1. Metabolomic Profiling in Drug Discovery: Understanding the Factors that Influence a Metabolomics Study and Strategies to Reduce Biochemical and Chemical Noise
Mark Sanders1;Serhiy Hnatyshyn2; Don Robertson2; Michael Reily2; Thomas McClure1; Michael Athanas3, Jessica Wang1, Pengxiang Yang1 and David Peake1
1Thermo Fisher Scientific, San Jose, CA;
2Bristol Myers Squibb, Princeton, NJ
3Vast Scientific, Boston, MA
Talk is too long and will need to be edited. Edits will depend on how data looks after it has been processed. Slides also need to be cleaned up in terms of consistent font sizes, bullet formats, etc. Some graphics need to be cleaned up.

2. Metabolomics in Drug Discovery
Identification and quantitation of endogenous “markers”
Compound selection
Target effects – efficacy markers
Off-target effects – toxicity/liability markers
Identification of markers provides mechanistic insights
Target validation
Mechanism of toxicity
Early evaluation of potential clinical markers
Ensure homogeneity within an animal study
Prescreen for outliers and remove them from the study

3. Metabolomics in Drug Discovery
Targeted Analysis
Metabolite target analysis
Analysis restricted to metabolites of an enzyme system that are known to be affected by a certain perturbation
Metabolite profiling
Analysis focused on a class of compounds associated with a particular pathway (e.g. nucleoside triphosphates, lipids, steroids, etc.)
Only find what you are looking for
Metabonomics
A comprehensive analysis of all metabolites
A measure of the fingerprint of biochemical perturbations – pattern recognition
Useful when you don’t know what to expect
Hypothesis generation

4. Metabolomics Analysis
Goals
Quantitative assessment of the biochemical makeup of the samples
Differential analysis between sample groups
Identify compounds responsible for changes
Challenges
Complexity of a biological sample
Diversity of small molecule metabolites
Wide range of metabolite concentration
Incomplete information – majority of components seen by LC/MS are unknowns
Structure elucidation of unknowns is expensive
Need sophisticated data reduction tools and strategies to minimize “noise”

5. Sources of Noise in a Metabolomics Experiment
Instrumental
Mass stability
Retention time stability
Sufficient resolution to resolve isobaric components
Chemical
Background from column/solvents
Multiple signals per compound
Biological
Different response rates to a stimulus
Stress status
Feeding status
Other health factors
Study Design
Proper controls and randomized sampling/analysis to minimize systematic errors
Statistical Analysis
Limited sampling
Over fitting data
Going to concentrate on Instrument, Chemical, Biological

6. Instrumentation: Q Exactive
Precursor ion selection for SIM and MS/MS functionality
HCD Collision Cell
Quadrupole Mass Filter
Quadrupole mass filter
Quadrupole: 4 mm, hyperbolic rods
Isolation down to 0.4 amu
HCD collision cell
Analogous to LTQ Orbitrap Velos

7. Instrumentation: Q Exactive
More sensitive ion source
S-lens
Stacked Ring Ion Guide
Analogous to Velos Pro
Shorter inject times for MS/MS and SIM
S-lens

8. Instrumentation: Q Exactive
Higher scan speed
Spectrum Multiplexing
Spectrum Multiplexing
MX SIM: Detect multiple C-trap fillings in one FT scan
MX MS/MS: Detect multiple HCD Cell fillings in one FT scan
Enhanced FT
Improved resolution Or
Faster acquisition speed
Enhanced FT

9. Instrumentation: HCD MS/MS to Confirm Identity
Extracted Rat Serum
Base Peak Chromatogram
1.7e7 60 80
100
120
140
160
180
200
m/z C 6 H 8 N 9 O 10 2 3 7 4
MS/MS of 209.1
10.01
Kynurenine
10.84
2.22
1.51
5.99
9.68
0.57
8.10
6.34
8.94
3.71
5.18 1 2 3 4 5 6 7 8 9 10 11
Time (min)

10. Instrumentation: Cycle Time for TOP10 HCD Method
100 80 60 40 20 20 25 30 35 40 45 50
Time (min)
1.086 sec
32.245
32.250
32.255
32.260
32.265
32.270
Time (min)
Apex Triggered MS/MS
2.8
2.9
3.0
Time (min)

11. Instrumentation: Q Exactive - Mass Stability
-0.45ppm
XIC ± 5ppm
4.28
Resolution = 82,000
7:51pm
Ext.Cal hrs
-0.04ppm
-0.72ppm
4.27
FWHM = 1.86 sec
11:14pm
-0.31ppm
4.28
-0.24ppm
3:32am
0.39ppm
4.27
-0.72ppm
8:08am
Ext.Cal hrs
-0.47ppm
4.15
4.20
4.25
4.30
4.35
4.40
Time (min)
185.5
m/z
186.0

12. Instrumentation: Q Exactive - Resolution
34S
Calculated
Measured
13C2
Exactive
25,000 Resolution
313.14
313.16
313.18
313.20
m/z
313.12
313.14
313.16
313.18
313.20
313.22
311.0
311.5
312.0
312.5
313.0
313.5
m/z
m/z

13. Instrumentation: Q Exactive - Speed
Example of a 3 sec wide peak run at 35K and 70K on Q Exactive

14. Instrumentation: Quantitation
Example of Full Scan and SIM for Quantitation
Does not have to be an endogenous compound
Markus should have this data

15. Need to be able to reduce the data to chemical entities
Chemical: Anatomy of a U-HPLC/Orbitrap Data Set
m/z window = ± 1 Da
100
200
300
400
500
600
700
800
900
1000
[M+H]+
m/z
853.0
853.5
854.0
854.5
855.0
z =+2 5
>1,000,000 data points
~100,000 extracted ion peaks.
Peak area ranges ~ 7 orders
Much irrelevant data
Much redundant data
High quality data from the Orbitrap allows for more precise automated data processing
Removing chemical noise +3 +2
+4
Need to be able to reduce the data to chemical entities +1

16. ~98% of lower intensity signals are eliminated
Chemical: Background Subtraction
Sample
Solvent blank
= Analyte signals
Removing chemical noise
~98% of lower intensity signals are eliminated

17. Chemical: Spectral Interpretation
m/z [M+H]+ 2 3 4 5
3.37
4e7
3e5
1e6
9e5
m/z [3M+Ca-H]+
m/z [3M+Fe-2H]+
m/z [2M+Fe-H]+
Time (min)
[M+H]+
180.07
Hippuric Acid
ESI+ 12 related ions
ESI - 24 related ions
[2M+H]+
[M+Na]+
359.12
202.05
413.04
576.13
100
150
200
250
300
350
400
450
500
550
600
m/z

18. Chemical: Spectral Interpretation
m/z [M+H]+ 2 3 4 5
3.37
4e7
3e5
1e6
9e5
m/z [3M+Ca-H]+
m/z [3M+Fe-2H]+
m/z [2M+Fe-H]+
Time (min)
Fe Isotope Pattern Detected
[M+H]+
180.07
590
592
594
m/z
Measured
Theoretical
C27H25N3O9Fe
[2M+H]+
[M+Na]+
359.12
202.05
413.04
576.13
100
150
200
250
300
350
400
450
500
550
600
m/z

19. Chemical: Varying Response with Different Ion Species

20. Chemical: Removing Noise from Statistics
Components
Loss of group separation
m/z Peaks
Increased intra group variability
Fed
Fasted
Female
Male
Clear distinction between groups once noise is removed

21. Software: Approach to Data Reduction
Sample
A complex mixture of compounds.
Component Ion
An ion that can be used to represent a detected compound.
Both dimensions of LC/MS analysis have challenges.
Reproducibility of chromatographic peak position from injection to injection
Electrospray ionization generates complex, redundant MS data
Automation
Software should automatically analyze the data as an analyst would manually
Interpret the spectra and identify component ions
Charge state
Isotopes
Adducts
Set the component parameters based on the run with the most intense signal
Slight shifts of chromatographic peak can be observed even for different adducts of the same component in the same sample.
Irregular retention time shifts between injection requires special peak alignment procedure to compare different samples.
The same chemical entity may be represented by several related peaks – isotopic peaks and/or adducts, dimers, fragments, etc.
Monoisotopic molecular weight of the component is calculate based of composition of m/z values of the peaks in the component’s group.
Quantitative representation for the component can be the sum of areas of all contributing peaks or any individual signal which shows the linear response to the component’s concentration.
Want a 1:1 correlation of component ions to compounds

22. Software: Sieve 2.0 Workflow
Provide screen shots when available

23. Biological: Study Description/Purpose
Study designed to monitor the effect of fasting on metabolic profiles
Don’t want to confuse fasting effects with drug or disease induced effects
Waiting on BMS for clearance on how detailed the study description can be.

24. Biological: Results in Sieve 2.0
Michael to provide plots once software is ready

25. Biological: Findings
Fasting has profound impact on metabolomic profiles
Most metabolic changes are modest in extent
Fasting-status may exacerbate or obscure drug-induced metabolic effects.
Fasting data help contextualize drug-induced changes in many metabolites
Fasting is neither “right” or “wrong” but it is a significant variable in model design
Waiting on BMS Toxicologist interpretation of the data and provide biological significance – should look similar to this

26. Summary
Comprehensive or untargeted metabolomics is very challenging. It is fraught with numerous sources of noise and the cost of going down the wrong path is high
With the right software and hardware many of the sources of noise can be minimized
Biological noise needs to be understood through systematic studies such as the one described here
More detailed summary once the data has been processed and interpreted.