BiopharmaTrend.com

The digital transformation of biopharmaceutical manufacturing is continuing at a rapid pace as companies attempt to mine the sources of data available. Innovations include predictive analytics, big data analytics, and creating the digital plant. Digital transformation offers a mechanism to revise its business model, to improve production processes, to design new drugs faster by using artificial intelligence to screen compounds and to increase responsiveness to customers. Furthermore, the volume of data processed by pharmaceutical firms shows no sign of slowing down. This means pharmaceutical companies must act quickly in terms of building core internal digital capabilities and moving beyond their traditional IT functions to all areas of the business.

 

There is a plethora of analytics reports, including ones by Deloitte, DKV Global, and Ernst and Young, all pointing out to a declining business performance of the pharmaceutical industry. They all convey a similar bottomline message: the decline is not due to a lack of innovation (the innovations are growing). And not because sales are falling or markets are shrinking (revenues are growing in general, and the markets are expanding with the expanding and ageing population). The key reason of the declining financial performance is the fact that research and development (R&D) costs are growing substantially faster over an average investment period, than the actual revenues over the same period. This kills operational profits, leading to a decline in the overall business gain. A direct consequence of that -- an increasingly stagnating industry, cutting sometimes promising R&D programs, jobs etc.  

There are two more relevant questions here: 

1) why R&D costs are growing faster than revenues, considering that technological progress is seemingly providing more and more optimal and powerful technologies to pharma companies at a constantly decreasing specific price (e.g. costs of computation, sequencing, screening and many other things are falling), and 

2) what to do about it to reverse the decline in pharma industry performance? 

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. 

Things like gene editing, stem cells, immunotherapies and new types of biologics are now mega-trends in the pharmaceutical industry, widely covered in media, and I guess there is little doubt that biology is the next big thing in medicine. However, in this post I would like to outline several hot areas in small molecule drug discovery, suggesting a lot of untapped potential and investment prospects in this more “traditional” pharmaceutical research space.

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.