Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial

• Published in: BMJ open Vol. 15; no. 4; p. e093920
• Main Authors: Teisseyre, Maxime, Destere, Alexandre, Cremoni, Marion, Zorzi, Kévin, Brglez, Vesna, Benito, Sylvain, Bailly, Laurent, Fernandez, Céline, Seitz-Polski, Barbara
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 02.04.2025; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: Adult; Algorithms; Antibodies; Antigens; Artificial Intelligence; Bioavailability; Creatinine; Drug dosages; Female; France; Glomerulonephritis; Glomerulonephritis, Membranous - drug therapy; Humans; Immunologic Factors - administration & dosage; Immunologic Factors - therapeutic use; IMMUNOLOGY; Immunology (including allergy); Kidney diseases; Life Sciences; Machine Learning; Male; Multicenter Studies as Topic; Nephrology; Nephrotic Syndrome - drug therapy; Patients; Pharmaceutical sciences; Pharmacology; Precision Medicine - methods; Prospective Studies; Proteins; Protocol; Randomized Controlled Trials as Topic; Remission (Medicine); Rituximab - administration & dosage; Rituximab - therapeutic use; Urine; France; Glomerulonephritis; Nephrology; IMMUNOLOGY; Machine Learning; Artificial Intelligence
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2024-093920

IntroductionMembranous nephropathy is an autoimmune kidney disease and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults. Rituximab is now recommended as first-line therapy for membranous nephropathy. However, Kidney Disease Improving Global Outcomes guidelines do not recommend any specific protocol. Rituximab bioavailability is reduced in patients with membranous nephropathy due to urinary drug loss. Underdosing of rituximab is associated with treatment failure. We have previously developed a machine learning algorithm to predict the risk of underdosing. We have retrospectively shown that patients with a high risk of underdosing required higher doses of rituximab to achieve remission. The aim of this prospective study is to evaluate the efficacy of algorithm-driven rituximab treatment in patients with membranous nephropathy compared to standard treatment.MethodsA multicentre, randomised, controlled, open-label, prospective superiority clinical trial will be conducted in 13 French hospitals. 130 consecutive patients with primary membranous nephropathy and active nephrotic syndrome will be randomised to either the standard protocol control group (two 1 g rituximab infusions on days 0 and 15) or the algorithm-driven rituximab treatment group. In the latter, the rituximab dose will depend on the algorithm-estimated risk of underdosing. Patients with an algorithm-estimated risk of underdosing ≤50% will receive 1 g of rituximab on days 0 and 15. Patients with an algorithm-estimated risk of underdosing between 51% and 75% will receive 1 g of rituximab on days 0, 15 and 30. Finally, patients with an estimated risk of underdosing >75% will receive 1 g of rituximab on days 0, 15, 30 and 45. The primary study outcome is the rate of clinical remission (complete or partial) at month 6 after treatment initiation. The secondary outcomes include clinical remission at month 12, immunological remission, proteinuria, albuminuria, serum creatinine, estimated glomerular filtration rate, phospholipase A2 receptor type 1 antibody titre, anti-rituximab antibody occurrence, lymphocyte count, serum rituximab level and related adverse events.Ethics and disseminationThe trial received ethics approval from the local ethics boards. The results of this study will confirm whether algorithm-driven rituximab treatment is more effective in inducing remission than the standard regimen and thus may contribute to improving management of patients with membranous nephropathy. The results of our study will be submitted to a peer-review journal.Trial registration numberNCT06341205 trial number. Registered on 2 April 2024.