Phase 2 Trial of Cemdisiran in Adult Patients with IgA Nephropathy: A Randomized Controlled Trial.

BACKGROUND: IgA nephropathy is the most common primary GN. Clinical features of IgA nephropathy include proteinuria, which is the strongest known surrogate of progression to kidney failure. Complement pathway activation is a critical driver of inflammation and tissue injury in IgA nephropathy. Cemdisiran is an investigational RNA interference therapeutic that suppresses hepatic production of complement component 5 (C5), thereby potentially reducing proteinuria in IgA nephropathy. We evaluated the efficacy and safety of cemdisiran in adult patients with IgA nephropathy at high risk of kidney disease progression. METHODS: In this phase 2, 36-week, double-blind study, adult patients with IgA nephropathy and urine protein ≥1 g/24 hours were randomized (2:1) to subcutaneous cemdisiran 600 mg or placebo every 4 weeks in combination with the standard of care. The primary end point was percentage change from baseline at week 32 in urine protein-to-creatinine ratio (UPCR) measured by 24-hour urine collection. Additional end points included change from baseline in UPCR measured by spot urine, serum C5 level, and safety assessments. RESULTS: Thirty-one patients were randomized (cemdisiran, N =22; placebo, N =9). Cemdisiran-treated patients had a placebo-adjusted geometric mean change in 24-hour UPCR of -37.4% (cemdisiran-adjusted geometric mean ratio to baseline [SEM], 0.69 [0.10]) at week 32. Spot UPCR was consistent with 24-hour UPCR placebo-adjusted change of -45.8% (cemdisiran-adjusted geometric mean ratio to baseline [SEM], 0.73 [0.11]). Mean (SD) change in serum C5 level from baseline at week 32 was -98.7% (1.2) with cemdisiran and 25.2% (57.7) with placebo. Over 36 weeks, most adverse events were mild or moderate and transient; the most common adverse event after cemdisiran treatment was injection-site reaction (41%). CONCLUSIONS: These findings indicate that treatment with cemdisiran resulted in a reduction of proteinuria at week 32 and was well tolerated.

The role of BAFF and APRIL in IgA nephropathy: pathogenic mechanisms and targeted therapies.

Immunoglobulin A nephropathy (IgAN), characterized by mesangial deposition of galactose-deficient-IgA1 (Gd-IgA1), is the most common biopsy-proven primary glomerulonephritis worldwide. Recently, an improved understanding of its underlying pathogenesis and the substantial risk of progression to kidney failure has emerged. The "four-hit hypothesis" of IgAN pathogenesis outlines a process that begins with elevated circulating levels of Gd-IgA1 that trigger autoantibody production. This results in the formation and deposition of immune complexes in the mesangium, leading to inflammation and kidney injury. Key mediators of the production of Gd-IgA1 and its corresponding autoantibodies are B-cell activating factor (BAFF), and A proliferation-inducing ligand (APRIL), each playing essential roles in the survival and maintenance of B cells and humoral immunity. Elevated serum levels of both BAFF and APRIL are observed in patients with IgAN and correlate with disease severity. This review explores the complex pathogenesis of IgAN, highlighting the pivotal roles of BAFF and APRIL in the interplay between mucosal hyper-responsiveness, B-cell activation, and the consequent overproduction of Gd-IgA1 and its autoantibodies that are key features in this disease. Finally, the potential therapeutic benefits of inhibiting BAFF and APRIL in IgAN, and a summary of recent clinical trial data, will be discussed.

Development and deployment of a nationwide predictive model for chronic kidney disease progression in diabetic patients.

Aim: Chronic kidney disease (CKD) is a major complication of diabetes and a significant disease burden on the healthcare system. The aim of this work was to apply a predictive model to identify high-risk patients in the early stages of CKD as a means to provide early intervention to avert or delay kidney function deterioration. Materials and methods: Using the data from the National Diabetes Database in Singapore, we applied a machine-learning algorithm to develop a predictive model for CKD progression in diabetic patients and to deploy the model nationwide. Results: Our model was rigorously validated. It outperformed existing models and clinician predictions. The area under the receiver operating characteristic curve (AUC) of our model is 0.88, with the 95% confidence interval being 0.87 to 0.89. In recognition of its higher and consistent accuracy and clinical usefulness, our CKD model became the first clinical model deployed nationwide in Singapore and has been incorporated into a national program to engage patients in long-term care plans in battling chronic diseases. The risk score generated by the model stratifies patients into three risk levels, which are embedded into the Diabetes Patient Dashboard for clinicians and care managers who can then allocate healthcare resources accordingly. Conclusion: This project provided a successful example of how an artificial intelligence (AI)-based model can be adopted to support clinical decision-making nationwide.