Drug Development Is Broken—And Change Is A Moral Imperative

The following is an excerpt from the book Inside the Cockpit: Navigating the Complexity of Drug Development with AI and Blockchain by Gunjan Bhardwaj.

***

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 silos. The present system of siloing data and information asymmetry characterizes the life sciences ecosystem and hampers the discovery of potentially lifesaving drugs.

The example of SB-742457 illustrates how broken our system of drug discovery and development actually is. Every day, people suffer and die because life sciences and pharmaceutical industries don’t have the technologies and capabilities they should and could have to bring effective drugs to market more quickly and effectively.

Drug discovery is hampered by a structural system that resembles driving a car by looking in the rearview mirror. Imagine a physician sitting in the cockpit of a race car. What is flying against his windshield is not wind, but data — the whole life sciences data universe. If the physician wants to drive fast and safely and get where he intends to go, he drives by looking out the windshield. But in life sciences, we drive the car by looking in the rearview mirror — that is, we look at historical data. Worse, we don’t even look at recent historical data; we look at old historical data. It’s as if there’s a time delay for what we’re seeing in our car’s rearview mirror.

Because insights about that data are manually curated and delayed by the process of scientific publication, we are not able to see the data in real time. Peer review of publishable data takes up to 250 days, and the time lag from completion of research to publication can be a year.6 That’s why GSK sold Axovant a promising Alzheimer’s drug for a pittance; the firm’s researchers were not able to see what the extant, current research on Alzheimer’s looks like.

Ramaswamy and Axovant were able to get a glimpse through the windshield, and that glimpse was worth $2 billion.

The System Is Structurally Flawed

Drug discovery and development are crippled by a threefold structural problem.

First, insights are not real-time. We drive the drug-discovery car while looking at data in the time-delayed rearview mirror. By the time you see something, you’re actually well beyond the moment when the researcher who did the work understood it. In life sciences, this delay is a result of slow publication of research data, even as the pace of drug research speeds up. Those delays limit insights.

Second, there is enormous information asymmetry in the life sciences universe. Some researchers and treatment centers have greater access to data and insights because they can afford to pay for it. Many small and medium biotech companies and smaller treatment companies, not to mention patients, have little or no access to these insights. The data is not democratized, which means fewer opportunities for researchers to work with the data.

Third, there is substantial innovation redundancy. In an efficient drug-discovery ecosystem, researchers know what other researchers are doing. Novelty requires competition, but competition doesn’t mean five people should conduct the same experiment that five others did before them. If someone conducted the experiment and the hypothesis that the researcher tested was not proven, why should others repeat that research to get the same results? Yet this happens because one of the principal currencies in science is not solving health problems — it’s publication credits that build reputation. The way science operates, researchers are rewarded with prestige, credentials, grant money, and other benefits when they are the first to publish a finding in a respected scientific journal. Researchers publish positive findings — not proof of a null hypothesis. No one is interested in learning about what didn’t work; they want to learn about what did work. They don’t publish data about proving the null hypothesis, and they don’t discuss their work until after publication. In fact, researchers are disincentivized to share their research or findings prior to publication. A German researcher at the University of Göttingen told me that this kind of competition leads to some researchers attending scientific congresses for the purposes of seeking to understand others’ current research projects and trying to rush their own competing work into publication first.

Think about that for a moment; think about how wrong that is. We live in an era when the public political narrative labels Big Pharma as “evil,” as nothing more than faceless corporations that only care about profits, not patients. Yet if that’s the case, then scientists are evil, too, because what they care about is getting published first, rather than helping patients. These perspectives are caricatures; of course scientists want to help patients. From the outside, the behaviors of Big Pharma companies and researchers may seem to be focused on other objectives, but they make more sense when we comprehend the underlying incentives. The way the system is built now, they have to achieve those objectives if they are going to help patients. A company that didn’t care about profits likely wouldn’t survive long enough to do meaningful work, and a researcher who was ineffective at gaining publication credits would not be able to continue his work.

Step back and look at this situation: who would create a system of drug discovery that actively disincentivizes the people working in the business from cooperating with each other? If, for example, you truly wanted to help people who are suffering from pancreatic cancer, why wouldn’t you want everyone who is working on pancreatic cancer, from a doctor treating patients in a rural clinic to the world’s largest drug manufacturers, to be communicating and cooperating with each other?

That’s not the system that exists right now. But it could be.

Right now, the incentives in the drug-discovery ecosystem are misaligned. Society wants drugs that cure diseases. Scientists want to publish original findings. Pharmaceutical companies want blockbuster drugs worth $1 billion or more.

I wrote this book to introduce a new paradigm for drug discovery: a way of ordering the life sciences ecosystem that relies on artificial intelligence (AI) and blockchain technologies to change incentives and enable faster discovery of more-effective drugs.

In the following chapters, I will describe what this ecosystem looks like and how it is already coming into being. As I described above, three primary problems limit drug discovery: lack of real-time information; information asymmetry; and innovation redundancy. The new paradigm is built on three major changes to the current system to address those problems:

The first change is real-time access to data. Researchers will be able to look forward out the windshield of their cockpit at the data universe, rather than continuing to drive toward drug discovery by looking in the rearview mirror.

The second change is sharing data to create large data training sets for artificial intelligence. This creates economies of scale and economies of scope, allowing AI to be brought to bear in the hunt for drugs. Artificial intelligence can be used not only to look at the data to evaluate researchers’ hypotheses; it also can develop previously unconsidered hypotheses and evaluate these.

The third change is to stop restricting access to critical data through data privacy laws. Rather than limiting access to data, as is the current trend, I will show how patients, researchers, and institutions can be incentivized to share health data and yet also retain their privacy using blockchain technology.

Change Is A Moral Imperative

Drug development is a complex business. Pharmaceutical companies spend decades and billions of dollars to bring drugs onto the market. In 2016, the top ten pharmas spent a combined $70.5 billion on R&D.7 Between 2010 and 2013, the US Food and Drug Administration reduced the average approval time on oncology drugs by 4 percent — and it still takes 9.8 years to get a drug approved. This reality pushes drug companies to work in the most potentially lucrative areas, such as oncology and neurodegenerative diseases, because they need a lot of financial headroom if they are to recoup enormous development costs. Research in rare diseases (those suffered by 200,000 individuals or fewer) sees much less investment, even though an estimated 350 million people worldwide are afflicted with one or more of the two thousand identified rare diseases.8

We should challenge the current drug discovery and development paradigm not because drug companies are evil, but because the system is slow and inefficient, and that is morally wrong. The current structure limits the ability of researchers to address all diseases, leading them to focus on the most profitable. We should be doing everything we can to get lifesaving drugs to the people who need them sooner, and one way to do that is to reduce the cost of drug discovery. When drugs are less expensive to develop, the opportunity to profitably investigate rare diseases expands. Morality demands it.

This work isn’t just a professional imperative for me; it’s a personal one. In 2010, a close friend and mentor in Frankfurt called me late at night to share the devastating news that he had been diagnosed with cancer. The following days were frustrating. I spent hours with him in the hospital, asking, “Why do you believe this doctor? Why do you believe what he says? Is there a way we could do research ourselves on these tumor types to find out about alternative therapies? Who are the key leaders in this field we can ask for second opinions?”

It was impossible. There was just no way to understand whether he was truly getting the best advice or the best treatment, based on his specific disease pattern.

I was very frustrated, and when I spoke to my father about it, he said, “Don’t complain. Change.” This is not the typical advice of a middle-class Indian father. Most want their children to follow a safe, secure career path. Although I had started several companies early in my career, I had settled onto that path. I was working as a consultant with Boston Consulting Group, where I had been a manager for two years and had a global program budget. I was on a secure track.

“Start,” my father said.

I understood what was wrong with the way we seek cures for disease. I had ideas about how to change things. My mother was very concerned that I was making a mistake, but I knew my purpose was right.

My vision was, and remains, this: any patient, anywhere, scared and facing a terrifying diagnosis, will know that the treatment being offered is the best available. That person will have the best chance to come through alive, not to relapse, and to live a rich life.

I knew I could make a difference if I could create a system that would enable those situations. I have started two companies, Innoplexus in Germany and CancerCoin in Switzerland, that do just this. This book is not intended to champion these companies. Rather, I am writing in order to share my vision and the progress that is being made toward that vision.

The paradigm is already changing. The combination of AI and blockchain technologies is fundamentally altering the way the life sciences ecosystem operates, creating new pathways for the discovery of lifesaving drugs. In the following pages, I will show you how.

***

To keep reading, pick up your copy of Inside the Cockpit: Navigating the Complexity of Drug Development with AI and Blockchain by Gunjan Bhardwaj on Amazon.

***

Footnotes

1 David W. Thomas, Justin Burns, John Audette, Adam Carroll, Corey Dow-Hygelund, and Michael Hay, Clinical Development Success Rates 2006–2015(Biotechnology Innovation Organization, Biomedtracker, and Amplion), 7, https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rates%202006-2015%20-%20BIO,%20Biomedtracker,%20Amplion%202016.pdf.

2 “Did a 29-Year-Old Show GlaxoSmithKline That It Made a Billion Dollar Mistake?” PharmaCompass, June 25, 2015, https://www.pharmacompass.com/radio-compass-blog/did-a-29-year-old-show-glaxosmithkline-that-it-made-a-billion-dollar-mistake.

3 Peter Densen, “Challenges and Opportunities Facing Medical Education,” Transactions of the American Clinical and Climatological Association 122, (2011): 48–58, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116346/.

4 Boris W. Kuvshinoff and Mark P. Bryer, “Treatment of Respectable and Locally Advanced Pancreatic Cancer,” Cancer Control 7, no. 5 (2000): 428 & ff,https://moffitt.org/File%20Library/Main%20Nav/Research%20and%20Clinical%20Trials/Cancer%20Control%20Journal/v7n5/428.pdf.

5 Richard K. Harrison, “Phase II and Phase III Failures: 2013–2015,” Nature Reviews 15 (2016): 817–818, http://www.whartonwrds.com/wp-content/uploads/2017/11/Nature-Paper-Richard_Harrison-Phase-II-Phase-III-Failure-Rates.pdf.

6 C.H.J. Hartgerink, “Publication Cycle: A Study of the Public Library of Science (PLOS),” Authorea, https://www.authorea.com/users/2013/articles/36067-publication-cycle-a-study-of-the-public-library-of-science-plos/_show_article.

7 Ben Adams, “The Top 10 Pharma R&D Budgets in 2016,” FiercePharma, https://pages.questexweb.com/rs/294-MQF-056/images/The%20top%2010%20pharma%20R%26D%20budgets%20in%202016%20REV2.pdf

8 “Investment in Research Saves Lives and Money,” Research America,https://www.researchamerica.org/sites/default/files/Rare%20Diseases%20Fact%20Sheet_2015.pdf.