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Preliminary Evaluation of Zanubrutinib-Containing Regimens in DLBCL and the Cerebrospinal Fluid Distribution of Zanubrutinib: A 13-Case Series.

Yan Zhang | Yanan Li | Zhe Zhuang | Wei Wang | Chong Wei | Danqing Zhao | Daobin Zhou | Wei Zhang
Frontiers in oncology | 2021

Zanubrutinib is a second-generation Bruton's tyrosine kinase inhibitor. Its safety and effectiveness in central nervous system (CNS) lymphoma along with its distribution in the brain and ability to cross the blood-brain barrier (BBB) remain unknown. This retrospective case series involved patients with diffuse large B-cell lymphoma (DLBCL) treated with zanubrutinib-containing regimens from August to December 2020 in PUMCH. The amounts of zanubrutinib in the plasma and brain were assessed by liquid chromatography-tandem mass spectrometry in paired plasma and cerebrospinal fluid (CSF) samples. In total, 13 patients were included: eight primary CNS lymphoma cases and five systemic DLBCL cases with 61.5% (8/13) refractory/relapsed and 84.6% (11/13) showing CNS involvement. The overall response rates were 84.5% in the entire population and 81.8% in the CNS-involved cases. A total of 23 time-matched plasma-CSF sample pairs were collected. The mean peak concentration of zanubrutinib in CSF was 2941.1 pg/ml (range, 466-9032.0 pg/ml). The corrected mean CSF/plasma ratio determined based on 94% protein binding was 42.7% ± 27.7% (range, 8.6%-106.3%). This preliminary study revealed the effectiveness of zanubrutinib-containing regimens in DLBLC, especially CNS-involved cases, for the first time. The excellent BBB penetration of zanubrutinib supports its further investigation for the treatment of CNS lymphoma.

Pubmed ID: 35004280

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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