Peer-reviewedCC BY 4.0Cell Reports Medicine
Uncovering combination therapies for immune-mediated inflammatory diseases through systems biology analysis on longitudinal patient data
18 affiliations
- 1IMIDomics, Inc., Barcelona, Spain
- 2Universitat de Barcelona (UB), Barcelona, Spain
- 3Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- 4Hospital Universitari Vall d'Hebron, Department of Rheumatology, Barcelona, Spain
- 5Laboratory of Experimental Immunology, Istituto Dermopatico dell′Immacolata-Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- 6Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
- 7HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- 8Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
- 9Fundació Clínic per a la Recerca Biomèdica, Department of Rheumatology, Barcelona, Spain
- 10Hospital Universitari Germans Trias i Pujol, Department of Dermatology, Badalona, Spain
- 11Hospital Universitari Germans Trias i Pujol, Department of Gastroenterology, and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Badalona, Spain
- 12Hospital Universitario de La Princesa, Department of Gastroenterology, Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Madrid, Spain
- 13Hospital General Universitario de Guadalajara, Department of Rheumatology, Guadalajara, Spain
- 14Charité-Universitätsmedizin Berlin, Department of Gastroenterology, Berlin, Germany
- 15Department of Medicine, Section of Dermatology, University of Verona, Verona, Italy
- 16CREATE Centre, Section of Rheumatology, Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK
- 17ICREA, Barcelona, Spain
- 18Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Abstract
While targeted therapies have reshaped the clinical management of immune-mediated inflammatory diseases (IMIDs), primary non-response remains a major obstacle for many patients. Combining two targeted therapies is an emerging strategy to overcome this therapeutic ceiling, but the number of possible drug pairs makes prioritization difficult. For this objective, we present mitigation of non-response signature (MNRS), a computational approach that uses longitudinal blood transcriptomic data from six IMIDs treated with different targeted therapies to identify the most promising drug combinations. The approach identifies complementary pairs of biologic agents and small molecules, as well as potentially incompatible pairs. In rheumatoid arthritis, anti-TNF and anti-interleukin 6 receptor therapy emerges as highly complementary; single-cell analysis localizes this effect to CD14+ monocytes, and a collagen-induced arthritis mouse model confirms that the combination outperforms monotherapy. These findings show that longitudinal patient data help prioritize drug combinations for clinical testing across immune-mediated diseases.
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BibTeX
@article{martinezmateu2026uncovering,
title = {{Uncovering combination therapies for immune-mediated inflammatory diseases through systems biology analysis on longitudinal patient data}},
author = {Sergio H. Martínez-Mateu and Yolanda Guillén and Edgar Angelats and María A. López-Lasanta and Stefania Madonna and Laura Jiménez-Gracia and Ivan Rodriguez-Nunez and Damiana Álvarez-Errico and Adrià Aterido and Raül Tortosa and Max Ruiz and Pau Serra and Juan D. Cañete and Jose M. Carrascosa and Eugeni Domènech and Javier P. Gisbert and Jesús Tornero and IMID-Consortium and Britta Siegmund and Giampiero Girolomoni and Ernest H.S. Choy and Richard M. Myers and Holger Heyn and Pere Santamaria and Sara Marsal and Antonio Julià},
journal = {Cell Reports Medicine},
year = {2026},
month = sep,
doi = {10.1016/j.xcrm.2026.103026},
url = {https://doi.org/10.1016/j.xcrm.2026.103026},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Uncovering combination therapies for immune-mediated inflammatory diseases through systems biology analysis on longitudinal patient data
AU - Sergio H. Martínez-Mateu
AU - Yolanda Guillén
AU - Edgar Angelats
AU - María A. López-Lasanta
AU - Stefania Madonna
AU - Laura Jiménez-Gracia
AU - Ivan Rodriguez-Nunez
AU - Damiana Álvarez-Errico
AU - Adrià Aterido
AU - Raül Tortosa
AU - Max Ruiz
AU - Pau Serra
AU - Juan D. Cañete
AU - Jose M. Carrascosa
AU - Eugeni Domènech
AU - Javier P. Gisbert
AU - Jesús Tornero
AU - IMID-Consortium
AU - Britta Siegmund
AU - Giampiero Girolomoni
AU - Ernest H.S. Choy
AU - Richard M. Myers
AU - Holger Heyn
AU - Pere Santamaria
AU - Sara Marsal
AU - Antonio Julià
PY - 2026
DA - 2026/09/11
JO - Cell Reports Medicine
DO - 10.1016/j.xcrm.2026.103026
UR - https://doi.org/10.1016/j.xcrm.2026.103026
AB - While targeted therapies have reshaped the clinical management of immune-mediated inflammatory diseases (IMIDs), primary non-response remains a major obstacle for many patients. Combining two targeted therapies is an emerging strategy to overcome this therapeutic ceiling, but the number of possible drug pairs makes prioritization difficult. For this objective, we present mitigation of non-response signature (MNRS), a computational approach that uses longitudinal blood transcriptomic data from six IMIDs treated with different targeted therapies to identify the most promising drug combinations. The approach identifies complementary pairs of biologic agents and small molecules, as well as potentially incompatible pairs. In rheumatoid arthritis, anti-TNF and anti-interleukin 6 receptor therapy emerges as highly complementary; single-cell analysis localizes this effect to CD14+ monocytes, and a collagen-induced arthritis mouse model confirms that the combination outperforms monotherapy. These findings show that longitudinal patient data help prioritize drug combinations for clinical testing across immune-mediated diseases.
SN - 2666-3791
ER -