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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Standard-of-care | Active Comparator | rituximab treatment 1gram x 2 (day-0, day-15) |
|
| Personalised treatment | Experimental | personalized treatment based on the algorithm for assessing the risk of having undetectable rituximab level after 3 months:
|
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| RiTUXimab Injection | Drug | Dose administered will depend on randomisation and for experimental Arm on the risk of having undetectable rituximab level after 3 months |
|
| Measure | Description | Time Frame |
|---|---|---|
| Clinical remission (complete or partial) after 6 months of rituximab initiation | Clinical remission (complete or partial) according to KDIGO and French guidelines:
| 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Complete clinical remission after 12 months of rituximab initiation | Complete remission: 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 | 12 months |
| Partial clinical remission after 12 months of rituximab initiation |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Barbara SEITZ-POLSKI, MD, PhD | Contact | +33492038828 | seitz-polski.b@chu-nice.fr | |
| Céline FERNANDEZ | Contact | +33492038828 | fernandez.c3@chu-nice.fr |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| CHU de BESANCON | Recruiting | Besançon | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40180405 | Derived | Teisseyre M, Destere A, Cremoni M, Zorzi K, Brglez V, Benito S, Bailly L, Fernandez C, Seitz-Polski B. Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial. BMJ Open. 2025 Apr 2;15(4):e093920. doi: 10.1136/bmjopen-2024-093920. |
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Partial remission: 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%). |
| 12 months |
| Immunological remission: anti-PLA2R1 depletion | Immunological remission: anti-PLA2R1 depletion (i.e., PLA2R1 titer < 14 RU/mL by ELISA method) at month-3, month-6 and month-12 | 12 months |
| Change in urine protein/creatinine ratio (UPCR) | Percentage of change in urine albumin/creatinine ratio (mg/g) from day-0 to month-3, month-6, month-9, month-12 | 12 months |
| Change in serum creatinine | Percentage of change in serum creatinine (μmol/L) from day-0 to month-3, month-6, month-9, month-12 | 12 months |
| Change in renal function | Percentage of change in Glomerular Filtration Rate estimated by CKD-EPI formula (mL/min/1.73m²) from day-0 to month-3, month-6, month-9, month-12 | 12 months |
| Change in the immunological status of the disease | Percentage of change in anti-PLA2R1 titer (RU/mL) by ELISA (EUROIMMUN Kit) from day-0 to month-3, month-6, month-9, month-12 | 12 months |
| Appearance of anti-drug antibodies after rituximab treatment | Serum anti-rituximab antibodies (ng/mL) at month-3, month-6, month-9, month-12 | 12 months |
| Rituximab underdosed patients | Percentage of patients with serum rituximab (μg/mL) >2 μg/mL 3 months after the last infusion | 3 months |
| Serious adverse events | Occurence of Serious adverse events reported | 84 months |
| Adaptation of symptomatic treatment | Number of dose modification of non-immunosuppressive anti-proteinuric treatment during study follow-up | 84 months |
| Model improvement through machine learning | serum creatinine and serum albumin levels, weight, anti-PLA2R1 and rituximab level will be combined to report the risk of having undetectable rituximab level after 3 months (in %) at day-0, day-15, day-30, day-45, month-3, month-6 | 6 months |
| Effect of rituximab on immune profiles | Cytokine levels in pg/mL (IFN-γ, IFN-α, IL-12p70, IL-17A, IL-4, IL-5, IL-10, IL-1, IL-6) at day-0 and month-6 | 6 months |
| CHU de BORDEAUX - Hôpital Pellegrin | Recruiting | Bordeaux | France |
|
| CHU de CAEN | Recruiting | Caen | France |
|
| AP-HP - Hôpital H. Mondor | Recruiting | Créteil | France |
|
| HCL - Hôpital E. Herriot | Recruiting | Lyon | France |
|
| AP-HM - Hôpital de la Conception | Recruiting | Marseille | France |
|
| CHU de NICE | Recruiting | Nice | France |
|
| CHU de Nîmes - Hôpital CAREMEAU | Recruiting | Nîmes | France |
|
| AP-HP - Hôpital Européen Georges Pompidou | Recruiting | Paris | France |
|
| AP-HP - Hôpital Necker | Recruiting | Paris | France |
|
| Hôpital Tenon | Not yet recruiting | Paris | France |
|
| CHU de TOULOUSE - Hôpital Rangueil | Recruiting | Toulouse | France |
|
| CHRU de TOURS - Hôpital Bretonneau | Recruiting | Tours | France |
|
| CH de Valenciennes | Recruiting | Valenciennes | France |
|
| ID | Term |
|---|---|
| D015433 | Glomerulonephritis, Membranous |
| ID | Term |
|---|---|
| D005921 | Glomerulonephritis |
| D009393 | Nephritis |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
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| ID | Term |
|---|---|
| D000069283 | Rituximab |
| ID | Term |
|---|---|
| D058846 | Antibodies, Monoclonal, Murine-Derived |
| D000911 | Antibodies, Monoclonal |
| D000906 | Antibodies |
| D007136 | Immunoglobulins |
| D007162 | Immunoproteins |
| D001798 | Blood Proteins |
| D011506 | Proteins |
| D000602 | Amino Acids, Peptides, and Proteins |
| D012712 | Serum Globulins |
| D005916 | Globulins |
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