BiopharmaTrend.com

The newly organized research project “MELLODDY” (Machine Learning Ledger Orchestration for Drug Discovery), involving ten large pharma companies and seven technology providers, is that kind of deals which can catalyze a transition of the pharmaceutical industry to a new level -- a “paradigm shift”, as one might refer to it in terms of Thomas Kuhn’s “The Structure of Scientific Revolutions”.

The project aims at developing a state-of-the-art platform for collaboration, based on Owkin’s blockchain architecture technology, which would allow collective training of artificial intelligence (AI) algorithms using data from multiple direct pharmaceutical competitors, without exposing their internal know-hows and compromising their intellectual property -- for the collective benefit of everyone involved. 

Scientists have put together a sensitive method to determine if bacteria carry a gene that can cause resistance to two common antibiotics. The test is rapid and has been tested against the bacterium which causes ‘strep throat’ and other respiratory illnesses.

New research, published in The Lancet Infectious Diseases, presents the first clinical results with CAL02 in patients suffering from severe pneumonia, the first cause of infectious mortality in the world.

The findings are of significance for pharmaceutical companies and the medical sector. This is in the context of a time of great struggle for antibiotic companies given the increase in instances of antibiotic resistant bacteria. What is of particular global concern is the acceleration of resistance. U.S. Centers for Disease Control and Prevention (CDC) data finds that many high-income countries are entering a “post-antibiotic era.”

A historically significant milestone has just been reached by Oxford, UK-based Exscientia, which used its artificial intelligence (AI)-based drug discovery platform Centaur Chemist™ to deliver the first drug candidate in the framework of their multiyear collaboration with GSK. This AI-derived small molecule is a highly potent in vivo active substance targeting a novel pathway for the treatment of chronic obstructive pulmonary disease (COPD). The milestone is among first early successes in drug discovery associated with the recent adoption of novel machine learning/AI methods and workflows.

In 1970-80s, the idea of virtual screening was regarded as a conceptual way to substitute costly and time-consuming experimental “screen-everything-you-have” approaches with a much faster and cheaper predictive modelling to cherry-pick only the best molecules for subsequent synthesis and validation in a lab. A great number of computational tools and approaches emerged, aiming at “pre-screening” new promising molecules, so called “hits”, or augmenting experimental screening programs to optimize efforts.