Transcript Story of Ricomica From a startup R&D lab to a Giant Agro company

Application of Computation to
Life Science Problems
throughout the Discovery
Development Process
Anuradha Acharya
CEO
Ocimum Biosolutions
The marriage of IT and medical
research may be just what traditional
pharmaceutical companies need to
survive in an increasingly competitive
field.
STEPHANIE OVERBY
CIO Magazine
Road Map
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Drug Discovery at the crossroads
Challenges for the industry
Can I help? says Genomics
Dealing with Complexity of Discovery
data
Creating a framework for success
Case Study of Drucogen
Discovery in India: A status report
Take Home message
Why do Drugs Fail?
Target-Related
Failures
(35%)
Other Failures
(10%)
Toxicity Failures
(10%)
Successful
NCE
(25%)
Metabolism
Failures
(10%)
Phamacokinetic
Failures
(10%)
Drug Discovery at the
Crossroads
• Anxiety in the Industry
• Too few validated targets in the pipeline
• Costs are too high- $897 million per
drug
• Time Taken is too long – 12-15 years
• Mergers and Acquisitions
• Many Discovery companies go bust
• Do we have a solution?
The Discovery process is
painful
• First find a disease or let the disease
find you
• Then find the lead molecules
• Optimise the lead molecules
• Validated Targets
• Trials
• FDA Approvals
• All the above are necessary evils as
it concerns our health
Drug Discovery – From then to now
The semi-original approach to Drug Discovery
– Major advancements in biochemistry and
molecular biology began to produce
changes in the way drugs were discovered.
– “Protein-to-Gene” Concept
• A protein implicated in disease was purified,
monitored by functional assays, cloned,
expressed and re-characterized.
• Drug screening performed against expressed
protein.
– Still, very laborious/time consuming.
Enter The Human Genome
Project
• Paradigm shifts in Drug Discovery resulting from
the HGP and other Genome Projects.
• From Protein->Gene, Its now Gene->Protein
• Target Validation: The new unmet need for Drug
Discovery
• Correlative Approaches to Target Validation
– Comparative Genomics
– Microarrays
– Proteomics
Enter The Human Genome
Project
• Causative Approaches to Target Validation
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Overexpression systems
Knockout mice/Gene Ablation
Chemical Genomics
Antibodies
Antisense
Interference RNA (RNAi)
Can I help? Says Genomics
I Promise
• More drugs
• Faster drugs
• Cheaper drugs
• Better drugs
• Personalized Medicine
Moore’s Law: The Effect
• The first to understand and deploy new
IT capabilities often seize great
competitive advantage.
• As improved uses of technology are
developed, “business” processes
change.
• Ultimately, access to appropriate IT
becomes essential for simple existence.
General Bottlenecks
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Discovery data is of inconsistent quality
Highly dispersed
Little to no standardization
Lack of quality man power
IP
Managing Discovery DataIssues
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Fragmented Databases
Massive amount of data
Different Formats
Public uncontrolled data
Private proprietary data
Where Bioinformatics will take
us
• Sanitization of data
– redundancy removal
– error correction
– collaborative centralized annotation
• Collaboration between in silico and wet
lab approaches
– Validation in the lab
New challenges for drug
discovery
• The industry is now faced with a highly
competitive target-rich environment.
• The key next steps in creating therapeutic
value from the “Genomics Revolution” are to
determine:
– The functions of the 35,000 human genes.
– The role of these genes in human disease.
– Which genes are the most attractive therapeutic
targets.
New challenges for drug
discovery
• Determining which genes are the best
for drug discovery (“Target Validation”)
is perceived as a major rate-limiting
step for drug discovery.
– Improved efficiency
– Increased productivity/reduced failure
– Intellectual property
New challenges for drug
discovery
• These investments by Pharma companies
have resulted in major advancements in new
technologies for the purpose of
validating/invalidating potential drug targets
on a very large scale.
Changing Bottle or Changing
Bottlenecks
Target Discovery
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Target Validation
& Selection
High-Throughput
Screening/
Combinatorial
Chemistry
Genome
Project
Completions
Small Molecule
Drug Discovery
Time
Early Old
Millenium
Late Old
Millenium
New
Millenium
Comparative Genomics
• Analysis of DNA sequence patterns between different
organisms to help define protein function.
– Orthologs
• Provides “1st-Pass” information on the function of a
putative protein based on the existence of conserved
protein sequence motifs.
• Advancements in computer software technologies
(Bioinformatics) has made comparative analysis of
genomes an extremely powerful approach for
functional genomics.
Human Resources Issues
Elbert Branscomb: “You must recognize
that some day you may need as many
computer scientists as biologists in your
labs.”
• Alternatively you might need a strategic
Bioinformatics partner
Case Study
-” Drucogen”
Story of “Drucogen” from a
startup to a Major Pharma
company
Story of a small discovery Lab
• Lets call the drug discovery company
“Drucogen” founded in late eighties.
• Number of employees in 1985 is 10
• Use manual means to record data
obtained
• Data is being collected at a rapid rate
• Some free websites being explored in
late eighties
Expansion phase for Drucogen
• Innovation and more scientists added to
the company
• A new patent filed
• More validation and data required to
enhance current research
• Logs of data are getting hard to
maintain
• Suddenly there is an exponential
increase of data
Data data everywhere, not a
tool to effectively shrink
• Scientists spend 7 valuable days doing
inventory of materials, when they could
be doing important experiments
• Scientists spend hours reading
molecular marker images
• Scientists spend hours on free websites
performing analysis and downloading
information.
Early nineties for Drucogen
• Staff increased to 50, data increases
almost a thousand times
• Need for tools to manage this vast
amount of data
• Well not just manage !
Data analysis for “Drucogen”
• Analyze this humongous amount of data
as well
• Analysis requires lots of computing
power
• And smart and scientifically correct
analysis also
Paradigm Shift for Drucogen
To use [the] flood of knowledge, which will pour
across the computer networks of the world, biologists
not only must become computer literate, but also
change their approach to the problem of
understanding life
Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature,
349:99.
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An Alliance is established with Ocimum- A
solutions provider
Alliance with Ocimum
The “New” Biology:
X-omics for Drucogen
• Traditional reductionistic approach was followed
earlier:
– One gene/protein/reaction at a time.
– Test/validate isolated models at bench.
• New “systems” approach:
– All DNA/RNA/proteins surveyed at once.
– Need to
• Manage data globally (across labs, sites, …)
• Analyze large batches of intermediate
results.
• Provide links to minute details when
required.
Ocimum Biosolutions – An
Introduction
 Ocimum Biosolutions is a life sciences R&D company
in the areas of Bioinformatics and contract bioresearch services, with operations in USA and India.
We are part of the $70 million Ficus Enterprises, the
world's largest producer of Sulpha-Methaoxazole, and
one of the top three producers of Ranitidine in India.
What do they bring to the table?
• Genchek™- The next generation LIMS
oriented Sequence analysis package
• Biotracker™- A 21 CFR Part 11 compliant
LIMS
• OptGene™- A gene optimisation software
• Nutrabase™- Database Archival and
Accessing System for Flavonoids
• Proteowiz™- The next generation protein
analysis software suite
• Genowiz™- A microarray data analysis and
management software package
Ocimum’s Solution
• A strategic informatics roadmap is
chalked out
• Drucogen’s sequencing facility now has
Genchek in place for analysis
• Genchek now does the following
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Primer Design
Trimming to remove vector contamination
Contig Assembly
Multiple Sequence Alignment
Blast Analysis
Gene Finding
SNP Analysis
Chemoinformatics
• Drucogen is now using cheminformatics as a
platform on which to bridge the gap between
chemistry and biology.
• In drug discovery, biology supplies the targets
through Genomics.
• Chemistry provides the compounds to be
screened, and assays are developed using
biology.
• Medicinal chemists take "hints" from those
screens and make more compounds to be tested
by biologists in animals. It is essential to tie
these processes together using informatics.
Chromatogram Viewer
The chromatograms from the sequencing
machines can be analysed
Multiple Sequence Alignment
Multiple sequences can be aligned for further study
Contig Viewer
Contig Viewer helps researchers to study the contigs
Sequence Patterns
Six Frame Analysis
ORF Analysis
ORF Analysis can now be done in a matter of seconds
Gene Finding
Genes of interest can be easily found using the gene finder,
which is built over a neural network algorithm
Three View: Annotated
Sequence
The annotated sequence can now be studied using
Genchek.
Primer Design
Primers can now be easily designed
NCBI Blast Results
To check for similar sequences on NCBI or a Local database
Helical Wheel View
This provides the helical wheel view of the sequence
Making Use of Data Analysis
• Result from PEST Analysis and other
such studies can be compared to data
obtained from assays such as protease
digest, electrophoresis gel and titration
• Such analytical data can now can help
identify a race/pathotype from the
database even in the absence of
sequencing and other such infrastructure
• Related information can also be retrieved
and used for further research and
intervention
Drucogen- New Frontiers
• Microarray and other advanced
instruments purchased
• A very advanced lab created
• Revenues increase 200 fold
• Genowiz now part of “Drucogen”
software suites
Data Distribution Plot - Genowiz
Linkage Clustering - Genowiz
Normalization
Three View
Pathway Editor
Reports
Reports
Clusters
Laboratory Information
Management (LIMS)
• Biotracker is now being used to manage the
lab data as lab size has grown to about 500
people and are spread in various parts of the
globe.
• Several Collaborations in place
• Research going on simultaneously in 20
global locations
• Following screenshots show Biotracker’s
different modules.
Authority levels to define the
degrees of freedom in
BiotrackerTM
Version controlled Protocols
Experiment - Biotracker
Graphs, Images, Documents and
Physical Samples in a
Collaboration
Experiment Run Report Biotracker
Resource Scheduling for better
Asset and Time Management
Audit Trails on guidelines of
USFDA 21 CFR Part 11
Work Flow of the Project
Schedule for a Project
Toxicity
Discovery Data Management
Take Home Message
• Discovery data is massive and complex
and needs to be managed effectively
• Ocimum enters the market to solve
biologist’s problem rather than create
another one
• BioIT tools will be essential for
existence of Drug Discovery/Pharma
companies in future
Thanks for listening
Anuradha Acharya,
CEO
Ocimum Biosolutions
[email protected]