The Scientific Method

This package of stories about the future of scientific innovation for Compass magazine challenged me, but I hope all of you find it informative. To see the complete package of stories, go to This is the lead story and it has just been published.



New technology is changing how research gets done

Scientific research isn’t producing as many breakthroughs as it once
did. But new capabilities and processes, from computerized simulations
to big-data mining and personalized medical data monitoring, offer the
potential to reinvent how scientific discovery is done.

In many respects, this is the worst of times in the world’s scientific community – but it could produce the best of times.

The reason for the duality? On one hand, researchers in the
pharmaceutical, biotechnology, materials and other scientific fields are
frustrated that tens of billions of dollars in research and development
are yielding so little.

It takes a major pharmaceutical company eight to 12 years, and from
US$1.2 billion to US$1.8 billion, to bring a new product to market,
according to IDC Health Insights in Framingham, Massachusetts (USA).

The era in which scientists could pick the low-hanging fruit of
conquering widespread diseases is giving way to a more challenging
period of confronting far more difficult diseases such as Alzheimer’s,
and those that affect relatively small populations, including multiple
sclerosis (MS), Lou Gehrig’s degenerative disease and rheumatoid

The complexity that researchers face as they begin to leverage the
human genome, analyze the deepest workings of complex organs such as the
heart and brain, or analyze data from personal fitness devices is
increasing the complexity by orders of magnitude, threatening to
overwhelm scientists with an explosion of data.

Materials research, which touches virtually every product used in the
world, is also wrestling with an explosion of complexity and data as
scientists learn how to analyze materials at the nano, or sub-atomic,
level. “We are facing a lot of the same issues faced by our friends in
the pharma industry,” said John Mauro, research manager of the glass
research group at Corning Incorporated, in Corning, New York (USA).


For every challenge, however, the marriage of technology and scientific
innovation offers an exciting opportunity. In the life sciences, for
example, wearable computing tools are collecting data on not only what
patients can observe, but also what they can’t, and that data is free of
human error and distortion.

As data is getting bigger, analytics are helping scientists to achieve
deeper insight. With increasing precision, scientists can model,
simulate and predict how individual molecules will affect human tissue
or how carbon graphite will endure in a jet aircraft when exposed to
wide variations in air pressure and temperature.

“The biggest game changer in the world is the ability to do
high-performance computing,” said Sanjay Mehta, manager of computational
modeling at Air Products, a manufacturer of industrial gases and
performance materials located in Allentown, Pennsylvania (USA). “It is
all about the ability to simulate thousands and millions of scenarios.
You can’t go into the lab and perform every experiment. It’s just not

This ability to design materials on demand is revolutionary. Corning,
for example, was able to use data-driven modeling, as well as
breakthroughs in predicting and understanding the core properties of
glass, to come up with the third generation of its trademarked Gorilla
Glass, which is on many mobile telephones and devices, to absorb energy
rather than break.

The life sciences have traditionally been slower to commercialize than
many sectors of materials research because of difficulties in linking
the work of drug discovery to drug development, manufacturing, clinical
trials, laboratory testing and, ultimately, review by government

“The whole idea of how you travel from the lab, whether for consumer
products or pharmaceuticals or a medical device, to your consumer or
patient in a more seamlessly manageable way is certainly a theme for
every industry,” said Paul McKenzie, senior vice president for
manufacturing and technical operations of Janssen Pharmaceuticals, the
pharma business of New Jersey (USA)-based Johnson & Johnson


To achieve a new era of dramatically improved innovation, the basic
model of scientific discovery and commercialization is experiencing
radical change.

For decades, the scientific research community – particularly in life
sciences – has been characterized, in simplified terms, by individual
scientists or small groups who labor in isolated silos. Successes have
been shared but failures have been forgotten, dooming others to repeat
them. When a group of scientists does identify a useful compound, the
information is transferred to a development team, which in turn “throws
it over the wall” to manufacturing. Still other teams manage the
clinical trials and seek regulatory approval. All of which generates a
certain measure of repetitive effort and lost intellectual property (IP)
as each group tries to understand the full context of the previous
group’s work.

“You’ve got a dysfunctional model,” argues Bernard Munos, who spent 30
years at Eli Lilly in Indianapolis, Indiana (USA), and is now senior
fellow at FasterCures, a center of the Milken Institute in Santa Monica,

In the new, improved model, scientists enter all their results –
successes and failures alike – in electronic laboratory notebooks
(ELNs). That data can be stored centrally and serve as benchmarks for
researchers in other projects and other geographies – perhaps even
shared with other companies. “Pharma organizations were once very much
vertically integrated,” said Andrew Brosnan, a UK-based pharmaceutical
industry analyst for research firm Ovum. “We’re seeing the unraveling of
that now.”


Today, successful research seldom occurs in a single lab or even a
single organization, but through the collaboration of many scientists
and developers.

“The reality of our world is moving toward more of a virtual integrated
network,” said Janssen’s McKenzie, who is based in Horsham,
Pennsylvania (USA). “I may do steps A, B and C internally on a drug
entity, but D, E and F may be done externally.”

The work of India’s Council for Scientific and Industrial Research
(CSIR) to combat the resurgence of deadly, drug-resistant forms of
tuberculosis (TB) could provide a new model for how discovery should
proceed. CSIR adopted an open-source R&D model and appealed to
scientists around the world to contribute their expertise. Ultimately,
830 scientists volunteered, achieving significant progress in combating
the disease in just four months.

“If you put 830 people on such a challenge, you can succeed and you can
succeed quickly,” Munos said. “That was 300 man-years of work. No
scientist in his right mind would take this as a lifetime project
because he would never see the end of it and never get recognition for


The TB project clicked because it came in response to a humanitarian
emergency. But in the corporate world, creating the systems that will
support broader collaboration outside a company, with commercial and
academic partners or even across competing  organizations, will be more
difficult. Who controls which piece of IP is a critical topic. So is the
need for common standards and protocols so that players who have never
communicated can begin to do so with ease.

Companies in every field of science define their research data
differently, making it impossible to compare results. “We all define the
data differently,” said Gerhard Noelken, Pfizer’s senior director of
technology and innovation based in the UK. “There is not one dictionary
for how you describe how you define an analytical result. If you can
agree on a proper definition, exchanging data and information between
companies and between partners would be so much easier.”

Agreeing on who should set the standards is proving to be a challenge.
Major end-users such as J&J are lobbying technology vendors to agree
on ways to make their IT offerings “platform agnostic.” If vendors
cannot, or will not, the industry risks erecting a new Tower of Babel
and creating more islands of “dark data” that are inaccessible outside a
single organizational silo.

“We have technologies from many, many companies,” Janssen’s McKenzie
said. “But we haven’t found a company yet that has completely got this
vision to really provide an approach where other systems can access each
other and really partner up. My hope is that vendor collaboration
around standards will be a success.”


The starting point for the new vision of research starts in the
laboratory. For many companies, the launch of ELNs was frustrating
because some scientists resented the imposition of a new housekeeping
chore or felt it exposed their ideas before they were ready for
evaluation. In the early going, the software and supporting databases
also were not robust enough. “Most of the early efforts didn’t deliver
the value that people had hoped for,” said Alan S. Louie, research
director of IDC Health Insights.

But increases in raw computing power have helped. For example, at North
America’s Merck & Company (MSD), scientists once had to wait 200
seconds – more than three minutes – before the database would admit it
could not supply an answer. Years later, however, Merck implemented a
database appliance solution that provides answers in about 15 seconds,
Louie said, dramatically improving the user experience.

Robert Wade, a Pfizer research fellow based in Groton, Connecticut
(USA), said that three departments within the pharmaceutical sciences
unit, the company’s pre-commercial development arm, have been linked
using an ELN. The company estimates that it saved US$2 million a year
when the system was used by only 230 people. Today, the ELN system has
900 users.

“Once the data became available and easily sharable, the fears about
using the system went right away,” Wade said. “Everyone demanded access
to the data of other scientists working on similar projects.”


Like Pfizer, the entire life sciences industry understands the promise
of extending these and other IT systems throughout their organizations,
into clinical trials, and ultimately linking them with regulatory

Janssen’s McKenzie, for example, envisions the possibility of using IT
systems to demonstrate to the US Food and Drug Administration (FDA) and
its global counterparts that products comply with regulatory
expectations and are consistent with the materials used in clinical
trials. Today, that system is largely paper-based, making it
time-consuming and labor-intensive.

“We should be able to do that with the hit of a button instead of
having a large amount of paper and reviews and people interacting with
documents,” McKenzie said. “I see a large efficiency to be gained.”


Add it all up and the promise of medicine personalized to specific
sub-groups of a disease – even to specific individuals – begins to
glimmer. The ultimate dream, of course, is that cancer patients could
have their genome decoded and their particular variation of cancer
analyzed to determine which compound, group of compounds or DNA sequence
will kill the disease with minimal side effects to healthy tissue.

In the materials field, scientists see the potential to improve the way
semiconductors are made, cars that can be held together by adhesives
rather than heavy metal welding and paints that can dry faster with
fewer environmental hazards. Despite all the challenges, the lure of
major advances is driving the world’s scientific community to make its
next great leap forward into, potentially, the best of times.  ◆

William J. Holstein is a New York-based business journalist and author. His most recent book is, “The Next American Economy: Blueprint For a Real Recovery.”

by William J. Holstein

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