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A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis

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dc.contributor.author Coto-Segura, Pablo
dc.contributor.author Segú-Vergés, Cristina
dc.contributor.author Martorell, Antonio
dc.contributor.author Moreno-Ramírez, David
dc.contributor.author Jorba Argemí, Guillem
dc.contributor.author Junet, Valentin
dc.contributor.author Guerri, Filippo
dc.contributor.author Daura, Xavier
dc.contributor.author Oliva Miguel, Baldomero
dc.contributor.author Cara, Carlos
dc.contributor.author Suárez-Magdalena, Olaya
dc.contributor.author Abraham, Sonya
dc.contributor.author Mas, José Manuel
dc.date.accessioned 2023-11-29T08:17:42Z
dc.date.available 2023-11-29T08:17:42Z
dc.date.issued 2023
dc.identifier.citation Coto-Segura P, Segú-Vergés C, Martorell A, Moreno-Ramírez D, Jorba G, Junet V, Guerri F, Daura X, Oliva B, Cara C, Suárez-Magdalena O, Abraham S, Mas JM. A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis. Front Immunol. 2023 Sep 20;14:1212981. DOI: 10.3389/fimmu.2023.1212981
dc.identifier.issn 1664-3224
dc.identifier.uri http://hdl.handle.net/10230/58412
dc.description.abstract Background: Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles. Methods: We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug. Results: The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed. Conclusion: Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.
dc.description.sponsorship The study was funded by UCB Pharma and Anaxomics Biotech. Copy-editing was funded by UCB Pharma. Article processing fees were provided by UCB Pharma. Public funders provided support for some of the authors’ salaries: VJ has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765158 (COSMIC; www.cosmic-h2020.eu); GJ has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 765912; FG has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 813545. The funder UCB Biopharma was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Front Immunol. 2023 Sep 20;14:1212981
dc.rights © 2023 Coto-Segura, Segú-Vergés, Martorell, Moreno-Ramírez, Jorba, Junet, Guerri, Daura, Oliva, Cara, Suárez-Magdalena, Abraham and Mas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fimmu.2023.1212981
dc.subject.keyword Anti-TNF
dc.subject.keyword Certolizumab pegol
dc.subject.keyword Mathematical modelling
dc.subject.keyword Mechanism of action
dc.subject.keyword Psoriasis
dc.subject.keyword Virtual population
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765158
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765912
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/813545
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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