- Respiratory disease drug identified as potential COVID-19 therapeutics
- AI-driven drug repositioning allowed a rapid search for COVID-19 drug candidates
- Exploring partners for clinical development
In this post, we decided to highlight eleven entrepreneurial women, leading the way in applying advanced computational technologies, such as machine learning (ML), deep learning (DL), and other artificial intelligence (AI) components, for tackling some of the hardest challenges of science -- in drug discovery and healthcare. This list is composed in alphabetical order.
Houston, TX (May 2020) – Pulmotect, Inc., a clinical-stage biotechnology company, has received approval from the U.S. Food & Drug Administration (FDA) to initiate two COVID-19 Phase-2 clinical trials of its innate immune-stimulating drug PUL-042. The Company plans to start accrual within the next week at up to twenty U.S. sites. The trials are for the prevention of infection with SARS-CoV-2 and the prevention of disease progression in patients with early COVID-19 disease. Funding for the trials came from the final closing of the Company’s offering of Series B Preferred stock in March.
Canada has a firm place in the global life sciences ecosystem, being the tenth largest market for pharmaceutical sales, and home to America’s second-largest life sciences corridor. The country has a long history of life science research, including the creation of the first pacemaker and discovery of stem cells.
According to a survey conducted by BIOTECanada and Deloitte in 2018, conducted in 2018, the Canadian life sciences sector includes many early-stage companies with substantial growth potential -- 67% of responders identified themselves as being in the discovery or emerging phase of development in 2017. Surveyed organizations reported intentions to raise additional capital in the coming years and reported access to capital as the primary issue in the life sciences sector in Canada.
Molecular mechanics (MM) is a traditional computational approach when it comes to modeling in synthetic organic chemistry, medicinal chemistry and versatile aspects of drug design. However, MM methods have significant limitations, for example, when used to study electron-based properties within the drug-receptor microenvironment. Quantum mechanical (QM) methods allow to substantially increase the accuracy of predictions and provide much more relevant models of chemical and biological objects and their interactions, but QM methods are extremely (often prohibitively) computationally costly.
However, a series of advancements over recent years allowed to expand horizons in this direction, for example, the emergence of density functional theory (DFT), the overall increase in the computation power and the emergence of distributed cloud-based computational infrastructures.