pharm202.ppt 1674KB Jun 23 2011 12:12:42 PM
Pharm 202
“Digitally Enabled Genomic
Medicine” and Its Role in Cancer
Treatment
Phil Bourne
bourne@sdsc.edu
http://www.sdsc.edu/pb -> Courses -> Pharm 202
Take Home Message
• We are undergoing a revolution in our approach to treating
disease
• This has been driven by the human genome project and the
technologies that go with it
• A key element is the integration of information derived
from genotype to phenotype
• Much of this information is now digital rather than analog
• This is much more than faster ways to develop drugs – it
has to do with diagnostic treatments, preventive medicine,
personalized medicine
• Remember the two applications associated with cancer
treatment
Today • Overview of the revolution
• Drug discovery specifically
• The much more part as it relates to cancer
– Improve the outcomes of radiotherapy in
treatment of breast and prostate cancer
– Predictive gene signatures to define treatments
for breast cancer
Approach Today
• Rather than discuss specific papers of work
completed we will take a broader perspective on
proposed work on large scale projects that have
the potential to impact people’s lives through
digitally enabled genomic medicine
• The grants we have studied are from Genome
Canada and should be treated as confidential
REPRESENTATIVE
DISCIPLINE
EXAMPLE
UNITS
Anatomy
MRI
Physiology
Heart
Cell Biology
Neuron
Proteomics
Genomics
Structure
Sequence
SCIENTIFIC RESEARCH
& DISCOVERY
Organisms
Protease
Inhibitor
Migratory
Sensors
Organs
Ventricular
Modeling
Cells
Electron
Microscopy
Macromolecules
Biopolymers
Infrastructure
Medicinal
Chemistry
REPRESENTATIVE
TECHNOLOGY
X-ray
Crystallography
Technologies
Atoms & Molecules
Training
Protein
Docking
Digital vs Analog
• The lower levels of biological complexity
have always been digital – the higher levels
analog
• This made it very hard to correlate across
biological scales
• Some good examples of digital phenotypic
data exist and it is now being collected in
earnest
Lower Levels – Digital (sort of)
This digital image
of cAMP dependant
protein kinase
(PKA) depicts years
of collective
knowledge.
We can only
interpret it in this
form and the
computer is vital
Higher Levels – The Patient Record
•
•
•
•
•
8% of patient records are lost
They are mostly paper (analog)
They can only be interpreted by humans
Errors are rampant
There are exceptions – tumor registries,
digitized x-rays, clinical trials, the Cockrane
library
Drug Discovery as an Example of
this Revolution
• Requires a higher level of digital
enablement
• Has been accelerated by the genome(s) and
associated technologies
Discovery and Development
• Discovery includes: Concept, mechanism,
assay, screening, hit identification, lead
demonstration, lead optimization
• Discovery also includes in vivo proof of
concept in animals and concomitant
demonstration of a therapeutic index
• Development begins when the decision is
made to put a molecule into phase I clinical
trials
Discovery and Development
• The time from conception to approval of a
new drug is typically 10-15 years
• The vast majority of molecules fail along
the way
• The estimated cost to bring to market a
successful drug is now $800 million!!
(Dimasi, 2000)
Drug Discovery - Status Today
• Somewhat digitally enabled (FDA still
requires paper submission)
• Will benefit from emergent technologies
• Human targets are relatively well defined
• Process for finding appropriate targets in
other organisms is evolving
• Process for finding leads is under revision
(we will see an example of that)
Drug Discovery Processes Today
Physiological
Hypothesis
Molecular
Biological
Hypothesis
(Genomics)
Primary Assays
Biochemical
Cellular
Pharmacological
Physiological
+
Chemical
Hypothesis
Sources of Molecules
Natural Products
Synthetic Chemicals
Combichem
Biologicals
Screening
Initial Hit
Compounds
Drug Discovery Processes - II
Initial Hit
Compounds
Secondary
Evaluation
- Mechanism
Of Action
- Dose Response
Hit to Lead
Chemistry
- physical
properties
-in vitro
metabolism
Initial Synthetic
Evaluation
- analytics
- first analogs
First In Vivo
Tests
- PK, efficacy,
toxicity
Drug Discovery Processes - III
Lead Optimization
Potency
Selectivity
Physical Properties
PK
Metabolism
Oral Bioavailability
Synthetic Ease
Scalability
Pharmacology
Multiple In Vivo
Models
Chronic Dosing
Preliminary Tox
Development
Candidate
(and Backups)
Remains Serendipity
• Often molecules are discovered/synthesized
for one indication and then turn out to be
useful for others
–
–
–
–
Tamoxifen (birth control and cancer)
Viagra (hypertension and erectile dysfunction)
Salvarsan (Sleeping sickness and syphilis)
Interferon- (hairy cell leukemia and Hepatitis C)
Issues in Drug Discovery
•
•
•
•
•
•
•
Hits and Leads - Is it a “Druggable” target?
Resistance
Pharmacodynamics and kinetics
Delivery - oral and otherwise
Metabolism
Solubility, toxicity
Patentability
What has changed in
identifying targets?
In principle we know all the
human targets The “Druggable Genome”
human genome
polysaccharides
lipids
nucleic acids
proteins
Problems with toxicity, specificity, and
difficulty in creating potent inhibitors
eliminate the first 3 categories...
human genome
polysaccharides
lipids
nucleic acids
proteins
proteins with
binding site
“druggable genome” = subset of genes which
express proteins capable of binding small drug-like
molecules
Relating druggable targets
to disease...
Analysis of pharm
industry reveals:
GPCR
Other 110
families
STY kinases
Cys proteases
Gated ionchannel
Zinc peptidases
Ion channels
Nuclear PDE
receptor
Serine
proteases
P450 enzymes
Fig. 3, Fauman et al.
• Over 400 proteins
used as drug targets
• Sequence analysis of
these proteins shows
that most targets fall
within a few major
gene families
(GPCRs, kinases,
proteases and
peptidases)
Remaining issues
• Characterization of human proteins is ongoing (see each revision from Ensembl)
• Our ability to locate coding regions is
improving
• Our ability to annotate putative proteins is
improving
• More targets will be identified
The Structural Genomics Pipeline
(X-ray Crystallography)
Basic Steps
Crystallomics
• Isolation,
Target • Expression,
Data
Selection • Purification, Collection
• Crystallization
Bioinformatics
• Distant
homologs
• Domain
recognition
Automation
Bioinformatics
• Empirical
rules
Automation
Better
sources
Anticipated Developments
Structure
Solution
Structure
Refinement
Software integration
Decision Support
MAD Phasing Automated
fitting
Functional
Annotation
Publish
No?
Bioinformatics
• Alignments
• Protein-protein
interactions
• Protein-ligand
interactions
• Motif recognition
From Structural Genomix
• FAST™ is a proprietary lead generation technology developed by SGX
for identification of novel, potent and selective small molecule
inhibitors of drug targets within a rapid six-month timeframe. The
FAST™ process involves crystallographic screening of lead-like drug
fragments followed by structure-guided elaboration of the fragments
by parallel chemical synthesis, guided by proprietary computational
tools. Iterative determination of crystal structures for multiple
target/compound complexes in parallel with assays, computational
design and synthesis results in optimized leads with high binding
affinities and low molecular weights. The combinatorial nature of
FAST™ provides access to expansive chemical diversity in the order
of 160 million compounds, while requiring only a small number of
compounds to be synthesized and screened. Thus the FAST™
approach generates novel and potent lead compounds within months
and with efficient deployment of chemistry resources.
Summary
• Need information flow from genotype to phenotype and
back
• Digital enablement provides that
• The human genome and the associated technologies has
accelerated this process dramatically
• Example – human genome provides more targets
• Example – structural genomics leads to faster
identification of leads
• Lets consider two examples related to cancer that illustrate
this more specifically….
“Digitally Enabled Genomic
Medicine” and Its Role in Cancer
Treatment
Phil Bourne
bourne@sdsc.edu
http://www.sdsc.edu/pb -> Courses -> Pharm 202
Take Home Message
• We are undergoing a revolution in our approach to treating
disease
• This has been driven by the human genome project and the
technologies that go with it
• A key element is the integration of information derived
from genotype to phenotype
• Much of this information is now digital rather than analog
• This is much more than faster ways to develop drugs – it
has to do with diagnostic treatments, preventive medicine,
personalized medicine
• Remember the two applications associated with cancer
treatment
Today • Overview of the revolution
• Drug discovery specifically
• The much more part as it relates to cancer
– Improve the outcomes of radiotherapy in
treatment of breast and prostate cancer
– Predictive gene signatures to define treatments
for breast cancer
Approach Today
• Rather than discuss specific papers of work
completed we will take a broader perspective on
proposed work on large scale projects that have
the potential to impact people’s lives through
digitally enabled genomic medicine
• The grants we have studied are from Genome
Canada and should be treated as confidential
REPRESENTATIVE
DISCIPLINE
EXAMPLE
UNITS
Anatomy
MRI
Physiology
Heart
Cell Biology
Neuron
Proteomics
Genomics
Structure
Sequence
SCIENTIFIC RESEARCH
& DISCOVERY
Organisms
Protease
Inhibitor
Migratory
Sensors
Organs
Ventricular
Modeling
Cells
Electron
Microscopy
Macromolecules
Biopolymers
Infrastructure
Medicinal
Chemistry
REPRESENTATIVE
TECHNOLOGY
X-ray
Crystallography
Technologies
Atoms & Molecules
Training
Protein
Docking
Digital vs Analog
• The lower levels of biological complexity
have always been digital – the higher levels
analog
• This made it very hard to correlate across
biological scales
• Some good examples of digital phenotypic
data exist and it is now being collected in
earnest
Lower Levels – Digital (sort of)
This digital image
of cAMP dependant
protein kinase
(PKA) depicts years
of collective
knowledge.
We can only
interpret it in this
form and the
computer is vital
Higher Levels – The Patient Record
•
•
•
•
•
8% of patient records are lost
They are mostly paper (analog)
They can only be interpreted by humans
Errors are rampant
There are exceptions – tumor registries,
digitized x-rays, clinical trials, the Cockrane
library
Drug Discovery as an Example of
this Revolution
• Requires a higher level of digital
enablement
• Has been accelerated by the genome(s) and
associated technologies
Discovery and Development
• Discovery includes: Concept, mechanism,
assay, screening, hit identification, lead
demonstration, lead optimization
• Discovery also includes in vivo proof of
concept in animals and concomitant
demonstration of a therapeutic index
• Development begins when the decision is
made to put a molecule into phase I clinical
trials
Discovery and Development
• The time from conception to approval of a
new drug is typically 10-15 years
• The vast majority of molecules fail along
the way
• The estimated cost to bring to market a
successful drug is now $800 million!!
(Dimasi, 2000)
Drug Discovery - Status Today
• Somewhat digitally enabled (FDA still
requires paper submission)
• Will benefit from emergent technologies
• Human targets are relatively well defined
• Process for finding appropriate targets in
other organisms is evolving
• Process for finding leads is under revision
(we will see an example of that)
Drug Discovery Processes Today
Physiological
Hypothesis
Molecular
Biological
Hypothesis
(Genomics)
Primary Assays
Biochemical
Cellular
Pharmacological
Physiological
+
Chemical
Hypothesis
Sources of Molecules
Natural Products
Synthetic Chemicals
Combichem
Biologicals
Screening
Initial Hit
Compounds
Drug Discovery Processes - II
Initial Hit
Compounds
Secondary
Evaluation
- Mechanism
Of Action
- Dose Response
Hit to Lead
Chemistry
- physical
properties
-in vitro
metabolism
Initial Synthetic
Evaluation
- analytics
- first analogs
First In Vivo
Tests
- PK, efficacy,
toxicity
Drug Discovery Processes - III
Lead Optimization
Potency
Selectivity
Physical Properties
PK
Metabolism
Oral Bioavailability
Synthetic Ease
Scalability
Pharmacology
Multiple In Vivo
Models
Chronic Dosing
Preliminary Tox
Development
Candidate
(and Backups)
Remains Serendipity
• Often molecules are discovered/synthesized
for one indication and then turn out to be
useful for others
–
–
–
–
Tamoxifen (birth control and cancer)
Viagra (hypertension and erectile dysfunction)
Salvarsan (Sleeping sickness and syphilis)
Interferon- (hairy cell leukemia and Hepatitis C)
Issues in Drug Discovery
•
•
•
•
•
•
•
Hits and Leads - Is it a “Druggable” target?
Resistance
Pharmacodynamics and kinetics
Delivery - oral and otherwise
Metabolism
Solubility, toxicity
Patentability
What has changed in
identifying targets?
In principle we know all the
human targets The “Druggable Genome”
human genome
polysaccharides
lipids
nucleic acids
proteins
Problems with toxicity, specificity, and
difficulty in creating potent inhibitors
eliminate the first 3 categories...
human genome
polysaccharides
lipids
nucleic acids
proteins
proteins with
binding site
“druggable genome” = subset of genes which
express proteins capable of binding small drug-like
molecules
Relating druggable targets
to disease...
Analysis of pharm
industry reveals:
GPCR
Other 110
families
STY kinases
Cys proteases
Gated ionchannel
Zinc peptidases
Ion channels
Nuclear PDE
receptor
Serine
proteases
P450 enzymes
Fig. 3, Fauman et al.
• Over 400 proteins
used as drug targets
• Sequence analysis of
these proteins shows
that most targets fall
within a few major
gene families
(GPCRs, kinases,
proteases and
peptidases)
Remaining issues
• Characterization of human proteins is ongoing (see each revision from Ensembl)
• Our ability to locate coding regions is
improving
• Our ability to annotate putative proteins is
improving
• More targets will be identified
The Structural Genomics Pipeline
(X-ray Crystallography)
Basic Steps
Crystallomics
• Isolation,
Target • Expression,
Data
Selection • Purification, Collection
• Crystallization
Bioinformatics
• Distant
homologs
• Domain
recognition
Automation
Bioinformatics
• Empirical
rules
Automation
Better
sources
Anticipated Developments
Structure
Solution
Structure
Refinement
Software integration
Decision Support
MAD Phasing Automated
fitting
Functional
Annotation
Publish
No?
Bioinformatics
• Alignments
• Protein-protein
interactions
• Protein-ligand
interactions
• Motif recognition
From Structural Genomix
• FAST™ is a proprietary lead generation technology developed by SGX
for identification of novel, potent and selective small molecule
inhibitors of drug targets within a rapid six-month timeframe. The
FAST™ process involves crystallographic screening of lead-like drug
fragments followed by structure-guided elaboration of the fragments
by parallel chemical synthesis, guided by proprietary computational
tools. Iterative determination of crystal structures for multiple
target/compound complexes in parallel with assays, computational
design and synthesis results in optimized leads with high binding
affinities and low molecular weights. The combinatorial nature of
FAST™ provides access to expansive chemical diversity in the order
of 160 million compounds, while requiring only a small number of
compounds to be synthesized and screened. Thus the FAST™
approach generates novel and potent lead compounds within months
and with efficient deployment of chemistry resources.
Summary
• Need information flow from genotype to phenotype and
back
• Digital enablement provides that
• The human genome and the associated technologies has
accelerated this process dramatically
• Example – human genome provides more targets
• Example – structural genomics leads to faster
identification of leads
• Lets consider two examples related to cancer that illustrate
this more specifically….