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Personalized medicine

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Personalized medicine

  1. 1. Personalized Medicine via molecular interrogation, data mining and systems biology Gerry Lushington KU Molecular Graphics & Modeling Lab K-INBRE Bioinformatics Core
  2. 2. Folk Medicine Baconian Hypothesis Validation Basic Science (Biology, Chemistry, Physics) Population-Based Clinical Research Personalized Analysis Computer Science Biomedical Research Biomarkers Personalized Medicine Evolution of Medical Discovery
  3. 3. 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
  4. 4. 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)
  5. 5. Process modulation: Chemical Biology Chemical Biology: how externally produced chemicals affect organismal biochemistry
  6. 6. Chemical Biology: how externally produced chemicals affect organismal biochemistry Inhibitor Process modulation: Chemical Biology
  7. 7. Chemical Biology: how externally produced chemicals affect organismal biochemistry Activator Process modulation: Chemical Biology
  8. 8. 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?
  9. 9. Molecular Docking Non-covalent inhibitor evaluation: Conformation search driven by Free energy estimation: E = Electrostatics + vdW + Entropy Structure based SAR Target specificity: bind well only to desired receptor, not to others
  10. 10. QSAR / Multivariate Regression Standard 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) + K j pIC50(i) = (cvj Vij + cEj Eij) + K j vdW + electrostatic probes Prop(i): simple physicochemical or constitutive property Vij, Eij: van der Waals + electrostatic fields
  11. 11. 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 Achievements of Functional Targeting Understand biochemical role of key genes/proteins + how to modulate these roles
  12. 12. 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 Molecular Mass  Time to reach detector MS has the potential to produce much more information than microarray studies, but poses very complex challenges
  13. 13. 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
  14. 14. 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
  15. 15. Data Mining Algorithm Example Expression (gene 2) Expression (gene 1) diseased healthy
  16. 16. Data Mining Algorithm Example Expression (gene 2) Expression (gene 1) diseased healthy Gene 1: no significant region of elevated diseased/healthy ratio
  17. 17. Data Mining Algorithm Example Expression (gene 2) Expression (gene 1) diseased healthy Gene 2: has significant region of elevated diseased/healthy ratio
  18. 18. Data Mining Algorithm Example Expression (gene 2) Expression (gene 3) diseased healthy Genes 2,3: strong region of elevated diseased/healthy ratio
  19. 19. 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
  20. 20. 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?
  21. 21. Chemical Biology: complex scenarios
  22. 22. ? ? ? ? ? Chemical Biology: complex implications! Need to quantify how modulating one node affects other biochemical pathways
  23. 23. 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
  24. 24. 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 ….
  25. 25. Systems Biology The study of how specific biochemical modulations affect pathways (e.g., signaling, metabolic, etc.), with organism-wide implications without observing intermediate steps …..
  26. 26. 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 …..
  27. 27. 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!
  28. 28. a b x 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 data Record multiple time points Perturb the system (i.e., add x) Fit concentrations to coupled equations
  29. 29. a b x 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
  30. 30. a b x 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
  31. 31. a b x 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
  32. 32. a b x 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]
  33. 33. a b x 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 data Record multiple dose response points Time average Fit concentrations to coupled equations
  34. 34. 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.)
  35. 35. Questions / Comments glushington@ku.edu 785-864-1140

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