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

Thanks for listening

Anuradha Acharya, CEO

Ocimum Biosolutions anu@ocimumbio.com