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).
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.
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.
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.