Personalized Rituximab Treatment Based on Artificial Intelligence in Membranous Nephropathy (iRITUX)

Condition(s) studied

Investigational drug(s) / intervention(s)

RiTUXimab Injection: Dose administered will depend on randomisation and for experimental Arm on the risk of having undetectable rituximab level after 3 months

Study summary

Membranous nephropathy is an autoimmune disease affecting the kidney, and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults. The course of this disease is highly variable from one individual to another, ranging from spontaneous remission to progressive chronic kidney disease.

The identification of autoantibodies - e.g., the phospholipase A2 receptor type 1 (PLA2R1) - has promoted the use of immunosuppressive drugs such as rituximab which is now a safe and effective first-line treatment for the management of membranous nephropathy. However, up to 40% of patients do not respond to a first course of rituximab treatment. In nephrotic patients, due to urinary drug loss, rituximab blood level is lower than in other autoimmune diseases treated with rituximab without proteinuria. This high urinary drug loss decreases the drug exposure, potentially explaining why rituximab regimen with low dose infusions (375 mg/m2) did not demonstrate efficacy after month-6 compared to a non-immunosuppressive antiproteinuric treatment in a previous study. In contrast, a regimen of two 1-g infusions two weeks apart was associated with a significantly greater remission rate after 6 months.

Recently, the investigators have shown that after two 1-g rituximab infusions, the rituximab blood level 3 months after the first rituximab infusion, was correlated with the likelihood of remission after 6 and 12 months of the rituximab treatment. Patients with positive rituximab blood level 3 months after treatment had a higher chance of remission at month-6 and at month-12 than patients with an undetectable rituximab level at month-3.

Nowadays, machine learning algorithms are increasingly used in medicine, especially in pharmacology, to predict the exposure to a drug, the initial dose to administer or the interval between two infusions.

The objective of this study is to use a machine learning algorithm predicting the risk of having an undetectable residual level of rituximab 3 months after treatment, in order to propose a personalized treatment management with early additional doses of rituximab for the patients at risk.

Eligibility

Primary outcome measure(s)

• Clinical remission (complete or partial) after 6 months of rituximab initiation — 6 months
  Clinical remission (complete or partial) according to KDIGO and French guidelines: * Complete: urine protein/creatinine ratio (UPCR) \<0.3 g/g and serum albumin\>30 g/L and Glomerular Filtration Rate (estimated by CKD-EPI formula) \>60 ml/min/1.73m2 * Partial: UPCR \<3.5 g/g with a decrease \>50% from baseline (i.e., at first rituximab infusion) and serum albumin improvement or normalization and stable serum creatinine (or increase \<30%).

Trial sites (14)

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