Evaluating duration of response to treatment in systemic lupus erythematosus clinical trials

Selection of indicators reporting response rate in pharmaceutical trials for systemic lupus erythematosus: preference and relative sensitivity

OBJECTIVE

SLE is a common multisystem autoimmune disease with chronic inflammation. Many efficacy evaluation indicators of randomised clinical trials (RCTs) for SLE have been proposed but the comparability remains unknown. We aim to explore the preference and comparability of indicators reporting response rate and provide basis for primary outcome selection when evaluating the efficacy of SLE pharmaceutical treatment.

METHODS

We systematically searched three databases and three registries to identify pharmacological intervention-controlled SLE RCTs. Relative discriminations between indicators were assessed by the Bayesian hierarchical linear mixed model.

RESULTS

33 RCTs met our inclusion criteria and we compared eight of the most commonly used indicators reporting response rate. SLE Disease Activity Index 4 (SLEDAI-4) and SLE Responder Index 4 were considered the best recommended indicators reporting response rate to discriminate the pharmacological efficacy. Indicator preference was altered by disease severity, classification of drugs and outcome of trials, but SLEDAI-4 had robust efficacy in discriminating ability for most interventions. Of note, BILAG Index-based Combined Lupus Assessment showed efficacy in trials covering all-severity patients, as well as non-biologics RCTs. The British Isles Lupus Assessment Group response and Physician's Global Assessment response were more cautious in evaluating disease changes. Serious adverse event was often applied to evaluate the safety and tolerability of treatments rather than efficacy.

CONCLUSIONS

The impressionable efficacy discrimination ability of indicators highlights the importance of flexibility and comprehensiveness when choosing primary outcome(s). As for trials that are only evaluated by SLEDAI-4, attention should be paid to outcome interpretation to avoid the exaggeration of treatment efficacy. Further subgroup analyses are limited by the number of included RCTs.

PROSPERO REGISTRATION NUMBER

CRD42022334517.

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.

Urine and serum S100A8/A9 and S100A12 associate with active lupus nephritis and may predict response to rituximab treatment

BACKGROUND

Approximately 30% of patients with the systemic autoimmune/inflammatory disorder systemic lupus erythematosus (SLE) develop lupus nephritis (LN) that affects treatment and prognosis. Easily accessible biomarkers do not exist to reliably predict renal disease. The Maximizing SLE Therapeutic Potential by Application of Novel and Systemic Approaches and the Engineering Consortium aims to identify indicators of treatment responses in SLE. This study tested the applicability of calcium-binding S100 proteins in serum and urine as biomarkers for disease activity and response to treatment with rituximab (RTX) in LN.

METHODS

S100A8/A9 and S100A12 proteins were quantified in the serum and urine of 243 patients with SLE from the British Isles Lupus Assessment Group Biologics Register (BILAG-BR) study and 48 controls matched for age using Meso Scale Discovery's technology to determine whether they perform as biomarkers for active LN and/or may be used to predict response to treatment with RTX. Renal disease activity and response to treatment was based on BILAG-BR scores and changes in response to treatment.

RESULTS

Serum S100A12 (p<0.001), and serum and urine S100A8/A9 (p<0.001) levels are elevated in patients with SLE. While serum and urine S100 levels do not correlate with global disease activity (SLE Disease Activity Index), levels in urine and urine/serum ratios are elevated in patients with active LN. S100 proteins perform better as biomarkers for active LN involvement in patients with SLE who tested positive for anti-double-stranded DNA antibodies. Binary logistic regression and area under the curve analyses suggest the combination of serum S100A8/A9 and S100A12 can predict response to RTX treatment in LN after 6 months.

CONCLUSIONS

Findings from this study show promise for clinical application of S100 proteins to predict active renal disease in SLE and response to treatment with RTX.

SLE clinical trials: impact of missing data on estimating treatment effects

Objective

A common problem in clinical trials is missing data due to participant dropout and loss to follow-up, an issue which continues to receive considerable attention in the clinical research community. Our objective was to examine and compare current and alternative methods for handling missing data in SLE trials with a particular focus on multiple imputation, a flexible technique that has been applied in different disease settings but not to address missing data in the primary outcome of an SLE trial.

Methods

Data on 279 patients with SLE randomised to standard of care (SoC) and also receiving mycophenolate mofetil (MMF), azathioprine or methotrexate were obtained from the Lupus Foundation of America-Collective Data Analysis Initiative Database. Complete case analysis (CC), last observation carried forward (LOCF), non-responder imputation (NRI) and multiple imputation (MI) were applied to handle missing data in an analysis to assess differences in SLE Responder Index-5 (SRI-5) response rates at 52 weeks between patients on SoC treated with MMF versus other immunosuppressants (non-MMF).

Results

The rates of missing data were 32% in the MMF and 23% in the non-MMF groups. As expected, the NRI missing data approach yielded the lowest estimated response rates. The smallest and least significant estimates of differences between groups were observed with LOCF, and precision was lowest with the CC method. Estimated between-group differences were magnified with the MI approach, and imputing SRI-5 directly versus deriving SRI-5 after separately imputing its individual components yielded similar results.

Conclusion

The potential advantages of applying MI to address missing data in an SLE trial include reduced bias when estimating treatment effects, and measures of precision that properly reflect uncertainty in the imputations. However, results can vary depending on the imputation model used, and the underlying assumptions should be plausible. Sensitivity analysis should be conducted to demonstrate robustness of results, especially when missing data proportions are high.