Identification, Synthesis, and Characterization of Potential Oxidative Impurities of Venetoclax: Application of Meisenheimer Rearrangement.

Venetoclax is a potent BCL-2 inhibitor that is used for the treatment of several blood cancers. During the oxidative stress degradation of venetoclax, we observed the formation of two potential impurities at levels of about 8-10%, which have similar molecular weights. The two impurities were isolated and identified as 4-(3-((1H-pyrrolo[2,3-b]pyridin-5-yl)oxy)-4-(((3-nitro-4-(((tetrahydro-2H-pyran-4-yl)methyl)amino)phenyl)sulfonyl)carbamoyl)phenyl)-1-((4'-chloro-5,5-dimethyl-3,4,5,6-tetrahydro-[1,1'-biphenyl]-2-yl)methyl)piperazine 1-oxide (venetoclax N-oxide, VNO) and 2-((1H-pyrrolo[2,3-b]pyridin-5-yl)oxy)-4-(4-((4'-chloro-5,5-dimethyl-3,4,5,6-tetrahydro-[1,1'-biphenyl]-2-yl)methoxy)piperazin-1-yl)-N-((3-nitro-4-(((tetrahydro-2H-pyran-4-yl)methyl)amino)phenyl)sulfonyl)benzamide (venetoclax hydroxylamine impurity, VHA). To confirm these two compounds, we have synthesized each impurity individually and analyzed it by high-performance liquid chromatography, mass spectrometry, 1H NMR, 13C NMR, and 2D NMR. VNO was synthesized by the oxidation of venetoclax using m-CPBA in dichloromethane to get the required N-oxide impurity. After the confirmation of the VNO impurity, the VNO impurity was heated with water at reflux in a sealed tube for 36 h to get the VHA impurity of about 6-8% after 36 h. After thorough analysis, it was confirmed that venetoclax N-oxide undergoes [1,2] Meisenheimer rearrangement to form the venetoclax hydroxylamine impurity. These two impurities may be relevant reference standards in manufacturing venetoclax Active Pharmaceutical Ingredient (API) (or) tablets.

Identification, Synthesis, and Characterization of Novel Baricitinib Impurities.

Baricitinib is a novel active pharmaceutical ingredient used in the treatment of rheumatoid arthritis, and it acts as an inhibitor of Janus kinase. During the synthesis of baricitinib, three unknown impurities were identified in several batches between 0.10 and 0.15% using high-performance liquid chromatography. The unknown compounds were isolated and identified as N-((3-(4-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-1H-pyrazol-1-yl)-5-oxotetrahydrofuran-3-yl)methyl)ethane sulfonamide (lactone impurity, BCL), 2-(3-(4-(7H-[4,7'-bipyrrolo[2,3-d]pyrimidin]-4'-yl)-1H-pyrazol-1-yl)-1-(ethylsulfonyl)azetidin-3-yl)acetonitrile (dimer impurity, BCD), and 2-(1-(ethylsulfonyl)-3-(4-(7-(hydroxymethyl)-7H-pyrrolo[2,3-d]pyrimidin-4-yl)-1H-pyrazol-1-yl)azetidin-3-yl) acetonitrile (hydroxymethyl, BHM). These compounds were synthesized and confirmed against the isolated samples. The structures of all the three impurities were confirmed by extensive analysis of 1H NMR, 13C NMR, and mass spectrometry. The lactone impurity formation was explained by a plausible mechanism. The outcome of this study was very useful for scientists working in process as well as in formulation development. To synthesize highly pure baricitinib drug substance, these impurities can be used as reference standards due to their potential importance.

Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network.

In this paper, a study is reported on the popular BraTS dataset for segmentation of brain tumor. The BraTS 2019 dataset is used that comprises four MR modalities along with the ground-truth for 259 high grade glioma (HGG) and 76 low grade glioma (LGG) patient data. We have employed U-Net architecture based 2D convolutional neural network (CNN) for each of the orthogonal planes (sagittal, coronal and axial) and fused their predictions. The objective function is aimed to minimize Dice loss between the binary prediction and its actual labels. Samples having tumor information are considered for each patient data to avoid training on non-informative data. The models are trained on 222 HGG data and tested on 37 HGG data using performance metrics such as sensitivity, specificity, accuracy and Dice score. Test-time augmentation is also performed to improve the segmentation performance. 7-fold cross validation is conducted to analyze the performance on different sets of training and testing data.

Effective electron-density map improvement and structure validation on a Linux multi-CPU web cluster: The TB Structural Genomics Consortium Bias Removal Web Service.

Anticipating a continuing increase in the number of structures solved by molecular replacement in high-throughput crystallography and drug-discovery programs, a user-friendly web service for automated molecular replacement, map improvement, bias removal and real-space correlation structure validation has been implemented. The service is based on an efficient bias-removal protocol, Shake&wARP, and implemented using EPMR and the CCP4 suite of programs, combined with various shell scripts and Fortran90 routines. The service returns improved maps, converted data files and real-space correlation and B-factor plots. User data are uploaded through a web interface and the CPU-intensive iteration cycles are executed on a low-cost Linux multi-CPU cluster using the Condor job-queuing package. Examples of map improvement at various resolutions are provided and include model completion and reconstruction of absent parts, sequence correction, and ligand validation in drug-target structures.