personalized medicine

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Personalized Medicine via molecular interrogation, data mining and systems biology Gerry Lushington KU Molecular Graphics & Modeling Lab K-INBRE Bioinformatics Core

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Page 1: Personalized medicine

Personalized Medicine

via molecular interrogation, data mining and systems biology

Gerry LushingtonKU Molecular Graphics & Modeling Lab

K-INBRE Bioinformatics Core

Page 2: Personalized medicine

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

Page 3: Personalized medicine

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

Page 4: Personalized medicine

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?

T C

R HF C GE

A C

G TA CG T

G TG CG T

KS

K HY C GD RT

R HF E WE KS1)

2)

3)

Page 5: Personalized medicine

Process modulation: Chemical Biology

Chemical Biology: how externally produced chemicals affect organismal biochemistry

Page 6: Personalized medicine

Chemical Biology: how externally produced chemicals affect organismal biochemistry

Inhibitor

Process modulation: Chemical Biology

Page 7: Personalized medicine

Chemical Biology: how externally produced chemicals affect organismal biochemistry

Activator

Process modulation: Chemical Biology

Page 8: Personalized medicine

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?

Page 9: Personalized medicine

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

Page 10: Personalized medicine

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

Page 11: Personalized medicine

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

Page 12: Personalized medicine
Page 13: Personalized medicine

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

Page 14: Personalized medicine

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

Page 15: Personalized medicine

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

Page 16: Personalized medicine

Data Mining Algorithm Example

Expression (gene 2)

Expression (gene 1)

diseased

healthy

Page 17: Personalized medicine

Data Mining Algorithm Example

Expression (gene 2)

Expression (gene 1)

diseased

healthy

Gene 1: no significant region of elevated diseased/healthy ratio

Page 18: Personalized medicine

Data Mining Algorithm Example

Expression (gene 2)

Expression (gene 1)

diseased

healthy

Gene 2: has significant region of elevated diseased/healthy ratio

Page 19: Personalized medicine

Data Mining Algorithm Example

Expression (gene 2)

Expression (gene 3)

diseased

healthy

Genes 2,3: strong region of elevated diseased/healthy ratio

Page 20: Personalized medicine

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

Page 21: Personalized medicine

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?

Page 22: Personalized medicine

Chemical Biology: complex scenarios

Page 23: Personalized medicine

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?

?

?

?

Chemical Biology: complex implications!

Need to quantify how modulating one node affects other biochemical pathways

Page 24: Personalized medicine

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

Page 25: Personalized medicine

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 ….

Page 26: Personalized medicine

Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,

signaling, metabolic, etc.), with organism-wide implications

without observing intermediate steps …..

Page 27: Personalized medicine

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 …..

Page 28: Personalized medicine

Systems BiologyThe study of how specific biochemical modulations affect pathways (e.g.,

signaling, metabolic, etc.), with organism-wide implications

can lead to erroneous conclusions!

Page 29: Personalized medicine

a

bx

c

d

e

f

A

B

C

[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:

Microarray, MS or chemical biology dataRecord multiple time pointsPerturb the system (i.e., add x)Fit concentrations to coupled equations

Page 30: Personalized medicine

a

bx

c

d

e

f

A

B

C

[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

Page 31: Personalized medicine

a

bx

c

d

e

f

A

B

C

[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

Page 32: Personalized medicine

a

bx

c

d

e

f

A

B

C

[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

Page 33: Personalized medicine

a

bx

c

d

e

f

A

B

C

[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]

Page 34: Personalized medicine

a

bx

c

d

e

f

A

B

C

[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

Page 35: Personalized medicine
Page 36: Personalized medicine

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.)

Page 37: Personalized medicine

Questions / Comments

[email protected]