ABSTRACT
There remains a need to develop novel SARS-CoV-2 therapeutic options that improve upon existing therapies by increased robustness of response, fewer safety liabilities, and global-ready accessibility. Functionally critical viral main protease (Mpro, 3CLpro) of SARS-CoV-2 is an attractive target due to its homology within the coronaviral family, and lack thereof towards human proteases. In this disclosure, we outline the advent of a novel SARS-CoV-2 3CLpro inhibitor, CMX990, bearing an unprecedented trifluoromethoxymethyl ketone warhead. Compared with the marketed drug nirmatrelvir (combination with ritonavir = Paxlovid), CMX990 has distinctly differentiated potency (~5x more potent in primary cells) and human in vitro clearance (>4x better microsomal clearance and >10x better hepatocyte clearance), with good in vitro-in vivo correlation. Based on its compelling preclinical profile and projected once or twice a day dosing supporting unboosted oral therapy in humans, CMX990 advanced to a Phase 1 clinical trial as an oral drug candidate for SARS-CoV-2.
ABSTRACT
The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly.