Is there a rational strategy for biomarkers that predict disease outcomes and translate into humans? Update on the rapidly...
Focus of Today’s Discussion <ul><li>Update on the rapidly advancing field of causal pathway measurements </li></ul>
Outline <ul><li>The problem:  Attrition, due to complexity and unpredictability </li></ul><ul><li>The need: Navigation sys...
The problem  <ul><li>Slow time for Pharma </li></ul><ul><li>The main problem is attrition </li></ul><ul><li>The primary re...
Attrition should not be a surprise:  Fundamental unpredictability of complex networks Drug/Nutrient Predicted Effect Unexp...
The Central Paradox of  Contemporary Biology and Medicine <ul><li>Dissonance between our understanding of the parts and th...
Is There An Answer to Unpredictability?  <ul><li>The answer: stop driving blind </li></ul><ul><li>Make development of medi...
The Need for Metrics that Capture the Activity of Disease-Driving Processes <ul><li>There are pathways that causally influ...
Causal Pathways of Disease Link Molecular Targets to Outcomes of Complex Systems Agent Molecular target Biochemical pathwa...
Fibrogenesis as Causal Pathway:  Complex Regulation, Simple Final Process WBC & Lymph M  HBV Target IFN   viral replicat...
Unifying Themes are Causality and Intrinsic Significance (Synonyms) <ul><li>“Causal pathways” </li></ul><ul><li>“Disease p...
Criterea for a Causal Pathway in a Disease <ul><li>It must make sense biochemically and physiologically, based on current ...
Would anyone disagree about:  <ul><li>Proliferation & death of tumor cells in cancer? </li></ul><ul><li>Synthesis & breakd...
Perhaps more controversial…  <ul><li>Reverse cholesterol transport in atherosclerosis </li></ul><ul><li>Synthesis & cleara...
General (cell biologic processes)  <ul><li>Flux through autophagosomes &proteosomes; dynamics of other organelles </li></u...
If you could measure any dynamic process in the body - what would you choose? <ul><li>If you could wave a magic wand: What...
Causal Pathways by Disease Category <ul><li>Neurodegenerative Diseases </li></ul>Neuro-psychiatry/ behavior Cancer Fibroti...
Two New Kinetic Technologies <ul><ul><li>Dynamic Proteome </li></ul></ul><ul><ul><li>“ Water World” (Deuteron Fluxes/ Meta...
<ul><li>Dynamic Proteome </li></ul>
Dynamic Proteomics 2 H 2 O-labelled cells plasma or tissues Enrich protein of interest (e.g., IP) SDS-PAGE Reduce, carboxy...
LC/MS/MS analysis of tryptic peptide Relative abundance LC Chromatogram SIM: m/z ≈ 876.45 (M+H) 2+ MS1: mass spectrum of i...
Mass Shift In Plasma Albumin Peptides Fractional synthesis calculated from the change in %m0 Mass isotopomer fractional ab...
Mass Shift In Plasma IgA Peptides natural 100% labeled Mass isotopomer fractional abundances %M1 %M2 %M3 %M4 %M0 Day 0 6 1...
RELATIVE ABUNDANCES  [ % ] UNLABELED PEPTIDE ISOTOPOMERS 2 H-PEPTIDE
F=0% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
F=25% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
F=50% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
F=75% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
ISOTOPOMERS RELATIVE ABUNDANCES [ % ] F=100%
Basic Principles of MIDA  Precursor Pool Polymer/Product Isotope Pattern M2 M3 M0 M1 (M+0) (M+1) (M+2) (M+3) M0 M1 M2 M3 M...
Serum Protein Synthesis Rates as Pathway Targets <ul><li>Clotting cascade </li></ul><ul><li>Complement activation cascade ...
Example <ul><li>Axonal Transport of Cargo in Human Brain </li></ul>
Axonal transport: essential to neuronal health Proteins made here Proteins transported here Proteins used here Secreted Pr...
Clinical Translational Tool: CSF-based secreted biomarkers of MT-mediated  fast  axonal transport in ALS MT-mediated  Fast...
Neuregulin-1, Chromogranin B and sAPPa exhibit altered CSF kinetics in symptomatic SOD1G93A mice   ***p< 0.001 *p< 0.05 **...
Pulse-chase Labeling – Healthy Control Patients 2 H-Enrichments
Pulse-chase Labeling – ALS Patient 0 10 20 30 40 50 0.0 0.5 1.0 1.5 2.0 Control  subject #7033 and ALS subject #6082 2H-la...
Pulse-chase Labeling - Parkinson’s Disease Patient 2 H-Body Water 2 H-Enrichments Control  subject #7033 and PD subject #6...
Sensitive clinical biomarker: kinetically distinct, disease-specific defects in neuronal transport? ALS- Defect #1: Gross ...
Peptide Isotopomer Analysis of Control Subject #6079 9-16-10 BH JC KL Cystatin-C peptide m/z=613.81, z=2 ALDFAVGEYNK
2 H-Enrichment in Pulse-Chase Subject #6079 (QTOF Analysis) #6079 Fraction Labeled
Proteome wide measure of protein turnover 1716 Unique proteins (total) 45 222 113 >10 peptides 71 244 165 5-10 peptides 11...
Rapid POC in Humans Skin Keratin and Keratinocyte Dynamics in Psoriasis
Epidermal Keratin Turnover:  Fsn Mice Glucocorticoid treatment has no effect on high turnover rate of keratin in fsn mice ...
Kinetics of Human Skin Keratin (Tape Strips)  Lindwall, JinvestDerm 2006 0.00 0.01 0.02 0.03 0.04 0.05 0 20 40 60 80 100 D...
Skin Turnover in Psoriatic Patients e
Next Generation for Dynamic Proteome <ul><li>Emerging Causal Pathway Metrics </li></ul>
Clotting Rate  in Vivo : Turnover (Consumption) of Coagulation Factors in Cascade
Complement Pathway: Turnover (Consumption)
“Water World” Platform for Causal Pathway Measurements: Deuteron Pathway Dynamics <ul><li>Cell kinetics </li></ul><ul><li>...
Label Pathways for Measuring DNA Synthesis G6P R5P PRPP Purine and Pyrimidine bases   Precursors NDP dNTP DNA dN 2 H 2 O G...
Application: Patient Subgroup Selection in CLL Messmer et al.,  J Clin Invest  2005 GMH, 02/10/2008 Fast Turnover/Bad Dise...
Application: Patient Subgroup Selection in CLL Messmer et al.,  J Clin Invest  2005 GMH, 02/10/2008 Slow Turnover/Stable D...
KineMed Test Distinguishes between Patients with Stable  vs  Progressive Disease  KineMarker 
Translating Cell Proliferation into Solid Tumors:  <ul><li>“ Histokinetics” </li></ul>
Micro-dissection of Normal & Tumor Tissues GMH, 02/10/2008 Tissue stained for SRPK1 (increased expression in tumors compar...
Micro-dissection of Normal & Tumor Tissues GMH, 02/10/2008 Determine tumor areas to excise
Micro-dissection of Normal & Tumor Tissues GMH, 02/10/2008 Micro-dissection of tumoral cells Into microfuge tube Derivatiz...
Human Breast cancer: DCIS  vs  normal tissue
Purification of PEC from Semen: GMH, 02/10/2008 Semen sample  Remove enriched PEC fraction Viscolytic Enzyme Percoll Gradi...
Proliferation Rates of ProstateTissue Microdissected Cells and Seminal Fluid PEC: Radical Prostatectomy Patients
Proliferation Rates of ProstateTissue Microdissected Cells and Seminal Fluid PEC
Correlation Between Proliferation Rates of Prostate Tissue Microdissected Cells and Seminal Fluid PEC PEC Tissue
Continuing Evolution of Histopathology: Adding the Dimension of Regional Molecular Kinetics <ul><li>Vital dyes: Structures...
Rapid POC Testing in Humans <ul><li>Modulators of reverse cholesterol transport/HDL function </li></ul>
Reverse Cholesterol Transport: Measurement of Efflux, Plasma Transport and Excretion Arms ABCB11 ABCG5/8 Pre   -HDL CETP ...
HDLc in Hypoalpha Patients ABCA1
Cholesterol Efflux in Hypoalpha Patients ABCA1
Esterification rate results in humans <ul><li>In seven subjects average esterification rate was 3.3 umol/kg/hr </li></ul><...
Effect of ALP-1 on  in-vivo  esterification rate in rabbit P < 0.05 P > 0.05 +93 +133 +20% +47%
Relation of RCT Fluxes to Reduction in Atherosclerosis with rrLCAT Treatment Vehicle rrLCAT *** Vehicle rrLCAT 0.00 2.00 4...
Torcetrapib inhibits absolute rate of esterification 8 cmpA and 7 cmpB Torcetrapib inhibits rate of esterification * Rate ...
Applications of Causal Pathway Metrics <ul><li>“Less guessing” about: </li></ul><ul><li>Picking targets </li></ul><ul><li>...
Applications: Picking targets and translating results quickly into humans <ul><li>Validating targets in a disease pathway ...
Identifying the right patients and translating POC rapidly into humans <ul><li>Identifying pathogenic subsets </li></ul><u...
Putting It All Together for Complex, Multifactorial Diseases <ul><li>Recent Technical Advances Make It Possible </li></ul>
How to dissect and control complexity? <ul><li>Informatics approach : Measure many things (“Omics”); find statistical corr...
Measurable Processes in Complex Diseases: AD/ Neurodegeneration APP Synthesis A   1-42 production Tau/MT Dysfunction Plaq...
Measurable Processes in Complex Diseases: Neoplasia (Prostate Cancer) Androgens Growth factors Mutagens etc PEC proliferat...
Measurable Processes in Complex Diseases: Atherogenesis Diet Activity Genes Etc. TG ApoB particles DNL Obesity VLDL TG tur...
Proving causality of process/pathway in disease  <ul><li>Two things are needed to prove causality of process/pathway in a ...
What is proven (and provable) <ul><li>Hypotheses are testable: </li></ul><ul><ul><li>drug X hits target Y? </li></ul></ul>...
Summary: Simple Message <ul><li>Development of medicines can be done better </li></ul><ul><li>The solution is to measure t...
Galileo’s Spyglass <ul><li>“… the nature of matter of the Milky Way itself, which, with the act of the spyglass, may be ob...
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Personalized & Translational Medicine - KineMed, Inc. - Marc Hellerstein, MD, PhD

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  • I can say this with conviction because of one of the great – and barely appreciated- insights of the past 20 years Insight from field of Metab Eng: that even when you control every gene and protein it is How many here read Metabolic Eng? But basic goal is same as Pharma- and insight are One doesn’t hear these terms in Med Chem: Robustness etc - the solution in Metab Eng is metab flux analysis Example: U Sauer, B Subtilis (137 gene mutations- only a few had any effect on fluxes Point is: Metab eng have learned, the only way to advance is to navigate (i.e., to measure) Max Planck’s quote: An exp’t is man asking nature a question; the answer is a measurement
  • On the right hand panels, we relate the delta in the abundances of each mass with heavy water labeling to the delta that would be predicted for a fully labeled peptide, based on mathematical modeling of the effect of deuterated water. As you can see, the shift for each isotopomer approaches the theoretical maximum, suggesting nearly complete fractional synthesis.
  • On the right hand panels, we relate the delta in the abundances of each mass with heavy water labeling to the delta that would be predicted for a fully labeled peptide, based on mathematical modeling of the effect of deuterated water. As you can see, the shift for each isotopomer approaches the theoretical maximum, suggesting nearly complete fractional synthesis.
  • He is moderately severe and slowly progressive. Subject 6082 weight is 79.3kg and ht is 190.9cm. Subject was male, white, non-hispanic and the only meds listed were marinol. Catherine Lomen-Hoerth, MD, PhD Director, ALS Center at UCSF 350 Parnassus Ave. Suite 500 San Francisco, CA 94117 (415) 514-0490 phone (415) 514-0491 fax
  • Epidermal Keratin : Turnover is complete in 4 days in FSN mice, takes 3 weeks in c57bl6 mice
  • Deuterium incorporation in keratin from skin tapes. Subject was administered deuterated water from day 0 to day 27 Deuterium incorporation in keratin from skin tapes. Subject was administered deuterated water from day 0 to day 27
  • HDL concentration does not reflect HDL function  Kinemed’s RCT measurement evaluates HDL function Ex Vivo Cholesterol efflux does not predict in vivo activity  Measurements done in vivo – hamsters, mice, rats, rabbits, humans Traditional outcomes in preclinical models of atherosclerosis does not predict clinical success (CETP, ACAT etc)  Symmetrical measurements done in humans – Phase I Therapy ½ life does not necessarily reflect effect on RCT or therapeutic duration  Identifies optimal dosing based on HDL function rather than concentration Subjects with low HDL concentrations may not be optimal population for proposed therapy.  Can pre-select subjects with low RCT as most likely to benefit from therapy
  • Personalized & Translational Medicine - KineMed, Inc. - Marc Hellerstein, MD, PhD

    1. 1. Is there a rational strategy for biomarkers that predict disease outcomes and translate into humans? Update on the rapidly advancing field of causal pathway measurements Marc K. Hellerstein, M.D., Ph.D. Co-Founder and Chief of SAB, KineMed, Inc., Emeryville, CA Professor (D.H. Calloway Chair), University of California, Berkeley; Professor of Medicine, UCSF
    2. 2. Focus of Today’s Discussion <ul><li>Update on the rapidly advancing field of causal pathway measurements </li></ul>
    3. 3. Outline <ul><li>The problem: Attrition, due to complexity and unpredictability </li></ul><ul><li>The need: Navigation systems </li></ul><ul><li>A solution: “Causal Pathway” metrics (translatable link between molecular targets and functional outcomes) </li></ul><ul><li>Survey of techniques and technology </li></ul><ul><li>Breadth of uses in drug discovery and development </li></ul><ul><li>Challenges and potential value </li></ul>
    4. 4. The problem <ul><li>Slow time for Pharma </li></ul><ul><li>The main problem is attrition </li></ul><ul><li>The primary reason is lack of predictability of molecular-target based interventions – for both efficacy and toxicities </li></ul>
    5. 5. Attrition should not be a surprise: Fundamental unpredictability of complex networks Drug/Nutrient Predicted Effect Unexpected Effect
    6. 6. The Central Paradox of Contemporary Biology and Medicine <ul><li>Dissonance between our understanding of the parts and the whole in complex biological systems </li></ul><ul><li>The radically uneven development in knowledge about components (reductionist understanding) vs intact assemblages (integrated understanding) tends to be taken for granted </li></ul><ul><li>Paradox is that the former has not led to parallel ability to control the latter, especially when things go wrong, as in disease </li></ul>
    7. 7. Is There An Answer to Unpredictability? <ul><li>The answer: stop driving blind </li></ul><ul><li>Make development of medicines a measurement science, not a series of guesses </li></ul><ul><li>Navigate through complexity, as doctors do in the ICU </li></ul>
    8. 8. The Need for Metrics that Capture the Activity of Disease-Driving Processes <ul><li>There are pathways that causally influence the initiation, progression, severity and therapeutic reversal of common diseases </li></ul><ul><li>These can be termed “Causal Pathways” </li></ul><ul><li>The activity of these pathways is difficult to predict and needs to be measured </li></ul><ul><li>The ability to measure these processes would transform the development of medicines </li></ul>
    9. 9. Causal Pathways of Disease Link Molecular Targets to Outcomes of Complex Systems Agent Molecular target Biochemical pathway Clinical outcome (Microscopic) (High throughput) (No functional significance) (Low throughput) (Macroscopic) (Functional significance)
    10. 10. Fibrogenesis as Causal Pathway: Complex Regulation, Simple Final Process WBC & Lymph M  HBV Target IFN  viral replication Cell Death Toxicants Target Steatosis Tissue Inflammation IL-6 Target TNF  IFN  Hepatocyte TNFR Fibroblast nucleus matrix metalloproteinases (MMPs) procollagenase AA peptides Lysyl oxidase Crosslinked collagen Transcription Procollagen Translation GlycOH AA hydroxyl Collagen mRNA acetaldehyde oxidative stress T-cell Target systemic & dietary protein degradation Extracellular Space Organ failure & Death PK3 NK PGE 2 M  Target CTGFR TGF  R IL-10 Target WBC & Lymph TGF-  CTGF Target Target STK Target MMP inhibitors Collagen buildup Fibrosis Collagen
    11. 11. Unifying Themes are Causality and Intrinsic Significance (Synonyms) <ul><li>“Causal pathways” </li></ul><ul><li>“Disease processes” </li></ul><ul><li>“Disease-modifying processes” </li></ul><ul><li>“Pathogenic mechanisms” </li></ul><ul><li>“Driving forces” </li></ul><ul><li>No matter what they are called, the theme is: </li></ul><ul><ul><ul><li>Causality </li></ul></ul></ul><ul><ul><ul><li>Intrinsic functional significance </li></ul></ul></ul><ul><li>These differ from most biomarkers and metrics proposed for drug development </li></ul>
    12. 12. Criterea for a Causal Pathway in a Disease <ul><li>It must make sense biochemically and physiologically, based on current knowledge (on explicit pathway to disease outcomes n) </li></ul><ul><li>It must have functional significance in its own right (intrinsic significance) </li></ul><ul><li>Factors that modulate it must alter the course of the disease (causal) </li></ul><ul><li>Must be measurable reproducibly and accurately in vivo (technically robust) </li></ul>
    13. 13. Would anyone disagree about: <ul><li>Proliferation & death of tumor cells in cancer? </li></ul><ul><li>Synthesis & breakdown of collagen in fibrosis? </li></ul><ul><li>Glc uptake by tissues and production by liver in T2DM? </li></ul><ul><li>Deposition & mobilization of amyloid plaque in AD? </li></ul><ul><li>Turnover and proliferation of skin in psoriasis? </li></ul><ul><li>Clot formation & lysis in thromboembolic disorders? </li></ul><ul><li>Synthesis & breakdown of myelin in demyelinating dz? </li></ul><ul><li>Axonal transport of key nutrients in ND diseases? </li></ul><ul><li>Production of Ag-specific Abs and T-cells in Auto-Imm? </li></ul><ul><li>Proliferation & death of pancreatic  -cells in T2DM? </li></ul><ul><li>etc. </li></ul>
    14. 14. Perhaps more controversial… <ul><li>Reverse cholesterol transport in atherosclerosis </li></ul><ul><li>Synthesis & clearance of A-  1-42 in brain in AD </li></ul><ul><li>Angiogenesis in cancer </li></ul><ul><li>Activation of complement cascade in CVD </li></ul><ul><li>DNA demethylation or histone deacetylation in cancer </li></ul><ul><li>Proliferation & differentiation of myoblasts in sarcopenia </li></ul><ul><li>Proliferation of specific anti-tumor T-cells in cancer </li></ul><ul><li>Muscle mitochondrial biogenesis in fitness and obesity </li></ul><ul><li>Proliferation of prostate cells in PIN </li></ul><ul><li>Adipogenesis in metabolic syndrome/insulin resistance </li></ul><ul><li>etc. </li></ul>
    15. 15. General (cell biologic processes) <ul><li>Flux through autophagosomes &proteosomes; dynamics of other organelles </li></ul><ul><li>Proliferation rate and differentiation of adult stem cells </li></ul><ul><li>The oxidation of nutrients, like fatty acids, by specific tissues including muscle, intestine or adipose </li></ul><ul><li>Tumor metabolic pathways </li></ul><ul><li>Caspases/apoptotic pathways </li></ul><ul><li>Non-invasivee measurement of synthesis of muscle protein, bone and cardiac collagen, or the proliferation of pancreatic beta cells </li></ul><ul><li>etc. </li></ul>
    16. 16. If you could measure any dynamic process in the body - what would you choose? <ul><li>If you could wave a magic wand: What would you really like to know when you hit a target? </li></ul>
    17. 17. Causal Pathways by Disease Category <ul><li>Neurodegenerative Diseases </li></ul>Neuro-psychiatry/ behavior Cancer Fibrotic Disorders Muscle Biology Dermatology Drug Toxicities Cardiometabolic Obesity/ T2DM Arthritis/ Inflammation Infection/ Immunity
    18. 18. Two New Kinetic Technologies <ul><ul><li>Dynamic Proteome </li></ul></ul><ul><ul><li>“ Water World” (Deuteron Fluxes/ Metabolic Pathways) </li></ul></ul>
    19. 19. <ul><li>Dynamic Proteome </li></ul>
    20. 20. Dynamic Proteomics 2 H 2 O-labelled cells plasma or tissues Enrich protein of interest (e.g., IP) SDS-PAGE Reduce, carboxymethylate Trypsinise LC/ESI-MS/MS analysis (Thermo OrbiTrap) Calculate fraction new protein ( f ) De Riva et al., in prep. Analysis of tryptic peptides from 2H2O-labelled proteins
    21. 21. LC/MS/MS analysis of tryptic peptide Relative abundance LC Chromatogram SIM: m/z ≈ 876.45 (M+H) 2+ MS1: mass spectrum of intact peptide m/z Retention time m/z MS2: fragmentation for sequencing A. De Riva et al., in prep.
    22. 22. Mass Shift In Plasma Albumin Peptides Fractional synthesis calculated from the change in %m0 Mass isotopomer fractional abundances %M1 %M2 %M3 %M4 %M0 m/z=1245 m/z=1487 m/z=826 Published half-life of albumin (17 d) Day 0 6 12 26 41
    23. 23. Mass Shift In Plasma IgA Peptides natural 100% labeled Mass isotopomer fractional abundances %M1 %M2 %M3 %M4 %M0 Day 0 6 12 26 41 h m/z=918 m/z=1172 Published half-life of IgA (6 d) Fractional synthesis calculated from the change in %m0 Predicted values CONFIDENTIAL PF BH KL JP 8-02.10
    24. 24. RELATIVE ABUNDANCES [ % ] UNLABELED PEPTIDE ISOTOPOMERS 2 H-PEPTIDE
    25. 25. F=0% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
    26. 26. F=25% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
    27. 27. F=50% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
    28. 28. F=75% ISOTOPOMERS RELATIVE ABUNDANCES [ % ]
    29. 29. ISOTOPOMERS RELATIVE ABUNDANCES [ % ] F=100%
    30. 30. Basic Principles of MIDA Precursor Pool Polymer/Product Isotope Pattern M2 M3 M0 M1 (M+0) (M+1) (M+2) (M+3) M0 M1 M2 M3 M4 p=1.1% Excesses (M+0) (M+1) (M+2) (M+3) p=5%
    31. 31. Serum Protein Synthesis Rates as Pathway Targets <ul><li>Clotting cascade </li></ul><ul><li>Complement activation cascade </li></ul><ul><li>Cytokines (IL-1, IL-6, TNF, IFN, etc.) </li></ul><ul><li>Insulin + pro-insulin </li></ul><ul><li>Adiponectin, leptin (adipokine sysnthesis) </li></ul><ul><li>Acute-phase GPs (  1-AGP, CRP, fibrinogen, etc.) </li></ul><ul><li>ApoB 100 (intestinal site assembly) </li></ul><ul><li>Serum CK [MB, BB], troponin </li></ul><ul><li>CDPs (bone derived, other tissue-specific) </li></ul><ul><li>Kallikrein system </li></ul><ul><li>Ag-specific antibodies </li></ul><ul><li>Ag-specific TCR </li></ul><ul><li>(Pancreas-derived proteins, for  -cell proliferation) </li></ul>
    32. 32. Example <ul><li>Axonal Transport of Cargo in Human Brain </li></ul>
    33. 33. Axonal transport: essential to neuronal health Proteins made here Proteins transported here Proteins used here Secreted Proteins
    34. 34. Clinical Translational Tool: CSF-based secreted biomarkers of MT-mediated fast axonal transport in ALS MT-mediated Fast axonal transport Healthy Neuron Degenerating Neuron Introduction of label Introduction of label Nucleus Time to Appearance in the CSF Nucleus 2 H – newly synthesized cargo molecules MTs Hyperdynamic MTs Time of CSF appearance 2 H – labeled secreted cargo molecules
    35. 35. Neuregulin-1, Chromogranin B and sAPPa exhibit altered CSF kinetics in symptomatic SOD1G93A mice ***p< 0.001 *p< 0.05 **p< 0.01 T max in CSF: 3d 2 H- labeled Chr-B (EM1) Time (day post-labeling) 2.0 0 2 4 6 8 10 12 0.0 0.5 1.0 1.5 * *** *** ** SOD1G93A Wild type 13 weeks ♀/♂ Body water 2 H- enrichment 2 H- labeled NeuR-1 (EM1) 0 2 4 6 8 10 12 0.0 0.5 1.0 1.5 2.0 Time (day post-labeling) SOD1G93A Wild type 13 weeks ♀/♂ 0 2 4 6 8 10 12 SOD1G93A Wild type 13 weeks ♀/♂ 0.0 1.0 2.0 3.0 4.0 Time (day post-labeling) ** *** ** * *** 2 H- labeled sAPP  (EM1) Time (day post-labeling) 0 2 4 6 8 10 12 0.0 1.0 2.0 3.0 4.0 5.0 6.0 *** * *** *** SOD1G93A Wild type 13 weeks ♀/♂
    36. 36. Pulse-chase Labeling – Healthy Control Patients 2 H-Enrichments
    37. 37. Pulse-chase Labeling – ALS Patient 0 10 20 30 40 50 0.0 0.5 1.0 1.5 2.0 Control subject #7033 and ALS subject #6082 2H-labeled Chr-B (EM1%) 0 10 20 30 40 50 0.0 0.5 1.0 1.5 2.0 2H-labeled sAPP  (EM1%) Time (day) Time (day) Chromogranin-B sAPP  CTRL 7033 ALS 6082 CTRL 7033 ALS 6082
    38. 38. Pulse-chase Labeling - Parkinson’s Disease Patient 2 H-Body Water 2 H-Enrichments Control subject #7033 and PD subject #6080 0 10 20 30 40 50 0.0 0.5 1.0 1.5 CTRL 7033 PD 6080 Time (day) 2 H-labeled Cargo (Fraction of Peak)
    39. 39. Sensitive clinical biomarker: kinetically distinct, disease-specific defects in neuronal transport? ALS- Defect #1: Gross delay representing global defect in bulk transport 0 10 20 30 40 50 0.0 0.5 1.0 1.5 2.0 2H-labeled Chr-B (EM1%) Time (day) PD - Defect #2: Transport is irregular uneven & less uniform CHASE 0 10 20 30 40 50 0.0 0.5 1.0 1.5 2.0 2H-labeled Chr-B (EM1%) Time (day) PULSE CTRL 7033 ALS 6082 CTRL 7033 PD 6080
    40. 40. Peptide Isotopomer Analysis of Control Subject #6079 9-16-10 BH JC KL Cystatin-C peptide m/z=613.81, z=2 ALDFAVGEYNK
    41. 41. 2 H-Enrichment in Pulse-Chase Subject #6079 (QTOF Analysis) #6079 Fraction Labeled
    42. 42. Proteome wide measure of protein turnover 1716 Unique proteins (total) 45 222 113 >10 peptides 71 244 165 5-10 peptides 111 343 353 2-4 peptides 107 313 379 1 peptide 334 1,122 1,010 Total proteins analyzed 1,968 7.226 4,619 Peptides utilized in protein analysis 3,340 11,643 10,389 Detected peptides (t0) Blood Liver Brain
    43. 43. Rapid POC in Humans Skin Keratin and Keratinocyte Dynamics in Psoriasis
    44. 44. Epidermal Keratin Turnover: Fsn Mice Glucocorticoid treatment has no effect on high turnover rate of keratin in fsn mice 0 0.02 0.04 0.06 0.08 0.1 0.12 0 5 10 15 20 25 Days on D2O EM1 FSN treated FSN untreated C57Bl/6
    45. 45. Kinetics of Human Skin Keratin (Tape Strips) Lindwall, JinvestDerm 2006 0.00 0.01 0.02 0.03 0.04 0.05 0 20 40 60 80 100 Day EM1 (Ala)
    46. 46. Skin Turnover in Psoriatic Patients e
    47. 47. Next Generation for Dynamic Proteome <ul><li>Emerging Causal Pathway Metrics </li></ul>
    48. 48. Clotting Rate in Vivo : Turnover (Consumption) of Coagulation Factors in Cascade
    49. 49. Complement Pathway: Turnover (Consumption)
    50. 50. “Water World” Platform for Causal Pathway Measurements: Deuteron Pathway Dynamics <ul><li>Cell kinetics </li></ul><ul><li>Histokinetics </li></ul><ul><li>Flux distributions/dynamic fingerprinting </li></ul><ul><li>Biosynthetic pathways </li></ul>
    51. 51. Label Pathways for Measuring DNA Synthesis G6P R5P PRPP Purine and Pyrimidine bases Precursors NDP dNTP DNA dN 2 H 2 O GNG Deoxy-ribonucleoside salvage 2 H 2 O 2 H 2 O Base Salvage DNPS 2 H 2 O Glycogen (RR) Glucose DNNS 3 H-dT, BrdU
    52. 52. Application: Patient Subgroup Selection in CLL Messmer et al., J Clin Invest 2005 GMH, 02/10/2008 Fast Turnover/Bad Disease Fraction labeled 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 50 100 150 200 250 WBCs (10 3 /mm 3 ) 100 10 Days Peak f = 68.1% f %/day = peak f days of labeling = 0.81% new cells/day (birth rate)
    53. 53. Application: Patient Subgroup Selection in CLL Messmer et al., J Clin Invest 2005 GMH, 02/10/2008 Slow Turnover/Stable Disease WBCs (10 3 /mm 3 ) Fraction labeled 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 50 100 150 200 250 100 10 Days Peak f = 15.2% f %/day = peak f days of labeling = 0.18% new cells/day (birth rate)
    54. 54. KineMed Test Distinguishes between Patients with Stable vs Progressive Disease KineMarker 
    55. 55. Translating Cell Proliferation into Solid Tumors: <ul><li>“ Histokinetics” </li></ul>
    56. 56. Micro-dissection of Normal & Tumor Tissues GMH, 02/10/2008 Tissue stained for SRPK1 (increased expression in tumors compared to normal epithelia) Benign epithelial Tumor (Grade 3) 1) Make a series of ~ six 5-10 micron thick tissue slides 2) Determine benign and tumoral areas to be excised from first and last stained slides 3) Micro-dissect mapped areas from slides in the middle
    57. 57. Micro-dissection of Normal & Tumor Tissues GMH, 02/10/2008 Determine tumor areas to excise
    58. 58. Micro-dissection of Normal & Tumor Tissues GMH, 02/10/2008 Micro-dissection of tumoral cells Into microfuge tube Derivatize & analyze
    59. 59. Human Breast cancer: DCIS vs normal tissue
    60. 60. Purification of PEC from Semen: GMH, 02/10/2008 Semen sample Remove enriched PEC fraction Viscolytic Enzyme Percoll Gradient Stain prostate-specific membrane antigen (PSMA-cell surface) & prostate-specific alkaline phosphatase (PAP-intracellular) SORT via flow cytometry Cytospin & Determine purity (via anti-CK staining) Pass through 50 μ m filter (removes squamous epithelial cells) sperm PEC +ve sort -ve sort fraction PSMA PAP
    61. 61. Proliferation Rates of ProstateTissue Microdissected Cells and Seminal Fluid PEC: Radical Prostatectomy Patients
    62. 62. Proliferation Rates of ProstateTissue Microdissected Cells and Seminal Fluid PEC
    63. 63. Correlation Between Proliferation Rates of Prostate Tissue Microdissected Cells and Seminal Fluid PEC PEC Tissue
    64. 64. Continuing Evolution of Histopathology: Adding the Dimension of Regional Molecular Kinetics <ul><li>Vital dyes: Structures visualized in living cells </li></ul><ul><li>Electron microscopy: images at angstrom level </li></ul><ul><li>Immunohistochemistry: Tissue characterization by molecular constituents </li></ul><ul><li>Histokinetics: molecular dynamics by tissue anatomic region </li></ul>
    65. 65. Rapid POC Testing in Humans <ul><li>Modulators of reverse cholesterol transport/HDL function </li></ul>
    66. 66. Reverse Cholesterol Transport: Measurement of Efflux, Plasma Transport and Excretion Arms ABCB11 ABCG5/8 Pre  -HDL CETP FC CE CE, FC CE, FC CE FC FC CE CYP7 FC BA FC BA LDLR FC/BA Cholesterol Efflux Cholesterol Excretion <ul><li>LCAT flux </li></ul><ul><li>CETP flux </li></ul>HDL LDL HDL ABCA1 ABCG1 SRBI
    67. 67. HDLc in Hypoalpha Patients ABCA1
    68. 68. Cholesterol Efflux in Hypoalpha Patients ABCA1
    69. 69. Esterification rate results in humans <ul><li>In seven subjects average esterification rate was 3.3 umol/kg/hr </li></ul><ul><li>Compared to average free C flux of 9.7 umol/kg/hr, esterification represents one third of the removal of free C from plasma </li></ul><ul><li>About 2g/day which is slightly excess compared to excretion of ca/ 1.5 g/day </li></ul>
    70. 70. Effect of ALP-1 on in-vivo esterification rate in rabbit P < 0.05 P > 0.05 +93 +133 +20% +47%
    71. 71. Relation of RCT Fluxes to Reduction in Atherosclerosis with rrLCAT Treatment Vehicle rrLCAT *** Vehicle rrLCAT 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Efflux Ra (mg/kg/hr) 0 5 10 15 20 25 30 Neutral Sterol Excretion (mg/day) 0.00% 20.00% 40.00% 60.00% Surface area with lesions (%) * Abdominal Aorta 60 - 40 - 20 - 0 0.00% 20.00% 40.00% 60.00% Surface area with lesions (%) * Abdominal Aorta 60 - 40 - 20 - 0 Vehicle rrLCAT Efflux Esterification Fecal Sterol Output 0 10 20 30 40 50 60 Rate of CE clearance (mg/day/rabbit) * Vehicle rrLCAT
    72. 72. Torcetrapib inhibits absolute rate of esterification 8 cmpA and 7 cmpB Torcetrapib inhibits rate of esterification * Rate of esterification Torcetrapib 0 5 10 15 Rate of esterification (mg/dl/6h) Vehicle
    73. 73. Applications of Causal Pathway Metrics <ul><li>“Less guessing” about: </li></ul><ul><li>Picking targets </li></ul><ul><li>Choosing chemical class and best compound in class </li></ul><ul><li>Identifying the right patients </li></ul><ul><li>Finding the best dose and regimen for early clinical trials </li></ul><ul><li>Selecting intermediate end-points to measure and variability to expect in patients </li></ul><ul><li>Anticipating toxicities </li></ul><ul><li>Testing whether personalization for features of the disease can improve response </li></ul><ul><li>Deciding whether to get out early </li></ul>
    74. 74. Applications: Picking targets and translating results quickly into humans <ul><li>Validating targets in a disease pathway </li></ul><ul><li>Differentiating from other therapeutic targets </li></ul><ul><li>Identifying targets with a big signal on the disease pathway </li></ul><ul><li>Selecting preclinical disease models that predict clinical response for the target </li></ul><ul><li>Rapidly testing targets in humans </li></ul>
    75. 75. Identifying the right patients and translating POC rapidly into humans <ul><li>Identifying pathogenic subsets </li></ul><ul><li>Establishing treatment-responsive and –unresponsive subsets </li></ul><ul><li>Understanding patient variability of response and effect size </li></ul><ul><li>Determining dose-response relationships in subsets </li></ul>
    76. 76. Putting It All Together for Complex, Multifactorial Diseases <ul><li>Recent Technical Advances Make It Possible </li></ul>
    77. 77. How to dissect and control complexity? <ul><li>Informatics approach : Measure many things (“Omics”); find statistical correlations, patterns, PCA, etc. </li></ul><ul><li>“ Convenient” biomarker approach : Measure static markers that can be measured </li></ul><ul><li>Causal pathway approach : As described here </li></ul>
    78. 78. Measurable Processes in Complex Diseases: AD/ Neurodegeneration APP Synthesis A  1-42 production Tau/MT Dysfunction Plaque accum. Neuronal Function Axonal Transport Neuro Transmitters Synaptic plasticity Plaque turnover Complement  -secr 1° NI Stimuli Neuro-inflammation Cytokine Synth Microglia Caspases, apoptosis Cell death
    79. 79. Measurable Processes in Complex Diseases: Neoplasia (Prostate Cancer) Androgens Growth factors Mutagens etc PEC proliferation Apoptosis Cellular pathways Clonal Selection PIN Prolif Angiogenesis ECM destruction Invasion Lymphangio-genesis Metastases Inflammation Cytokine Synth
    80. 80. Measurable Processes in Complex Diseases: Atherogenesis Diet Activity Genes Etc. TG ApoB particles DNL Obesity VLDL TG turnover FFA Release Adipose dynamics Macrophage proliferation HDL LDL RCT Complement Activation Cytokine Synth Coagulation Cascade Vessel Wall APGP Synth Inflamm-asomes C-VD Events
    81. 81. Proving causality of process/pathway in disease <ul><li>Two things are needed to prove causality of process/pathway in a disease: </li></ul><ul><li>Ability to measure the process </li></ul><ul><li>Testing of the measured process (against disease activity and outcomes) </li></ul>
    82. 82. What is proven (and provable) <ul><li>Hypotheses are testable: </li></ul><ul><ul><li>drug X hits target Y? </li></ul></ul><ul><ul><li>alters disease process Z? </li></ul></ul><ul><ul><li>alters outcomes in disease A? </li></ul></ul><ul><li>Easily proven (when technology there): </li></ul><ul><ul><li>Engaging target alters disease process </li></ul></ul><ul><li>What takes longer: </li></ul><ul><ul><li>Altering disease process alters outcomes (if and when) </li></ul></ul>
    83. 83. Summary: Simple Message <ul><li>Development of medicines can be done better </li></ul><ul><li>The solution is to measure the activity of pathways that causally influence desired and toxic outcomes, instead of guessing at each step in development </li></ul>
    84. 84. Galileo’s Spyglass <ul><li>“… the nature of matter of the Milky Way itself, which, with the act of the spyglass, may be observed so well that all the disputes that for so many generations have vexed philosophers are destroyed by visible certainty. And we are liberated from wordy arguments.” </li></ul><ul><li>Galileo </li></ul>

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