# Presentation: 120348

**Technologies for Metabolic Profiling**

**Stephen C. Brown**
**Research Fellow, Chemistry**

***Esperion Therapeutics, a ***
***division of Pfizer Global R&D***
***3621 S. State St.***
***Ann Arbor, MI 48108***

***Metabolomics ***
***NCI, Rockville MD***

## Metabolomics: A Rosetta Stone bridginggenomics, proteomics to drug discovery?

***Metabolomics: The complete characterization of endogenous ***
***metabolites in a biological system at a defined state ***

**DNA **

**mRNA**

**Proteins**** **

**Metabolites**

**Genotype**

**Environment**

**Drug Target**

**Biomarker**

**MOA**

**Toxicology**

**Phenotype**

## Systems Biology and Biomarkers

**NIH Roadmap ($70M/5 years)**
**Understanding disease and interventions**
**LIPID MAPS (RAW 264.7, E. Dennis UCSD)**
**Surrogate markers for clinical endpoints**
**FDA Initiatives**
**Concept Paper: “Drug-Diagnostic Co-Development” April 2005**
**MolPAGE (EU)**
**MGED-MIAME ()**

## Strategic Issues

**Separations Required?**
**“****Universal Detector”**
**Metabolism Models and Databases**
**Fluxes or Levels?**
**Accuracy and Precision Requirements?**
**Subset Analytes?**
**Lipomics**
**Glycomics**
**Peptidomics**
**RedOx actives**

## Technologies for Metabolic Profiling (Metabolomics, Metabonomics)

**Bioanalytical Methods**
**Mass Spectrometry***** ******	******Best sensitivity, quantitation variable***
**FTMS**** (FT-ICR)**
***Highest Mass Resolution***
***Highest Mass Accuracy***
**LC/MS/MS**
**GC/MS/MS**
**NMR  ****	****	****	*****Low sensitivity but very quantitative***
**Solution NMR of biofluids (Metabonomics)**
**Solid State NMR of tissues**
**LC-Coulometric Electrochemical Array**
**X-ray Fluorescence (metal cations)**
**Proteomic**
**Multiplexed Luminex (e.g., RBM)**

**Notes:**
Coulometric Electrochemical Array: PharmaGenomics (January, 2004), 46-52

## Data Analyses and Modeling

**Sampling Considerations**
**Cell Compartments, Tissue Composition, Diurnal Variance**
**Nutritional Status**
**“****Reactivity”, Preservatives**
**Data Analyses**
**Unsupervised Multivariate (HCA, PCA)**
**Supervised PLS “variable influence on projection”**
**False Discovery Rates!! (Benjamini-Hochberg Adjustment)**
**Models**
**Animals**
**Systems Biology-Mathematical Models**

## Example 1NMR-Metabonomics

** ****Metabonomic Differentiation of Organ-Specific Toxicants**

## Toxicology Study Details

**4 male Wistar rats/dose group**
**Two dose groups + control group**
**Urine samples collected daily before and after acute dose (5 time points)**
**Compounds:**
** ****a-Napthylisothiocyanate (ANIT)**
** ****Biliary liver toxin 10 & 100 mg/kg**
*** ******p*****-aminophenol (PAP)**
**Proximal tubular kidney toxin 15 & 150 mg/kg**

## ANIT 100 mg/kg

- citrate

- 2-oxoglutarate

- creatinine

- creatine

- hippurate

- TMAO

- succinate

## PAP 150 mg/kg

- glucose

## Control Rat

## Slide 12

## Pattern recognition techniques capture the  systematic variation within the spectral variables and can be used to construct  classification models

## Example 1 Conclusions

**Different stressors produce different metabolite profiles**
**Profiles are reproducible enough to build classification models**
**Lays groundwork for metabonomics-based toxicity screening paradigm**

## Example 2FTMS-Metabolomics

** ****Drug Mechanism of Action**

## Advantages of FTMS Metabolic Profiling

**Ultra High Resolution:****	****       *****Resolve Complex Mixtures***
**Ultra High Mass Accuracy:      *****Unambiguous Molecular ******	******	******	******	******	******Formula***
**Ultra high peak capacity:****	****	*****Less need for fractionation***
***                                                         ******	******Simple sample preparation***
**Global profiling:    *****Simultaneous analysis by +/- ESI, APCI***
**Detailed profiling: ****	*****Structural elucidation by MS******n****** ***

## Slide 17

## Slide 18

## ESP 55016 vs. Vehicle in Rats

**SD rats treated with 100 mg/kg (p.o. gavage)**
**Sacrificed 1.5 hr. post-dose**
**Livers Freeze-clamped**
**Liver tissue aliquot extracted (17:0)-CoA IS added**
**Extract passed through C18 Sep-Pak, eluted MeOH**
**Infused into FTMS, ESI****-**** ****mode**
**FTMS peaks normalized to (17:0)-CoA**

## PCA Scores  (71.4%,9.7%,8.2%)

**Notes:**
PCA

# of Included Samples:	10
# of Included X vars:	91
Preprocessing:	Mean-center
Rotation:	None
Maximum factors:	9
Optimal factors:	5
Validation:	None
Prob. threshold:	0.9500
Calib Transfer:	Not enabled
Transforms:
   SNV

Algorithm date:     02/09/2004 15:29:13.0132

## PCA Loadings

**Notes:**
9: 255.2322 palmitate
14: 281.2477 oleate
16: 283.2634 stearate
63: 391.2855 deoxycholate, chenodeoxycholate
68: 405.2646 dihydroxy-oxo-cholanoate
73: 407.2804 cholate
78: 408.2834 cholate 13C
104 : 462.2860 oxo-glycocholate
106: 464.3020 glycocholate
119: 498.2900 taurodeoxycholate, taurochenodeoxycholate
129: 512.2689 taurooxocholate
131: 513.27235 taurooxocholate 13C
133: 514.2848 taurocholate
136: 515.2879 taurocholate 13C
207: 715.5914 
243: 885.5498 
264: 1018.3528 (17:0)-CoA
275: 1040.3301 (17:0)-CoA Na
296: 1092.3492 ESP 41139
327: 1156.3818 cholyl-CoA

## ESP55016 vs. CoA-ESP55016

## Cholate vs. Palmitate

## Glycocholate vs. Cholate

## Cholyl-CoA vs. Cholate

## Slide 26

## Example 2 Conclusions

**In livers of ESP-55016 treated rats (100 mg/kg)**
**Free fatty acids are down-regulated, especially (16:0) and (18:1)**
**Bile Acids are up-regulated, but glycine conjugates have been suppressed.**
**Variance of both ESP-55016 and the CoA conjugate in livers is high.**

## Acknowledgements

**Esperion Therapeutics, PGRD**
**Stephen C. Brown**
**Clay Cramer**
**David Thibault**
**Rose Ackermann**
**Ann Arbor , PGRD**
**Michael Reily**
**Don Robertson**
**David Baker**
**Dale Wells**

## Pirouette 2D-plot comparisons

**Pirouette 2D-plot comparisons**
**ESP-55016**
**Bile Acid Metabolism**

## Hierarchical Clustering Analysis (HCA)

**Notes:**
HCA-8

Preprocessing:	None
Distance:	Euclidean
Linkage method:	Group Average
Orientation:	Sample
Transforms:
   SNV

Algorithm date:     02/09/2004 14:57:25.0260

## Principle Components Analysis (PCA) Convergence Measures

**Convergence of Factors (5)**

**Outlier Diagnostics**

## Cholate vs. Stearate

## Taurocholate vs. Cholate

## Cholyl-CoA vs. CoA-55016