study was funded by Nimbus Discovery, Inc. and Takeda Development Center Americas, Inc. Writing assistance was provided by Tina Borg, PhD, of Oxford PharmaGenesis and funded by Takeda Development Center Americas, Inc. Nimbus refers to the group of entities including Nimbus Therapeutics LLC, Nimbus Discovery Inc., and Nimbus Lakshmi Inc. Disclosure of Interests: Nada Elbuluk has received royalties from McGraw Hill, has stock options in VisualDx, is a consultant, advisory board member, and/or speaker for AbbVie, Allergan, Avita, Beiersdorf, Dior, Eli Lilly, Galderma, Incyte, Janssen, La Roche Posay, L'Oreal, McGraw Hill, Medscape, Pfizer, Sanofi, Takeda, Unilever, VisualDx, and has received grant funding from Pfizer, Melinda Gooderham is an investigator, speaker and/or advisor for AbbVie, Akros, Amgen, AnaptysBio, Apogee, Arcutis Biotherapeutics, Aristea, Bausch Health, Boehringer Ingelheim, Bristol Myers Squibb, Dermavant, Dermira, Eli Lilly, Galderma, GSK, Incyte, Inmagene, JAMP, Janssen, Kyowa Kirin, LEO Pharma, MedImmune, Meiji, MoonLake Immunotherapeutics, Nimbus, Novartis, Pfizer, Regeneron, Sanofi Genzyme, Sun Pharma, Takeda, Tarsus Pharmaceuticals, UCB, Union Therapeutics, Ventyx Biosciences and Vyne Therapeutics, Jessamyn Blau is an equity holder and an employee of Takeda, Wenwen Zhang is an equity holder and an employee of Takeda, Jonathan Uy is an equity holder and an employee of Takeda, Warren Winkelman is an equity holder and an employee of Takeda, Mark Lebwohl is a consultant for Almirall, AltruBio, Apogee Therapeutics, Arcutis Biotherapeutics, AstraZeneca, Atomwise, Avotres, Boehringer Ingelheim, Bristol Myers Squibb, Castle Biosciences, Celltrion, CorEvitas, Dermavant, Dermsquared, Evommune, Facilitation of International Dermatology Education, Forte Biosciences, Galderma, Genentech, Incyte, LEO Pharma, Meiji Seika Pharma, Mindera, Pfizer, Sanofi-Regeneron, Seanergy, Strata, Takeda, Trevi Therapeutics and Verrica Pharmaceuticals, is an employee of Mount Sinai, and receives research funds from AbbVie, Arcutis Biotherapeutics, Avotres, Boehringer Ingelheim, Cara Therapeutics, Clexio Biosciences, Dermavant, Eli Lilly, Incyte, Inozyme, Janssen, Pfizer, Sanofi-Regeneron and UCB. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.029
GPT teacher head0.314
Teacher spread0.286 · 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 designRandomized 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

Citations0
Published2025
Admission routes1
Has abstractyes

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