Rituximab is a chimeric monoclonal antibody targeting the CD20 protein expressed on B cells, leading to their depletion. Initially approved for the treatment of non-Hodgkin's lymphoma, its use has expanded to several autoimmune diseases, including glomerular diseases (GD) such as idiopathic nephrotic syndrome (INS)—which encompasses minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS)—as well as membranous nephropathy (MN) and antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV).1,2

Rituximab dosages and frequencies in GD have been extrapolated from other indications; however, this population shows differential characteristics that could alter rituximab pharmacokinetics and compromise treatment exposure and efficacy.3 In this context, the aim of this study is to analyze the influence of rituximab exposure on clinical outcomes in GD.

A total of 35 cases (30 patients) were finally included in the analysis (Supplementary Fig. 1). The baseline characteristics of the cohort have been previously reported5; for completeness, they are provided again in Table 1. The cohort mainly consisted of older adults, predominantly male, with overweight and moderately impaired kidney function at baseline. MN was the most common diagnosis, followed by AAV and INS. A high proportion of patients had prior exposure to rituximab and were receiving immunosuppressive therapy, together with marked baseline proteinuria.

Six months after rituximab administration, 9 (25.7%) cases achieved CR, 19 (54.3%) cases reached partial response, and no response was detected in 7 (20.0%). At month 12, 33 cases were assessed; two cases were lost to follow-up, one due to death unrelated to rituximab treatment and the other due to transfer to another community. Of the 33 cases evaluated at month 12, 10 (30.3%) showed CR, 15 (45.5%) partial response, 4 (12.1%) showed no response to treatment, and 4 (12.1%) cases of relapse were detected. The results were analyzed by pathology, showing a similar response rate across all pathologies, with slightly less efficacy observed in AAV. The outcome of the generalized linear mixed model with binomial distribution and logit link fitted, including a random intercept for patient to account for within-subject correlation, was comparable – coefficient, −4387, p=0.003–, demonstrating no influence of prior exposure to rituximab and repeated inclusion in the study. No infusion reactions or serious adverse events occurred during the time of the study.

Rituximab exposure, measured as AUC, concentration in plasma at day 28 (Cp28) and concentration in plasma at day 45 (Cp45) was consistently higher, after 6 and 12 months, in the CR group compared to no-CR (Table 2); however, differences were not statistically significant. When the relationship between Cp28 and CR-6m was further analyzed, Cp28>100μg/mL showed the highest predictive capacity for CR-6m, with a sensitivity of 55.6% (95% confidence interval [CI]: 23.1–88.0), a specificity of 84.6% (95% CI: 70.7–98.5), a positive likelihood ratio of 3.6 (95% CI: 1.2–10.6), and a negative likelihood ratio of 0.5 (95% CI: 0.2–1.1) (Fig. 1, Supplementary Table 1). The Cp28>100μg/mL threshold represented the optimal cutoff according to the Youden index. The Youden index value (J=0.402) indicated a moderate discriminative ability. Internal validation using bootstrap resampling with 1000 iterations yielded B=1.928 (p=0.010), suggesting that the observed effect was consistent. However, the 95% confidence interval ranged from −0.203 to 22.732 and was sufficiently wide to preclude drawing definitive conclusions from these findings.

Fig. 1.

Impact of rituximab plasma concentration on day 28 (Cp28) on the 6-month response. n=33. Cases are shown in orange, ordered from lowest to highest Cp28. The axes indicate the cutoff point of Cp=100μg/mL. The upper horizontal bars represent cases with complete remission at 6 months (n=9), and the inner bars represent cases without remission at 6 months (n=24). The calculated area under the curve-ROC is 0.701 and the 95% confidence interval is 0.485–0.917.

Cp28 was >100μg/mL in 9 cases, of whom 5 (55.6%) achieved CR-6m, and Cp28 was <100μg/mL in 26 cases, of whom only 4 (15.4%) achieved CR-6m. Fig. 2 shows the survival curves for cases achieving CR-6m and CR-12m for the Cp28>100μg/mL group compared with the Cp28<100μg/mL group. A significantly higher proportion of CR-6m was observed in cases with Cp28>100μg/mL vs. Cp28 < 100μg/mL (p=0.026). This difference was maintained at 12 months, but did not reach statistical significance (p=0.063); however, when CR at 6 and 12 months was combined, a significantly higher response was observed in cases with Cp28>100μg/mL (p=0.038). Disease-specific efficacy and rituximab exposure data is shown in Supplementary Table 2.

Fig. 2.

Survival curve showing complete remission at 6 and 12 months according to rituximab concentration on day 28.

The influence of demographic factors (sex and age), analytical parameters (serum albumin and proteinuria) and use of immunosuppression were studied; none of them showed significant association or modified the observed association between rituximab exposure (Cp28>100μg/mL) and CR-6m. Only serum albumin showed an independent significant association with CR-12m (p=0.013), which did not affect the main effect nor showed collinearity with Cp28>100μg/mL.

The present study shows that rituximab exposure is related to a better outcome in patients with GD; specifically, cases with Cp28>100μg/mL exhibit significantly better clinical outcomes compared with those with lower concentrations, suggesting that early drug exposure may be a key determinant of therapeutic response.

Previous studies indicate that there is a certain rituximab concentration required to achieve a complete B cell depletion across different clinical entities due to rituximab pharmacodinamics.4 Our results align with emerging evidence that adequate rituximab exposure is critical for efficacy in patients affected by GD, where altered pharmacokinetics (including increased clearance and reduced half-life), have been reported due to factors such as proteinuria and disease-specific characteristics, potentially leading to suboptimal drug levels and treatment failure.3,5,6

Specifically, Teisseyre et al. concluded that undetectable rituximab in plasma at 3 months after drug administration was a predictor of treatment failure in patients with MN and INS.7 Also, Destere et al. used Machine learning to identify risk factors for rituximab underexposure and suggested an intensive monitoring of those patients with MN at risk in order to readily provide additional rituximab doses in cases with poor evolution.6 Interestingly, Liang et al. developed a pharmacokinetic model of rituximab in GD and suggested dose individualization in order to optimize exposure and reduce costs.8 Finally, Hartinger et al. explored rituximab pharmacokinetics in patients with GD and lupus, concluding that dose intensification might be beneficial in certain cases.9 Regarding AAV, Pasquiers et al. suggested that 1000mg every 4 months might be preferred over less intense alternative dosages.10 Together, all these recent studies point out that current rituximab schedules might lead to insufficient drug exposure in GD, which could jeopardize treatment efficacy; however, even if some of them suggested alternative doses and frequency they have not proved its efficacy in the clinical setting; therefore, the optimal schedule remains to be established, warranting further investigation.

This study has certain limitations. First, this was a low-intervention clinical study carried out in the real world setting, therefore, our priority was to reduce the burden on patients by collecting the fewest number of samples, which has limited our ability to identify the optimal time to measure Cp in order to predict response. Second, patients with a baseline GFR≤20mL/min were excluded from the clinical trial; therefore, the findings may not be generalizable to more complex clinical situations. Moreover, due to the small number of patients and the diversity of diagnoses, the power of the study is modest; for instance, a larger patient cohort might have allowed the detection of statistically significant associations between AUC, Cp28 and Cp45 and CR-6m and CR-12m. Given the limited sample size, the findings of this study should be interpreted with caution. Further research is needed to confirm and extend these results.

Cp28>100μg/mL predicts a higher likelihood of CR-m6 in patients with GD treated with rituximab. Rituximab exposure was consistently higher in case of CR; however, no significant correlation was observed between AUC, Cp28 or Cp45 and CR-6m or CR-12m. Further studies are needed to confirm the target rituximab exposure and define the optimal dose schedule in order to ensure adequate treatment management in GD.

This work has been funded by the Catalan Society of Clinical Pharmacy with 12.000€.

The authors declare no conflicts of interest.