The Patient Equation

The Precision Medicine Revolution in the Age of COVID-19 and Beyond

Glen de Vries, Jeremy Blachman

• Verlag: Wiley
• Jahr: 2020
• ISBN:
  9781119755753
• Auflage: 1
• Thema: Medicine
• Thema: Clinical Medicine

How the data revolution is transforming biotech and health care, especially in the wake of COVID-19—and why you can't afford to let it pass you by

We are living through a time when the digitization of health and medicine is becoming a reality, with new abilities to improve outcomes for patients as well as the efficiency and success of the organizations that serve them. In The Patient Equation, Glen de Vries presents the history and current state of life sciences and health care as well as crucial insights and strategies to help scientists, physicians, executives, and patients survive and thrive, with an eye toward how COVID-19 has accelerated the need for change. One of the biggest challenges facing biotech, pharma, and medical device companies today is how to integrate new knowledge, new data, and new technologies to get the right treatments to the right patients at precisely the right times—made even more profound in the midst of a pandemic and in the years to come.

Drawing on the fascinating stories of businesses and individuals that are already making inroads—from a fertility-tracking bracelet changing the game for couples looking to get pregnant, to an entrepreneur reinventing the treatment of diabetes, to Medidata's own work bringing clinical trials into the 21st century—de Vries shares the breakthroughs, approaches, and practical business techniques that will allow companies to stay ahead of the curve and deliver solutions faster, cheaper, and more successfully—while still upholding the principles of traditional therapeutic medicine and reflecting the current environment.

• How new approaches to cancer and rare diseases are leading the way toward precision medicine
• What data and digital technologies enable in the building of robust, effective disease management platforms
• Why value-based reimbursement is changing the business of life sciences
• How the right alignment of incentives will improve outcomes at every stage of the patient journey

Whether you're a scientist, physician, or executive, you can't afford to let the moment pass: understand the landscape with this must-read roadmap for success—and see how you can change health care for the better.

SECTION 1
From Hippocrates to Epocrates

1
Before We Cured Scurvy

What do we know about a person? If you asked Hippocrates, he might not have that much to say. Hot or cold. Big or small. Dead or alive. Ask a physician today, and the answer is much more complex. There are thousands of medical tests we can run on a person, inside and out. Blood chemistry, urinalysis, X-rays, Dopplers, and more. We can track these results over time, in various systems, or research information online, with powerful programs like Epocrates, a medical reference app, and others. We can sequence the genome. Or we can count how many steps someone takes in a day.

Categorizing all of these observations about a person is important as we think about them as inputs to patient equations. Whether ancient or modern, these observations come with different levels of reliability and resolution. For example, movement and mood have been observed by physicians for centuries, but we can now check them digitally, reliably, and automatically—without the biases or endurance limitations of a human observer. Hippocrates could certainly count steps—but nowhere near the way a fitness tracker can.

A useful first step in our categorization comes from what most people learned in high school biology: the difference between genotype and phenotype. Before Gregor Mendel's experiments with the physical attributes of peas in the 1800s, we had little knowledge about inheritance from a medical perspective. And until James Watson and Francis Crick's famous work with DNA less than a hundred years ago, we had no notion of the mechanisms by which our genetic makeup was stored and transmitted to subsequent generations. Our genome is incredibly important in determining our health—but it is merely a starting point.

Phenotype, on the other hand, includes every observable aspect of ourselves that is not encoded in our DNA. Everything about us and how we exist in the world is phenotype: our hair color, eye color, height, weight, and so much more. The observation of phenotypes begins well before the days of Hippocrates. Imagine an ancient doctor simply using a hand to determine if a person had a fever. Or, not even a doctor—we should instead use the term “healer” in that example, since people were likely checking for fevers long before any notion of the structured discipline of medicine.

Of course, this technique continues today. Imagine a parent touching a child to check for the same. These kinds of observations certainly go under the heading of phenotype. But even what goes on in our heads—our cognition—and how those thoughts manifest in what we do every day—our behavior: it's all phenotype.

Over time, the precision with which phenotypes can be measured has continued to evolve. The hand, to start, was replaced by a thermometer to check for a fever. A modern mercury or alcohol-based thermometer can be read to a tenth of a degree of precision. 37.0° Celsius is the widely accepted average “normal” value of a healthy person's temperature. On a modern analog thermometer, that is distinguishable from 37.1° or 36.9°. A digital thermometer might be even more precise, perhaps to the level of hundredths or even thousandths of a degree.

These digital readings show a greater resolution—which is another useful dimension that we can use to categorize phenotypes. An inexperienced hand might be able to distinguish between two states: fever and no fever. For those familiar with the language of computers, we can represent this in binary as a zero or a one. Perhaps a more experienced nurse, physician, or mom can distinguish between a low fever and a high fever. Add hypothermia (the body becoming too cold for normal functioning) and we've got four possible outcomes of the measurement. The computer-literate will realize that this is now not one binary bit, but two digits, each a zero or one. If we want to know if a patient is recovering from a fever (or hypothermia), we probably need to grab that liquid thermometer and measure the temperature more precisely, so that we can see the value change over time.

As we look at more complex problems in disease diagnosis, or, for instance, predicting fertility, we may indeed need the digital version. As we take these more-and-more-precise measurements (and need more and more computer bits to store them), you can start to see how the convergence of biology and digital technologies is inextricably linked to the resolution at which we measure phenotype.

Nanometers to Megameters

Beyond resolution or precision, we can think of the available knowledge about a person in terms of scale. Starting small: individual atoms combine to form molecules that define the tiniest end of our scale, at least when it comes to our current knowledge about how to observe our state of health. (A keen futurist—or a particle physicist—might predict that future editions of this book will reflect not-yet-uncovered findings about subatomic interactions being relevant to predicting or managing our health. But, for now, the atom is as small as it gets.)

Let's begin with our DNA, at a couple of nanometers in size, as the starting point. When our genes are turned on—activated as a first step in a cascade of observable phenotypes—they are transcribed to RNA. We're still talking nanometers. Ultimately, those genes produce proteins, protein complexes, organelles (just as our body has organs, so do the cells that make it up), and we reach the next milestone of scale: a cell, at tens of micrometers in size. Figure 1.1 illustrates this continuing progression of phenotypic scale.

Figure 1.1 A multiscale view of health

Our organs, in centimeters, are next. And if we look at the ways phenotype has been measured over time, the organs were the smallest level at which we could observe for many, many generations. The Greek anatomist Herophilus, around the year 300 BC, is said to be the first to systematically dissect and start to understand the human body.¹ He described the cardiovascular system, the digestive system, the reproductive system, and more.

Perhaps embarrassingly, more than 2,000 years later, Herophilus's work still dictates much of how we divide up medical specialties. Doctors are trained in and specialize in the brain, the heart, the liver, and more—disciplines in medicine are largely still organ-based. But as we look at how impactful observations as well as medical interventions are now happening at smaller and smaller scales, the inevitable need for specialization at these smaller dimensions will become obvious. It's not that one scale is more important than others. Of course the brain and all of its complexities merits its own field of study. But as we look at cancers, and how interactions at nanometer and micrometer scales determine what kinds of treatments will be most beneficial for different patients, specialization in molecules, in pathways, and in fields that allow us to recognize that cancer isn't one disease but many will all be critical.

Professor Paul Herrling, who among several distinguished positions in academia and industry was the head of research at Novartis Pharma AG as well as a scientific advisor to Medidata, once told me that evolution was the ally of the drug discoverer. He was referring to the fact that once the molecular mechanisms that function in our bodies emerge through evolutionary processes, they are reused, sometimes over and over again.² They will perform the same—as well as sometimes different—functions in different types of cells, and in different organs. This is a fact that life scientists ought to keep in mind. A drug that is useful for one particular purpose in treating a specific disease probably has other uses, in other diseases.

Imagine having no tools and deciding you need to tighten a particular bolt on a specific model of refrigerator (a somewhat ludicrous analogy, but I think also a useful one). You end up designing something to perform that function—much like creating a drug to treat a particular kind of cancer in a particular organ. Depending on the size of the bolt, the tool you create may well end up being able to tighten (and loosen) lots of other bolts as well, on lots of different models of refrigerators—not to mention on lots of other things too. Similarly, if that cancer treatment works in one specific instance, it may have the potential to be used in other cancers, as well as for noncancerous conditions.

As we move up the scale to our bodies, in meters, we realize that much of what we can see now has been detectable since the beginning of mankind—our moods are often quite obvious, our knowledge can be tested, our movements tracked—but not truly measurable in the way it is today. Going even bigger—if we start to not just count our steps but observe how our cognition drives the behavior of where we go and what we do in the world—we reach the scale of kilometers. Sometimes by the hundreds or thousands. Scaling up, sometimes what we think about or what we do can affect entire societies, entire countries, or the whole world.

We need to be open to these different levels of observation, these different scales. We need to look smaller and larger than the organ-based classifications modern medicine has often settled at. Joel Dudley, executive vice president for precision health for the Mount Sinai Health System as well as director of the Institute for Next Generation Healthcare and associate professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai, spoke about this at a recent Medidata event, explaining that humans are complex adaptive systems and we simply can't understand the entire person by looking at the individual parts.³

Organizing our study by symptoms and anatomy, he said, is like learning about the world from shadows. It is critical that we redefine our understanding of human disease with data—seeing clearly the overlaps between, say, brain disease and skin disease. Our assumptions about the relationships between systems in our body, and therefore the relationships between diseases, are outdated and incorrect, Dudley insists. We haven't even begun to define what health actually is, he says. Today, health is crudely defined as the absence of our flawed concepts of disease. But the remainder of what it truly means to be healthy is still to be fully figured out.

If we think about our journey since Herophilus, it is indeed only a few hundred years ago that we started to be able to look at things at a cellular level at all, with the invention of the microscope and the discovery of these tiny building blocks inside of us. And about halfway along the road between Anton van Leeuwenhoek viewing the first live cells in the late 1600s and the development of modern cell theory in 1839 (and the realization that everything in our body is made of cells), the world of clinical trials began.⁴ It is there that we could truly start building objective knowledge about how our bodies work.

Scurvy

James Lind, a surgeon in the British Navy in 1747, saw seaman after seaman...