application of computation to life science problems throughout the discovery development process...
TRANSCRIPT
Application of Computation to Life Science Problems throughout the Discovery
Development Process
Anuradha AcharyaCEO
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
• 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?
Other Failures(10%)
Toxicity Failures(10%)
PhamacokineticFailures(10%)Metabolism
Failures(10%)
SuccessfulNCE
(25%)
Target-RelatedFailures(35%)
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– 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
• Discovery data is of inconsistent quality
• Highly dispersed• Little to no standardization• Lack of quality man power• IP
Managing Discovery Data- Issues
• 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
Target Validation& Selection
Small MoleculeDrug Discovery
High-ThroughputScreening/CombinatorialChemistry
GenomeProjectCompletions
?
TimeEarly OldMillenium
Late OldMillenium
NewMillenium
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 bio-research 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
– 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
