Alongside the topic of gene editing technologies that keeps making headlines these days, there is also a wave of breakthroughs in the field of RNA-targeting medicines, primarily falling into the following two categories: antisense oligonucleotides (ASO), and RNA interference (RNAi) technologies. (another promising approach, dealing with editing RNA itself by ADAR enzymes, is not covered in this post).
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The number of immunotherapies in clinical trial rolls over 5000 now, and immunology has become a common approach in some cancers. Cell technologies, as a growing sub-field in the immunotherapy landscape, have progressed considerably and now represent a $26 billion financial opportunity by 2030, according to a report by Roots Analysis.
The race for adopting new machine learning (ML), deep learning (DL) and related technologies (for simplicity -- “artificial intelligence”/”AI”) keeps rapidly unfolding in the pharmaceutical industry, albeit with varying rate of progress across different use cases.
Let’s review retrospectively some of the key developments in the drug discovery area in 2019 and see how they characterize the current state of AI in the pharmaceutical industry (“hype vs reality”). Note, that I do not cover the healthcare sector in this post (diagnostics, medical applications of AI, digital health etc) -- those will be discussed in one of the future posts.
Major companies on the scene include Second Genome, Enterome, and EpiBiome. In addition, several new startups have entered the field. Amongst the most active investors, Global Engage reports, are Seventure Partners, Flagship Pioneering and BioGaia. In fact there are some 120 companies investing in analyzing data relating to the human microbiome. To take one example, companies such as uBiome are developing genomic tests meant to identify and diagnose harmful microbes in the body.
(Last updated 08.10.2018)
The type of artificial intelligence (AI) which scares some of the greatest minds, like Elon Musk and Stephen Hawking, is called “general artificial intelligence” -- the one which can “think” pretty much like humans do, and which can quickly evolve into a dangerous “superintelligence”. There is a notion that it might be invented in the nearest decades, but today we are definitely not there yet. The AI which is making headlines these days is a “narrow artificial intelligence”, a limited type of machine “intelligence” able to solve only a specific task or a group of tasks. It can’t go anywhere beyond specifics of the problem for which it is designed, so apparently, it will not hurt anyone in the nearest time. But already now it can provide meaningful practical results on those narrow tasks, like natural language processing, image recognition, controlling self-driving cars, and helping develop new drugs more efficiently. With the ability to find hidden and unintuitive patterns in vast amounts of data in ways that no human can do, AI represents a considerable promise to transform many industries, including pharma and biotech.