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.
Pharmaceutical companies are increasingly outsourcing research activities to academic and private contract research organizations (CROs) as a strategy to stay competitive and flexible in a world of exponentially growing knowledge, increasingly sophisticated technologies and an unstable economic environment.
The R&D tasks that firms choose to outsource include a wide spectrum of activities from basic research to late-stage development: genetic engineering, target validation, assay development, hit exploration and lead optimization (hit candidates-as-a-service), safety and efficacy tests in animal models, and clinical trials involving humans.
According to a report by Clearwater International (autumn 2019), the global CRO market will potentially rise to a $45 billion industry by 2022, as compared to an estimated $30 billion valuation (by Objective Capital Partners), exhibiting the current rate of market growth of around 10% CAGR with projected acceleration up to 12%. This is in line with Vantage’s alliance benchmarking study, revealing that over 80% of bio-pharma respondents reported increased alliance activity compared to previous periods. Getting ideas and expertise from external sources is a well-established practice in the pharmaceutical industry with about one-third of all drugs in the pipelines of the top ten pharmaceutical companies initially developed elsewhere, according to a 2014 WSJ article by Jonathan D. Rockoff.
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?
/Last update -- 24 Dec 2019/
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.