Inside the Cockpit: Navigating the Complexity of Drug Development With Ai and Blockchain

By Gunjan Bhardwaj

The modern system of drug discovery and development is broken. Development costs are so high that pharmaceutical companies can only afford to develop blockbuster drugs that will be worth at least $1 billion annually and take a decade to come to market. Researchers have incentives to hide their work rather than collaborate. And the most valuable data for drug discovery is hidden in silos or unpublished research.

Entrepreneur Gunjan Bhardwaj, CEO of Innoplexus and Cancercoin, is pioneering a new paradigm to bring drug development to meet the moral imperative of bringing lifesaving drugs to market faster and cheaper. His innovative combination of blockchain technology and second- and third-wave artificial intelligence changes the incentives for all the players in the drug development ecosystem.

The broken drug development system exacts an enormous toll of human pain and suffering. Fixing it is a moral imperative. Inside the Cockpit shows how.

• Language: English
• Publisher: BookBaby
• Release date: Jul 9, 2019
• ISBN: 9781544513089

Book preview

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Copyright © 2019 Gunjan Bhardwaj

All rights reserved.

ISBN: 978-1-5445-1308-9

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Contents

Introduction

1. The Pain of Drug Discovery

2. The Three Waves of Artificial Intelligence

3. AI Limitations in Drug Discovery

4. The Role of Blockchain in Opening Up Data

5. Blockchain plus AI in Drug Discovery

Conclusion

Acknowledgments

About the Author

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Introduction

In December 2014, a twenty-nine-year-old investor named Vivek Ramaswamy bought a patent from GlaxoSmithKline (GSK) for $5 million. The patent covered an Alzheimer’s drug candidate that had failed GSK’s clinical testing. This isn’t uncommon; in fact, most drug candidates fail (one study found that between 2006 and 2015 only 9.6 percent of drug development programs made it to market).1 GSK, seeking to recoup some of its investment in what appeared to be a dead-end project, was more than willing to sell its intellectual property.

Ramaswamy knew something GSK didn’t. He was eager to buy.

Typically, drug testing involves a stage gate process characterized by multiple decision points, or stage gates, along the way. If a drug fails to meet performance requirements at any decision point (DP), development is stopped. Drugs like the one Ramaswamy bought, known as SB-742457, fill decision-point graves. GSK had conducted thirteen trials involving 1,250 patients before concluding at DP-02, or decision point two, that further study was unwarranted. In 2010, it abandoned SB-742457, evidently one more in a long line of failures that characterize the hunt for blockbuster drugs.

In early 2014, Ramaswamy, a hedge fund partner, had information that helped him understand something that the GSK researchers did not: SB-742457, taken with another Alzheimer’s drug, Aricept, slowed cognitive decline for a certain subset of dementia patients. In May 2014, Ramaswamy left his hedge fund. Within eight months, he raised $360 million to launch his new company, Axovant Sciences, built around SB-742457. When Axovant went public in mid-2015, even though the firm had done no further clinical studies, the new company’s market valuation topped $2 billion—based on a single $5 million purchase of a failed drug.2

GSK had sold Ramaswamy a treasure for almost nothing.

He had taken advantage of the information asymmetry that’s pervasive in the life sciences market. Companies like GSK often don’t even know the value of what they have because they can’t see a complete picture; data is scattered across the life sciences ecosystem in hidden pockets and walled gardens. Making connections and drawing insights from that data, consequently, is very difficult.

In order to understand the value of the data in the drug development value chain, it’s crucial to understand the context, which is dauntingly complex. To obtain a view of the landscape in, say, Alzheimer’s research, one would have to manually curate and annotate all the relevant research data, then look at the relationships within it. For most researchers today, this is essentially impossible. Companies that do this work employ armies of analysts and sell the results for a substantial premium. Big pharmaceutical firms may be able to pay for their research, which gives them a leg up in the hunt for blockbuster drugs (though no guarantee they’ll be the only ones finding them, as Vivek Ramaswamy showed).

The result is information asymmetry, and the value of that asymmetry is growing exponentially, given the growth in the volume of life sciences data. In 1950, medical knowledge was believed to double every fifty years; by 2020, it is expected to double every seventy-three days.3 Even at a huge pharma like GSK, they were—and are—sitting on an unexploited treasure trove in terms of the value of the research and development work. But they didn’t know that in the case of SB-742457. They didn’t know that the drug could be repurposed and used for a certain subset of patients. In all likelihood, they are sitting on other treasures they don’t recognize because they are not able to glean the appropriate insights from the life sciences data landscape.

Drug Development Is Broken

Ramaswamy and GSK’s experience is one example of what’s broken in drug discovery and development. Data is hidden away in carefully protected silos, and that secrecy means that important discoveries are not happening at the pace at which they should.

This problem is not limited to big pharmaceutical companies; consider a story I heard from a research scientist at the University of Göttingen in Germany who works on the epigenetics of pancreatic cancer—one of the deadliest cancers in the world, a cancer with one of the lowest five-year survival rates, less than 5 percent.4 Many candidate drugs for treating pancreatic cancer have failed miserably. This scientist described a drug that was in clinical trials but had been abandoned because it failed to meet the safety and efficacy criteria of the US Food and Drug Administration (FDA). The reality was that this drug actually cured the tumor in a select subset of patients. If one were to look at the epigenetics of the drug’s efficacy and stratify the patient population appropriately, it could be a wonder drug for a smaller segment of patients. But it wasn’t being pursued.

Failed, or apparently failed, drug experiments go to the valley of death. During the period from 2013 to 2015, 218 drugs failed at Stage II or Stage III trials.5 The decision has been made by their creators that they are not worth pursuing, yet valuable data is locked up in those experiments. That data still is useful. Some researcher somewhere, if he had that data, might see a connection to something that otherwise seems unrelated, and see possibility, just as Vivek Ramaswamy did. Instead, the data about those failed experiments is not published, not searchable, and not available—no one can even find out that a researcher conducted an experiment. Researchers only want to publish what seems to work, yet a broader understanding of what doesn’t work can also be useful in the search for drugs.

Big Pharma companies might be holding hidden treasures, but they can’t get a real-time look at the entire gamut of drug candidates and the intellectual property (IP) of those candidates. They can’t combine their own research with outside research to come up with drugs that will help patients, which of course is why pharmaceutical companies should exist. The direct advantage of looking at the entire research universe, internal and external, is that researchers can see insights and correlations that they could not previously see when data was trapped in