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B i o p h a r m a c e u t i c a l s L t d. Drug Discovery and Genomics. How the Sequencing of the Human Genome and Related Developments has Impacted Drug Discovery. B i o p h a r m a c e u t i c a l s L t d. “ Fortunes will be won and lost in the genome grab. The race to secure the - PowerPoint PPT Presentation
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Drug Discovery and Genomics
How the Sequencing of the Human Genome and Related Developments
has Impacted Drug Discovery
B i o p h a r m a c e u t i c a l s L t d.
“Fortunes will be won and lost in thegenome grab. The race to secure thesequence patents will be over in fiveYears.”
The World in 2001. The New Economist on pharma-ceuticals.
B i o p h a r m a c e u t i c a l s L t d.
The Promise
The Concern
The Human Genome Project andrelated technologies has generatedthousands of novel potential drug targets.
Validating those targets and their drugability and generating therapeutic options are now the rate limiting steps in drug development.
Bottom Line
B i o p h a r m a c e u t i c a l s L t d.
Topics
• What is Genomics ?
• What is the relationship between genes and disease?
• What are the steps in developing a drug?
• What impact has genomics had on the process of drug development?
B i o p h a r m a c e u t i c a l s L t d.
What is Genomics ?
• Study of information stored in the genome– structural and functional information
• Structural genomics — the sequence– Information is encoded linearly and digitally in four
coding molecules-bases– Three bases = codon = amino acid– A number of codons strung together code for a
gene which codes for a protein
• Functional genomics — what the genes do
B i o p h a r m a c e u t i c a l s L t d.
B i o p h a r m a c e u t i c a l s L t d.
B i o p h a r m a c e u t i c a l s L t d.
Comparative Sequence Sizes (Bases)
(yeast chromosome 3) 350 Thousand
Escherichia coli (bacterium) genome 4.6 Million
Largest yeast chromosome now mapped 5.8 Million
Entire yeast genome 15 Million
Smallest human chromosome (Y) 50 Million
Largest human chromosome (1) 250 Million
Entire human genome 3 Billion
B i o p h a r m a c e u t i c a l s L t d.
Structural Genomics: The Human Genome
• Three billion bases long (=800 Tanachim) • Codes for 30,000 to 80,000 genes• 23 chromosome pairs (24 in chimp)• 97% of genome does not code for
translatable protein products• June 26, 2000: Clinton and Blair announce
rough draft
B i o p h a r m a c e u t i c a l s L t d.
Functional Genomics
• Sequence/structural motifs in proteins ie functional class of protein
• Homology to model organisms/gene knockouts: worms, flies, mice, fish, etc.
• Antisense in cell culture• Microarrays of gene expression• Proteomics• Pharmacogenomics
B i o p h a r m a c e u t i c a l s L t d.
Functional Genomics: Motifs
• Gene families– Super families of related activities such as
dehydrogenases, glucocorticoid receptor-like etc.
– Bioinformatic tools; data mining
B i o p h a r m a c e u t i c a l s L t d.
Functional Genomics: Microarrays of Gene Expression
Normal tissue
cDNA
Diseased
Diseased associated
normal
B i o p h a r m a c e u t i c a l s L t d.
Functional Genomics: Model Organisms
“Genes are just chunks of software that canRun on any system: they use the same codeAnd do the same jobs.”
Matt Ridley in Genome 1999 Perennial
Example: Homeotic genes whichdetermine macro form of animalFly mouse
B i o p h a r m a c e u t i c a l s L t d.
Functional Genomics: Proteomics
Differential display of protein expression indiseased and normal tissue
May be a better approach to target identification thanmicroarrays of gene expression
Not all expressed genes produce proteins
B i o p h a r m a c e u t i c a l s L t d.
Functional Genomics: Pharmacogenomics
Genetic differences between individuals(SNP) can cause large differences in drugeffects both agonist and antagonist and toxic
Stratification of patients into genotypesmay increase the probability of drug efficacy/therapeutic window
eg: drug metabolizing enzymes, transportersand drug receptors
B i o p h a r m a c e u t i c a l s L t d.
Relationship between Genes & Disease
• Genes do not cause disease, defective genes cause disease
• One gene one enzyme (Beadle and Tatum 1940s)
• Mendelian inherited diseases
• Polygenic diseases
B i o p h a r m a c e u t i c a l s L t d.
Relationship between Genes & Disease
• A gene is missing or defective– Replace protein– Replace activity
• Gene is overexpressed– Develop inhibitors of synthesis or activity
• Poly-genic disease– eg asthma where up to 15 genes may be
involved
B i o p h a r m a c e u t i c a l s L t d.
Relationship between Genes & Disease
• As of February 2, 2001 in GenBank– 12265 human gene entries– 8912 established gene locus– 845 multi loci disease associations
B i o p h a r m a c e u t i c a l s L t d.
Genomics New Drug Targets
More Rapid Drug Development
Thesis
B i o p h a r m a c e u t i c a l s L t d.
Use of genomics to discover new drug targets began in 1993
Today, percent of research projects based ongenomics in pharma: 10-25% average
Only handful of drugs currently in the clinicutilizing genomic information
Expect percent of genomic based drugs toincrease considerably in the next 5-10 years
“Is Genomics Delivering?” “Yes but slower than Expected.” Lehman Brothers
B i o p h a r m a c e u t i c a l s L t d.
Gene Sequences Genome Targets Validated Targets
Drug Screening Drug Leads Validated Candidate
Clinical Trials Market
B i o p h a r m a c e u t i c a l s L t d.
The Drug Development Process
Gene Sequences Genome Targets Validated Targets
Drug Screening Drug Leads Validated Candidate
Clinical Trials Market
B i o p h a r m a c e u t i c a l s L t d.
The Drug Development Process
Reality One
0102030405060708090
Gene
Targe
ts
Assay
Dev
elopm
ent
Drug
Candid
ate
Mar
ket
Data from Biocentury (Jan 29, 2001) CuraGen/Bayer
B i o p h a r m a c e u t i c a l s L t d.
Reality Two
• Millenium: 44% targets to leads
• Vertex: 85% targets into phase 1
• Bayer: 25% targets into phase 1
B i o p h a r m a c e u t i c a l s L t d.
Genomic Based Drug Development: What Next?
• Improvement of bio-validation tools– Cell based– In-vivo based
• Better understanding of physiologic pathways and networks and their control– Model organisms
• Better bio-informatic tools for protein structure and better chemo-informatic tools for medicinal chemistry
B i o p h a r m a c e u t i c a l s L t d.