4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Therapeutic drug monitoring in dermatology: the way towards dose optimization of secukinumab in chronic plaque psoriasis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Background

          Despite the favourable efficacy profile of secukinumab, clinicians encounter varying clinical responses among patients potentially associated with under‐ and overdosing. As biologics are expensive, their rational use is crucial and evident. Therapeutic drug monitoring could guide clinicians in making decisions about treatment modifications.

          Aim

          In this multicentre, prospective study, we aimed to develop and validate a secukinumab immunoassay and searched for the therapeutic window in patients with psoriasis.

          Methods

          We determined secukinumab concentrations at trough in sera from 78 patients with psoriasis at multiple timepoints (Weeks 12, 24, 36, 48 and 52; after Week 52, measurements could be taken at an additional three timepoints) during maintenance phase, using an in‐house secukinumab immunoassay consisting of a combination of MA‐SEC66A2 as capture antibody and MA‐SEC67A9, conjugated to horseradish peroxidase, as detecting antibody. At each hospital visit, disease severity was assessed using the Psoriasis Area and Severity Index (PASI).

          Results

          After quantification, 121 serum samples were included for dose–response analysis. Based on a linear mixed‐effects model, secukinumab trough concentrations were found to decrease with increasing body mass index (BMI). Based on receiver operating characteristic (ROC) analysis, we concluded that the minimal effective secukinumab threshold was 39.1 mg/L in steady state, and that this was associated with a 92.7% probability of having an optimal clinical response (PASI ≤ 2 or reduction in PASI of ≥ 90%).

          Conclusions

          Monitoring and targeting a secukinumab trough concentration of 39.1 mg/L may be a viable treatment option in suboptimal responders. In patients with higher BMI, weight‐based dosing may be needed in order to prevent underdosing.

          Abstract

          Using our in‐house immunoassay, we determined secukinumab concentrations in sera from 78 patients with psoriasis at multiple timepoints (ensuring these were trough levels, just before the next administration) during maintenance phase. We deduced that the minimal effective secukinumab threshold is 39.1 mg/L, and this was associated with a 92.7% probability of having an optimal clinical response [Psoriasis Area and Severity Index (PASI) ≤ 2 or reduction in PASI of ≥ 90%].

          Related collections

          Most cited references50

          • Record: found
          • Abstract: found
          • Article: not found

          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The REDCap consortium: Building an international community of software platform partners

            The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              pROC: an open-source package for R and S+ to analyze and compare ROC curves

              Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
                Bookmark

                Author and article information

                Contributors
                jo.lambert@uzgent.be
                Journal
                Clin Exp Dermatol
                Clin Exp Dermatol
                10.1111/(ISSN)1365-2230
                CED
                Clinical and Experimental Dermatology
                John Wiley and Sons Inc. (Hoboken )
                0307-6938
                1365-2230
                25 April 2022
                July 2022
                : 47
                : 7 ( doiID: 10.1111/ced.v47.7 )
                : 1324-1336
                Affiliations
                [ 1 ] Department of Dermatology Ghent University Hospital Ghent Belgium
                [ 2 ] Department of Pharmaceutical and Pharmacological Sciences KU Leuven Leuven Belgium
                Author notes
                [*] [* ] Correspondence: Professor Dr Jo Lambert, Department of Dermatology, Ghent University Hospital, C. Heymanslaan 10, Ghent, Belgium

                E‐mail: jo.lambert@ 123456uzgent.be

                Author information
                https://orcid.org/0000-0002-7625-6630
                https://orcid.org/0000-0001-6415-2044
                https://orcid.org/0000-0002-7546-4454
                https://orcid.org/0000-0002-0785-2930
                https://orcid.org/0000-0003-3375-7467
                https://orcid.org/0000-0002-2595-2994
                https://orcid.org/0000-0003-1259-9105
                https://orcid.org/0000-0003-3075-9846
                https://orcid.org/0000-0001-5303-9310
                Article
                CED15157 CED-2021-1668.R1
                10.1111/ced.15157
                9320967
                35245966
                26a23297-5c43-4e69-80cb-9d461c028df1
                © 2022 The Authors. Clinical and Experimental Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 11 February 2022
                : 10 November 2021
                : 27 February 2022
                Page count
                Figures: 5, Tables: 2, Pages: 1336, Words: 8029
                Funding
                Funded by: Fonds Wetenschappelijk Onderzoek , doi 10.13039/501100003130;
                Award ID: 12X9420N
                Award ID: T003218N
                Award ID: T003716N
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                July 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:26.07.2022

                Dermatology
                Dermatology

                Comments

                Comment on this article