Complex Made Simple

AI in pharma will one day trump the spread of Corona-like viruses

When the chips are down and there is disease and death all around, who you're gonna call? AI !

Researchers have used AI to track the spread of the Wuhan coronavirus AI Virtual Screening involves interrogating vast compound libraries via computer simulation and expedites drug development Numerous studies have demonstrated the ability of AI systems to provide accurate diagnosis and treatment decisions for cancers

Over 2100 died and 43,000 remain in hospitals as a result of the Coronavirus. Could this have been prevented or at least halted much more quickly?

In fact, no antidote has been discovered. According to the Center for Disease Control and Prevention (CDCP), “There is currently no vaccine to prevent coronavirus disease 2019 (COVID-19). The best way to prevent illness is to avoid being exposed…,” CDCP said.  

Living in today’s high tech world, it seems ridiculous to needlessly let people die because we are unable to quickly intervene in discovering a drug to deal with diseases.

That’s beside the cost involved: Pandemics and efforts to contain them eat away an estimated 1% of global GDP each year.

But this is about to change. AI has the brains to put a stop to all that. 

Researchers have used AI to track the spread of the Wuhan coronavirus, and the technology is also being deployed to tackle the American opioid crisis.

And that’s just the beginning.

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Drugs under the AI influence  

The combination of AI, big data, IoT technologies, cloud computing, augmented reality, virtual reality and blockchain are being used extensively in the Pharmaceutical industry’s digital transformation.

AI can be subdivided into machine learning, deep learning, natural language, robotics and visual analytics.

Virtual screening (VS) is a great example of how powerful AI can be. VS involves interrogating vast compound libraries via computer simulation and helps expedite the development of a drug inexpensively. AI methods help predict which compounds will be “most favorable” in terms of binding to the therapeutic target.

A computer system can come up with and mine through different molecules, comparing them against different parameters and learning the most promising compounds faster than a human could

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AI pharma funding and treatments

And Artificial intelligence hit a big milestone in January, when the first drug designed entirely using AI entered human clinical trials. The compound, created by Oxford-based biotech company Exscientia, is aimed at treating obsessive-compulsive disorder and reached this stage in less than a year — five times faster than it usually takes to get a drug to this stage.

Exscientia’s specialism is using a combination of deep learning and neural networks to tackle problems even when there is very little data available.

Exscientia also recently signed a near $260 million deal with Bayer to do AI research for new cardiovascular and cancer drugs.

Hong Kong-based Insilico Medicine raised $37 million last year  to find a promising drug candidate for fibrosis and take it to pre-clinical validation in just 50 days.

There are many variants in the way artificial intelligence is used. The UK’s BenevolentAI, for example, scans data on existing drugs to find new uses for them. 

Budapest-based Turbine.ai built a cell model that can be used to simulate how cancer works, with the aim of using artificial intelligence to find effective cancer medicines.      

Biomarker development and drug discovery are benefiting from AI, but two “key players”, pathology and radiology, are thriving.

 Numerous studies have demonstrated the ability of AI systems to provide accurate diagnosis and treatment decisions for cancers including; prostate, breast, and brain

Last year researchers developed a novel AI-based tool to predict risk of breast cancer, using a deep learning model

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AI cost advantages  

Clinical Research Cost is being addressed by AI. “An enormous sum of $2.5bn is spent on research and development (R&D) and clinical trials before every successful drug enters the market,” reasoned Divesh Singla, vice president, Parexel

It takes on average 10 to 12 years to bring a new drug to market, and failure rates are high.  

The cost of bringing a drug to market has gone from $1.2tn to $2tn in the last ten years, but sales have more than halved. Ten years ago, the pharma industry was making a 10.1% return on investment, last year it is just 1.8%, according to the latest Deloitte report on the industry.

Andrew Hopkins, founder and chief executive of Exscientia believes that AI could cut the cost of bringing a drug to market by 30%.

Can AI be trusted in pharma? 

The speed gains from artificial intelligence look impressive, but industry experts caution that there is still some way to go before this method is proven.

“None of these drugs has gone all the way through clinical trials yet, so there is no proof yet for how well this works,” says Karen Taylor, director at the Deloitte Centre for Health Solutions. “Nothing has gone all the way through the system.”

Though a drug might perform well against a cell simulation, an actual human body may still throw some surprises. 

“The tricky part about drug discovery is that we have huge areas of ignorance about human biology, in both health and disease,” says Derek Lowe, a longtime drug discovery researcher who writes a blog about drug discovery and the pharmaceutical industry.