Clinically relevant patient clusters identified by machine learning from the clinical development programme of secukinumab in psoriatic arthritis.

arthritis biological therapy inflammation psoriatic t-lymphocyte subsets tumor necrosis factor inhibitors

Journal

RMD open
ISSN: 2056-5933
Titre abrégé: RMD Open
Pays: England
ID NLM: 101662038

Informations de publication

Date de publication:
11 2021
Historique:
received: 26 07 2021
accepted: 29 10 2021
entrez: 19 11 2021
pubmed: 20 11 2021
medline: 5 1 2022
Statut: ppublish

Résumé

Identify distinct clusters of psoriatic arthritis (PsA) patients based on their baseline articular, entheseal and cutaneous disease manifestations and explore their clinical and therapeutic value. Pooled baseline data in PsA patients (n=1894) treated with secukinumab across four phase 3 studies (FUTURE 2-5) were analysed to determine phenotypes based on clusters of clinical indicators. Finite mixture models methodology was applied to generate clinical clusters and mean longitudinal responses were compared between secukinumab doses (300 vs 150 mg) across identified clusters and clinical indicators through week 52 using machine learning (ML) techniques. Seven distinct patient clusters were identified. Cluster 1 (very-high (VH) - SWO/TEN (swollen/tender); n=187) was characterised by VH polyarticular burden for both tenderness and swelling of joints, while cluster 2 (H (high) - TEN; n=251) was marked by high polyarticular burden in tender joints and cluster 3 (H - Feet - Dactylitis; n=175) by high burden in joints of feet and dactylitis. For cluster 4 (L (Low) - Nails - Skin; n=209), cluster 5 (L - skin; n=283), cluster 6 (L - Nails; n=294) and cluster 7 (L; n=495) articular burden was low but nail and skin involvement was variable, with cluster 7 marked by mild disease activity across all domains. Greater improvements in the longitudinal responses for enthesitis in cluster 2, enthesitis and Psoriasis Area and Severity Index (PASI) in cluster 4 and PASI in cluster 6 were shown for secukinumab 300 mg compared with 150 mg. PsA clusters identified by ML follow variable response trajectories indicating their potential to predict precise impact on patients' outcomes. NCT01752634, NCT01989468, NCT02294227, NCT02404350.

Identifiants

pubmed: 34795065
pii: rmdopen-2021-001845
doi: 10.1136/rmdopen-2021-001845
pmc: PMC8603280
pii:
doi:

Substances chimiques

Antibodies, Monoclonal, Humanized 0
secukinumab DLG4EML025

Banques de données

ClinicalTrials.gov
['NCT01989468', 'NCT02294227', 'NCT01752634', 'NCT02404350']

Types de publication

Clinical Trial, Phase III Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: EP: Shareholder and Employee of Novartis. MK: Shareholder and Employee of Novartis. PN: Speaker’s bureau: Novartis, Eli Lilly and AbbVie. CTR: Research grants: AbbVie, Amgen, UCB; Consultant for: AbbVie, Amgen, UCB, Novartis, Pfizer, Lilly, Janssen, BMS. BWK: Research grants, consultation fees, or speaker honoraria: AbbVie, Gilead, Janssen, Lilly, Novartis, Pfizer and UCB. GL: Shareholder and Employee of Novartis. LP: Shareholder and Employee of Novartis. AO: Consultant: AbbVie, Amgen, BMS, Celgene, Corrona, Gilead, Janssen, Lilly, Novartis, Pfizer, UCB. Research grants: Novartis (to Penn), Pfizer (to Penn), Amgen (to Forward). Royalties to husband from Novartis. LCC: Grant/research support: AbbVie, Amgen, Gilead, Janssen, Lilly, Novartis, Pfizer Consultant/speaker for: AbbVie, Amgen, Biogen, Celgene, Pfizer, UCB, Boehringer Ingelheim, Novartis, Lilly, Janssen, Gilead, Medac. GS: Speakers honoraria from AbbVie, BMS, Celgene, Janssen, Lilly, Novartis, Roche and UCB. IBM: Research grants, consultation fees, or speaker honoraria: AbbVie, Amgen, BMS, Celgene, Janssen, Lilly, Novartis, Pfizer and UCB.

Références

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pubmed: 32296743
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pubmed: 31228101
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Auteurs

Effie Pournara (E)

Immunology, Heptatology and Dermatology, Novartis AG, Basel, Switzerland.

Matthias Kormaksson (M)

Advanced Exploratory Analytics, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA.

Peter Nash (P)

School of Medicine, Griffith University School of Medicine, Gold Coast, Queensland, Australia.

Christopher T Ritchlin (CT)

Department of Medicine, Allergy/Immunology and Rheumatology (SMD), University of Rochester, Rochester, New York, USA.

Bruce W Kirkham (BW)

Rheumatology, Guy's and St Thomas' NHS Foundation Trust, London, UK.

Gregory Ligozio (G)

Immunology, Heptatology and Dermatology, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA.

Luminita Pricop (L)

Immunology, Heptatology and Dermatology, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA.

Alexis Ogdie (A)

Rheumatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Laura C Coates (LC)

Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Georg Schett (G)

Rheumatology, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany.

Iain B McInnes (IB)

College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK iain.mcinnes@glasgow.ac.uk.

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