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

Sponsor
Centre Hospitalier Universitaire de Nice
Study ID
NCT06341205
Phase
PHASE3
Status
Recruiting
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Conditions

  • Membranous Nephropathy

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

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

Study Details

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.

Key Dates

Start date
Feb 4, 2025
Status verified
Sep 2025
Primary completion
Aug 31, 2031
Completion
Sep 30, 2031

Study Design

Enrollment
120 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT

Arms

  • Active Comparator: Standard-of-care
    rituximab treatment 1gram x 2 (day-0, day-15)
  • Experimental: Personalised treatment
    personalized treatment based on the algorithm for assessing the risk of having undetectable rituximab level after 3 months: * Patients with a risk between 0 and 50% will receive 1gram x2 (day-0, day-15) * Patients with a risk between 51 and 75% will receive 1gram x 3 (day-0, day-15, day-30) * Patients with a risk between 76 and 100% will receive 1gram x 4 (day-0, day-15, day-30, day-45)

Primary Outcome Measure

Clinical remission (complete or partial) after 6 months of rituximab initiation [ Time Frame: 6 months ]

Central Contacts

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