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One of the major problems in our medical system is the prescription of medicines that, although well validated over a general group of clinical trial patients for specific ailments, may produce unhelpful or even harmful results in some individuals. A major emerging goal in the pharmaceutical and biomedical industries is the ability to tailor medicines to the individual. This can be achieved, but in practice still requires careful analysis of an extensive array of data and thus has not yet entered the mainstream medical practice.
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Personalized Medicine
via molecular interrogation, data mining and systems biology
Gerry LushingtonKU Molecular Graphics & Modeling Lab
K-INBRE Bioinformatics Core
Folk Medicine
BaconianHypothesis Validation
Basic Science(Biology, Chemistry, Physics)
Population-Based Clinical Research
Personalized Analysis
Computer Science
BiomedicalResearch
Biomarkers
Personalized Medicine
Evolution of Medical Discovery
How do you personalize medicine?
Need to: Via:
Understand what biochemical processes occur in our bodies
Know how to effectively + selectively modulate these processes
Know which processes cause specific diseases
Predict what will happen to a patient if you modulate the disease-causing processes
Sequence-based gene & protein characterization
Chemical biology + molecular modeling
Molecular interrogation: microarrays, mass spec, data mining
Systems biology modeling
Biochemical understanding: Sequence Analysis
Genomics: coding / non-coding alternative splicing relevant mutations (SNPs)
Proteins: homolog detection functional motifs structure prediction
Implications: What biomolecules are we made of? What do these biomolecules do? How can we target them with therapeutics?
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Process modulation: Chemical Biology
Chemical Biology: how externally produced chemicals affect organismal biochemistry
Chemical Biology: how externally produced chemicals affect organismal biochemistry
Inhibitor
Process modulation: Chemical Biology
Chemical Biology: how externally produced chemicals affect organismal biochemistry
Activator
Process modulation: Chemical Biology
Chemical Biology Technologies
Therapeutic optimization (efficacy + selectivity):
• Structure-based modeling• QSAR (multivariate regression) modeling
Experimental methods:
• targets (proteins or cells) stored in multi-well plates• compounds delivered robotically into wells• activity read via fluorescence emissions or microscopy
Experimental insight:
• Which chemicals interact with a given target?• How strongly?
Molecular DockingNon-covalent inhibitor evaluation:
Conformation search driven byFree energy estimation:
E = Electrostatics + vdW + Entropy
Structure based SAR
Target specificity: bind well only to desired receptor, not to others
QSAR / Multivariate RegressionStandard property-based QSAR:• fairly simple method• potentially quite accurate• often not very intuitive
3D QSAR (CoMFA):• Prop(i) are vdW and
electrostatic field terms• more informative
pIC50(i) = cj Prop(i) + Kj
pIC50(i) = (cvj Vij + cEj Eij) + Kj
vdW + electrostatic probes
Prop(i): simple physicochemical or constitutive property
Vij, Eij: van der Waals + electrostatic fields
Therapeutic LimitationNo single gene/protein bears complete responsibility for a given disease
Coping Strategies
Analyze microarray data to identify which genes are disproportionately more or less active in performing protein translation in diseased tissue
Use mass spec to identify specific molecules with abnormally high or low abundance
Use informatics techniques to determine which anomalies are significant and causative
Achievements of Functional TargetingUnderstand biochemical role of key genes/proteins + how to modulate these roles
Molecular interrogation: mass spectrometry
supports rapid assessment of the tissue prevalence of functionally relevant biomolecules, including:
- Proteins (native, spliced or modified) - Lipids - Metabolites - Transmitters - Toxins - Therapeutics - etc.
Ablation
Sample
Force
MolecularMass
Time to reach detector
MS has the potential to produce much more information than microarray studies, but poses very complex challenges
How do you know which are: - significant vs. incidental? - causative vs. symptomatic?
How can you correct the imbalance?
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
Practical Applications & Extensions
How do you know which are: - significant vs. incidental? - causative vs. symptomatic?
How can you correct the imbalance?
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
Practical Applications & Extensions
Datamining over healthy vs. diseased samples
Data Mining Algorithm Example
Expression (gene 2)
Expression (gene 1)
diseased
healthy
Data Mining Algorithm Example
Expression (gene 2)
Expression (gene 1)
diseased
healthy
Gene 1: no significant region of elevated diseased/healthy ratio
Data Mining Algorithm Example
Expression (gene 2)
Expression (gene 1)
diseased
healthy
Gene 2: has significant region of elevated diseased/healthy ratio
Data Mining Algorithm Example
Expression (gene 2)
Expression (gene 3)
diseased
healthy
Genes 2,3: strong region of elevated diseased/healthy ratio
How do you know which are: - significant vs. incidental? - causative vs. symptomatic?
How can you correct the imbalance?
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
Practical Applications & Extensions
Knockouts: genetic engineering or chemical biology
How do you know which are: - significant vs. incidental? - causative vs. symptomatic?
How can you correct the imbalance?
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
Practical Applications & Extensions
Chemical biology?
Chemical Biology: complex scenarios
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?
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Chemical Biology: complex implications!
Need to quantify how modulating one node affects other biochemical pathways
Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
Single genechip microarray, mass spec and chemical biology experiments give dependency snapshots
Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
Comparing instantaneous data snap shots with clinical outcomes ….
Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
without observing intermediate steps …..
Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
that play key roles in determining the outcomes …..
Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
can lead to erroneous conclusions!
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[d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n
[a] = 1 KaxA [a]k [x]j [A]l
[b] = KxA [x]j [A]l
KaA [a]k [A]l
Systems Biology Models
[Conc]
time
[a][d]
[f][c]
[b]
[e]
x administered
Procedure:
Microarray, MS or chemical biology dataRecord multiple time pointsPerturb the system (i.e., add x)Fit concentrations to coupled equations
a
bx
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A
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[c] = KaxA [a]k [x]j [A]l
KcB [c]m [B]n
[d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n
[a] = 1 KaxA [a]k [x]j [A]l
[b] = KxA [x]j [A]l
KaA [a]k [A]l
Systems Biology Models
[Conc]
time
[a][d]
[f][c]
[b]
[e]
x administered
Results:
Network sensitivities can pinpoint possible side effects
a
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A
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[c] = KaxA [a]k [x]j [A]l
KcB [c]m [B]n
[d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n
[a] = 1 KaxA [a]k [x]j [A]l
[b] = KxA [x]j [A]l
KaA [a]k [A]l
Systems Biology Models
[Conc]
time
[a][d]
[f][c]
[b]
[e]
x administered
Procedure:
Examine difference patient responses
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[c] = KaxA [a]k [x]j [A]l
KcB [c]m [B]n
[d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n
[a] = 1 KaxA [a]k [x]j [A]l
[b] = KxA [x]j [A]l
KaA [a]k [A]l
Systems Biology Models
Results:
Patient 2 has decreased susceptibility to side effects
May be able to boost dosage without negative consequences
[Conc]
time
[a][d]
[f][c]
[b]
[e]
x administered
a
bx
c
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e
f
A
B
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[c] = KaxA [a]k [x]j [A]l
KcB [c]m [B]n
[d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n
[a] = 1 KaxA [a]k [x]j [A]l
[b] = KxA [x]j [A]l
KaA [a]k [A]l
Systems Biology Models
[Conc]
time
[a][d]
[f][c]
[b]
[e]
x administered
Results:
Patient 3 has diminished therapeutic response
May need to find another drug or target or also address [c]
a
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A
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[c] = KaxA [a]k [x]j [A]l
KcB [c]m [B]n
[d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n
[a] = 1 KaxA [a]k [x]j [A]l
[b] = KxA [x]j [A]l
KaA [a]k [A]l
Systems Biology Models
[Conc]
[x]
[d][f]
[a]
[b]
[c]
[e]
Procedure:
Microarray, MS or chemical biology dataRecord multiple dose response pointsTime averageFit concentrations to coupled equations
Personalized Medicine: Synopsis
Functional Targeting: gene / protein characterization and chemical biology yielding an arsenal of effective / specific target modulators
Molecular interrogation: microarray, mass spec identifying specific targets with anomalous behavior in diseased tissue
Data mining: highlight specific combinations of anomalies that characterize specific disease states (biomarkers)
Systems biology: identify complementary targets, characterize side-effects, personalize medicine (doses, cocktails, etc.)
Questions / Comments