A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

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Biopharma Insights

[Interview] The Rise of Quantum Physics in Drug Discovery

   by Andrii Buvailo    4026
[Interview] The Rise of Quantum Physics in Drug Discovery

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.  

Pharma R&D Outsourcing Is On The Rise

   by Andrii Buvailo    54337
Pharma R&D Outsourcing Is On The Rise

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.  

The Evolution Of Pharmaceutical R&D Model

   by Andrii Buvailo    4753
The Evolution Of Pharmaceutical R&D Model

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? 

Current State of AI in Pharma: Key Achievements Beyond Hype

   by Andrii Buvailo    4941
Current State of AI in Pharma: Key Achievements Beyond Hype

/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.

[Interview] Applying AI To Shape Business Strategies At European Pharma Organizations

   by Andrii Buvailo    1219
[Interview] Applying AI To Shape Business Strategies At European Pharma Organizations

The application of artificial intelligence (AI) in the pharmaceutical industry has become a long-term strategic priority for most companies. However, the efficiency of this endeavor depends greatly on the availability of large volumes of properly curated quality data, which is not always the case.

While pharma organizations generate huge volumes of data across all stages of drug discovery, development, and commercialization, not all types of data are equally useful for building efficient machine learning (ML) pipelines. For instance, it is relatively easier to apply AI-tech to consumer-related business processes, where lots of well-understood and properly labeled data is available, than it is for basic research tasks, where data is complex, often poorly labeled and extremely domain-specific.

The above situation leads to a faster pace of progress with AI application in such areas as financial analysis, consumer-behavior prediction, patient classification, marketing, and so on.

One of the important hurdles that pharma companies are trying to solve using AI tech is brand management. Indeed, understanding peculiar features of various patient categories, their purchasing behaviors, reactions to different products, revealing possible risks and side-effects for each class -- those things become essential for pharma companies to be able to develop and implement truly patient-centric brand management strategies. Luckily, this is one of the most fruitful areas for the application of machine learning (especially deep learning) models.

To get a better understanding of how it can be done, I have asked several questions to Agnieszka Wolk, Senior Director, Data Science, IQVIA, who recently presented this topic at the PMSA 2019 European Summit in Basel, Switzerland. .