Actively Recruiting
Personalized Rituximab Treatment Based on Artificial Intelligence in Membranous Nephropathy (iRITUX)
Led by Centre Hospitalier Universitaire de Nice · Updated on 2025-09-11
120
Participants Needed
13
Research Sites
347 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Personalized Rituximab Treatment Based on Artificial Intelligence in Membranous Nephropathy (iRITUX)
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age 18 years or older
- Current diagnosis of membranous nephropathy confirmed by anti-PLA2R1 antibodies detected by ELISA (≥ 14 RU/ml), validated before randomization
- Nephrotic syndrome defined by proteinuria > 3.5 g/24h (or UPCR > 3.5 g/g) and serum albumin < 30 g/L at diagnosis
- Estimated glomerular filtration rate (CKD-EPI formula) > 30 mL/min/1.73 m2
- Indication for rituximab treatment according to KDIGO and French guidelines
- Stable dose of non-immunosuppressive antiproteinuric treatment for at least 2 weeks, including renin-angiotensin-aldosterone system inhibitor, diuretic, and low-salt diet at maximum tolerated dose without orthostatic hypotension or creatinine increase > 30%
You will not qualify if you...
- Secondary membranous nephropathy related to cancer, infection, systemic lupus, or drug
- Diagnosis of PLA2R1-associated membranous nephropathy not confirmed by the Coordination team
- Pregnancy or breastfeeding
- Immunosuppressive treatment, including rituximab, within 6 months before inclusion
- Presence of anti-rituximab antibodies detected by Central Lab
- Active cancer under treatment
- Active, severe infections
- Hypersensitivity to rituximab or its excipients
- Severe immunocompromise
- Severe heart failure or uncontrolled severe cardiac disease
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 13 locations
1
CHU de BESANCON
Besançon, France
Actively Recruiting
2
CHU de BORDEAUX - Hôpital Pellegrin
Bordeaux, France
Actively Recruiting
3
CHU de CAEN
Caen, France
Actively Recruiting
4
AP-HP - Hôpital H. Mondor
Créteil, France
Actively Recruiting
5
HCL - Hôpital E. Herriot
Lyon, France
Actively Recruiting
6
AP-HM - Hôpital de la Conception
Marseille, France
Actively Recruiting
7
CHU de NICE
Nice, France
Actively Recruiting
8
CHU de Nîmes - Hôpital CAREMEAU
Nîmes, France
Actively Recruiting
9
AP-HP - Hôpital Européen Georges Pompidou
Paris, France
Not Yet Recruiting
10
AP-HP - Hôpital Necker
Paris, France
Not Yet Recruiting
11
CHU de TOULOUSE - Hôpital Rangueil
Toulouse, France
Actively Recruiting
12
CHRU de TOURS - Hôpital Bretonneau
Tours, France
Actively Recruiting
13
CH de Valenciennes
Valenciennes, France
Not Yet Recruiting
Research Team
B
Barbara SEITZ-POLSKI, MD, PhD
CONTACT
C
Céline FERNANDEZ
CONTACT
How is the study designed?
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
TREATMENT
Number of Arms
2
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