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INTRODUCTION: Belimumab is a recombinant human immunoglobulin G1λ monoclonal antibody indicated as an intravenous (IV) 10 mg/kg and subcutaneous (SC) 200-mg dose for the treatment of systemic lupus erythematosus (SLE). Belimumab 10 mg/kg IV has been approved for the treatment of patients with SLE in China. This phase 1 study investigated the pharmacokinetics (PK), safety, and tolerability of belimumab 200 mg SC and the approved IV formulation in a healthy Chinese population. METHODS: This was a 13-week open-label, randomized, parallel-group study in healthy Chinese volunteers. Eligible volunteers were randomized (1:2) to receive a single dose of IV or SC (via auto-injector) belimumab 200 mg. PK and safety endpoints were evaluated using descriptive statistics. RESULTS: Thirty-six healthy Chinese volunteers were enrolled and all completed the study. Concentration-time profiles were as expected for both formulations. Overall, 130 adverse events (AEs) were reported, with 28 AEs reported in 11 (91.7%) volunteers in the IV group and 102 AEs in 24 (100%) volunteers in the SC group. Of the 130 AEs, 104 (80.0%) were considered to be treatment-related (27 [20.8% of total AEs] treatment-related AEs in the IV group; 77 [59.2% of total AEs] in the SC group). Although the occurrence of AEs was higher in the SC group, most volunteers (91.7%) experienced AEs of mild intensity. The most frequently reported AEs included injection site pain (n = 19 [79.2%]) and oropharyngeal pain (n = 5 [20.8%]) in the SC group, and positive bacterial test, upper respiratory tract infection, blood uric acid increase, white blood cell count increase, asthenia, and diarrhea (n = 2 [16.7%], each) in the IV group. CONCLUSIONS: PK profiles of 200 mg SC and IV belimumab administrations were similar to previous studies, and safety profiles were acceptable, supporting the use of the SC dose in Chinese patients with SLE. TRIAL REGISTRATION: NCT04136145.
Systemic lupus erythematosus (SLE) is a long-term autoimmune disease that affects patients' quality of life. Belimumab is an antibody used in several countries in combination with standard therapy to treat patients with SLE. Belimumab can be given monthly either via a vein (intravenous, IV) or weekly under the skin (subcutaneous, SC). In China, only the IV belimumab has been approved for the treatment of patients with SLE. Therefore, we carried out a study in healthy Chinese volunteers to compare the concentration of a single dose of IV or SC belimumab in the body over time, and to investigate the safety of SC belimumab to assist its approval in China. In our study, the concentration and safety of both administration methods were similar; however, more volunteers from the SC treatment group had urinalysis-related side effects compared with the IV treatment group. All of these side effects were of mild intensity and did not require treatment. These results suggest that SC belimumab could be used for the treatment of Chinese patients with SLE.
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Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.
