Aberrant activation of Bruton’s tyrosine kinase (BTK) plays an important role in pathogenesis of B-cell lymphomas, suggesting that inhibition of BTK is useful in the treatment of hematological malignancies. The discovery of a more selective on-target covalent BTK inhibitor is of high value. Herein, we disclose the discovery and preclinical characterization of a potent, selective, and irreversible BTK inhibitor as our clinical candidate by using in vitro potency, selectivity, pharmacokinetics (PK), and in vivo pharmacodynamic for prioritizing compounds. Compound BGB-3111 (31a, Zanubrutinib) demonstrates (i) potent activity against BTK and excellent selectivity over other TEC, EGFR and Src family kinases, (ii) desirable ADME, excellent in vivo pharmacodynamic in mice and efficacy in OCI-LY10 xenograft models.

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Target detection is one of the most important research directions in computer vision. Recently, a variety of target detection algorithms have been proposed. Since the targets have varying sizes in a scene, it is essential to be able to detect the targets at different scales. To improve the detection performance of targets with different sizes, a multi-scale target detection algorithm was proposed involving improved YOLO (You Only Look Once) V3. The main contributions of our work include: (1) a mathematical derivation method based on Intersection over Union (IOU) was proposed to select the number and the aspect ratio dimensions of the candidate anchor boxes for each scale of the improved YOLO V3; (2) To further improve the detection performance of the network, the detection scales of YOLO V3 have been extended from 3 to 4 and the feature fusion target detection layer downsampled by 4× is established to detect the small targets; (3) To avoid gradient fading and enhance the reuse of the features, the six convolutional layers in front of the output detection layer are transformed into two residual units. The experimental results upon PASCAL VOC dataset and KITTI dataset show that the proposed method has obtained better performance than other state-of-the-art target detection algorithms.