BiopharmaTrend.com

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.

Over the past decade, pull incentives as a solution to the broken antibiotic market have been proposed to entice companies into antibiotic research and development.  These incentives would essentially provide a market, and therefore a return on investment for pharmaceutical companies. Almost all of today’s inadequate antibiotic pipeline is provided by biotech and small pharma.  All are threatened with loss of investor interest because of the failed marketplace and many are experiencing difficulty in raising funds either from public or private markets.  One alternative to providing money to the “evil” pharmaceutical industry via a substantial pull incentive is to create publicly funded non-profit organizations or public-private ventures that would essentially replace the industry in antibiotic research, development and commercialization. Two proponents of this approach are Lord Jim O’Neill (of the O’Neill Commission or Antimicrobial Resistance Review fame) and Ramanan Laxminarayan of the Center for Disease Dynamics, Economics and Policy and of GARDP. Both, clearly, are key thought leaders in the area. 

A background context -- opportunities and challenges

Current widespread interest towards artificial intelligence (AI) and its numerous research and commercial successes was largely catalyzed by several landmark breakthroughs in 2012, when researchers at the University of Toronto achieved unprecedented improvement in the image classification challenge ImageNet, using their deep neural network “AlexNet” running on graphics processing units (GPUs), and when that same year Google’s deep neural network managed to identify a cat from millions of unlabeled Youtube videos, representing a conceptual step in unsupervised machine learning.