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1 Metabolomic Profiling in Drug Discovery: Understanding the Factors that Influence a Metabolomics Study and Strategies to Reduce Biochemical and Chemical Noise Mark Sanders 1 ;Serhiy Hnatyshyn 2 ; Don Robertson 2 ; Michael Reily 2 ; Thomas McClure 1 ; Michael Athanas 3, Jessica Wang 1, Pengxiang Yang 1 and David Peake 1 1 Thermo Fisher Scientific Inc., San Jose, CA; 2 Bristol Myers Squibb, Princeton, NJ 3 Vast Scientific, Boston, MA

2 Metabolomics in Drug Discovery Pattern recognition Good profile verses bad profile 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

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 Untargeted Analysis A comprehensive analysis of all metabolites A measure of the fingerprint of biochemical perturbations 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 Multiple sources of variability 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 Study Instrumental Mass and retention time stability Robust and stable detector response Sufficient resolution to resolve isobaric interference Chemical (Data Processing) Background from column/solvents Multiple signals per compound Setting the threshold Biological Different response rates to a stimulus between individuals Stress status Feeding status Other health factors Study Design Proper controls and randomized sampling/analysis to minimize systematic errors Sampling, sample preparation and storage Statistical Analysis Limited sampling, over fitting data

6 The First Benchtop Quadrupole Orbitrap LC-MS/MS

7 Q Exactive LC-MS/MS: Benchtop Quadrupole Orbitrap Quadrupole mass filter Quadrupole: hyperbolic rods Isolation down to 0.4 amu HCD collision cell Analogous to Thermo Scientific LTQ Orbitrap Velos Hybrid S-lens Stacked Ring Ion Guide Analogous to Thermo Scientific LTQ Velos LC-MS n Shorter inject times Parallel Processing Ions collected in C-trap while orbitrap is scanning Advanced Signal Processing Improved resolution Faster acquisition speed Precursor ion selection for SIM and MS/MS functionality Higher scan speed

8 What do we Want in a Good HRAM Instrument? Accurate mass stability Robust accuracy over extended periods set it and forget it Ability to do pos/neg switching within a run and maintain accuracy Speed Compatibility with the most demanding UHPLC separations Resolution Primary discriminator for the analytes of interest Want as much as we can get without compromising sensitivity Sensitivity As good if not better than a triple quad

9 Q Exactive LC-MS/MS: Mass and Response Stability D 5 -hippuric acid, external calibration, resolution = 82,000 Chromatograms XIC ± 5ppm FWHM = 1.86 sec :51pm 11:14pm 3:32am ppm -0.72ppm -0.24ppm Mass Spectra -0.04ppm ppm CV = 2.4% Time (min) Instrumentation 8:08am ppm Ext.Cal hrs m/z 0.39ppm ppm

10 Q Exactive LC-MS/MS: Speed Resolution Setting: 35,000 Resolution Setting: 70, Relative Abundance Relative Abundance Time (min) Time (min) Peakwidth (FWHM) ~ 1 sec Scans/peak = 21 Peakwidth (FWHM) ~ 1 sec Scans/peak = 11 Instrumentation

11 Q Exactive LC-MS/MS: Resolution Setting - 70, S C m/z m/z Calculated 35,000 Resolution C 17 H 27 O 3 S m/z Instrumentation

12 Q Exactive LC-MS/MS: High Sensitivity Quantitation Testosterone 10pg/mL in Serum Area Ratio Standard pg/ml % Difference Testosterone 10 pg/ml in Serum Concentration (pg/ml) Instrumentation

13 Mass Accuracy with Polarity Switching (External Calibration) 1 positive + 1 negative scan in 1 second Instrumentation

14 Apex Triggered Data Dependant MS/MS O 0.8ppm HO NH 2 O NH 2 Kynurenine ppm ppm 0.8ppm ppm ppm 0.4ppm ppm Time (min) m/z Instrumentation

15 Anatomy of a UHPLC/Orbitrap Data Set Other (5%) [M+H] m/z window = Da z = m/z Z=2 12% Z=3 (10%) Data Processing Z=1 (73%) Adduct % Assignments [M+H] [M+Na] [M-H2O+H] [2M+H] [M+NH 4 ] [2M+Na] [M+K] [M-2(H 2 O)+H] [M+CH 3 CN+H] >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 mass analyzer allows for more precise automated data processing Need to be able to reduce the data to chemical entities

16 Background Subtraction Sample - Solvent blank = Analyte signals Data Processing ~98% of lower intensity signals are eliminated

17 Spectral Interpretation Rat Urine [M+H] m/z [M+H] e7 Hippuric Acid O N H O OH m/z [2M+Fe-H]+ 3e5 ESI+ ESI - 12 related ions 24 related ions m/z [3M+Ca-H]+ 1e6 [M+Na]+ [2M+H] m/z m/z [3M+Fe-2H]+ 9e Time (min) Data Processing

18 Spectral Interpretation Rat Urine [M+H] Fe Isotope Pattern Detected Measured m/z [M+H] e m/z [2M+Fe-H]+ 3e Theoretical C 27 H 25 N 3 O 9 Fe m/z [3M+Ca-H]+ 1e m/z [M+Na]+ [2M+H] m/z m/z [3M+Fe-2H]+ 9e Time (min) Data Processing

19 Varying Response with Different Ion Species Rat Urine Hippuric Acid M+H [2M+Fe-H]+ [3M+Ca]+ [3M+Fe-2H]+ 122,738, ,212 2,576, , ,451, ,794 2,471, , ,092, ,807 2,234, , ,057, ,152 2,167, , ,387, ,090 2,138, , ,957, ,476 2,157, , ,947, ,089 2,291, ,314 2,858, , ,409 47, % 19% 8% 10% Trp Phe 415,983 2,574, ,085 2,614, ,093 2,494, ,342 2,479, ,358 2,448, ,844 2,439, ,951 2,508,562 6,047 70, % 2.8% Data Processing

20 Varying Response with Different Ion Species Rat Urine Hippuric Acid M+H [2M+Fe-H]+ [3M+Ca]+ [3M+Fe-2H]+ 122,738, ,212 2,576, , ,451, ,794 2,471, , ,092, ,807 2,234, , ,057, ,152 2,167, , ,387, ,090 2,138, , ,957, ,476 2,157, , ,947, ,089 2,291, ,314 2,858, , ,409 47, % 19% 8% 10% Trp Phe M+H M+H 415,983 2,574, ,085 2,614, ,093 2,494, ,342 2,479, ,358 2,448, ,844 2,439, ,951 2,508,562 6,047 70, % 2.8% Data Processing

21 Importance of Spectral Interpretation m/z = Dosed Control [M+H] e7 [M+H] Time (min) Component ion e Time (min) [2M+H] m/z Data Processing

22 Removing Noise from the Statistics m/z Peaks Components Large intra group variability Female Fed Fasted No group separation Male Data Processing

23 SIEVE Analysis Platform Statistically rigorous automated label-free LC/MS differential analysis platform State 1 Raw file State 2 raw file State raw file Workflow Align Detect Identify Reports: Components Identification Relative Quantitation Statistical Analysis Trend information Data Processing Applied to: peptide, protein, small molecule data

24 SIEVE Workflow Alignment Unaligned basepeaks Data Processing

25 SIEVE Workflow Alignment Aligned basepeaks Alignment scores Data Processing

26 SIEVE Workflow Alignment 1. Full scan spectra are acquired with high mass accuracy. Data File 1 Reference Data File 2 Data Processing

27 SIEVE Workflow Alignment 1. Full scan spectra are acquired with high mass accuracy Data File 1 Reference 2. The spectra are binned. Data File 2 Data Processing

28 SIEVE Workflow Alignment 1. Full scan spectra are acquired with high mass accuracy. Data File 1 Reference 2. The spectra are binned. 3. A dot product correlation is calculated between each pair of spectra X Data File 2 Data Processing

29 SIEVE Workflow Alignment Scan # data file 1 Scan-to-scan correlation: Red High Green Low Scan # data file 2 Data Processing

30 SIEVE Workflow Alignment 5 An overlapping tile is constructed from the next region starting from the middle of the optimal path. Data Processing

31 SIEVE Workflow Alignment 6 An overlapping tile is constructed from the next region starting from the middle of the optimal path. The full plane is tiled and a final alignment score is calculated. Overlapping measurements are averaged Data Processing

32 Component Detection Adducts, fragments and multimers , z=1, I=4.2E+08, 100% , z=1, I=1.0E+08, 24.6% , z=1, I=1.1E+06, 0.3% [M+H]+ [M+Na]+ [M+K]+ Isotopic peaks A , I=1.2E+08, 28.9% A , I=2.3E+07, 5.5% A+3 A , I=3.0E+06, 0.7% , I=3.9E+05, 0.1% A+1 Isotopic peaks A , I=2.9E+07, 27.8% , I=5.6E+06, 5.4% A , I=9.0E+05, 0.9% Data Processing Constituents are represented by base component

33 Frame / Feature Frame: a well defined rectangular region in the M/Z versus Retention Time plane. L-Epicatechin MW = Data Processing

34 L-Epicatechin # Blend Location 1 zinfandel Lake 10 petite sirah Lake 13 zinfandel Lake 36 cabernet sauvignon Mendocino 37 petite sirah Mendocino 2 cabernet franc Napa 3 cabernet franc Napa 20 petite verdot Napa 21 cabernet franc Sonoma 25 cabernet sauvignon Sonoma 33 merlot Sonoma 35 merlot Sonoma 44 cabernet sauvignon Sonoma L-Epicatechin MW = Data Processing

35 Accurate Mass Identification Component MW chemspider web service Local database Data Processing MolWt Expression Name L-Epicatechin Epigallocatechin D-glycoside of vanillin Vellokaempferol 3-5-dimethyl ether Velloquercetin 4 -methyl ether Epigallocatechin 3-O-(4-hydroxybenzoate) Epigallocatechin 3-O-cinnamate Quercetin 4 -galactoside Epigallocatechin 3-O-caffeate Epigallocatechin 3-O-(3-O-methylgallate) Isorhamnetin 7-alpha-D-Glucosamine;Quercetin 3 -methyl ether 7-alpha-D-Glucosam Q i 7 l id List of candidates

36 Rat Fasting Study Study designed to monitor the effect of fasting on metabolic profiles Biology Group Male Fasting Time 1 1: Dark Cycle Control (no Fast) hr Fast hr Fast hr Fast hr Fast hr Fast 1 Rats fasted during a 6 p.m. to 6 a.m. dark cycle to capture peak feeding time Samples: 50uL Serum ppt with cold MeOH MS: Q Exactive 70K resolution, ESI+ and ESI- UHPLC: Accela 1250 Pump Column: Hypersil GOLD aq 2.1x150mm, 600µL/min, 50ºC Buffers: A: 0.1% formic acid in H 2 O, B: 0.1% formic acid in 98:2 ACN:H 2 O

37 Pooled Quality Controls Same Sample IS citrulline Tyr Phe Trp _1.07 pooled QC 20,903, ,474 18,350,003 19,904,399 20,685,704 10,918,321 pooled QC 22,076,315 1,041,539 20,227,547 20,984,429 22,968,636 9,599,500 pooled QC 22,088,562 1,182,143 20,853,789 21,040,901 23,310,086 9,010,457 pooled QC 22,052,324 1,205,426 20,390,553 21,477,887 23,583,964 8,213,523 pooled QC 22,042,181 1,153,795 21,417,740 22,061,286 23,215,235 6,456,432 pooled QC 22,778,779 1,244,100 21,862,115 21,822,323 23,765,745 3,499,156 3% 9% 6% 4% 5% 33% Inj. # Sample 1 Sample 2 Sample 3 Component of Interest Pooled QC Treated Control Sample 52 Pooled QC *Sangster, et. al., Analyst, 2006, 131, Biology QC Treated Control

38 Finding the Differences PCA, rat plasma negative mode 4hr 12hr Control - Fed Pooled Controls 16hr Biology

39 Examples of Metabolite Changes on Fasting 450,000, ,000, ,000,000 Methionine 1,200,000,000 1,000,000,000 Proline 300,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000,000 50,000, ,000, ,000, ,000,000 QC Blank DC 2h 4h 8h 12h - QC Blank DC 2h 4h 8h 12h 140,000, ,000, ,000,000 Arachidonic Acid Linoleoyl-lyso-PC (18:2) 50,000, ,000,000 80,000,000 60,000,000 40,000,000 20,000,000 - QC Blank DC 2h 4h 8h 12h - QC Blank DC 2h 4h 8h 12h Biology

40 Overall Method Robustness Uric Acid: Positive and Negative Data 300,000, ,000,000 Negative ion 200,000, ,000, ,000,000 50,000, ,000,000 90,000,000 80,000,000 70,000,000 60,000,000 50,000,000 40,000,000 30,000,000 20,000,000 10,000,000 QC Blank DC 2h 4h 8h 12h Positive ion Time between analysis 30 hrs QC Blank DC 2h 4h 8h 12h Biology

41 Study Findings Fasting has profound impact on metabolomic profiles Most metabolic changes are modest in extent Fasting-status may exacerbate or obscure druginduced metabolic effects. Fasting data help contextualize drug-induced changes in many metabolites As part of the study design, fasting is neither right or wrong but it is a significant variable in model design Biology

42 Summary Metabolomics is very challenging. It is fraught with numerous sources of noise and the cost of going down the wrong path is high Instrumentation Needs to be precise and robust good quality in, good quality out Q Exactive LC-MS/MS provides an ideal platform Excellent mass accuracy with external calibration Ultra high resolution without loss of sensitivity High performance quantitation Discovery and validation on the same platform Chemical Noise (Data Processing) The right software and the right controls can make all the difference Intelligent data reduction tools can significantly reduce noise Biological Noise Needs to be understood through systematic studies Metabolomic prescreening can identify biological outliers Ensure homogeneity within the study

43 The Power of SIEVE software for Differential Analyses

44 Comparison of Palm Oil Samples Relative Abundance Relative Abundance Adulterated 4.89 Control Time (min) E E10

45 Comparison of Palm Oil Samples Relative Abundance Relative Abundance Adulterated Control T 2 T g Time (min)

46 SIEVE software for Differential Analysis Easy to use wizard walks you through the process and parameters of a differential analysis and unknown identification

47 SIEVE software for Differential Analysis

48 SIEVE software for Differential Analysis

49

50 Thanks! Serhiy Hnatyshyn Michael Reily Don Robertson Jessica Wang Pengxiang Yang Michael Athanas Thomas McClure David Peake Kate Comstock Yingying Huang Patrick Bennett Markus Kellmann Catharina Crone Thomas Moehring Alexander Makarov Eugen Democ Frank Czemper Sebastian Kanngiesser Andreas Wieghaus

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