A Study of Structure and Dynamics in Hydrated Active Pharmaceutical Ingredients

Over 50% of active pharmaceutical ingredients (APIs) are crystallized as simple salts, and of these, over 50% are HCI salts. In many instances, APIs can crystallize into pseudopolymorphic forms (e.g., hydrates or solvates), which have structures distinct from the non-hydrated or non-solvated solid phases. The polymorphic form of an API can influence such factors as the bioavailability, shelf life, toxicity, and solubility in the body. Additionally, each unique hydrate or solvate of an API represents unique intellectual property, and may be distinctly patented. As such, it is very important to precisely structurally characterize all solid forms of APIs. The focus of this project is to use 35CI SSNMR, pXRD, and quantum-chemical calculations to systematically study hydrates of HCI APIs. By analyzing the 35CI and 2H SSNMR spectra of different hydrated and anhydrous forms of various HCI salts, we hope to determine the nature by which water molecules directly and indirectly affect the molecular structures. First principles calculations of 35CI electric field gradient and chemical shielding tensors will aid in rationalizing symmetry/structure/spectral relationships. Preliminary studies on Cimetidine HCl monohydrate and Arginine HCl monohydrate have shown that quantum-chemical calculations do not accurately match experimental data for these hydrated systems. This may be due to dynamic motion of the water molecules in these compounds. By conducting 2H variable temperature SSNMR studies, it will be possible to address this specific hypothesis. It is our hope that these findings will be of interest to the pharmaceutical industry, for use in high throughput analysis of APIs, hydrate identification and detection of impurities, and disproportionation products

Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction

© 2020, CARS. Purpose: In the context of analyzing neck vascular morphology, this work formulates and compares Mask R-CNN and U-Net-based algorithms to automatically segment the carotid artery (CA) and internal jugular vein (IJV) from transverse neck ultrasound (US). Methods: US scans of the neck vasculature were collected to produce a dataset of 2439 images and their respective manual segmentations. Fourfold cross-validation was employed to train and evaluate Mask RCNN and U-Net models. The U-Net algorithm includes a post-processing step that selects the largest connected segmentation for each class. A Mask R-CNN-based vascular reconstruction pipeline was validated by performing a surface-to-surface distance comparison between US and CT reconstructions from the same patient. Results: The average CA and IJV Dice scores produced by the Mask R-CNN across the evaluation data from all four sets were 0.90 ± 0.08 and 0.88 ± 0.14. The average Dice scores produced by the post-processed U-Net were 0.81 ± 0.21 and 0.71 ± 0.23 , for the CA and IJV, respectively. The reconstruction algorithm utilizing the Mask R-CNN was capable of producing accurate 3D reconstructions with majority of US reconstruction surface points being within 2 mm of the CT equivalent. Conclusions: On average, the Mask R-CNN produced more accurate vascular segmentations compared to U-Net. The Mask R-CNN models were used to produce 3D reconstructed vasculature with a similar accuracy to that of a manually segmented CT scan. This implementation of the Mask R-CNN network enables automatic analysis of the neck vasculature and facilitates 3D vascular reconstruction