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Pharmaceutical industry trends

Section: Biopharma Insights     View all sections


The Evolution Of Pharmaceutical R&D Model

   by Andrii Buvailo    1625
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    2942
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.

Is Pharma Ready For Serialization? The Answer Lies In Digital Technology

   by Tim Sandle    834
Is Pharma Ready For Serialization? The Answer Lies In Digital Technology

New legislation requiring pharmaceutical companies to implement 'serialization' is now coming into force. This means that no counterfeit product should enter the supply chain and no legitimate product is diverted from its intended destination. To work effectively, serialization requires a comprehensive system to track and trace the passage of prescription drugs through the entire supply chain. The application of track and trace principles can help to avoid counterfeit medicines from entering the supply chain. To be effective, digital technologies such as blockchain and RFID-enabled tag and trace systems need to be embraced.

The Overview of AI in Drug Discovery in 2019: The “Proof-of-concept Year”

   by Andrii Buvailo    2684
The Overview of AI in Drug Discovery in 2019: The “Proof-of-concept Year”

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.

 

Becoming Pharma 4.0: How Digital Transformation Is Reshaping Pharmaceuticals

   by Tim Sandle    2217
Becoming Pharma 4.0: How Digital Transformation Is Reshaping Pharmaceuticals

The digital transformation of biopharmaceutical manufacturing is continuing at a rapid pace as companies attempt to mine the sources of data available. Innovations include predictive analytics, big data analytics, and creating the digital plant. Digital transformation offers a mechanism to revise its business model, to improve production processes, to design new drugs faster by using artificial intelligence to screen compounds and to increase responsiveness to customers. Furthermore, the volume of data processed by pharmaceutical firms shows no sign of slowing down. This means pharmaceutical companies must act quickly in terms of building core internal digital capabilities and moving beyond their traditional IT functions to all areas of the business.