personalized medicine

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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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

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[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

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

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

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

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]

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

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

glushington@ku.edu785-864-1140