Ticopa Serum Analysis: an Analysis of Blood Samples From the TICOPA Study

NCT ID: NCT05329896

Last Updated: 2022-04-15

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 82 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-04-01
• Study Completion Date: 2024-03-31

Brief Summary

A range of different drugs are available to treat psoriatic arthritis (PsA) inflammation. However, clinicians are unable to predict who will respond well to a given drug, who will fail to respond and who will develop side effects. Responder/non-responder effects may also differ for the skin and joint domains of PsA. Patients currently undergo a trial and error phase of treatment, sometimes withstanding a period of nonresponse, and thus pain and discomfort, for a period of time. Treatment failures also waste resources and undermine patient confidence. There is a pressing need to identify predictors for response / non response and side effects, and this study will utilise novel bioinformatics approaches to address this need. The samples and clinical information collected from participants in the TICOPA (Effect of tight control of inflammation in early psoriatic arthritis) study (1) are a valuable resource. The investigators aim to use these existing serum samples to determine the potential of molecular markers to predict patients' response to treatment both with regard to effects and side effects. This analysis could potentially lead to the identification of serum and clinical parameters which when measured in a defined combination would be predictive of patients' response to treatment.

1 https://doi.org/10.1016/S0140-6736(15)00347-5

Detailed Description

1. INTRODUCTION 1.1. Background Psoriatic Arthritis (PsA) is estimated to occur in 10-15% of individuals with psoriasis, and accounts for 13% of people attending early arthritis clinics.Two thirds of patients with PsA suffer chronic inflammation causing progressive joint damage, disability and reduced life expectancy. With an increasing awareness of the poor outcomes associated with PsA, and the availability of new effective, but costly, treatments, there is an urgent need to establish the optimal treatment for patients with PsA.

The TICOPA study was a UK multicentre, open-label, randomised controlled, parallel group trial of 206 patients with early PsA, randomised on a 1:1 basis to receive either standard care (12 weekly review) or intensive management (4 weekly review) for a period of 48 weeks. Patients assigned to the intensive management group followed a strict treatment protocol whereby dose continuation/escalation was determined through the objective assessment of the minimal disease activity (MDA) criteria. Patients assigned to the standard care group received standard care as felt appropriate by the treating clinician, with no set protocol. The aim of the TICOPA study was to establish whether, in treatment naïve early PsA patients, intensive management with evidence based therapies would improve clinical outcome, the principle hypothesis being that tight control of inflammation in psoriatic arthritis using a treatment protocol and pre-defined objective targets for treatment would lead to an improvement in patients disease activity. The trial aimed to establish the superiority of intensive management as compared to standard care as defined by the ACR 20 (American Society of Rheumatology).

The TICOPA trial provided direct evidence that the use of early and intensive treatment in PsA in routine clinical care leads to an improvement in patients' disease activity. Participants received the following systemic therapies: MTX (methotrexate), sulfasalazine, TNFi (tumor necrosis factor inhibitor), leflunomide and CsA (cyclosporine). Almost all patients received initial or ongoing treatment with MTX.

The folic acid antagonist MTX remains in clinical use as a standard systemic therapy for psoriatic inflammation of both the skin and joint components of PsA. MTX inhibits dihydrofolate-reductase competitively, reducing metabolism dihydrofolic acid to tetrahydrofolic acid which results in suppression of the intracellular synthesis of various folic acid derivatives. MTX has a range of known side-effects , particularly hepatotoxicity.5 Quite a significant proportion of patients discontinue MTX due to side effects, including nausea and hepatotoxicity. Clinicians are currently unable to predict who is likely to suffer from side effects leading to discontinuation of the drug. This study will seek to predict which treatment works best for a given patient and which treatment will cause side effects.

The samples and clinical information collected from participants in the TICOPA study are a valuable resource. The investigators aim to use existing serum samples to determine the potential of molecular markers to predict patients' response to treatment both with regard to effects and side effects. This analysis could potentially lead to the identification of serum and clinical parameters which when measured in a defined combination would be predictive of patients' response to treatment.

Previously, blood samples from the TICOPA study have undergone mass spectrometry (MS) analysis which identified over 200 candidate peptides that might predict treatment response.6 However MS analysis is limited in its ability to detect proteins in low abundance, and this is true for pathogenetic relevant cytokines and other inflammation and metabolic related mediators. Furthermore, MS analysis is restricted to proteins and - as performed - fail to measure lipid mediators or small RNA molecules such as microRNAs.

For these reasons, the investigators propose to analyse these samples using ELISA (enzyme-linked immunosorbent assay)based techniques. By using multiplex ELISA based, and other standard techniques to measure inflammation or metabolic related parameters in these serum samples, and performing AI (artificial intelligence) based analysis methods, the investigators aim to identify predictive "markers and patterns" for response to therapy in this well-defined patient cohort.

1.2. Rationale for the proposed study A range of different drugs are available to treat psoriatic arthritis (PsA) inflammation. However, clinicians are unable to predict who will respond well to a given drug, who will fail to respond and who will develop side effects. Responder / non-responder effects may also differ for the skin and joint domains of PsA. Patients currently undergo a trial and error phase of treatment, sometimes withstanding a period of nonresponse, and thus pain and discomfort, for a period of time. Treatment failures also waste resources and undermine patient confidence. There is a pressing need to identify predictors for response / non response and side effects, and this study will utilise novel bioinformatics approaches to address this need.

2. STUDY AIM AND OBJECTIVES 2.1. Study aim This study aims to analyse inflammation and metabolic parameters utilizing serum samples provided by a cohort of TICOPA study participants. These results will be used in combination with TICOPA clinical data to determine the predictive value of parameters regarding patients' response to therapy. The analysis will make use of AI analysis methods to achieve this goal.

2.2. Primary objective Predict effects and response to therapy using TICOPA clinical data in combination with measured serum parameters.

2.3. Secondary objective(s) Predict response to therapy using TICOPA clinical data in combination with measured serum parameters specific to the skin and MSK (musculoskeletal) components of PsA.

3. STUDY ENDPOINTS Primary Endpoint Completion of an AI prediction model for AE Secondary Endpoint(s) Completion of an AI prediction model for treatment response

4. SELECTION AND WITHDRAWAL OF SUBJECTS The sample pool comprises those TICOPA participants who signed an informed consent form allowing their data and samples to be used for future studies.

5. METHODS OF ASSESSMENT 5.1. Data handling All patient clinical data and serum samples were collected in line with the ethics of the original TICOPA trial. Data involved in this project is linked-anonymised with only the patients' trial number available as reference. Only the CI and research coordinator have access to this linkage, which is held on the University of Leeds' password protected secure server. The CI for this study was the CI for the TICOPA study and the research coordinator was also involved in the TICOPA study. No other members of the current study team will have access to the linkage.

5.2. Sample handling Samples are currently held in storage at the Leeds Institute of Rheumatic and Musculoskeletal Medicine (UoL) laboratory based at Chapel Allerton Hospital, aliquots will be transported to the UoL School of Molecular and Cellular Biology for analysis by one of the study team. Analysis will mainly use ELISA based multiplex techniques for inflammation and metabolic related parameters. However, if indicated by new findings in the literature relevant lipid mediators or microRNAs may also be determined using appropriate standard laboratory techniques.

6. DATA EVALUATION Univariate analysis will be performed for each new variable measured and the predictive targets of this project. Predictive algorithms such as random forest and support vector machines will then be trained and evaluated on the identified predictive targets using the data set as a whole and selected subsets.

Conditions

Psoriatic Arthritis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• treatment naive patients with early psoriatic arthritis

Exclusion Criteria

• previous treatment with systemic therapy
• other inflammatory arthritis

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of Leeds

OTHER

Sponsor Role: lead

Responsible Party

Philip Helliwell

Professor (Clinical Rheumatology)

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

University of Leeds

Leeds, , United Kingdom

Countries

United Kingdom

Other Identifiers

22/EM/0059

Identifier Type: OTHER

Identifier Source: secondary_id

310813

Identifier Type: -

Identifier Source: org_study_id