A transformative technology for gene editing - clustered regularly interspaced short palindromic repeats (CRISPR) -- has become ubiquitous, at least in research laboratories, where scientists efficiently adopted CRISPR technology to manipulate genes of interest. Still, applying it for humans is associated with greater risks and faces ethical issues. However, alongside a too reckless experiment of a Chinese scientist who edited babies’ genomes, plenty of companies have made well-thought progress toward transferring gene editing technologies into the clinic. Here we make a brief but pithy review on up-to-date gene editing approaches and start-up companies active in this area.
As we enter a new decade, our belief in the the impact of Artificial intelligence (AI) is only getting stronger. Supporting the industry to drive the right drug to the right patient at speed is a huge responsibility that we take very seriously. Towards the end of the last decade we have seen great progress made within life sciences and the use of AI, but moving into 2020 the spotlight on commercial teams and gaining competitive advantage with AI will intensify.
Pharmaceutical companies are increasingly outsourcing their R&D activities, including early-stage research programs, to third party organizations -- academic institutions, biotech startups, and private contract research organizations (CROs) -- as a means to stay competitive, flexible, and profitable against all odds.
Economically, there are factors such as increasing downward pressure on drug pricing by governments, an impending “patent cliff” threatening $198 billion worth of sales during 2019-2024), and downturns in income due to the increasing competition from generics and biosimilars.
From the innovation's point of view, there is a boom in life sciences, stimulating the emergence of novel biological targets, therapeutic modalities, and even whole new areas of drug discovery -- adding opportunities, but also complexity and uncertainty to research programs. In fact, according to Deloitte’s report, return on late-stage pipelines dropped for the top 12 pharma companies from 10.1% in 2010 down to 3.7% in 2016.
Technologically, there is an unfolding “digital revolution”, bringing even further complexity and investment cost to the table -- in a form of artificial intelligence (AI), data mining and big data technologies, data-driven diagnostics, and digital health.
Finally, the rise of the personalized medicine paradigm forces companies to rethink their research pipelines and “one-size-fits-all” product development programs, as well as reconsider their market strategies.
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.
Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.
The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.
In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.