Predicting the Treatment Response of Certolizumab for Individual Patients with Rheumatoid Arthritis: An Individual Participant Data Meta-Analysis

Lead Investigator: Yan Luo, Kyoto University
Title of Proposal Research: Predicting the Treatment Response of Certolizumab for Individual Patients with Rheumatoid Arthritis: An Individual Participant Data Meta-Analysis
Vivli Data Request: 5276
Funding Source: None.
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Rheumatoid arthritis (RA) is a chronic inflammatory disease, for which we cannot currently expect complete cure. The drugs that can slow the disease progression are known as disease modifying anti-rheumatic drugs (DMARDs), which have many types. Most RA patients experience long-term treatment. According to the strategy proposed by the practice guideline, a physician should select an intervention for a patient, then re-evaluate after 3 to 6 months, based on which a next-step strategy is given. In order to improve long-term prognosis of RA (such as delaying the progression of bone fusion or functional deficiency), we should assure 3-6 months short-term clinical outcome primarily. To achieve this goal, physicians should select the optimal intervention based on 3 to 6 months clinical response for the patient.

It is not an easy task for doctors to choose the right drug from so many choices. Especially the evidence usually used now is in the form of randomized controlled trials (RCTs) or their meta- analyses (MAs) at the aggregate level, which can only provide average results. But patients’ responses differ, based on their particular genetic, biologic, and clinical features. Thus it is highly necessary to identify the subgroups of patients who show different responses, to build a prediction model of treatment response that depends on the individual characteristics, and further help physicians personalize treatment strategy. For this purpose, we decide to use the individual participant data meta-analysis (IPD-MA) of RCTs, which has recently been recommended as a proper approach by many articles. It is superior than usual observational study-based prediction models of treatment effects in: (1) RCTs have more rigorously collected data and are more powerful in dealing with missing data; (2) RCTs can be used to predict the relative treatment effect under alternative treatment conditions. Furthermore, IPD-MA, as a collection of several RCTs, can increase the statistical power in dealing with complex models.

As for feasibility, several promising modeling approaches of using IPD-MA to predict treatment response have been established and validated in some simulation studies, yet only one study has applied the method to real clinical settings (depression). The lead researcher of that study is also a co-researcher of our study. Until now no such studies have been conducted for RA. As a result, we are motivated to develop a prediction model of treatment effects based on individual characteristics of RA patients through IPD-MA. The reason why we choose certolizumab (CTZ) is that TNFa inhibitors are the most classic and widely used biologic DMARDs for RA, with plenty of IPD. We may consider to expand the model to compare several drugs as an IPD-NMA (network meta-analysis) in the future. The general study process is: (1) search-select-risk of bias assessment of eligible studies; (2) applying for IPD; (3) identify effect modifiers (affect treatment/control group’s outcome differently) for the CTZ; (4) developing the prediction model of treatment effects, including both effect modifiers and prognostic factors (affect the outcome in
treatment/control group equally).

Statistical Analysis Plan:

1. Modeling: It is basically a linear regression model, with a term that indicates treatment or control, a term used for prognostic factors and their coefficients, an interaction term of treatment and covariates (indicating effect modification). According to several previous studies, one-stage approach using both within-trial factors and across-trial factors appears to be the most optimal approach for identifying effect modifiers and developing prediction models including treatment interaction terms. As our secondary objective for this study is to explore the methodology in modeling, we may consider some other approaches and compare between them.
2. Identify effect modifiers (EMs) and prognostic factors (PFs) by conducting IPD-MA using the above model based on all the samples.
3. Develop the prediction model for treatment effects using the above model in addition with proper shrinkage methods such as LASSO and Ridge regression to deal with overfitting and select the terms.
4. Evaluation of performance of the model
5. Validation: internal validation: leave-one out cross validation (leave one patient out every time), based on all the samples; external validation: leave one RCT out (every time) cross validation.

Requested Studies:

Efficacy and Safety of CDP870 400 mg in Combination With Methotrexate Compared to Methotrexate Alone in theTreatment of the Signs and Symptoms of Patients With Rheumatoid Arthritis Who Are Partial Responders to Methotrexate
Sponsor: UCB
Study ID: NCT00544154
Sponsor ID: C87014

A Phase III Multi-center, Double-blind, Placebo-controlled, Parallel Group 24-Week Study to Assess the Efficacy and Safety of Two Dose Regimens of Liquid Certolizumab Pegol as Additional Medication to Methotrexate in the Treatment of Signs and Symptoms of Rheumatoid Arthritis and in Prevention of Joint Damage in Patients With Active Rheumatoid Arthritis Who Have an Incomplete Response to Methotrexate.
Sponsor: UCB
Study ID: NCT00160602
Sponsor ID: C87050

A Phase III Multicentre, Double Blind, Placebo-controlled, Parallel Group 52-week Study to Assess the Efficacy and Safety of 2 Dose Regimens of Lyophilised CDP870 as Additional Medication to Methotrexate in the Treatment of Signs and Symptoms and Preventing Structural Damage in Patients With Active Rheumatoid Arthritis Who Have an Incomplete Response to Methotrexate
Sponsor: UCB
Study ID: NCT00152386
Sponsor ID: C87027

A Phase IIIb Multicenter Study With a 12-week Double-blind, Placebo-controlled, Randomized Period Followed by an Open-label, Extension Phase Evaluating Safety/Efficacy of Certolizumab Pegol Given to Patients With Active Rheumatoid Arthritis.
Sponsor: UCB
Study ID: NCT00717236
Sponsor ID: C87094

A Phase IIIB, Multi-center, Double-blind, Placebo-controlled, Parallel Group, 52-week Study to Evaluate the Safety and Efficacy of Certolizumab Pegol, Administered With DMARDs, in Patients With Low to Moderate Disease Activity Rheumatoid Arthritis
Sponsor: UCB
Study ID: NCT00674362
Sponsor ID: C87076

A Phase IIIb Open-label run-in Double-blind, Placebo Controlled, Randomized Study to Evaluate the Safety/Efficacy of Certolizumab Pegol Administered Concomitantly With Stable-dose Methotrexate in Patients With Active Rheumatoid Arthritis.
Sponsor: UCB
Study ID: NCT00580840
Sponsor ID: C87077

A Multi-center, Randomized, Double-blind, Placebo-controlled Study to Evaluate the Efficacy and Safety of Certolizumab Pegol in Combination With Methotrexate for Inducing and Sustaining Clinical Response in the Treatment of DMARDNaïve Adults With Early Active Rheumatoid Arthritis
Sponsor: UCB
Study ID: NCT01521923
Sponsor ID: RA0055 Period 2

A 16-week Double-blind, Placebo-controlled (for Initial 2 Weeks) Randomized Period, Followed by a 24-week Open-label Extension to Assess Magnetic Resonance Image (MRI) – Verified Early Response to Certolizumab Pegol in Subjects With Active Rheumatoid Arthritis (RA)
Sponsor: UCB
Study ID: NCT01235598
Sponsor ID: RA0028

Public Disclosures:

Luo, Y., Chalkou, K., Funada, S., Salanti, G. and Furukawa, T.A., 2023. Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis. JAMA Network Open, 6(6), pp.e2321398-e2321398. doi: 10.1001/jamanetworkopen.2023.21398