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.

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

2. Evolution of Medical Discovery
Folk Medicine
Baconian
Hypothesis Validation
Population-Based Basic Science
Clinical Research (Biology, Chemistry, Physics)
Biomedical Computer
Research Science
Personalized
Biomarkers
Analysis
Personalized
Medicine

3. How do you personalize medicine?
Need to: Via:
Understand what biochemical Sequence-based gene & protein
processes occur in our bodies characterization
Know how to effectively + Chemical biology + molecular
selectively modulate these modeling
processes
Know which processes cause Molecular interrogation:
specific diseases microarrays, mass spec, data
mining
Predict what will happen to a patient Systems biology modeling
if you modulate the disease-causing
processes

4. Biochemical understanding: Sequence Analysis
Genomics: coding / non-coding
T C G T G A T C
alternative splicing
relevant mutations (SNPs)
A C G T G G T C
Proteins: homolog detection 1) F R E H E W S K
functional motifs
structure prediction 2) F R C H E G S K
3) Y K C H D G T R
Implications:
What biomolecules are we made of?
What do these biomolecules do?
How can we target them with therapeutics?

5. Process modulation: Chemical Biology
Chemical Biology: how externally produced chemicals affect organismal
biochemistry

6. Process modulation: Chemical Biology
Chemical Biology: how externally produced chemicals affect organismal
biochemistry
Inhibitor

7. Process modulation: Chemical Biology
Chemical Biology: how externally produced chemicals affect organismal
biochemistry
Activator

8. Chemical Biology Technologies
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?
Therapeutic optimization (efficacy + selectivity):
• Structure-based modeling
• QSAR (multivariate regression) modeling

9. Structure based SAR
Molecular Docking
Non-covalent inhibitor evaluation:
Conformation search driven by
Free energy estimation:
E = Electrostatics + vdW + Entropy
Target specificity: bind well only to desired receptor, not to others

10. QSAR / Multivariate Regression
Standard property-based QSAR: pIC (i) =  c Prop(i) + K
50 j
j
• fairly simple method
• potentially quite accurate Prop(i): simple physicochemical
• often not very intuitive or constitutive property
3D QSAR (CoMFA): pIC50(i) = (cvj Vij + cEj Eij) + K
• Prop(i) are vdW and j
electrostatic field terms Vij, Eij: van der Waals + electrostatic fields
• more informative
vdW + electrostatic probes

11. Achievements of Functional Targeting
Understand biochemical role of key genes/proteins + how to modulate
these roles
Therapeutic Limitation
No 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

13. Molecular interrogation: mass spectrometry
supports rapid assessment of the tissue prevalence of functionally
relevant biomolecules, including:
- Proteins (native, spliced or modified)
- Lipids
- Metabolites
- Transmitters Force
- Toxins
- Therapeutics Ablation
- etc.
Sample
MS has the potential to
produce much more
information than microarray
Molecular
Mass
 Time to reach
detector
studies, but poses very
complex challenges

14. Practical Applications & Extensions
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
How do you know which are:
- significant vs. incidental?
- causative vs. symptomatic?
How can you correct the imbalance?

15. Practical Applications & Extensions
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
How do you know which are:
- significant vs. incidental?
- causative vs. symptomatic?
How can you correct the imbalance?
Datamining over healthy vs. diseased samples

16. Data Mining Algorithm Example
diseased
healthy
Expression
(gene 2)
Expression (gene 1)

17. Data Mining Algorithm Example
diseased
healthy
Expression
(gene 2)
Expression (gene 1)
Gene 1: no significant region of elevated diseased/healthy ratio

18. Data Mining Algorithm Example
diseased
healthy
Expression
(gene 2)
Expression (gene 1)
Gene 2: has significant region of elevated diseased/healthy ratio

19. Data Mining Algorithm Example
diseased
healthy
Expression
(gene 2)
Expression (gene 3)
Genes 2,3: strong region of elevated diseased/healthy ratio

20. Practical Applications & Extensions
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
How do you know which are:
- significant vs. incidental?
- causative vs. symptomatic?
How can you correct the imbalance?
Knockouts: genetic engineering or chemical biology

21. Practical Applications & Extensions
Genomics microarray: over/under-expressed genes
Mass spectrometry: over/under-abundance of functional biomolecules
How do you know which are:
- significant vs. incidental?
- causative vs. symptomatic?
How can you correct the imbalance?
Chemical biology?

22. Chemical Biology: complex scenarios

23. Chemical Biology: complex implications!
?
? ?
?
?
Need to quantify how modulating one node affects other biochemical
pathways

24. Systems Biology
The 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

25. Systems Biology
The 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 ….

26. Systems Biology
The study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
without observing intermediate steps …..

27. Systems Biology
The 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 …..

28. Systems Biology
The study of how specific biochemical modulations affect pathways (e.g.,
signaling, metabolic, etc.), with organism-wide implications
can lead to erroneous conclusions!

29. Systems Biology Models c B e
a
A
b
x administered x d C f
[e]
[Conc] [b] [a] = 1
KaxA [a]k [x]j [A]l
[c]
[f] [b] = KxA [x]j [A]l
[a] KaA [a]k [A]l
[d]
[c] = KaxA [a]k [x]j [A]l
time
KcB [c]m [B]n
Procedure: [d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
Microarray, MS or chemical biology data
Record multiple time points [e] = KcB [c]m [B]n
Perturb the system (i.e., add x)
Fit concentrations to coupled equations [f] = KdC [d]m [C]n

30. Systems Biology Models c B e
a
A
b
x administered x d C f
[e]
[Conc] [b] [a] = 1
KaxA [a]k [x]j [A]l
[c]
[f] [b] = KxA [x]j [A]l
[a] KaA [a]k [A]l
[d]
[c] = KaxA [a]k [x]j [A]l
time
KcB [c]m [B]n
Results: [d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
Network sensitivities can pinpoint possible side
effects [e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n

31. Systems Biology Models c B e
a
A
b
x administered x d C f
[e]
[Conc] [b] [a] = 1
KaxA [a]k [x]j [A]l
[c]
[f] [b] = KxA [x]j [A]l
[a] KaA [a]k [A]l
[d]
[c] = KaxA [a]k [x]j [A]l
time
KcB [c]m [B]n
Procedure: [d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
Examine difference patient responses
[e] = KcB [c]m [B]n
[f] = KdC [d]m [C]n

32. Systems Biology Models c B e
a
A
b
x administered x d C f
[e]
[Conc] [b] [a] = 1
KaxA [a]k [x]j [A]l
[c]
[f] [b] = KxA [x]j [A]l
[a] KaA [a]k [A]l
[d]
[c] = KaxA [a]k [x]j [A]l
time
KcB [c]m [B]n
Results: [d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
Patient 2 has decreased susceptibility to side
effects [e] = KcB [c]m [B]n
May be able to boost dosage
without negative consequences [f] = KdC [d]m [C]n

33. Systems Biology Models c B e
a
A
b
x administered x d C f
[e]
[Conc] [b] [a] = 1
KaxA [a]k [x]j [A]l
[c]
[f] [b] = KxA [x]j [A]l
[a] KaA [a]k [A]l
[d]
[c] = KaxA [a]k [x]j [A]l
time
KcB [c]m [B]n
Results: [d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
Patient 3 has diminished therapeutic
response [e] = KcB [c]m [B]n
May need to find another drug
or target or also address [c] [f] = KdC [d]m [C]n

34. Systems Biology Models c B e
a
A
b
x d C f
[e]
[c]
[Conc]
[b] [a] = 1
KaxA [a]k [x]j [A]l
[a] [b] = KxA [x]j [A]l
[d] KaA [a]k [A]l
[f]
[c] = KaxA [a]k [x]j [A]l
[x]
KcB [c]m [B]n
Procedure: [d] = KbA [b]k [A]l
KdxC [d]m [x]j [C]n
Microarray, MS or chemical biology data
Record multiple dose response points [e] = KcB [c]m [B]n
Time average
Fit concentrations to coupled equations [f] = KdC [d]m [C]n

36. 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.)

37. Questions / Comments
glushington@ku.edu
785-864-1140