Selected posts from Editor
Updated: 10.01.2019. Newly added content is marked in the text with "Update" sign.
The idea of using artificial intelligence (AI) to accelerate drug discovery process and boost a success rate of pharmaceutical research programs has inspired a surge of activity in this area over the last several years. In 2018, things are getting even “hotter” with the increase in the amount of partnerships, investments and other important events, summarized and grouped below into “mini-trends”.
(Last updated 08.10.2018)
The type of artificial intelligence (AI) which scares some of the greatest minds, like Elon Musk and Stephen Hawking, is called “general artificial intelligence” -- the one which can “think” pretty much like humans do, and which can quickly evolve into a dangerous “superintelligence”. There is a notion that it might be invented in the nearest decades, but today we are definitely not there yet. The AI which is making headlines these days is a “narrow artificial intelligence”, a limited type of machine “intelligence” able to solve only a specific task or a group of tasks. It can’t go anywhere beyond specifics of the problem for which it is designed, so apparently, it will not hurt anyone in the nearest time. But already now it can provide meaningful practical results on those narrow tasks, like natural language processing, image recognition, controlling self-driving cars, and helping develop new drugs more efficiently. With the ability to find hidden and unintuitive patterns in vast amounts of data in ways that no human can do, AI represents a considerable promise to transform many industries, including pharma and biotech.
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 recent analytical report by Visiongain, drug discovery outsourcing will continue to grow over the next decade and will rise to a $43.7 billion dollar industry by 2026, as compared to an estimated 19.2 billion in 2016 (or $21.2 billion according to Kalorama Information). This is in line with Vantage’s fresh alliance benchmarking study, revealing that over 80% of bio-pharma respondents report increased alliance activity compared to five years ago. 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.
Being under ever-increasing pressure to compete in a challenging economic and technological environment, pharmaceutical and biotech companies must continually innovate in their R&D programmes to stay ahead of the game.
External innovations come in different forms and originate in different places -- from university labs, to privately held venture capital-backed startups and contract research organizations (CROs). Let’s get to reviewing some of the most influential research trends which will be “hot” in 2018 and beyond, and summarize some of the key players driving innovations.
A historically significant milestone has just been reached by Oxford, UK-based Exscientia, which used its artificial intelligence (AI)-based drug discovery platform Centaur Chemist™ to deliver the first drug candidate in the framework of their multiyear collaboration with GSK. This AI-derived small molecule is a highly potent in vivo active substance targeting a novel pathway for the treatment of chronic obstructive pulmonary disease (COPD). The milestone is among first early successes in drug discovery associated with the recent adoption of novel machine learning/AI methods and workflows.
(Last updated: 23.08.2018)
Online marketplaces are websites with a “many-to-many” business logic. They can host multiple suppliers trading with multiple buyers via different e-commerce tools available as a part of a website functionality.
Why are online marketplaces great?
Online marketplaces can provide a substantial added value to its users. For example, buyers can quickly compare and select better offerings without the need to research multiple websites and surf online for price comparisons or product specifications. Additionally, marketplaces bring more transparency, trust, and standardization to the whole process of sourcing.
Machine learning and artificial intelligence have become widely discussed topics in the area of life sciences and healthcare over the last several years. While a lot of pharmaceutical companies and healthcare organizations express considerable interest in possible new opportunities, associated with the use of artificial intelligence for early drug discovery, clinical trials optimization, and business intelligence, a considerable gap still exists when it comes to understanding new technologies by pharmaceutical professionals and leaders. The key questions here are these:
What machine learning / AI can and can’t do for pharmaceutical industry
What should be done to harness practical and measurable value out of machine learning / AI?
How it should be done and what are the timelines for getting returns on investments?
Nowadays mobile devices are ubiquitous with an estimated number of smartphones and tablet PCs to exceed two billion globally.
The availability of internet connection in most public places, powerful processors, and user-friendly touch screen technologies make mobile devices useful not only for spare time activities but also for education and science.
Specialized mobile apps are ubiquitous in the area of healthcare providing value for medical doctors, as well as patients involved in various healthcare programs and therapies. Those include various apps for assisting clinical decision making by doctors, apps for monitoring physiological parameters of patients in real time, apps for managing doctor-patient interactions, apps for self-monitoring various health conditions and physiological parameters (for example, did you know you can identify a dangerous wart on your body using your mobile phone?) etc.
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.