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Mr
Pankaj Sharma
CEO and co-founder, LeadInvent Technologies
The author is a biotechnologist and a computer scientist. He worked at
IIT Delhi, where he developed simulation algorithms for biological
systems. He is the CEO and co-founder of LeadInvent Technologies that
was awarded the Asia Pacific Emerging Company of the Year 2011 Award by
BioSpectrum.
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Biotechnology in particular, and life sciences studies in general,
provide an ideal field for ground breaking innovation and discoveries
because much less is known about biology when compared with some of our
other mature scientific pursuits. Biology seems to be far ahead than
our current understanding of it. After all, life has more than three
billion years’ of head start over us. And it is in this attempt of
understanding and interaction with biological systems lies our greatest
promise. One such long-standing quest has been in the area of
understanding diseases at the cellular level and trying to come up with
solutions to fix them.
A disease is nothing but an end point observation of multiple small
events that go wrong inside the body. The only way to deal with such a
situation is to deconstruct a disease into a meaningful scientific
quest. There are multiple approaches that could enable us to do this.
One such approach is popularly known as computational biology that
literally translates to computer simulations of biological systems.
At the core of this discipline is our ability to study protein
molecules and their interaction with small molecules. Computational
biology receives a somewhat mixed reaction. One school of thought
completely ridicules its usage with generalization that computers don’t
give drugs. The other, somewhat curious, attempted the logic between
computer simulation and their correlation to experimental results. The
accuracy of such simulation predictions versus experimental results has
always been debated.
Both disciplines have made considerable progress over the last two
decades. Our experimental techniques have advanced with newer methods:
from PCRs for reading whole genome to micro arrays. Miniaturization and
automation of assays have allowed experimentalists to evaluate millions
of compounds with unmatched ease and accuracy.
The newer advances have also created challenges of their own. Our
genomics’ scientists are able to read terabytes of data within a short
span of time, but are grappling with challenges of assembling such huge
data and interpreting key insights. Techniques using ultra fast X-ray
spectroscopy hold the promise to truly understand different
conformations of protein molecule samples but remains in development.
Our Förster resonance energy transfer (FRET)-based assays are
allowing accurate understanding of interacting molecules but requires
more standardizations than those, which are currently available in the
market.
Computer hardware has consistently caught up with Moor’s law leading to
doubling computational capabilities of chips every 18 months. We are
now at a point where the fastest supercomputer in the world can compute
more than eight quadrillion calculations per second (petaflop/s). This
is serious computing by any dimension and reminds me of how far the
computate capabilities have reached from the early days of 2002 when
our team was building the first dedicated supercomputer for
computational biology at IIT Delhi. The challenge, however, is for
computational biologists to capitalize this opportunity and make use of
such compute power.
One of the key advantages that simulations provide is the ability to
understand our biological system at the atomic level. The world seems
to operate on two fundamental levels. One that is beautifully and
intuitively explained through Newton’s equations, popularly known as
classical mechanics or molecular mechanics, and the other more
fundamental, known as quantum mechanics.
The challenge for computational techniques is to walk the fine line
between these two worlds and balance the approximations within the
details of the study. The most
important step in understanding a problem is formulation of the right
question. This should be followed by setting up simulation protocol
within the framework of available compute resources and simulation
technology capabilities.
The right start in this direction stems from collaboration between the
experimental team and the computational team. At LeadInvent, our
greatest learning curve has been working with experimentalists. In our
observation, when simulation answers start guiding experimental effort,
that’s when the true realization of molecule simulations effort is
felt.
Edge over others
LeadInvent has gone through a transition as a company. The company was
started purely as a bioinformatics company with a business plan to
package its simulation technologies as products and eventually sell
them as standalone application in the drug discovery market. We quickly
realized that the targeted industry’s challenges were far deeper in
demands of drug discovery than unavailability of appropriate
bioinformatics tools. LeadInvent metamorphosed and we started working
more closely with experimental teams on our client side. Framing the
right simulation questions and providing simulation feedback were key
to our engagement with clients. This gave LeadInvent the market edge
over others.
Meanwhile, we collaborated extensively with top notch academic centers,
such as the All India Institute of Medical Sciences, Delhi University,
the Indian Institutes of Technology, Tata Institute of Fundamental
Research and Harvard Medical School for broadening our overall
knowledge and capabilities in experimental science for specific
projects. We generated support from the Department of Biotechnology
under the small business innovation research Initiative programs and
invested in upgrading missing links in technology and ensuring that the
technology was in tune with experimentalist requirements.
The benefit has been felt on both the sides. While our partners have
had access to LeadInvent
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s team, technology and computational platform
as one billable resource, LeadInvent got the opportunity to re-evaluate
how best to apply its simulation technologies in drug discovery
pursuit. The question again appears before us. Is it safe to say that
the recent development in computational biology coupled with newer
computing platform is ushering renewed interest in simulation results?
And, are we close to making an opinion for the selected few who
generalize that computer’s don’t give drugs?
I guess the greater knowledge is in our understanding that
computational biology plays a pivotal role when coupled with
experimental team in pursuit of right questions. There is definitely no
single technology that could alone give rise to drugs. Why than
generalize it for computational biology? After all, more than 55 drug
candidates were recently reported in a review to have benefited from
computational biology approaches. It’s not the technology that is at
the heart of drug discovery, it’s the team. After all it’s not the
rocket that puts a man on the moon, it’s the people.
The challenges for any computational biology company are many. The
underlying technology is definitely a place to start with, followed by
constant revaluation in this era of computing growth and experiment.