No. 6 May 2013
THE DIGITAL FUTURE OF
Rethinking FDA Regulation
Peter Huber, Senior Fellow, Manhattan Institute for Policy
Pharmacology is fast becoming an information industry. Biochemists can read every letter of life's core genetic code
and determine the composition and structure of all its molecular progenythe downstream proteins and other biochemicals that shape our health, for better or worse. They have the tools to design a drug that can control almost any
molecular target. The power in nature's code and our mirror-image drugs resides in minuscule packets of material that
technologies now in hand can read, copy, and manipulate. And these technologies are getting cheaper and improving
even faster than their digital siblings.
But biochemists have arrived on the scene billions of years behind nature, which neglected to provide manuals
that explain how all the molecular slivers of code that it has created fit together and interact. The search for a new
drug is increasingly a search for information about how a molecule of our design will interact with different arrays
of molecules that it will encounter in future patients and how those interactions will affect a patient's health. That
search accounts for a rapidly rising fraction of the front-end cost and medical value of most drugs. Repeated again
and again, with one drug after the next, the information acquired will end up in massive and very valuable databases.
The analysis of the data using extremely powerful computers will expose the architectures and dynamics of countless
molecular networks that make human bodies function well or badly and that the right drugs can control.
The private sector is already actively engaged in collecting and analyzing the data. Led by a rapidly growing
group of companies as diverse as IBM, Myriad Genetics, and 23andMe, the digital community has graspedfar ahead
of the FDA and much of the medical communityhow fast molecular medicine can now advance by taking full advantage of the recent convergence of astonishingly powerful biochemical and digital technologies. Never before have
two such powerful technological revolutions converged to advance a single objective of such universal importance.
But unleashing the enormous power and economies of innovation on this last frontier of the information revolution
will require fundamental changes in public policy.
The FDA has spent the last 30 years pondering how, if at all, molecular science might be shoehorned into the
clinical trial protocols that Washington first used over 70 years ago and formalized in licensing rules developed in the
1960s. The regulatory system is now frozen in the headlights. Its drug-testing protocols cannot handle the torrents of
complex data that propel the advance of modern molecular medicine. For all practical purposes, those protocols make
it impossible to license most of the drugs and complex treatment regimens that are needed to control the biochemically complex disorders that these data torrents reveal.
Developed at a time when nobody could see or track the molecules that matter, the FDA's current testing
protocols rely entirely on empirical studies and statistical correlations. They aim to guard, above all, against just one
kind of error in the licensing process: selection bias. But modern pharmacology hinges on the scientific selection of
the right drug-patient molecular combinations. The only practical way to work out most of the drug-patient science
is to study how the drug actually performs in patients. And the first opportunity to do that systematically is during the
As recommended in a recent report issued by President Obama's Council of Advisors on Science and Technology,
the FDA should use its existing accelerated approval rule as a starting point for developing adaptive trial protocols to
be used "for all drugs meeting … an unmet medical need for a serious or life threatening illness …." These protocols
should promote the meticulous, data-intensive study of the drug's molecular performance during clinical trials. And
they should use modern statistical designs to choreograph the adaptive trials needed to ascertain when a drug that
provides only some degree of clinical benefit to some subsets of patients can become a useful component of complex
Part 1 of this paper discusses the rapidly widening chasm that now separates modern pharmacology and
the practice of molecular medicine from the drug-patient science developed and certified the Washington way. The
chasm reflects obsolete policies and rules put into place to regulate ignorance, not knowledge; it reflects the dearth
of molecular medical science, not the science itself or its efficient, orderly development. Part 2 discusses what it will
take to unleash the full power of the precision molecular medicine that biochemical science, powered by digital technology, can now deliver.
The Digital Future of Molecular Medicine: Rethinking FDA Regulation
by Dr. Andrew C. von Eschenbach, Chairman Project FDA
Former commissioner, U.S. Food and Drug Administration; director, National Cancer Institute
During my tenure as commissioner of the U.S. Food and Drug Administration, the federal agency tasked with evaluating the safety and efficacy of medical products that touch the lives of tens of millions of Americans every day, it
became increasingly clear to me that a revolution in biomedical science augured the need for significant changes to
assure the future success of the agency.
It made less and less sense to evaluate the effectiveness of promising new medicines through traditional clinical trials,
in which a cross-section of the intended patient population is randomly selected to receive an interventionand then
compared with a similar population receiving a placebo or the standard of care. Neither population truly reflected the
real world of diverse patients who will eventually receive the therapy, if it is approved.
The decoding of the human genome and rapid advances in molecular biology were also making it clear that patients and
their diseases that we had once considered homogenoussuch as cancer and diabeteswere vastly different by virtue
of a constellation of gene or metabolic dysfunctions that modern science could now identify. The historical conundrum
of why drugs would work for some, but not for others, could not be explained by these historically "gold standard"
clinical trials, but these "responders" could now be prospectively identified by specific "biomarkers." Traditional trials
could notand were not designed totake into account rapid advances in our understanding of the mechanistic
causes of disease, rather than just clinical symptoms. In short, it is time to rethink what our gold standard should be.
In his new paper, Peter Huber tells the story of this biomedical revolution, and he maps out a path for guiding the
agency into a new era of precision medicine that holds unprecedented benefits for patients and the American economy.
By embracing new tools and technologies, the FDA can help unleash a new golden age of biomedical innovation.
Huber carefully explains why the FDA can no longer delay change and simply cling to the traditional way of evaluating the safety and efficacy of new medicines. There is talented leadership and staff at the FDA; butlike many large
organizations committed to sustaining their core products, mission, and internal cultureit can be overtaken by rapid
changes in market structures and technology. IBM, AT&T, and the "Big Three" automakers are only a few examples
of once-successful firms that have had to adapt themselves to new technologies and new customer expectationsor
Federal agencies are no less immune to disruptive technologies than private firms, and Huber details how rapid advances in molecular biology and quantum leaps in information technology have progressed far beyond the 70-year-old
double-blind, placebo-controlled trials that the agency (for the most part) still uses to evaluate new medicines.
Huber builds a powerful, well-argued case for regulators and researchers to "remove their blindfolds" and fully embrace
the latest advances in molecular biology, adaptive clinical trial designs (which can shift patients toward more effective
treatments as evidence accumulates), and powerful new statistical tools to identify and validate the biomarkers that
will allow companies to match promising new drug candidates with the patients who are most likely to benefit from
them and least likely to suffer serious adverse effects. Along the way, the FDA and the drug companies will also weed
out unpromising or dangerous drugs much more quickly (and less expensively) than they can by using traditional
clinical trial designs.
Suffice it to say that the FDA's current protocols are designed to gauge a drug's average effects for both safety and efficacy in clinical trials with "representative" populations. Good drugs are licensed, and bad drugs are relegated to the
scrap heap, based on what is essentially a clinical popularity contest. The problem is that human biological diversity is
much broader than regulators and researchers had assumed for much of the twentieth century. Matching the right drug
to the right patient requires knowing just as much about the biochemistry of the patient as we do about the medicine.
For instance, cancer isn't a single disease; uncontrolled cell growth is driven by hundreds of different defects in cell
metabolism and growth that vary widely among patients. Other common diseases, like diabetes, share common clinical
effects (such as low blood sugar) but probably have myriad different biochemical causes. Patients are just as likely to
vary in their susceptibility to serious side effects. Unless you test the right drug together with the right patients, you
are often likely to draw the wrong conclusions about both drug safety and efficacy.
This mismatch between science and regulation has critical implications for patient health. The FDA's one-size-fits-all
regulatory pathway has become breathtakingly expensive and time-consuming: it takes well over $1 billion and a
decade to develop a single FDA-approved medicine, according to recent estimates. These enormous sunk costs mean
that some diseases will never be cured because it costs too much to develop drugs for them.
Some drugs that might work well in small populations are also abandoned because they work poorly or produce toxic
side effects in large, untargeted populations. And the process of developing drugs for complex indications, such as
neurological diseases, is so slow and unwieldy that it will take decades for researchers to match the right treatments
for the right subgroups of patients.
Thalidomide, a case study that Huber discusses in depth, is the poster child for the tremendous complexity of molecular biology. Prescribed to pregnant women at a key juncture in fetal development, thalidomide produced horrific
birth defectsforcing the drug's withdrawal in 1962. But it was returned to the market decades later as evidence
accumulated that it could be used to effectively to treat leprosy, AIDS, and several types of cancer.
Huber's key argument is that the best time to begin generating information about how a given drug interacts with
a given patient's biochemistry is at the "front end," in small, biomarker-driven clinical trials that can then be used
to license the drug for very specific uses in targeted patient populations. These studies will be ongoing and iterative
and will both inform and be informed by information gleaned from large post-market databases of electronic health
records that combine phenotypic and genotypic information.
Companies such as IBM are already operating such databases, offering powerful tools for combating HIV (the EuResist database) and, in the not-too-distant future, cancer. Google and Amazon update their databases thousands of
times every day based on precise algorithms that improve their ability to predict who is likely to be looking for what,
and when, and why. Similar algorithms and computing platforms can be linked with molecular diagnostics to help
researchers, regulators, and companies match new drugs with the molecular profiles of patients who are likely to
benefit from themor, conversely, who should avoid them.
It will not be simple or easy for the FDA to embrace these transformative tools. First, the FDA should take stock of
how best to deploy its staff, expertise, and budget to respond to ongoing changes in basic science and product development. The agency will need to reform the clinical trial process and engage additional research partners to help
validate new biomarkers, especially by collaborating with other federal agencies, such as the National Institutes of
Health (which funds critical research in molecular biology), and with academic medical centers that can bring together
the large distributed groups of patients, researchers, and hospitals that will be needed to run new, molecularly guided
adaptive clinical trial designs.
To its credit, the FDA knows this and has already taken initial steps to embrace several of these tools. But the FDA's
advance in embracing new models of regulation has been glacially slow and largely limited to just a few diseases such
as cancer, HIV/AIDS, and some orphan drugs.
For precision medicine to flourish, Congress must explicitly empower the agency to embrace new tools, delegate other
authorities to the NIH and/or patient-led organizations, and create a legal framework that protects companies from
lawsuits to encourage the intensive data mining that will be required to evaluate medicines effectively in the postmarket setting. Last but not least, Congress will also have to create a mechanism for holding the agency accountable
for producing the desired outcomes.
The FDA, like any other large, bureaucratic organization, will find it difficult to change and to embrace new models
of "doing business" until its "customer" (Congress and society at large) has clearly signaled that the "product" that
the agency is delivering is no longer acceptable. Huber has done the agency a tremendous favor by drawing our attention to the need for such change in the agency's clinical trial protocolsand in a way that allows Congress and
the agency to chart a clear path toward modernizing the agency's role and functions.
The process of creating a truly precise framework for molecular medicine will be the work of years, not a few months.
But, if done as Huber suggests, it can become a self-advancing, self-correcting process that will put the patient at
the center of decisions about how and when to deploy or remove new medicines in the battle against complex, lifethreatening ailments. Medicine has always aspired to offer patients "personalized" treatments. Huber shows how it
can become both personal and precise.
Previous FDA modernizations effortsincluding Accelerated Approval, the Orphan Drug Act, and the Prescription
Drug User Fee Acthave saved countless lives and helped establish the U.S.-based biopharmaceutical industry as the
world's most innovative source of new medicines.
For the first time, we can see how medicine can attack the molecular roots of complex chronic diseases, rather than
simply ameliorate them. For the millions of patients at risk of developing devastating ailments such as Alzheimer's, science holds the hope of fuller, more productive lives. For America, it means trillions of dollars in lower health-care costs
spent treating chronic disease, better-paying jobs in a flourishing life-sciences industry, and a reenergized economy as
life sciences transform every sector, from agriculture to energy and even defense.
But for all these technologies to reach fruition, we need the Food and Drug Administrationwhich has done so much
for so long to keep our food supply safe and evaluate new medicinesto adjust and adapt to new challenges. Other
stakeholders, from patients' groups to companies, also have their own critical roles to play in advancing medical progress.
The goal of the Manhattan Institute's Project FDA is to encourage an ecosystem for U.S. medical innovation where
many partners work seamlessly together to advance truly disruptive medical innovations. With Huber's paper, and the
painstaking work of the many experts and scientists that he catalogs, this vision is one step closer to becoming reality.
About the Author
PETER HUBER is a senior fellow at the Manhattan Institute writing on the issues of drug development, energy,
technology, and the law. He is the author of the forthcoming book, The Cure in the Code: How 20th Century Law Is
Undermining 21st Century Medicine (Basic Books, Fall 2013).
Huber most recently wrote The Bottomless Well, coauthored with Mark Mills, which Bill Gates said "is the only book
I've ever seen that really explains energy, its history and what it will be like going forward." Huber's previous book,
Hard Green: Saving the Environment from the Environmentalists (Basic Books, 2000), which was called "the richest
contribution ever made to the greening of the political mind" by William F. Buckley, Jr., set out a new conservative
manifesto on the environment which advocates a return to conservation and environmental policy based on sound
science and market economics. In 1997 he authored two books, Law and Disorder in Cyberspace: Abolish the FCC
and Let Common Law Rule the Telecosm (Oxford University Press), which is an examination of telecommunications
policy, and (with the University of Pennsylvania's Kenneth Foster) Judging Science, Scientific Knowledge and the Federal
Courts (MIT Press). Previous books include Orwell's Revenge: The 1984 Palimpsest, (Free Press, 1994), Galileo's Revenge:
Junk Science in the Courtroom (Perseus Book Group, 1991); and Liability: The Legal Revolution and its Consequences
(Basic Books, 1988).
Huber has also published articles in scholarly journals such as the Harvard Law Review and the Yale Law Journal, as well
as many other publications, including Science, The Wall Street Journal, Reason, Regulation, and National Review. He has
appeared on numerous television and radio programs, including Face the Nation and The NewsHour with Jim Lehrer.
Before joining the Manhattan Institute, Huber served as an assistant and later associate professor at MIT for six years.
He clerked on the D.C. Circuit Court of Appeals for Judge Ruth Bader Ginsburg, and then on the U.S. Supreme Court
for Justice Sandra Day O'Connor. Huber is also a partner at the Washington, D.C. law firm of Kellogg, Huber, Hansen,
Todd, Evans & Figel.
Huber earned a doctorate in mechanical engineering from MIT and a law degree from Harvard University.
The author is grateful for the excellent research and editorial assistance received from Matthew Hennessey, Yevgeniy
Feyman, Kaitlin Keegan, and Janice Scheindlin.
Part 1: The Fading Myth of the FDA's "Gold Standard"
No drug may be licensed until the FDA is convinced
that it will perform safely and effectively in future
patients. All such predictions hinge, of course,
on both the drug's chemistry and the patient's;
pharmacology is not a science of one hand clapping. So the
FDA does not license drugsit licenses specified drug-patient
combinations: the license's implicit promise of future safety and
efficacy applies only "under the conditions of use prescribed,
recommended, or suggested in the labeling thereof."
The FDA has plenary authority to police how that science is
developed. The agency has played a large and valuable role in
developing protocols for laboratory tests, particularly for drug
But the 1962 amendments to the federal drug law
demanded, above all, "substantial" evidence, derived from "adequate
and well-controlled" clinical trials. Fifty years ago, the FDA started
drafting elaborate rules and protocols that spell out how Washington
oversees the development of drug science. If, in the FDA's view,
the science that is developed in this way predicts future benefits
for certain patients, the FDA licenses the drug accompanied by a
label that delineates who they are. These protocols, it is often said,
establish the "gold standard" for drug science.
To this day, the drug-licensing process thus remains anchored
in protocols developed at a time when pharmacology aspired,
but mostly failed, to target molecules that no one could see, and to control biochemical processes that no one could
unravel. Today's biochemists and doctors, however,
have the power to diagnose and treat with molecular
precision from the bottom up.
Statistical analysis of the clinical symptoms of crowds
is what medical science uses to pluck the most
primitive form of cause-and-effect understanding
out of the depths of ignorance. In Victorian London,
it helped ferret out the cause of the city's periodic
cholera epidemics. A doctor, John Snow, made the
right connection in 1853: after a particularly nasty
outbreak of cholera in Soho, he saved an unknown
number of lives by persuading parish authorities to
remove the handle from the neighborhood's Broad
Street water pump. Germ science and the isolation
of the cholera bacterium still lay three decades in
The vaccines and antibiotics that followed worked
wonders, but they owed their success to one brilliant
trickvaccines use biochemical fragments of the
enemy microbe to fire up the human immune
systemand lots of luck. Many of the early
antibiotics were discovered by searching for microbes
that had developed these molecules to kill their
rivals. The first synthetic antibiotics were developed
by chemists who happened to notice that some
industrial dyes preferentially stained certain types of
microbes; all the rest was intuition and guesswork.
Insulin and estrogen, two pioneering drugs that
tinkered directly with human chemistry, had likewise
been designed by nature first. Most of the small
number of other people-tuning drugs that emerged
before 1962 were designed mainly by hunch, trial,
and errormostly error.
When prescribing the drugs of that era, doctors
were guided almost entirely by clinical symptoms.
Routine lab tests tracked only a few dozen infectious
germs and a limited number of simple molecular
"biomarkers"blood-sugar levels, for examplethat
had clear, direct links to known diseases. Doctors
knew little more about the molecular processes that
made drugs perform well or badly.
The prevailing pharmacological model pictured
magic-bullet molecules aimed at simple progenitors
of disease. In this view, simple, clear lines separated
disease and health. A discrete cause produced a
discrete set of clinical symptomsfluxes, fevers,
lesions, or lumpsthat uniquely defined the disease.
The drug's story was the disease's, told in reverse. A
single, straight line linked the drug to the root cause
of the disease and the patient's return to health. A new
drug did not need to be tested for long, nor did trials
have to involve many patients. Medical science had
scarcely begun to glimpse how one patient's chemistry
can differ from another's, and it had little reason to
suppose that such differences mattered much.
When a single visible cause is quickly and tightly
connected to a visible effect that is easily tracked,
simple statistical analyses are quite good at making
the right connections. They correctly link a cluster
of symptoms called "cholera" to a polluted well, or
a bacterium, and the prevention or cure of cholera
to the removal of the handle on the pump, or to the
administration of a vaccine or antibiotic.
In implementing the 1962 federal drug law, the
FDA accepted that view of things and expanded and
standardized what Washington had begun doing in
1938, when the U.S. Public Health Service conducted
a randomized trial of the pertussis vaccine in Norfolk,
The FDA would scrutinize what the drug
delivers up herewhere patients ache and worry and
clinicians diagnose and treatnot down there, where
tetracycline (we now know) latches on to a specific
receptor on the surface of the cholera bacterium.
A good drug had to have the same effect in a large
majority of patients suffering from the same, clinically
defined disease because medical science lacked a way
to distinguish patients whom the drug would help
from those whom it wouldn't. The assumptionthe
blind hope, reallywas that the FDA knew how to
decide how many patients had to be tested, and for
how long, to arrive at a robust statistical correlation
and a label that would allow the drug to be prescribed
safely and effectively to future patients.
The biggest worry was that wishful thinking by
doctors or patients"selection bias"might culminate in the licensing of drugs that did more
harm than goodhence the randomized, "doubleblind" clinical trials. To this day, Washington almost
always requires and relies on the same kind of
evidencestatistical comparisons of the health of
two crowdsto decide whether a drug should be
licensed. Typically, one crowd gets the real thing, the
other a placebo; when a reasonably good treatment
is already available, the comparison may instead be
drug versus drug. Doctors track clinical symptoms.
The newly healthy and the still sick, the living and
the dead, vote the drug up or down.
These trial protocols, in short, are structured to
regulate ignorance, not the systematic acquisition of
reliable knowledge. They assume that the molecular
science is impossibly difficult; the best we can do
is search for strong statistical correlations linking a
drug to its clinical effects. They do indeed set the
gold standardfor dealing with blind ignorance. But
when the cause-and-effect connections are complex,
writing a good trial script requires information that
only the trial itself can reveal.
If we were all exact biochemical clones of one
another, testing a new drug in just two patients would
sufficeone receiving the drug, the other a placebo.
To expose how we differ in ways that affect a drug's
clinical performance, many more patients have to
be tested, for a long time. But just how many, and
for how long, depends on how many patient-side
biochemical factors can affect the drug's performance
and how evenly or otherwise those factors are
distributed among patients who will end up using
the drugbiochemical facts that only extensive tests
are likely to reveal.
Statisticians call this the "reference class problem,"
or the problem of "external validity." The relentless
growth of FDA-mandated clinical trials since 1962
reflects the emperor's own dawning realization that
his wardrobe was furnished by Victoria's Secret.
Washington began losing confidence in quick, small
clinical trials as science began to expose the slow,
complex diversity of human chemistry. In the last
decade, our newfound power to scrutinize everything
down at the molecular level has exposed vastly more biochemical diversity and complexity. And any
molecular difference between two bodies might be
the difference that allows the same drug to perform
well in one body and badly in another.
The FDA's conventional trial protocols deliberately
lose all such details in the crowd, collapse biochemically
complex phenomena into misleadingly simple, onedimensional, yes/no verdicts, and will often reject
good drugs that many patients need. They test too
many of the wrong patients, and they develop the
selection criteria for prescribing the drug to the
right patients much too slowly, if at all. Today's
gold-standard molecular medicine is anchored in
biochemical facts that the FDA takes pains to keep
out of the sight of doctors conducting the front-end
clinical trials, and it uses reams of empirical data
that no drug company could collect and disseminate
without risking prosecution for the promotion of
Science learns how to make consistently reliable
predictions only by mastering the fundamental
mechanics of cause and effect. Drugs are molecules
that interact with other molecules in ways determined
by mechanistic biochemical rules. The science is
complex because drugs operate in the extremely
complex biochemical environments of human
bodies. But the rock-solid science that we are
seeking is, ultimately, chemistryprecise, logical,
Drug designers have understood this for decades.
The modern tools of "structure-based" drug design
were first used successfully in the 1970s. The details
are hard, but the idea is simple: hold a molecular
blueprint of the disease up to a mirror, and you
will see in the reflection molecular blueprints for
one or more antidotes. With a promising molecular
target in hand, drug designers now rely heavily
on raw computing power to analyze the structure
of the target and design mirror-image molecules.
Alternatively, designers enlist the immune system of
a genetically engineered laboratory animal to design
Thalidomide, the notorious sedative that caused
thousands of birth defects in the countries where it
was licensed and, though never licensed in the United
States, spurred the enactment of the 1962 drug-law
amendments, would end up bridging the old era of
pharmacology and the new. In 1964, shortly after it
had become the most reviled drug in history, Jacob
Sheskin, an Israeli physician, admitted to his ward
a frantic woman suffering from the excruciatingly
painful skin lesions and mouth ulcers that often
develop in the later stages of leprosy.
In an attempt
to calm her down, he prescribed some left-over
thalidomide that he happened to find on his shelf.
Overnight, to his astonishment, her skin lesions
and mouth ulcers were dramatically reduced. Dr.
Sheskin's colleagues were skeptical; they couldn't
imagine how a sedative could help treat a bacterial
infection. To convince himself, Dr. Sheskin went
to Venezuela, where leprosy was common, and
conducted successful clinical trials. But he still had no
clue as to why the drug worked, and medical science
didn't have the tools to find out.
By the late 1980s, it did. Thalidomide doesn't attack
the leprosy bacterium; it alleviates symptoms that
develop when the infection sends the human immune
system into overdrive. Researchers at Rockefeller
University tracked the connection to a human
protein called tumor necrosis factor, one of three
intercellular signaling molecules (cytokines) that
thalidomide suppresses. TNF plays important roles
in the communication system that the body uses to
fight both germs and cancerous human cells. But
when engaged in a losing battle, the body sometimes
produces too much TNF, which can then cause
painful lumps and lesions on the skin. TNF overloads
can also cause wasting syndrome, a common
condition in the late stages of AIDS. Doctors treating
AIDS patients grasped the implications and began
prescribing thalidomide to treat ulcers and weight
loss. Other doctors were soon investigating the
drug's effects on a variety of skin disorders and other
inflammatory conditions, as well as autoimmune
diseases such as lupus and rheumatoid arthritis.
Meanwhile, other drug designers had begun designing
precisely targeted drugs from scratch. In the 1970s, three researchers at Squibb set out to tame a protease
enzyme that snaps proteins apart in the process of
manufacturing a hormone that helps control our
blood pressure. In 1981, the FDA approved captopril,
the first of the now widely used ACE inhibitors.
Gleevec was another early triumph of structure-based
design. The first solid molecular link between a
cancerchronic myelogenous leukemia (CML)and
a flawed human gene had been discovered in 1960.
The culprit is a corrupted version of a gene that codes
for one of our many kinase enzymes. Scientists at the
company now called Novartis developed computer
models of the enzyme, used them to design various
structures that might latch on exclusively to the CMLkinase binding pocket, synthesized them, tested the
most promising ones, and got to Gleevec. It worked
astonishingly well. Medicine now has "tools to probe
the molecular anatomy of tumor cells in search
of cancer-causing proteins," the National Cancer
Institute exulted when the license was issued in 2001.
Gleevec is "proof that molecular targeting works."
A new disease called AIDS surfaced a month after
the FDA licensed captopril. Soon after HIV was
isolated, biochemists found the gene for a protease
enzyme that the virus uses to assemble its protein
shell, manufactured the enzyme itself, worked out
its three-dimensional structure, and identified a
key point of vulnerability. Then they designed the
first HIV-protease inhibitor (saquinavir), which
completed a lightning-fast trip through the FDA in
1995. Other drugs targeting other aspects of HIV's
chemistry soon followed. As the National Academy
of Sciences would observe in 2000, the remarkably
fast development of HIV-protease inhibitors had a
"revolutionary effect on modern drug design."
The formerly blind doctors now have keen molecular
vision, too. In early 2012, scientists at Stanford
University described how they had spent the previous
two years tracking DNA, RNA, cell proteins,
antibodies, metabolites, and molecular signals
some 40,000 biomarkers that yielded billions of
data pointsin the body of geneticist Michael Snyder, the team's senior member, to create the
first-ever "integrative Personal ‘Omics' Profile": an
Though Snyder had no family history or
conventional risk factors, the data revealed a genetic
predisposition to type 2 diabetes. Later in the study,
the data tracked the onset of the disease in what has
been described as "the first eyewitness accountviewed on a molecular levelof the birth of a disease
that affects millions of Americans." Then the iPOP
team watched the diabetes markers revert to their
normal state in response to treatment.
The technologies for designing and mass-producing
the diagnostic biochemicals that power the iPOP,
along with many less ambitious molecule sniffers
already on the market, have been mastered. Arrayed
on chip-size, micro-electro-mechanical laboratories,
sniffers are now becoming complete bio-scanners that
can, for a few dollars a whiff, search a cheek swab or a
drop of blood for hundredsand soon, thousandsof genes, proteins, and other biomarkers. Sensor
chemicals on the surface of plastic or paper cards
mounted in a breathalyzer can detect lung cancer,
tuberculosis, and 100 other biomarkers associated
with other disorders. More complex sequences of
assays are now performed by automated banks of
compact diagnostic machines that can quickly and
cheaply diagnose germs, genes, and biochemical
imbalances of every kindin as many specimens
of bodily fluids or tissues as anyone cares to supply.
The problem for medicine in general, and the FDA
in particular, is that our keen molecular vision
is revealing a great deal of complex biochemical
diversity down there. Most diseases, as defined by
their clinical symptoms, can't be tracked back to a
single, magic-bullet cause. The clinical symptoms
that once defined "the disease" are, in fact, produced
by complex sequences and webs of biochemical cause
and effect, configured in different ways in different
bodies. There are many more biochemically distinct
disorders at the bottom than clinical symptoms
reveal to doctors up at the top. And drug-safety
issues are almost always complex because side effects
can potentially involve any part of the complex
biochemistry of all the different bodies in which a
drug may land.
The question, then, is how we develop the science
that can reliably predict when, if at all, a drug can
be safely and effectively prescribed to some patients
when its performance may be determined by its
interactions with different combinations of molecules
in patients who are suffering from what looks,
superficially, like the same disease.
The Crowd of One
The iPOPing of Michael Snyder began when he
was, by all clinical appearances, perfectly healthy,
and it thus established a biochemical baseline for
his personal clinical health. The early genetic scan,
however, revealed a genetic propensity for high
cholesterol, which he already knew about, and for
diabetes, which came as a surprise. He then watched
his cholesterol level drop sharply when he started
taking a cholesterol drug. After his blood-sugar level
suddenly jumped on day 301 of the tracking, he
watched aspirin, ibuprofen, exercise, and a low-sugar
diet wrestle it back down.
For Michael, the patient, that might have been
enough; but for Professor Snyder, the scientist,
there was more to learn. Analysis of the iPOP data
also revealed how his RNA was activating different
genes at different points of the study. As the patient
recounts, "we generated 2.67 billion individual
reads of the [relevant RNA molecules], which gave
us a degree of analysis that has never been achieved
before…. This enabled us to see some very different
processing and editing behaviors that no one had
suspected. We also have two copies of each of our
genes and we discovered they often behave differently
The researchers suspected a possible link between
a viral infection and Snyder's blood-sugar surge 12
days after its onset, and they zeroed in on about
2,000 genes that were fired up during that period
and another 2,000 that throttled down. They found
among them links involving inflammatory proteins
and antibodies, among them an auto-antibody that
targets a human insulin receptor. The data pointed
to "unexpected relationships and pathways between
viral infection and type 2 diabetes." As one of Snyder's colleagues notes, an analysis of this kind reveals how
a patient's complex control systems interact with his
own chemistry and the environment and thus point
to how medicine "can best target treatment for many
other complex diseases at a truly personal level."
In the iPOP world, it isn't just the medicine that
gets personal; the science does, too. The science that
describes the biochemical structure and dynamics
of the disease and determines the efficacy and safety
of the antidotes still involves a comparison of two
or more patients, but they have the same name. "In
a study like this, you are your own best control,"
says Professor Snyder. "You compare your altered,
or infected, states with the values you see when you
The development of this personal science does,
however, build on a large body of knowledge
previously acquired from other patients, and the
data gleaned from Snyder's body will help refine how
other diabetics use iPOP technology going forward.
As Snyder notes, researchers with access to such data
should be able to converge on a much smaller number
of variables that can predict future blood-sugar health
and track the rise and fall of diabetes and other
diseases. But the picture that will likely emerge from
this bottom-up, data-extravagant science isn't likely to
please the crowd doctors. There are probably "many
reasons why someone is at risk" of type 2 diabetes.
"Diabetes is really hundreds of diabetes, and they
just have one common characteristic, which is a high
level of glucose." Different patients therefore require
different treatments. "Some respond to metformin [a
drug that suppresses glucose production in the liver],
some don't. Some respond to anti-inflammatory
medicine, some don't." And with diabetes, as with
many other diseases, the key to effective prevention
or treatment is "to catch it earlier."
The in-depth study of individual patients is the
starting point for exposing such details. Prescribing
one or more drugs and watching what happens in
some larger group of biochemically similar patients
is the surest way to pin down the causal connections.
High blood sugar is a proximate cause of the clinical
symptoms of diabetes; the best proof is supplied by
treatments that alleviate the symptoms by controlling
the sugar. An inflammatory protein may be an
antecedent cause, disrupting the insulin chemistry
that ordinarily controls the sugar; medicine has
been studying this possibility for some years and
will confirm it by testing anti-inflammatory drugs
in present or prospective diabetics. Using a drug to
verify the link between a biomarker and a clinical
effect is the molecular version of removing the handle
from the pump. Or it may take several drugs, used
simultaneously or sequentially, to establish that the
complex cause that underlies the disorder can be
beaten only with a complex treatment.
There is no practical substitute for this approach;
biochemically complex diseases don't have a single
handle. By studying patients alone, researchers are
rapidly exposing many promising targets that have
clear statistical associations with the many intractable
disorders that we still face. Drug designers have the
tools to create molecules that will control many of
those targets, and lab tests often confirm that the
precisely targeted drugs perform as expected. Yet
the drugs often don't perform as hoped in clinical
trials. It's the patients and their diseases that aren't
cooperating. The magic molecular bullets work one
on one but fail to consistently deliver the hoped-for
clinical effects in FDA-scripted trials.
Given what we now know about the biochemical
complexity and diversity of the environments in
which drugs operate, the unresolved question at the
end of many failed clinical trials is whether it was the
drug that failed or the FDA-approved script. It's all
too easy for a bad script to make a good drug look
awful. The disease, as clinically defined, is, in fact, a
cluster of many distinct diseases: a coalition of nine
biochemical minorities, each with a slightly different
form of the disease, vetoes the drug that would help
the tenth. Or a biochemical majority vetoes the
drug that would help a minority. Or the good drug
or cocktail fails because the disease's biochemistry
changes quickly but at different rates in different
patients, and to remain effective, treatments have
to be changed in tandem; but the clinical trial is set
to continue for some fixed period that doesn't align
with the dynamics of the disease in enough patients
Or side effects in a biochemical minority veto a drug
or cocktail that works well for the majority. Some
cocktail cures that we need may well be composed
of drugs that can't deliver any useful clinical effects
until combined in complex ways. Getting that kind
of medicine through today's FDA would be, for all
practical purposes, impossible.
For a drug to perform well, we need to select the
patients to fit it. Ideally, the in/out selection criteria
will span all the patient-side molecules that will
affect a drug's performance, in all the different
combinations that occur in different patients. But
most of the time, we don't know what all or even
most of those biomarkers areand we won't find
out until we test the drug or drug cocktail in enough
patients to expose them.
The FDA doesn't know, eitherand it doesn't want
biomarkers involved in the licensing process until
it does. That is the biggest obstacle that now stands
between us and the future of molecular medicine.
So we arrive at what the FDA calls "validating"
biomarkers. They aren't manufactured by drug
companies, but that detail aside, we are back to
1962. Once again, the agency is struggling to decide
how to decide when a molecule is likely to affect
clinical health, for better or worse. At issue now are
the patient-side molecules that a candidate drug
will interact with, directly or indirectly, in good
ways or bad.
For over two decades, the FDA has acceptedin principlethe use of biomarkers in drug
licensing. The FDA, NIH, and Congress have been
issuing general and vaguely encouraging biomarker
pronouncements and guidelines, as well as launching
related studies, since the late 1980s. In 1997,
Congress directed the FDA to establish a progam to
accelerate the process.
The FDA, however, has unlimited discretion to
remain dissatisfied with the quality of biomarker
science, and, by and large, it has. The FDA points
out, correctly, that linking what happens down there
to what then happens up here can be tricky, and if we
get the linkage wrong, the FDA may end up licensing
drugs that are useless or worse. So the FDA won't
accept the use of biomarkers until it is convinced that
their use is "reasonably likely"to translate into clinical
benefits. What kind of convincing should it take?
Molecular medicine often determines how strongly
moleculescholesterol, for example, or a highcholesterol geneare linked to clinical problems by
searching for statistical correlations in large databases
of patient records that include both molecular and
clinical data. Strong links then point to promising
drug targets. And they can be found before a clinical
trial of, say, a cholesterol-targeting drug begins.
The same statistical tools can then be used to analyze
links between drug-biomarker combinations and
clinical effects. As it's acquired, this information
can be used to refine prescription protocols in ways
that improve both efficacy and safety. Such studies
have identified genetic biomarkers that can tell
you in advance whether you will respond well or
badly to a growing number of drugs, among them,
anticoagulants, antidepressants, painkillers, and
drugs used to treat heart disease, high blood pressure,
hepatitis, and various cancers.
But here's the catch: most of the drug-biomarker
science can't be developed before human trials begin.
FDA protocols allow very little of it, if any, to be
developed during the front-end licensing trials. So
most of this invaluable predictive molecular science
is developed after a drug has been licensed and
prescribed to many patientsmany of whom, we
discover, should never have used it.
A drug designed to target an estrogen receptor, for
example, should obviously be tested only in the ER+
breast-cancer patients whose tumors present that
target. But if the breast-cancer drug's performance
also depends on how it is metabolized in the patient's
liver, as tamoxifen's does, the existence of a genetic
marker that identifies the patients with the right
liver often won't be discovered until doctors begin
exploring why different ER+ patients respond
differently to the same targeted drug. A drug's
selective efficacy can also depend on a wide range of
other biomarkers that are hard to identify in advance.
Hitting the drug's intended target may not suffice:
complex diseases may respond only to multipronged
attacksin which case, the selection criteria for
testing today's drug ought to include the selection
of other drugs needed to complete the synergistic
cocktail. Which means that it may be impossible to
test the drug in the right biochemical environment
until complementary drugs are availableand the
same may be true for each of those other drugs.
Before a trial begins, it is even more difficult to specify
selection criteria for excluding patients in whom the
drug will cause serious side effects. The FDA itself
helped launch a nonprofit consortium of ten drug
companies and academic institutions to compile a
global database of genetic links to drug side effects. In
2010, the group released data that help predict when
drugs are likely to cause serious harm to a patient's
liver or trigger a potentially lethal allergic response.
Similar initiatives have exposed genetic variations
that make other drugs ineffective and are developing
genetic-profile standards to guide more accurate
prescriptions. Better late than never; but detecting
these links during front-end licensing trials would
have been very much better.
Many of these links could, and should, be detected
earlier, because another way to develop drug-biomarker
science is to study how individual bodies interact with
drugs at the molecular levelas was done, for example,
in Stanford's iPOP study. The FDA knows that, too,
and it recently began approving a range of what are,
by Washington's standards, innovative "adaptive" trial
protocols that allow that to happen.
But the FDA
remains slow and reluctant to approve such trials and
unwilling to accept the complex analytical tools that
extract reliable scientific knowledge efficiently from
extremely complex data sets.
The problem for the FDA is that robust drugbiomarker science can't be fully developed without
testing drugs in a broad range of biochemically
different patients and carefully studying and
comparing their responses. That means removing
the FDA's cherished blindfolds and replacing
simple trial protocols that analyze comparatively
tiny amounts of data with complex protocols that
analyze torrents of datanot the kind of change
that ever happens quickly in Washington. The
FDA, as currently structured and funded, lacks
the institutional resourcesand perhaps also
the expertiseto keep up with the converging, synergistic power of the biochemical and digital
revolutions. Bureaucratic inertia may also be a
factorthe indiscriminate testing required by the
FDA's current trial protocols is familiar and much
easier to regulate. At stake, unfortunately, is the
entire future of molecular medicine.
The New Gold Standard of Drug Science
Three significant loopholes in the existing drug law
have already shown us how today's gold-standard
molecular medicine evolves when doctors are given
enough latitude to develop much of the drug science
from the bottom up. The first two loopholes can
bring the FDA fairly close to what might be called
"tool-kit licensing": license drugs as molecular scalpels
or sutures in search of the right disease. The third
(and, by far, the largest) loophole allows doctors to
start using drugs in exactly that way as soon as they
are licensed. Ignore the label, and prescribe the drug
to patients whose disorder presents the target that
the drug was designed to control. Use the available
molecular tools simultaneously or sequentially, in a
way that makes mechanistic sense, much as surgeons
use their tools. Work out the connections between
molecular and clinical effects on your own, one
patient at a time.
The 1983 Orphan Drug Act directs the FDA to
be flexible in evaluating evidence that a drug is
effective. The act covers drugs directed at a rare
disease, many of which are caused by a single, rare
genetic disorder, associated with a single protein
that an effective drug can target. This makes it easy
to frame trials that fit the drug to the right patients
from the get-go and track at least one key aspect of
its performance at the molecular level. The act then
gives the FDA broad flexibility to license drugs on
the strength of individual patient case reports, or
even studies conducted in animals or laboratory
glassware. Drugs designed and licensed this way are,
in effect, recognized and used as molecular tool-kit
drugs from the start.
The FDA has designated as orphans about 7,000 rare
conditions that collectively affect some 30 million
Americans, and it has approved about 350 orphan
The orphanage currently fosters about onethird of the FDA's successful graduates and is now
home to "the most rapidly expanding area of drug
development." This is widely viewed as a "roaring
success." Over half of all certified orphans end up
as wards of Big Pharma, and quite a few end up
treating big crowds, when it turns out that the drug's
molecular target propels other diseases as well.
Then there is the FDA's own accelerated-approval
rule, promulgated in 1993, codified and somewhat
expanded by Congress in 1997, and endorsed again
in 2012. When the disease is sufficiently serious
and available treatments are inadequate, a new drug
can get to market by demonstrating that it does
indeed produce its intended molecular-scale effectlowering blood-sugar levels, for exampleor, more
generally, that it produces favorable changes inwhat
the FDA calls "surrogate end points" without waiting
for favorable changes in clinical effects that often take
much longer to surface. The front-end trials need not
resolve concerns about how the drug's performance
might be affected by many aspects of biochemical
diversity or about long-term side effects. The
manufacturer must still complete controlled trials
after the drug is conditionally licensed; meanwhile,
the drug can be prescribed by unblinded doctors
who can gather information that clarifies how it
can be used well. The license is rescinded if things
don't pan out.
Finally, the 1962 law left doctors free to prescribe
licensed drugs "off-label." Once a drug is licensed
for one purpose, however narrow, it may legally
be prescribed for any purpose. The doctor and
patient will have some assurance that the drug isn't
immediately toxic, but efficacy is entirely up to them.
The FDA itself brazenly relied on this aspect of the
law to help rush thalidomide into the U.S. market.
After desperate HIV patients began smuggling the
drug into the U.S., the FDA asked drug companies to
consider cashing in on the leprosy epidemic that was
not sweeping across America. Celgene accepted the
invitation, presented leprosy-related clinical data, and
the FDA licensed thalidomide for saleto leprosy
patientsin 1998. Sales boomed, overwhelmingly
to HIV-positive patients.
But for these three major licensing loopholes, millions
of people alive today would have died in the 1990s.
Almost all the early HIV- and AIDS-related drugsthalidomide among themwere designated as
orphans. Most were rushed through the FDA under
the accelerated-approval rule. Many were widely
prescribed off-label. Oncology is the other field in
which the orphanage, accelerated approval, and offlabel prescription have already played a large role.
Between 1992 and 2010, the rule accelerated patient
access to 35 cancer drugs used in 47 new treatments.
For the 26 that had completed conventional followup trials by the end of that period, the median
acceleration time was almost four years.
Together, HIV and some cancers have also gone on
to demonstrate what must replace the binary, yes/
no licensing calls and the preposterously out-of-date
Washington-approved label in the realm of complex
molecular medicine. The new gold standard of
molecular medicine looks nothing like the old.
Engine versus Experts
The first HIV drug to arrive in WashingtonAZThad been developed (as a cancer drug) in the early
1960s but never licensed. Tested against HIV in the
lab two decades later, it looked promising. But HIV
is typically invisible and seemingly harmless for about
five years after the initial infection. What if HIV was
able to mutate its way into an AZT-resistant form
faster than that? Or caused grave side effects that
took four years to surface? AZT couldn't prove that it
was good for patientsat least, not to Washington's
satisfactionany faster than HIV killed them.
So the FDA approved a first AZT trial limited to
HIV patients who had also been infected with a rare
form of fungal pneumonia, one of the most common
epitaph killers when the patient develops full-blown
AIDS. The trial had to be terminated prematurely,
when the dead-patient count reached 19-1 against the
placebodoctors can't ethically keep prescribing a
placebo just to run up the score once it becomes clear
that the drug works. The FDA immediately licensed
AZT for use by HIV-plus-fungus-positive patients.
The fungus restriction was, of course, widely ignored.
It took another three years for the FDA to broaden
AZT's license to cover early-stage treatment. Soon
after, the FDA formalized its accelerated-approval
rule. By early 1998, the rule had expedited the
licensing of some 27 cancer and HIV drugs, along
with 16 drugs for other conditions, several of which
most commonly occur in cancer or AIDS patients.
HIV quickly developed resistance to AZT. In the
interim, however, biochemists had been designing
other HIV drugs. The FDA gunned its licensing
engine, and doctors were soon concocting threedrug cocktails that the virus isn't nimble enough to
evade. About 20 HIV drugs have since been approved
worldwide; they are typically used in about ten fairly
standard cocktails. The efficacy of each cocktail
depends on which strain launched the infection and
how it has evolved inside the patient being treated.
Different forms of the disease predominate in
different countries and track gender, sexual practices,
and other factors.
So, viewed from the treatment perspective, medicine
is now dealing here with about ten different diseases,
each of which is forever poised to mutate into some
new, untreatable form. Treatments work well when
the doctor selects just the right trio of molecular
scalpels from the available drug tool kit. Selecting
them isn't easy because so many different variables
can affect how each possible combination of drugs
performs in different patients. Until quite recently,
trial and error played a large role. The doctor started
with one mix, monitored viral loads and other
biomarkers in the patient's bloodstream, and adjusted
the treatment accordingly.
Today, the process is guided by sophisticated
analytical engines fueled by huge collections of
patient records that include data on HIV genotypes,
treatment histories, and responses, along with
patient age, gender, race, and route of infection;
patient genotypes will undoubtedly be added
sooner or later. As of late 2011, the largest such
engineEurope's EuResist Network, powered by
IBM technologywas using data from 49,000
patients involving 130,000 treatment regimens
associated with 1.2 million records of viral genetic sequences, viral loads, and white blood-cell counts.
As described by its manager, the EuResist database
is "continuously updated with new data in order
to improve the accuracy of the prediction system."
When tested against 25 actual case histories that
weren't already in its database, EuResist beat nine
out of ten international experts in predicting how
well the treatments had performed. The study was
dubbed "Engine versus Experts."
Whatever we may call it up here, there is no single
disease down there, and the disease down there
tomorrow will be different from today's. When the
FDA licensed the individual drugs or the cocktails,
it clearly lacked "substantial evidence" that the drugs
or cocktails would perform effectively when directed
against any substantial fraction of all the variations
in HIV and patient chemistry that they might
encounter in the future. That evidence was acquired
later and is now translated into complex treatment
protocols by experienced doctors or analytical
engines like EuResist. The virus continues to evolve,
so the cocktails will remain safe and effective, in
any meaningful sense of those words, only so long
as we continue to prescribe them as directed by
continuously updated databases. Whatever they
permit or proscribe, the FDA's licenses and labels
will always lag far behind the virus.
Algorithms Replace Labels
Cancer drugs were the other early beneficiaries of
the three main licensing loopholes. But cancer cells
present a far broader range of biochemical complexity,
and the FDA has licensed only a tiny fraction of the
drugs that oncologists need to beat them.
Gleevec got the benefit of both the orphanage and
the accelerated-approval rule. In the Gleevec-versusCML trials launched in 2000, doctors assessed
the drug's performance by tracking two types of
cell counts. The FDA reviewed the results in three
months, conceded that it didn't yet know whether the
drug would keep patients alive longer, and in 2001
licensed Gleevec, anyway.
Almost immediately, oncologists began experimenting
with Gleevec in the treatment of other cancers,
and it soon landed a second license to treat a rare
gastrointestinal cancer. Other orphan designations
followed, and the drug has been widely prescribed
off-label. At its peak, little orphan Gleevec was
raking in $5 billion a year. Gleevec and other
orphan billionaires epitomize the gulf between
the old medicine and the new. The orphanage still
defines disease from the top down. Biochemists and
doctors fit one drug to multiple diseases by finding
a molecular target that they share.
But Gleevec also fails to help about one CML
patient in ten. To put it another way, it is the old
medical taxonomy that has failed: CML, we now
know, is one name for at least two distinct diseases,
each of which can spawn others. About two out of
every five patients on Gleevec benefit at first but
then relapse because their cancer cells mutate into a
Gleevec-resistant form. Most cancers exhibit similar
behaviorthey mutate so frequently that, viewed
from a biochemical perspective, "the cancer" is really
an engine for spawning a limitless number of different
cancers. At major research hospitals, oncologists now
sequence the complete genome of different parts of a
single tumor, in a search for targets that will be used
to guide treatment. The therapies that work often
consist of complex drug cocktails that are tailored
toand repeatedly adjusted to trackthe disease's
Working with the drugs that they do have, oncologists
routinely prescribe cancer drugs and cocktails far
outside the boundaries that were tested in blinded
licensing trials and are set out in the FDA-approved
label. A nonprofit alliance of 21 leading cancer centers
evaluates and publishes information on off-label uses.
Off-label and cocktail therapies sometimes end up
being steered through the rigid, slow, and expensive
trials scripted by the FDA. But as a practical matter,
the vast majority never will be; there are just too many
combinations of drugs, dosages, and patient profiles
to explore and calibrate.
With breast cancer, the bottom-up development of
the drug science has already traveled a good distance
down the same path as HIV. Defined by its clinical
symptoms, breast cancer is a single disease that kills
about 40,000 Americans a year. For oncologists, however, the disease now comes with initialsER,
PR, and HER2, for examplewith a plus or minus
sign attached to each one, depending on whether
the malignant cells have receptors for estrogen,
progesterone, or a human epidermal growth factor.
The medical literature first mentioned the "triple
negative" form in late 2005; it has since been the
subject of hundreds of research papers. Drugs are
prescribed accordingly. Tamoxifen, for example, is
used to block estrogen receptors on the ER+ form of
breast cancer. But estrogen itself is used to treat other
ER- forms, and some studies indicate that estrogen
can be used prophylactically to lower the incidence
of breast cancer in some postmenopausal women.
"The story of estrogen's role in breast cancer," an
article in the Journal of the National Cancer Institute recently observed, "is starting to look like Dr. Jekyll
and Mr. Hyde."
Over a decade after tamoxifen (an estrogen blocker)
was licensed, studies revealed that most of the
effective blockers are produced when tamoxifen is
metabolized in the liver. But significant numbers of
women (the numbers vary significantly across ethnic
lines) have two copies of a gene that produces the
enzyme in a form that can't activate the drug, and
women who have one copy activate much less of it.
Multidrug breast-cancer regimens have to be tailored
to fit all the biochemical variations in tumors, livers,
and other parts of the patient's body that may affect
each drug's performance. The regimens are often
adjusted during the course of treatment, as the
mutating cancer cells develop resistance to some
drugs and susceptibility to others. Prophylactic
drugs may well have to address a quite different
set of biochemical processes. Some women, for
example, are very likely to develop cancer in at least
one breast because they carry flawed versions of a
gene that produces a protein involved in repairing
So much for the magic-bullet disease struck by
a magic-bullet drug. Oncologists now speak of
treatment "algorithms"sets of rules for selecting
and combining the array of available drugs in
a much broader array of cocktails. A consensus
statement released by breast-cancer specialists in
2009 announced that "the previous attempt to
produce a single-risk categorization and a separate
therapy recommendation are no longer considered
appropriate." Three years later, a major international
study of genes that promote or suppress breast
cancer concluded that breast cancer is now an
"umbrella term" for "10 quite distinct diseases."
Biochemists and oncologists now have in hand a
new "molecular map" to guide both treatment and
the development of new drugs. The maps and
algorithms will undoubtedly continue to be refined
for years to come.
Digital engines will almost certainly end up doing
most of the refining. In February 2013, IBM
announced the arrival of a new engineInteractive
Care Insights for Oncology, powered by Watsonthat apparently aims to do for oncology what
EuResist does for HIV. Developed in partnership
with WellPoint and Memorial Sloan-Kettering, and
powered by the supercomputer that won the engineversus-experts challenge on Jeopardy, the engine
was initially drawing on "600,000 pieces of medical
evidence, two million pages of text from 42 medical
journals and clinical trials in the area of oncology
research. Watson has the power to sift through 1.5
million patient records representing decades of cancer
treatment history, such as medical records and patient
outcomes…. Watson continues to learn while on the
job, much like a medical resident, while working
with the WellPoint nurses who originally conducted
The magic bullets beat the easy problems. Most
diseases that medicine is now struggling with will, in
all likelihood, turn out to be much more difficult
more like HIV or breast cancer than cholera. In the
grand biological scheme of things, simple, static,
one-size design is the path to extinction. Survival lies
in complexity: the temporal complexity of viruses
such as HIV, which thrive by mutating very fast; or
the complexity of cancers, which reveal the human
body's capacity to spawn biochemical complexity at
its malignant worst.
Researchers investigating the wild mutability of
cancer cells recently discovered that humans share
with apes a biochemical quirk that introduces "copy
number variations" (CNVs) into our genome.
When our cells replicate, whole paragraphs and pages
of genetic code are sometimes duplicated, written
backward, abridged, or ripped out. CNVs occur in
our reproductive cells, too. Their discovery, in the
words of one geneticist, has lifted the veil "on a whole
new level of genetic diversity."
Each of us also carries thousands of genetic spelling
errors"single nucleotide polymorphisms," or
"snips." A recently published study analyzed snips in
potential "drug target genes" of 14,000 individuals
thought to be particularly susceptible to heart attacks,
strokes, obesity, and other health problems. The
average subject was found to carry about 14,000 snips,
about 12,000 of which were very rare. Each subject
carried an estimated 300 genes with rare variants
(found in less than 0.5 percent of the population)
that would disrupt a protein's functionality in ways
that were likely to undermine health and affect how
the individual might respond to drugs. Most of the
rare variants, as the Science News report on the study
put it, are "practically secret family recipes. Others
reveal the distinct flavor of geographic regions, much
like wines or cheeses."
Biochemists used to assume that when common
disorders ran in families, they were caused by a
common variation in a single gene, or perhaps a small
cluster of genes. But seemingly common disorders,
it now appears, often reflect large numbers of rare,
distinct genetic flaws that cause similar clinical
symptoms. A neural connection that depends on the
interaction of two different proteins, for example, can
be disrupted by a flaw in either of the two underlying
genes.Evidence in favor of the "common-disease rarevariant" hypothesis is rapidly accumulating. Hundreds
of different proteins that control the interfaces between
nerve cells, for example, can apparently play roles in
choreographing Alzheimer's, Parkinson's, epilepsy, and
more than 130 other brain disorders.
The endlessly diverse biochemical ecosystems that
shape our health also determine how a drug performs
in different bodies. To beat most biochemically
complex diseases, we will need a pharmacy stocked
with a concomitantly large and diverse array of
targeted drugs, together with complex protocols for
prescribing complex treatments. We will develop this
cornucopia of drugs and treatment regimens only by
extracting vast amounts of biochemical information
from a very large number of human bodies and
working out how the pieces interact.
The gold-standard drug science that gets these drugs
licensed will be anchored in mechanistic facts about
how specific arrays of other molecules will affect
a drug's clinical effects. Those facts alone aren't
sufficient but are necessary: without them, many
drugs that we need can't perform well, and most
diseases won't be cured. As the EuResist engine and
breast-cancer treatment algorithms illustrate, the best
predictions of how drugs will perform are provided
by a sophisticated and continuously improving mix of
the rock-solid biochemical facts and empirical datawith the mix shifting steadily toward the former.
These engines and algorithms still rely on empirical
databut they do so not to pass final judgment on
any single drug or drug cocktail but to reveal more
complex patterns that can be used to transform
the core, patient-specific biochemical facts into a
personalized prediction of likely clinical effects, good
and bad, that targeted drugs will have in the unique
biochemical environment of an individual body.
Every advance in the biochemical science diminishes
the need for empirical correlations by narrowing the
scope of the biochemical uncertainty. As Dr. Janet
Woodcock, director of the FDA's Center for Drug
Evaluation and Research, put it in 2004, "biomarkers
are the foundation of evidence based medicinewho
should be treated, how and with what…. Outcomes
happen to people, not populations."
Part 2: Precision Medicine and the FDA
In late 2011, a committee convened by the
National Research Council (NRC) at the request
of the National Institutes of Health (NIH)
released a landmark report addressing the need for "a new taxonomy of human diseases based on molecular
biology" and outlining how that taxonomy might be
developed. Redefining diseases on the basis of their
biochemistry, it concludes, will require the analysis
of "biological and other relevant clinical data derived
from large and ethnically diverse populations," in
a dynamic, learn-as-you-go collaboration among
biochemists, clinical specialists, patients, and others.
As it happens, good drug science requires much the
samea drug is just one more molecule added to
the molecular ecosystem that constitutes a body. The
NRC report assumes as much when it recommends
that doctors be allowed to consult the proposed
network to find out how other patients have fared
when already-licensed drugs are prescribed outside
the FDA-approved boundaries. As the NRC report
makes clear, the objective is "precision medicine." A
molecular taxonomy of disease is only the starting
point that leads to precisely targeted drugs and precise
The several elements of precision medicine are
tightly linked. Every time we prescribe a targeted
drug, whether during a licensing trial or thereafter,
we simultaneously test and have the opportunity to
improve our molecular understanding of the disease
that it targets. We confirm that the bacterium is
the cause of the disease, for example, by targeting
it with an antibiotic and watching the patient
recoveror discover that the microbe has mutated
into some new form when the previously effective
drug fails. Every drug is also a potential cause of
other diseases. Tamoxifen suppresses some forms
of breast cancer but raises the risk of some forms
of uterine cancer.
Precision medicine hinges on systematic patient
selectionselection that is based on the drug's
intended target and on unintended targets associated
with side effects and on other drugs that may be
prescribed at the same time. The include/exclude
calls will often have to be repeated on the fly, as the
patient's biochemistry changes (or fails to change)
during the course of treatment. A good clinical trial
of a good drug will develop the information that
future doctors will use to select the patients who have
what it takes to make the drug perform well. The
best protocols will be based on molecular markers
and effects; the whole point, after all, is not to wait
for clinical effects to reveal whether the drug was
But the FDA's current protocols treat patient selection
as a problem that the drug company must solve before
the clinical trial begins or, to a limited extent, when
it is in its very early phases. At best, this means that
the drug is prescribed to many patients whom it fails
to help or even harms during the trials, and to still
more of the wrong patients after it's licensed, until
enough post-licensing data accumulates and reveals
how to prescribe the drug more precisely. At worst,
drugs that some patients desperately need don't get
licensed because the trials include too many of the
wrong patients. Either way, testing a drug in many
of the wrong patients wastes a great deal of time and
money. At some point, the cost of relying on this
very inefficient process to try to solidify the science
up front surpasses how much the drug is likely to
earn years later in the market. We then have an
economically incurable disease.
The PCAST Proposal
Nine months after the NRC issued its report,
President Obama's Council of Advisors on Science and
Technology (PCAST) released a report, "Propelling
Innovation in Drug Discovery, Development, and
Evaluation," which picks up roughly where the NRC
report leaves off. The FDA's standard trial protocols,
the PCAST report notes, "have only a very limited
ability to explore multiple factors … [among them]
individual patient responses to a drug, the effects
of simultaneous multiple treatment interventions,
and the diversity of biomarkers and disease subtypes." These protocols lead to clinical trials that are
"expensive because they often must be extremely large
or long to provide sufficient evidence about efficacy."
The report goes on to outline a proposal for ushering
the FDA into the future of molecular medicine. It
has five main elements:
• The FDA should use its existing acceleratedapproval rule, which "allowed for the development
of pioneering and lifesaving HIV/AIDS and cancer drugs over the past two decades," as the
foundation for reforming the trial protocols used
for all drugs that address an unmet medical need
for a serious or life-threatening illness.
• The molecular science used to select targets and
patients should be anchored in human rather than
cell or animal data, and it can be developed, in
part, during the clinical trials. Before the trials
begin, statistical studies of naturally occurring
genetic variations can provide valuable guidance
on biomarkers to target and track and will grow
increasingly useful as databases that combine
genomic and clinical data grow larger. "Clinical
investigational studies with small numbers of
patients but extensive data gathering" are an
"extremely valuable" alternative.
• The FDA should adopt "modern statistical designs"
to handle the data-intensive trials and explore
multiple causal factors simultaneouslyamong
them, "individual patient responses to a drug,
the effects of simultaneous multiple treatment
interventions, and the diversity of biomarkers and
disease subtypes." These designs are much more
efficient than the FDA's conventional protocols,
and the patients involved receive, on average,
• The FDA should also "expand the scope of
acceptable endpoints" used to grant accelerated
approval. Specifically, the FDA should make wider
use of "intermediate" end pointsindications
that a drug provides "some degree of clinical
benefit to patients" though the benefits "fall
short of the desired, longterm meaningful clinical
outcome from a treatment." The FDA has granted
only 11 such approvals in the past 20 years.
It should "signal to industry that this path for
approval could be used for more types of drugs"
and "specify what kinds of candidates and diseases
• These initiatives should be complemented
by greater rigor in enforcing and fulfilling
requirements that follow up confirmatory studies
that demonstrate the actual efficacy of drugs on
clinical outcome, and the FDA should continue
and possibly expand its use of reporting systems
that track both efficacy and side effects in the
marketplace. The FDA should also consider a
process of incremental licensing that begins with
accelerated approval for use of the drug only in
treating "a specific subpopulation at high risk
from the disease" when larger trials would take
much longer or wouldn't be feasible. The license
could then be broadened to authorize broader use
upon the successful completion of broader trials.
The FDA would "strongly discourage"but not
forbidoff-label use in the interim.
Vigorously implemented, these proposals could go
a long way toward aligning FDA regulation with
the drug development tools and practice of modern
molecular medicine. The accelerated-approval rule
puts the focus on molecular-scale or other lowlevel effects from the start. Protocols that allow the
efficient, integrated development of drug-biomarker
science lead to smaller, less expensive trials because
they lead simultaneously to narrower, safer, and more
effective prescription protocolsor to the conclusion
that the drug has no useful role to play.
Broadening the standard for accelerated approval
to include successful achievement of "intermediate"
end points is a good starting point in addressing the
most fundamental issue of all: What should it take to
meet the federal drug law's demand for "substantial
evidence" in the age of molecular medicine backed by
the pattern-recognition power of digital technology?
The PCAST report addresses that question only
indirectly; it needs to be addressed head-on.
To acquire the tools that medicine needs to deal
successfully with complex diseases that require
complex treatments, we will have to develop
treatment regimens piece by piece, each piece
consisting of a drug and a solid understanding of
how a cluster of biomarkers can affect that drug's
performance. Demanding a front-end demonstration
that each piece will deliver clinical benefits on its own
will only ensure that no treatment for the disease is ever developed. An intermediate end point"some
degree of clinical benefit"suggests that the drug is
interacting in a promising way with a molecular factor
that plays a role in propelling the disease; that is the
best we can expect from any single piece. Even that
requirement may be too demandingused on their
own, the individual constituents of some multidrug
treatments that we need may never be able to deliver
any clinical benefit at all.
When the first HIV protease inhibitor, for example,
showed that it did its job and thus lowered viral
loads, it was a drug that medicine clearly wanted
to have on the shelfeven though it would take
several more years to develop additional drugs and
assemble cocktails that could suppress the virus
almost completely, and for a long time; likewise with
the first estrogen inhibitor for breast-cancer patients.
A drug may have some modest, short-term effect on
a patient's clinical health but have no lasting effect
on the progress of the disease because the virions
and cancer cells are quick to mutate their way past
any single-pronged attack. But if the drug offers a
biochemically new approach to attacking the disease,
it should be licensed, anyway. A successful attack on
a biochemically nimble virus or cancer has to begin
somewhere, and the place to begin is with a targeted
drug that has demonstrated its ability to disrupt some
molecular aspect of the disease's chemistry in a way
that had some promising effect, in some patients, at
some point further along in the biochemical process
that propels the disease.
By allowing broader use of the drug by unblinded
doctors, accelerated approval based on molecular
or modestand perhaps only temporaryclinical
benefits launches the process that allows more doctors
to work out the rest of the biomarker science and
spurs the development of additional drugs. The FDA's
focus shifts from licensing drugs, one by one, to
regulating a process that develops the integrated drugpatient science to arrive at complex, often multidrug,
prescription protocols that can beat biochemically
complex diseases. The FDA already has the authority
to monitor and regulate that follow-up process and
to modify or rescind the initial license if the clinical
benefits don't materialize.
Adaptive trials can be structured in many different
ways; the details are beyond the scope of this paper.
The PCAST report includes a description of the
I-SPY 1 (2002–06) and I-SPY 2 (ongoing as of early
2013) trials of breast-cancer drugs.
In brief, adaptive trials gather a great deal of data,
focusing at first on effects down at the bottomtracking genes, proteins, microbes, and other
biomarkers that control the trajectory of the disease
and cause different patients to respond differently
to the same treatment regimens. As Stanford's iPOP
study demonstrated, and the PCAST report notes,
the data-intensive study of quite small numbers of
patients can substitute for the statistical analyses of
crowds. The protocols evolve as the trial progresses
and the collective understanding of the drug-patient
molecular science improves.
Data-pooling networks and pattern-recognition
computers should be used to systematize the process
from the outset. Informed by a constantly expanding
database of patient experience, the computers will
be engaged in the rigorous process of learning
incrementally from uncertain observations of
complex phenomenaa process, as discussed shortly,
that relies on Bayesian (or similar) statistical methods.
The selection of additional biomarkers for use
in refining the selection of additional patients to
include in the trials can be guided by a mechanistic
biochemical understanding of why the biomarkers
are relevant, along with the types of data already
used by the FDA when licensing orphan drugs.
Laboratory tests, such as those already developed to
mimic various aspects of the human liver or heart
cells, can be used to confirm that a drug can indeed
interact with a biomarker in a way likely to affect
the drug's performance. The in-depth investigation
of the response of individual patients, coupled
with today's sophisticated laboratory tests, can do
much to ensure that the biomarkers that are used to
stack the patient deck in a drug's favor are based on
objective criteria rather than on wishful thinking. The
analytical engines that quantify the strength of links
between drug-biomarker combinations and clinical
effects need not even know whether the effects are medically good or bad; regulators can see to it that
the computers wear the blindfolds.
If the analytical engine is doing its job well, the
adaptive trial will progressively hone in on the
taxonomic aspects of the diseaseif anythat
determine when a drug can perform well down at the
molecular and cellular level, along with biomarkers
that determine when the drug causes unacceptable
side effects. The drug's clinical performance should
steadily improve as treating doctors gain access to
the information that they need to predict when the
drug will fit the patient. If performance does not
improve, either the drug or the engine is failing;
either way, the trial should stop. If performance
does keep improving, the trial can start expanding
againmore clinicians can enlist and treat more of
the right patients.
If the drug's numbers continue to improve, what
next? One possibility is to revert to conventional
blinded trials that use patient-selection criteria
supplied by an engine powered by data collected up
to that point. But with comprehensive tracking and
reporting systems in place, a better alternative is to
allow biochemists, unblinded clinicians, and Bayesian
engines to continue to develop the patient-selecting
biomarkers as long as the drug is used. The FDA
already relies on this process to expose rare, long-term
side effects that don't surface during front-end trials.
Adaptive licensing is a necessary corollary to the
adaptive and open-ended development of the drugpatient science, and formalizing it would also force
Washington to be more candid about the scientific
realities of the drug-licensing process. When, if ever,
a drug company should be able to start selling a drug
for profit, and for what medical purposes, can be
guided and limited by the accuracy of the constantly
evolving databases and analytical engines that link
known molecular effects to desired clinical effects.
But who decidesand how they decidethat the
engines are accurate enough to justify using a drug to
treat a particular patient or disorder are not strictly
scientific questions, and Washington should stop
pretending that they are. As the databases grow and
the analytical engines improve, the authority to
make the final calls should shift progressively from
Washington to professional medical associations
whose members are engaged in the battle against a
disease, on down to front-line doctors and patients.
As others take charge of judging when it is in a
patient's best interest to start tinkering with his own
molecular chemistry, the FDA will be left with a
narrower taskone much more firmly grounded
in solid science. So far as efficacy is concerned,
the FDA will verify the drug's ability to perform a
specific biochemical task in various precisely defined
molecular environments. It will evaluate drugs not as
cures but as potential tools to be picked off the shelf
and used carefully but flexibly, down at the molecular
level, where the surgeon's scalpels and sutures can't
reach. The FDA will retain the power to require that
the drug be prescribed only by certain specialists and
only to patients who are tested and tracked to ensure
that the drug is prescribed in ways consistent with
what is known about its effects. The data gathering
and analytical engines used in adaptive trials can
also be used to systematize the essential and rapidly
expanding sphere of off-label drug prescription.
Safety is (and will forever remain) a trickier issue
than efficacy. All drugs will continue to be screened
at the threshold for toxicity before adaptive trials
begin. Genetic factors that are linked to some fairly
common side effects, such as those linked to the
body's ability to metabolize a drug, have already
been identified, and unblinded trials can search
systematically for others. But other side effects
may always be lurking just over the horizon. Some
balancing between known benefits and unknown
risks will always be required, and the balancing
should itself be an ongoing process, as clinical
experience accumulates. The best that science
and regulation can do for the individual patient is
provide the best possible estimates of how much
confidence can be placed in the personalized
prediction made by a well-designed analytical
engine. If the drug is effective for some purposes
and the engines are doing their job, the drug's overall
performance should steadily improve as we steadily
improve our ability to link both good effects and
bad to patient-specific biomarkers.
Though much of Washington will recoil at the
idea, we should conduct systematic comparative
effectiveness studies of the regulatory process itself.
However the front-end trial is scripted, one of its
purposes is to establish a reliable basis for prescribing
a drug safely and effectively to future patients.
Conventional FDA trials provide one familiar path
to that end, centered on human expertise and one
specific type of statistical investigation. Adaptive trials
and Bayesian analyses of large patient databases offer a
different path to the same end. Those two alternatives
can be tested against each other. As was done in the
"Engine versus Experts" study of EuResist, there are
systematic ways to find out if adaptive trials used
to educate a Bayesian computer can provide better
predictive guidance and provide it sooner than trials
scripted by the FDA, with the details of what was
learned collapsed into FDA-approved labels. Clinical
experience with a drug that is widely prescribed
off-label in ways later vindicated in FDA-approved
clinical trials offers further opportunities to test
how the Bayesian computers measure up against the
empirical and analytical methods of the past.
Enlisting the Right People
What it will take to get drug companies, doctors,
and patients engaged in adaptive trials is a separate
question. Experience with HIV and AIDS drugs
and an early adaptive trial of a Pfizer drug for acute
stroke therapy indicates that patients are considerably
more willing to volunteer for trials in which they are
guaranteed some kind of treatment than for trials in
which they take their chances on the flip of a coin.
Drug companies and doctors, however, may hesitate
to start prescribing new drugs under less tightly
controlled conditions until they are confident that the
data acquired will be analyzed using rigorous statistical
methods, not cherry-picked in an unscientific search
for anecdotes that can be used to condemn a drug at
the FDA or launch lawsuits. The FDA side is easily
addressed. The vaccine compensation law already
provides one reasonably fair and accurate alternative
to the wildly unpredictable tort system.
We also need to find reasonable ways to integrate
the clinical development of drug science with the
sale of drugs for treatment. Manufacturing drugs
(particularly monoclonal antibodies) in small
quantities can be very expensive, and small biotechs
do much of the pioneering work. Developing drugs
to treat complex, slow-moving diseases will require
many years of involvement by many patients.
We should revive rules drafted in the HIV-driven
1980s (and still on the books) that, in appropriate
circumstances, allowed manufacturers to charge
patients for the cost of manufacturing drugs
distributed under investigational licenses. Pay-forperformance schemes, already used in Europe, should
be considered in the United States, too.
To keep private capital engaged in the long-term
pursuit of ever more complex diseases, we will also
need to address intellectual property rights. Much of
the development cost and value of new drugs is now
anchored in the development of databases that link
molecular scale to clinical effects. Current patent and
data-exclusivity rules address the right issuesbut
not broadly enough to span the continuous, dynamic
process of developing the drug-patient science that
That a drug trial must often begin with an imperfect
molecular understanding of a disease's biochemistry
also raises a question of institutional competence.
At present, the FDA passes judgment, implicitly or
explicitly, on two scientifically distinct issues: a drug's
ability to control a molecule down there; and the role
that the same molecule plays in causing clinical effects
up here. The first obviously involves the drug. But the
molecules that precisely define a disease and control
its progress are matters of biological science. The FDA
has quietly emerged as America's chief taxonomist
of health and disease, policing not just drug-disease
interactions but also the disease-defining science and
all the diagnostic and prognostic measurements used
to judge whether a disease is headed north or south
inside the individual patient.
But the NIH, not the FDA, is the agency with
the deep expertise in diseases, and it is therefore
the agency best qualified to decide when specific,
measurable, molecular-scale changes in a patient's
body have some reasonable prospect of playing a role in changing the trajectory of a disease for the
better. The NIH should, at the very least, have
independent authority to identify the biomarkers
that can play such an important role in improving
the quality of drug science and the speed at which
drugs are licensed. NRC report cochair Dr. Susan
Desmond-Hellmann has suggested that biomarker
validation might also come from "other regulators
or the American Heart Association or the American
Sooner or later, the individual doctor and patient
should be added to that list. The accumulation of
molecular and clinical data in public and private
databases will steadily improve medicine's ability
to make an accurate, personal, biomarker-based
prognosis of how the untreated disease is likely to
progress inside the patient. The doctor and patient
will thus gain access to concomitantly accurate
estimates for how much benefit the individual patient
is likely to derive from drugs that modulate molecules
involved in propelling the disease. Together, the
patient and doctor will then be better qualified than
anyone else to decide when it makes sense to start
fighting the clinical future of the disease by using
one or more drugs to address molecular problems
here and now.
Bayesian Statistics and Alternative States
In their basic conception, Bayesian and other
"adaptive" clinical trial protocols aren't radical or
new. It was the advent of digital technology, however,
that made them powerful enough to deal with the
complexity of molecular medicine.
In 1948, a century after John Snow tracked cholera
to the Broad Street pump and removed the handle,
his successors at the NIH began searching for
handles that might be removed to quell America's
rising epidemic of heart disease. They signed up
5,209 residents of the small town of Framingham,
Massachusetts, to participate in a long-term study
that would track their cardiovascular health and an
array of possible risk factors. But the researchers
faced an immediate practical problem: using
conventional statistical methods to analyze every
possible combination of ten high-medium-low risk
factors would have required tracking hundreds of
thousands of people to get a sufficient number of
representatives of each possible combination.
At about the same time, Jerome Cornfield, one of the
NIH's own statisticians, set about rediscovering the
genius of Thomas Bayes and Pierre-Simon Laplace,
the two eighteenth-century fathers of Bayes' theorem.
The one-line Bayes formula provides a systematic way
to calculate "reverse probability": how confidently
we can attribute an observed effect (lung cancer or
a heart attack, for example) to a suspected cause
(cigarettes or high cholesterol). Cornfield's landmark
1951 paper demonstrated how statistical methods
based on that theorem could be used to establish
with high confidence that most lung cancers had been
caused by cigarettes. As Sharon Bertsch McGrayne
recounts in her 2011 book, The Theory That Would
Not Die, Bayes has since emerged as "arguably the
most powerful mechanism ever created for processing
data and knowledge."
Pinning down reverse probabilities with high
confidence is extremely difficult when a single effect
might be the product of many causes that occur in
different combinations or interact in complex ways.
When conventional statistical tools are used, getting
robust answers for all possible combinations of all
relevant factors requires massive amounts of data.
Bayesian statisticians converge on correct answers
much more quickly and efficiently by adding science
to the analysis in a way that progressively narrows
the range of uncertainty that must be addressed by
purely statistical correlations.
Richard Wilson, a Harvard professor of physics,
provides a simple illustration: How believable is a
child's report that "I saw a dog running down Fifth
Avenue"? To answer the question using conventional
"frequentist" tools, one might conduct a study of
children randomly assigned to walk a path with
Fifth Avenue–like pedestrian traffic and distractions
for all, but a dog briefly included only half the time.
Statisticians can tell us how many children would
have to be tested to arrive at a reliable measure of how much we can trust such reports, assuming that
all the factors that they can't control forthe child's
eyesight, veracity, yearning for a puppy of his own,
and so onare randomly distributed among children.
Dog size may be a factor, too; so if FDA statisticians
were in charge, they would want a representative mix
of breeds, from Great Danes to Chihuahuas. Reports
of a lion sighted on Fifth Avenue would require new
trials with the right mix of lions, and likewise with
stegosaurus reports. The FDA could handle them all,
so long as someone was willing to pay for each trial.
A Bayesian, however, would start at a different
point, and arrive at reliable answers much faster. We
are dealing here with a typical reverse probability
problem: we have an observed effectchild
chatterand we are wondering how confidently
we can attribute it to the suspect cause. But we are
talking Fifth Avenue, where dogs are quite common.
Accepting an "I-saw-a-lion" report requires additional
information: Were the Ringling Brothers in town,
and did their truck crash? "I saw a stegosaurus" is
never believable, not even if Steven Spielberg is in
town. The reliability of each report depends not only
on the child but also on knowledge that has nothing
to do with the childknowledge about where lions
roam and dinosaurs don't.
Bayes provides a systematic, rigorous way to insert
that kind of external knowledge into the analysis
when calculating reverse probability. Indeed, the
rise of modern Bayesian analysis began with the
recognition that (in McGrayne's words) "statistics
should be more closely entwined with science than
with mathematics." As one Bayesian analyst put it:
"The limit of [frequentist] approaches just isn't obvious
until you actually have to make some decisions. You
have to be able to ask, ‘What are the alternative states
of nature, and how much do I believe they're true?'
[Frequentists] can't ask that question. Bayesians, on
the other hand, can compare hypotheses."
We already have good numbers for many of the
alternative states of nature on Fifth Avenue, and if
we didn't, we could acquire them without conducting
a long series of double-blind trials. The example
often used in medical textbooks addresses the use of
mammograms in the routine screening of 40-year-old womenthe results have an 80-20-10 accuracy
rate, with the ten being "false positive" reports of a
tumor that isn't there. So these mammogram reports
are, of course, wrong 97 percent of the time29
out of every 30 frightened patients whom they send
scurrying for a biopsy or some other test don't need
it. If you have no idea where that "of course" came
from, and don't believe it, you're in good company:
surveys indicate that many American doctors don't
either. But for Bayesians, this is a simple calculation.1
Mammograms are usually wrong not because
radiologists are incompetent but because breast
cancer is raremore lion than dog. When used in
routine screening for rare diseases, any test that is even
a bit less than perfect will report many more false
positives than true positives because the number of
healthy individuals screened will dwarf the number
who are sick.
Cornfield helped design the Framingham heartdisease study in 1948; a decade later, it still hadn't
lasted long enough, nor was it large enough, to pin
down any risk factor with high confidence. But using
Bayesian analysis, statisticians can refine probabilities
as fast as new evidence is acquired, and in the search
for rare causes, we often acquire information about
suspects that do not matter much more quickly than
information about those that do. In the first decade of
the study, 92 of 1,329 adult males had experienced a
heart attack or serious chest pain. Based on a Bayesian
analysis of the various combinations of risk factors
presented by those who had and hadn't, Cornfield was
able to reframe the study around just four risk factors:
cholesterol, smoking, heart abnormalities, and blood
pressure. The "multiple logistic risk function" that
he developed has been called "one of epidemiology's
Using the limited amounts of data obtained during
the early phases of a trial to narrow the trial's focus is not the same as concluding that we are now highly
confident that we know which risk factors matter;
we are just more confident than we were a while
ago, we can calculate how much more, and, when
the numbers look encouraging enough, we can focus
more of our attention on some factors and less on
others. Later results may either reinforce that early
confidence boost or undermine itthe process is
self-correcting. And it worked well in Framingham.
Using data acquired in the early years of the study
to narrow the range of what remained to be explored
statistically, Cornfield hastened the arrival of
statistically robust correlations that have since helped
save millions of lives.
Statistics Bounded by Molecular Reality
Biologists now rely almost entirely on Bayes or
closely related analytical tools to track complex
diseases, or their absence, back to genetic and
other molecular factors. Figures 2, 3, and 4 (below)
illustrate the probabilistic causal networks that
emerge when modern Bayesians use powerful
computers to analyze large amounts of detailed
molecular and clinical data. As those analyses
demonstrate, Bayesians can begin with many suspect
biomarkers, each one linked to all the othersthe
strength of each link initially based on such things
as biochemical logic, laboratory experiments, and
experience with other diseases and drugs, and
therefore not much better than a guessand then
systematically adjust all the numbers until they
align with all the available data on combinations of
biomarkers that were present or absent in patients
who did or didn't develop the disease. With enough
data to analyze, the biomarkers that play no role
will drop out of the picture. Those most strongly
associated with the disease will, in one typical
graphical representation, migrate toward the center
of the graphic, closest to the point that represents
the disease itself.
The same Bayesian statistical methods can be used
to analyze links between unusually good health and
the underlying causesgenes, for examplethat
keep some heavy smokers cancer-free or that allow
some patients to control HIV on their own. And they
can add lung-cancer drugs and various measures of
a cancer's advance or retreat to their analyses as well.
The FDA itself is often a closetalbeit an ad hoc
and therefore ineptBayesian. Sometimes there is
no other ethical or practical alternative. A separate
team of unblinded doctors typically monitors the
results of clinical trials from a distance and can halt
the trial if the results seem so clearly good or bad
that continuing the trial would be unethical; the
trial of AZT, the first HIV drug, ended that way.
And the investigation of drug side effects that aren't
bad enough to halt a trial invariably involves an ad
hoc Bayesian process to identifythough not to
pin down with high statistical confidencepossible
side effects. The FDA's trial protocols make do, for
the most part, with careful monitoring for adverse
responses during the course of the trial and ad hoc
searches for patterns that suggest that the drug is
to blame. If the drug gets licensed, the label will
warn doctors to look out for such effects, and the
FDA has in place various processes for collecting
reports of other side effects that the drug may have
caused thereafter. But ad hoc Bayes is a far cry
from the real thing, and most of the statistically
robust molecular-based safety science emerges, if
ever, after the drug is licensedat which point
the FDA itself uses Bayesian statistical methods to
analyze the data.
Much of the efficacy side of drug science is now
developed in the same way because the only practical
way to learn how to treat biochemically complex
diseases is to get the molecular tools into the hands
of front-line doctors and let them learn about efficacy
in the same way that they learn about side effects:
by learning as they treat, with eyes wide open. As
we have seen, the flexibility of the Orphan Drug Act
and the accelerated-approval rule allows the FDA
to accept limited or uneven evidence of efficacy
and allows doctors to work out the algorithms and
details in an ad hoc, adaptive process later on; the
off-label loophole allows doctors to launch the
same process without any relevant FDA-approved
evidence of efficacy at all. But here, too, the ad hoc
Bayesian analysis gets started late, if at all, and isn't
The NRC report includes an illustration. Until
recently, clinicians divided lung cancers into two
main types: small-cell and non-small-cell. In 2003
and 2004, the FDA granted accelerated approval to
two drugs (Iressa and Tarceva) on the strength of their
dramatic effects in about one in ten non-small-cell
patients. During the next two years, the drugs were
prescribed to many patients whom they didn't help,
and several follow-up clinical trials seemed to indicate
that the drugs didn't work, after allprobably, we
now know, "because the actual responders represented
too small a proportion of the patients."
Meanwhile, the NRC report continues, the molecular
disassembly of lung cancer had begun its explosive
advance. In 2004, researchers had identified the
specific genetic mutation that activates the epidermal
growth factor (EGF) receptor for the enzyme that
these two drugs inhibit. "This led to the design
of much more effective clinical trials as well as
reduced treatment costs and increased treatment
effectiveness." By conditionally licensing a pair of
one-in-ten drugs, the FDA had launched an adaptive
process that finished the job.
Under current FDA trial protocols, however, such
launches often depend on luck and circular science.
The original clinical trial happens to include enough
of the right patients to persuade the FDA to license
the one-in-ten drug. The fortuitously and justbarely-successful completion of the FDA-approved
trial starts the process that may ultimately supply the
information that ideally would have been used to
select the patients to include in that first trial.
Countless other valuable drugs have almost certainly
been abandoned not because they didn't work
but because medicine hadn't yet found out how
to contract the clinical trial, while Washington's
statisticians insisted on expanding it willy-nilly.
According to a recent consensus report issued by a
coalition of cancer experts drawn from the industry,
academia, and the FDA itself, the agency still usually
relies on "traditional population-based models of
clinical trials … designed to guard against bias of
selection." Such trials "may form the antithesis of
personalized medicine, and accordingly, these trials
expose large numbers of patients to drugs from which
they may not benefit."
Tarceva remains on the U.S. market, but not Iressa.
In early 2005, Iressa became the first cancer drug
to be withdrawn after the required follow-up trials
failed to confirm its worth to the FDA's satisfaction.
In 2011, after further trials failed to establish that
Iressa extends average patient survival and serious
side effects surfaced, the manufacturer halted further
testing in the U.S. The drug had been licensed in
Europe and other countries, subject to further study
on how to identify patients whom it can help. So
Iressa may yet return to the U.S., after doctors and
patients in Europe and elsewhere finish developing
the biomarker science that medicine needs to
prescribe Iressa more precisely.
Iressa survival times and side effects vary widely
among patients. As Bruce Johnson, a researcher at
Boston's Dana-Farber Cancer Institute and a doctor
involved in the original Iressa trials, remarked in
2005: "For us as investigators, at this point, there are
at least 20 different mutations in the EGF receptors
in human lung cancers, and we don't know if the
same drug works as well for every mutation … which
is why we want as many EGFR inhibitor drugs
available as possible for testing." When the FDA
rescinded Iressa's license, it allowed U.S. patients
already benefiting from its use to continue using
it. One such patient who started on Iressa in 2004,
when he had been given two to three months to live,
was still alive eight years later, and walking his dogs
several miles daily.
* * *
A series of frequentist drug trials can eventually yield
the same answers as a single adaptive Bayesian trial:
each separate trial will test a different combination of
suspect causes in a suitably large number of patients,
and when every combination of biomarkers has been
tested, we will be statistically confident that we know
how likely it is that the drug's good or bad effects can
be attributed to each combination. But if the disease
is biochemically complex, a great deal of time and
money will be spent testing suspect causes that don't
play any role.
The doctors conducting FDA-approved blinded trials
have no choice. The patient-selection criteria must be
specified and approved at the outset of the trial. The
FDA's "controlled" trials deliberately exclude controls
that unblinded doctors guided by Bayesian statisticians
might otherwise develop and use to guide the inclusion
of new patients and the exclusion of older ones as the
trial progresses. FDA protocols do allow "subgroup
analysis" of the results at the conclusion of some trials
but only using statistical analyses that are heavily
stacked against approving the drug.
Instead, the FDA's frequentist statistical methods
consign to chance everything that isn't understood and
addressed at the outset and let statistical analysis take it
from there. These methods assume that when a drug
lands inside a human body, anything is possible, but
some things are just less likely than others; assuming
a specific probability-distribution for the limitless
number of unknown drug-patient interactions keeps
the statistics and the trials manageable. But while
new drugs can surprise us in many unanticipated
ways, biochemistry is not a realm in which anything
is possible. How drugs and human bodies interact is
constrained by solid rules of biochemical science, and
we now have the power to identify those constraints,
molecule by molecule, and thus narrow how much we
need to rely on blind statistics.
The alternative states of nature that can affect a drug's
performance are largely defined by all the biomarkers
that can interact with the drug in all the different
combinations that occur in patients who use it. A
clinical trial of a drug that targets a biochemically
complex disease will always begin with an uncertain
and incomplete understanding of the drug-biomarker
science. Bayesian choreographers of clinical trials can
deal with many suspect biomarkers and recalculate
the strength of the links among drug-biomarker
clusters and various measures of the patient's health as
fast as they acquire data about how different patients
respond well or badly. Bayesians can likewise deal
with complex, multidrug regimens from the start
and continue refining them forever.
They can, for example, incorporate what science
has long knownor just found outabout how
different breast-cancer or HIV molecular receptors
affect a drug's performance, or about how fast cancer
cells or HIV mutate at different stages of their assault
on our bodies. The EuResist analytical engine takes
into account the fact that three important classes of
HIV drugs are used to target three different aspects
of HIV's chemistry. Bayesians can start quantifying
the likelihood that a new drug will perform well as
soon as any possibly relevant biochemical information
is acquired. They can begin with evidence acquired
in glassware and test animals. As we shall shortly
see, they can start quantifying the likelihood that
a drug will successfully reach its intended target
without causing side effects by considering the
experience gained and biomarkers validated with
other chemically similar drugs.
None of these sources of data can finish the job.
But they can help launch an efficient, robust, selfcorrecting process that can, as it tracks a drug's effects
across biochemical space and time, steadily improve
our confidence in our ability to select the patients in
whom the drug will perform well. Unlike the FDA,
Bayesians need not select some arbitrary number of
patients to be tested in a trial that will end at some
arbitrary point in time. However simple or complex
the disorder, the accumulation of valuable data
canand shouldcontinue for as long as the drug
In the early stages of a drug trial, the negative
information will be more valuable than the positive.
The negative data points are the ones that allow the
trial to hone in on the molecules that do matter
and then stack the patient deck to increase the
likelihood of a positive outcome in the next patient
tested. As data accumulate, multi-patient analyses
expose the patterns that can be used to understand
the implications of the torrents of data extracted
from a single patient, spot molecular changes that
foreshadow clinical benefits or problems, and guide
This process will systematically converge on the
science that ultimately matters: the complex, datarich, integrated drug-patient science. As the FDA's
own Dr. Janet Woodcock put it in 2004, drug science is, at best, a "progressive reduction of uncertainty"
about effectsor an "increasing level of confidence"
about outcomes that comes with the development
of "multidimensional" databases and "composite"
measurements of outcomes.
* * *
The calculations required to extract cause-and-effect
patterns from large volumes of complex data are so
difficult that an appreciation of the full power of
Bayesian analysis had to await the digital revolution.
The digital wizards, as it happened, needed the power
themselves; their devices and networks are constantly
racing to link what matters to you right now with
just the right puff of data stored somewhere in the
vast digital cloud that surrounds you. Doing that
efficiently is essential, which means anticipating
what you want before you ask for it, which digital
Bayesians do by learning from experience about the
alternative states of nature commonly found in your
microprocessor or brain.
Digital Bayesians can handle the rest of your body, too.
Andy Grove, a founder and, for many years, pioneering
CEO of Intel, has urged the FDA to catch up with the
advent of computing power that "now makes possible
a process that, in its early phases, enlists patients much
more flexibly, to "provide insights into the factors that
determine … how individuals or subgroups respond
to the drug, ... facilitate such comparisons at incredible
speeds, … quickly highlight negative results, … [and]
liberate drugs from the tyranny of the averages that
characterize [FDA-scripted] trial information today."
23andMe, a provider (with Google and Genentech
connections) of genetic sequencing services, recently
announced that it would allow other providers and
software services to develop applications that would
interact with the data entrusted to 23andMe by
its customers. Hundreds soon did. Their interests,
Wired reported, include "integrating genetic data
with electronic health records for studies at major
research centers and … building consumer-health
applications focused on diet, nutrition and sleep."
For individuals, 23andMe's platform will, in the words
of the company's director of engineering, serve as "an
operating system for your genome, a way that you can
authorize what happens with your genome online."
Meanwhile, Washington remains focused on why
ordinary citizens should not be permitted to read their
own biochemical scripts. The FDA is determined
to protect us from reports provided by diagnostic
sniffers or companies like 23andMe that, however
biochemically accurate they may be, might lead to
"unsupported clinical interpretations." But the fastest
way to develop support for clinical interpretations is
to do exactly what 23andMe wants to help lots of
people start doing today: feed a steady stream of
biochemical data into the rapidly expanding digital
cloud of biochemical-clinical data, to be continuously
probed by Bayesian engines, to progressively refine
our understanding of all the biochemical factors that
do or don't affect clinical health.
When 23andMe and others let the rest of us catch
up with the iPOPing professors at Stanford and gain
easy access to the digital engines that can discern
causal patterns in torrents of data, the first thing
each of us should do is establish a baseline profile of
our excellent health and keep it up to date thereafter.
With that information securely stored and pooled
with enough data from other patients, the Bayesian
engines will take it from there. When we suddenly
find ourselves diabetic, they will probably be able
to tell us whether a viral infection, a bad diet, or
some other factor was to blame. When we try a
cure, we will be able to track and at least tentatively
evaluate its efficacy almost immediately, down at
the molecular level. To establish a control baseline
for its crowd science, the FDA directs doctors to
prescribe placebos. But as Stanford professor Snyder
noted, the patient's own healthy, unmedicated history
can provide the best possible control for tracking a
disease to its root cause, starting treatment earlier,
and tracking the performance of a drug prescribed
to cure it.
Why isn't the FDA already on board? It accepts
Bayesian methods when licensing devicessuch
things as lenses, implants, artificial hips, and
diagnostic sniffers. In February 2010, it did finally
issue a "Draft Guidance" for adaptive drug trials,
and, as noted earlier, the FDA has taken a few
small, hesitant steps that point to the possibility of
a fundamental shift in the way it will script clinical trials and pass judgment on drug science. But it has
clearly failed to proceed at the pace that many outside
experts have been advocating for years.
One of the FDA's legitimate technical concerns is,
apparently, the Bayesian "prior." In deciding what
to make of reports from children or radiologists,
or from doctors engaged in a drug trial, Bayesian
analysts require estimates of how often lions or
women with breast cancer stroll down Fifth Avenue,
or how strongly a suspect biomarker affects the
drug's performance. These estimates can affect
how quickly a Bayesian analysis will converge on
a reliable answer, and drug trials must often begin
with speculative estimatestoo speculative, the FDA
worriesof how various biomarkers might affect
a drug's performance. The FDA, however, begins
with initial guesses, tooabout how many patients
must be tested for how long to expose enough
detail about our complex biochemical diversity. The
main difference is that the FDA buries its estimates
in trial protocols and reductionist, unscientific
pronouncements about "safe" and "effective" for the
crowd. There is, of course, only one reality out there,
and if the drug is prescribed to enough patients, the
Bayesian and frequentist analyses of the results will
invariably converge on the same understanding of
how a drug's clinical effects are shaped by the various
biomarker combinations presented by different
patients. Without enough data, they can both make mistakes. Because they are willing and able to deal
with much more data, the Bayesians will correct their
Bayesian analyses look messy, mainly because they
dare to deal forthrightly with complexity. But
Bayesians don't choke when biochemical reality gets
complex, either. "Far better an approximate answer
to the right question," as one Bayesian put it, "than
an exact answer to the wrong question."
The New Science of Molecular Crowds
By combining what we know about drugs with
what we know about bodies, researchers are already
beginning to systematize pharmacological science
from end to end. The predictive power of integrated
drug-patient science is rapidly moving far beyond
anything that pharmacology has previously seen.
In their 2012 paper "Quantifying the Chemical
Beauty of Drugs," one research team describes how
it pooled information about multiple aspects of the
molecular structures of drugs successfully licensed
in the past to arrive at a general algorithm for
predicting the likelihood that a candidate drug will be
successfully absorbed by the human body and won't
have toxic side effects. The team used similar tools
to quantify the beauty of potential binding sites that
a new drug might attempt to target.
To the eyes of
a biochemist, the measure of a drug's beauty is how
likely it is to hook up smoothly with its target.
Another research group combined a catalog of 809
drugs and the 852 side effects known in 2005 with
information about each drug's chemical properties
and molecular targets in the human body.Network
analysis software was then able to predict almost
half of the additional side effects that have emerged
since then. "We were pleasantly surprised," said Ben
Reis, director of the predictive medicine group at
Boston Children's Hospital. Part of the network's
power comes from the inclusion of information not
previously considered in attempts to assess side-effect
risksthe drug's molecular weight and melting
point, for example, and what specific part of the body the drug targets. As Reis notes: "The network encodes
a lot of information from other worlds." The team
is now investigating what types of biochemical data
have the most predictive value and is studying drugdrug interactions. "We're moving from a paradigm
of detectionwhere it takes sick people to know
something is wrongto prediction."
By mining ten years' of clinicians' notes on the
treatment of 4,700 patients at a large psychiatric
hospital, another team uncovered some 800
unexpected pairings of health problems. Adding
gene and protein data relevant to about 100 of
these pairs revealed previously unknown molecular
connections between such conditions as migraines,
hair loss, gluten allergy, and schizophrenia. Yet
another team developed what one member describes
as an opposites-attract dating service for drugs and
diseases. Using public databases that contain
thousands of genomic studies, the digital matchmaker
searches for diseases that push a specific human
biochemical north and drugs that push it south.
Early results suggest that an epilepsy drug might
also be useful in treating certain inflammatory bowel
disorders, while an ulcer drug might also help treat
some forms of lung cancer.
Biochemists call this "repurposing." Many more such
odd couples are certainly out there waiting for us. Life
has been repurposing molecules from the beginning,
so we now find identical or very similar ones scattered
all over the place. And when we find them, we find new commonalities among diseases that point to new
uses for old drugs.
This is the new science of crowds: crowds defined not
by shared clinical symptoms but by shared clusters of
molecules that propel our diseases and interact with
our drugs. Life is intrinsically social. Nucleic acids and
the proteins that they define, along with all the rest
of the chemistry that proteins assemble and control,
flow through the river of life as surely as cholera once
flowed through London's water supply. Mapping
human chemistry exposes the differences between
many forms of breast cancerbut it also exposes the
shared biochemistry of leukemia and gastrointestinal
cancer; and, by way of a drug called pentamidine, the
molecular kinship of sleeping sickness in Gambia and
fungal pneumonia in AIDS patients; and, by way of
thalidomide, insomnia, leprosy, Kaposi's sarcoma (a rare
form of skin cancer) and "wasting syndrome" in AIDS
patients, and at least two bone-marrow and blood cancers
in other patients; and unsightly facial hair and sleeping
sickness again (eflornithine). There are differences
everywhere, but there are also matches, overlaps, and
widely shared forms of molecular strength and weakness.
Going forward, molecular science will link the
performance of more drugs to more genes, proteins,
and other molecular constituents of the sick and
healthy parts of our bodies. The more we learn, the
easier it will get to learn still more, and the cheaper
it will get to translate what we know into powerful
medicine. The accumulation of increasingly detailed
descriptions of how biochemical ecosystems work will
progressively lower the cost of designing new drugs
and determining, quickly and cheaply, how they can
be prescribed safely and effectively.
Databases will expand to include the results of
laboratory experiments on microbes and test
animals and thus expose biochemical webs and
processes that operate in the same way in different
species. No bacterium, rat, monkey, or other
animal is a good model for an entire person,
but some are enough like us in the ways that are
relevant to beating a particular disease, and they
are now bioengineered to incorporate humanand
sometimes the individual patient's—immunesystem or cancerous-tumor genes. A good animal
model for a human disease is often what launches
the ultimately successful search for a drug to beat
the disease in people. Extended across species,
molecular cartography can thus steadily improve
our ability to develop solid human-drug science
outside human bodies.
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