Comprehensive Metabolite Profiling in a Discovery...

Comprehensive Metabolite Profiling in a Discovery Environment, Coupled Workflows Enabled by HRMS

Jonathan L. Josephs1, C. Emily Luk1, Kate Comstock2,

Tim Stratton2, Yingying Huang2, and William G. Humphreys1

1Bristol-Myers Squibb, Hopewell, NJ 2Thermo Fisher Scientific, San Jose, CA

1 1

Thermo NE Users Meeting, Somerset, NJ October 12, 2011, 1:50pm

Where we look and why

2

The Success of SRM quantiation on Triple Quads has led to a mindset of targeted

quantitation methods

Establishment of departments

devoted to targeted quantitation

All quantitation done in a targeted manner

https://fscimage.fishersci.com/images/F80639~wl.jpg�


3

A New Mechanism of Selectivity with Full Scan HRMS

Full Scan High Resolution Accurate Mass Complete spectrum acquired for every scan All data for all components acquired all the time

Selectivity achieved by isolating only the “exact mass” of the component of interest Like focusing a camera or microscope on the particular item

of interest – Difference is that “focusing” may be done post acquisition

such that you may focus on something that you had no knowledge of prior to the experiment.

“Method free”quantitation of multiple components High mass accuracy aids structural elucidation Faster structural assignments Improved certainty of assignments Greater suitability for automated structural assignment

approaches Integrated Metabolite ID/Quantitation datasets 5

286.5 287.0 287.5 288.0 288.5 289.0 289.5 290.0 290.5 291.0 291.5 292.0 m/z

288.04

289.12 290.06

287 288 289 290 291 292 m/z

288.0258

289.0292 290.0215


6

Drug Discovery and Data Drug discovery and development is an integrated science

Expert scientists from a number of disciplines with a broad knowledge of drug discovery collaborate on integrated teams

HRMS allows the collection of more integrated datasets Streamlining of experiments and sample collection/distribution Interrelated data are obtained from the same system

– Metabolic rate/metabolite structures – PK/PD in the same animals – Metabolite profiling/estimation in FIH studies

Interpreted data streams normally integrated by a collaborative team Collection of integrated datasets increases the speed and

quality of interpreted data Facilitates the use of the combined knowledge in decision

making

7

Parent PK

TOX

Parent TK Biomarkers

Biotransfromation

Metabolite Profiles

Metabolite ID

Future ADMET Process?

DMPK Bioanalytical Analytical

BIOL CHEM

HRMS Data Collection Lead Profiling/Bioanalytical

/Analytical /Biotransformation Raw Data Acquisition

Discovery Working Group

Process/Interpret Data

Convert Interpreted Data into Knowledge

Drive Program Decisions

8

LC/MS is not a Universal Detector LC/MS is normally effective in detecting “drug like” compounds.

However it is not a universal detector Compound response is dependent on

– Compound structure – Mobile phase composition – Coeluting matrix

Quantitation by LC/MS Normally requires a calibration curve prepared from an authentic

standard. Quantitation of “unknowns” requires a “universal” detector

Radioactivity is considered to be universal UV detection at 220nm is more universal than MS response Preparation of “metabolite standards” from a high substrate

concentration in vitro incubation allows determination of UV response (or radioactivity response)

Sample dilution and analysis by LC/MS affords a UV:MS response factor (or RAD:MS response factor)

Samples run by LC/MS may then be corrected for response. 9

*Petia Shipkova, Jonathan Josephs, Mary Grubb, Robert Langish, Weiqi Chen, Mark Sanders, ASMS 2004

HRMS Integrated Qual/Quan Approach

In vivo quantitation Parent and metabolites

Metabolite “Standards” Buspirone Metabolite Time Course

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0Time (min)

UV

Co

rre

cte

d A

rea

0

1

10

100

10 20 30 40 50 60

0

1000

2000

3000

4000

5000

6000

7000

0 1 2 3 4 5 6 7 8 9

Time (hr)

Cal

cula

ted

UV

area

High substrate concentration in vitro

incubation

In vitro quantitation Parent and metabolites

Low substrate concentration in vitro incubation

In vitro metabolite ID

Accurate Mass LC/UV/MS/MS

10

* **Yanou Yang, Mary F. Grubb, Chiuwa E Luk, W. Griffith Humphreys and Jonathan L. Josephs, Rapid Commun. Mass Spectrom. 2011, 25, 3245

*

HRMS Integrated Qual/Quan Approach

t0 t45

+NADPH 30 µM

t0 t2.5 t5 t10 t15 t20 t30 t60

+NADPH 0.5 µM

t0 t2.5 t5 t10 t15 t20 t30 t60

+NADPH 0.5 µM

t0 t2.5 t5 t10 t15 t20 t30 t60

-NADPH 0.5 µM

= 2 Samples

= 24 Samples

1 compound = 2 + 1 + 24 = 27 Samples 6 compounds = 27 x 6 = 162 Samples Analysis time = 162 x 10 = 1620 mins = 27 hrs

t45/dil

Dilute = 1 Sample

Accurate Mass LC/UV/MS/MS RT-m/z MET ID


RT-m/z MET ID

Accurate Mass LC/UV/MS/MS RT-m/z

MET ID


RT-m/z MET ID


RT-m/z MET ID

HRMS Integrated Qual/Quan Approach with Sample Pooling

Pooled Metabolite

“Standards”

High substrate concentration (30 µM) in vitro

incubations


12

S

O

O

O

NHN

N

S

F

F F

O

O

HN N

N

FFF

O

ON

NH2

NO O

N

N

NN

F

O

HO OH

HN

O

N

N

F

t45 t45 t45 t45 t45 t45 t0 t0 t0 t0 t0 t0

RT-m/z MET ID

12 Qualitative samples generated

0.5 µM Parallel Incubations with Sample Pooling

13

t0

t2.5

t5

t10

t15

t20

t30

t60

Pooled Metabolite

“Standards”

t0

t2.5

t5

t10

t15

t20

t30

t60

8 + 1 = 9 Samples

Throughput improvements with pooled analysis Traditional Approach

6 compounds 25 Quantiative + 2 Qualitative samples/cmpd 6 x 27 = 161 Samples Analysis time = 162 x 10 = 1620 mins 27 hrs total

Pooling approach 6 compounds 25 Quantiative + 12 Qualitiative samples total 25 + 12 = 37 total samples Analysis time = 37 x 10 = 370 mins 6 hrs total 1 hr/compound

Full qualitative dataset 8 Time point t1/2 in duplicate with control 8 Timepoint metabolite formation quantitation in duplicate

Pooling makes sample analysis more challenging 6 x dilution factor Samples are now more complex

Instrument requirements For quantitation

High resolution (>30,000 FWHM) High mass accuracy/stability Fast scanning (> 3Hz at 30,000) Sensitive (detect metabolites at all time points in 6 x diluted 0.5 µM incubations)

For structural elucidation High resolution (>30,000 FWHM) in MS and MS/MS modes High mass accuracy in MS and MS/MS modes

Q Exactive: Max resolution: 140,000 (m/z 200) Scan speed: up to 12 Hz (17,500 @ m/z 200) <5ppm external <1 ppm internal +/- switching within 1 sec

Samples for pooled analysis

S

O

OO HN

NN

Esomeprazole

Chemical Formula: C17H19N3O3SExact Mass: 345.11471

S

F

F

F

O

OHN

NN

Lansoprazole

Chemical Formula: C16H14F3N3O2SExact Mass: 369.07588

F

O

N

NChemical Formula: C20H21FN2O

Exact Mass: 324.16379

Citalopram

F

FF

O

ON

NH2Chemical Formula: C15H21F3N2O2

Exact Mass: 318.15551

Fluvoxamine

F

O OH

OH

HN

Chemical Formula: C18H20FNO3Exact Mass: 317.14272

N

O

O

N NN

N

Chemical Formula: C21H31N5O2Exact Mass: 385.24778

BuspironeParoxetine

Esomeprazole

17

RT: 2.80 - 4.37

3.0 3.2 3.4 3.6 3.8 4.0 4.2Time (min)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

220000

240000

260000

280000

300000

320000

340000

uAU

Esomeprazole 0.5 µM Incubation 0 min

3.0 3.2 3.4 3.6 3.8 4.0 4.2

Time (min)

4.24

3.24

3.77

3.94

3.15

NL: 2.48E8 m/z= 346.1203-346.1237 F: FTMS + p ESI Full ms MS 042911_Esomeprazole_ RLM_HL_MQ_01



XICs +/- 5ppm

Esomeprazole

4.20 4.21 4.22 4.23 4.24 4.25 4.26 4.27 4.28 4.29 Time (min)

4.24

4.24 4.23

4.25

4.23

4.25

4.26 4.22

4.26

4.27 4.22 4.27 4.27

NL: 2.48E8 m/z= 346.1203-346.1237 F: FTMS + p ESI Full ms MS 042911_Esomeprazol e_RLM_HL_MQ_01

3 sec

12 scans/3s 4Hz

Theoretical 346.12199 Observed Mass

Error (ppm)

Variation from mean (ppm)

346.12103 -2.77 -0.08 346.12106 -2.69 0.01 346.12097 -2.95 -0.25 346.12097 -2.95 -0.25 346.12106 -2.69 0.01 346.12100 -2.86 -0.17 346.12097 -2.95 -0.25 346.12094 -3.03 -0.34 346.12088 -3.21 -0.51 346.12119 -2.31 0.38 346.12128 -2.05 0.64 346.12134 -1.88 0.82

Average 346.12106 -2.69 STD 0.00014 STD ppm 0.40787

0.5 uM Incubation 0 min

Esomeprazole

100 200 300 400 500 600 700 800 900 1000 m/z

346.1209

713.2178 198.0580

[M+H]+

[2M+H]+

0.5 µM Incubation 0 min

[M+Na]+

[M+H – H2O]+

346.0 346.5 347.0 347.5 348.0 348.5 m/z

346.1209 R=57513

347.1245 R=56547

348.06 348.08 348.10 348.12 348.14 348.16 348.18 348.20

348.1169

-2.4 ppm C17H20N3O3

34S = 348.1178

348.1275 12C16

13CH20N3O332S = 348.1332

-5.67 ppm

R = 59200

R = 56100

0

100

200

300

400

500

600

0 10 20 30 40 50 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Esomeprazole in RLM

2.96_332 3.07_362 3.60_362 3.79_362

Esomeprazole

21

0.5 µM Incubations

y = 78.478e-0.164x R² = 0.9627

1

10

100

0 10 20 30 40 50 60

%R

emai

ning

Time (min)

Half-Life of Esomeprazole in RLM %Remaining

%-NADPH

Expon. (%Remaining)

t1/2 4.2 min

y = 95.099e-0.151x R² = 0.9645

1

10

100

0 10 20 30 40 50 60

%R

emai

ning

Time (min)

Half-Life of Esomeprazole in RLM Pooled Analysis %Remaining

%-NADPH

Expon. (%Remaining)

t1/2 4.6 min

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Esomeprazole in RLM Pooled Analysis

2.96_332

3.07_362

3.60_362

3.79_362

0

100

200

300

400

500

600

0 10 20 30 40 50 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Esomeprazole in RLM

2.96_332 3.07_362 3.60_362 3.79_362

Fluvoxamine

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y = 117.8e-0.104x R² = 0.9577

1

10

100

0 20 40 60 80

%R

emai

ning

Time (min)

Half-Life of Fluvoxamine in RLM

%Remaining

%-NADPH

Expon. (%Remaining)

t1/2=6.66 min

y = 100.76e-0.109x R² = 0.9869

1

10

100

0 20 40 60 80

%R

emai

ning

Time (min)

Half-Life of Fluvoxamine in RLM Pooled analysis %Remaining

%-NADPH

Expon. (%Remaining)

t1/2=6.36 min

0 20 40 60 80

100 120 140

0 10 20 30 40 50 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Fluvoxamine in RLM

5.14_305

5.20_319

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Fluvoxamine in RLM Pooled Analysis

5.14_305

5.20_319

0.5 µM Incubations

Buspirone

y = 85.318e-0.216x R² = 0.9629

0

1

10

100

0 20 40 60

%R

emai

ning

Time (min)

Half-Life of Buspirone in RLM

%Remaining

t1/2 3.2 min

y = 78.724e-0.211x R² = 0.9708

0

1

10

100

0 10 20 30 40 50 60

%R

emai

ning

Time (min)

Half-Life of Buspirone in RLM Pooled Analysis

%Remaining

%-NADPH

Expon. (%Remaining)

t1/2 3.3 min

0

500

1000

1500

2000

2500

0 20 40 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Buspirone in RLM Pooled analysis

3.03_402

3.22_402

3.67_402

3.77_418

4.01_402

4.27_418

4.44_402

4.93_402 0

200

400

600

800

1000

1200

1400

1600

0 20 40 60

Cor

rect

ed M

et A

rea

Time (min)

Metabolite Profile of Buspirone in RLM

3.03_402

3.22_402

3.67_402

3.77_418

4.01_402

4.27_418

4.44_402

4.93_402

0.5 µM Incubations

Conclusion Hybrid Quadrupole-Orbitrap platform (Q Exactive) is well suited to

integrated Qual/Quan workflows High resolution (>30,000 FWHM)

57,000 @ m/z 346 High mass accuracy/stability

< 5ppm/<1ppm @ 57,000 for mass m/z 346 Fast scanning (> 3Hz at 30,000)

4 Hz @ 57,000 for mass m/z 346

Sample pooling for analysis can greatly reduce the data acquisition time With a sufficiently selective and sensitive system comparable data may

be obtained compared to running samples individually Integrated Qual/Quan workflows may be feasible in a high

throughput environment Structural assignments are the limiting step Extensive Qual/Quan datasets may be more suited to computational

modeling than quantiative datasets alone

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Beyond the horizon

In Vitro Incubations and HRMS Data Collection Lead Profiling

In Vitro Incubations and HRMS Data Collection “Cloud Instrumentation”

Beyond the horizon

Automated Structural Assignment of Metabolites

Automated Calculation of Initial Rate of Formation of Primary Metabolites

Automated Determination of Relationship of Primary and Secondary Metabolites

Building of Metabolic Softspot SAR by Data Mining of Quan/Qual Datasets

Recommendation of Structural Solutions

In silico prediction of rate/site of metabolic stability

In Vivo Samples and HRMS Data Collection PCO

DWG

Scientists

Automated t1/2 Determination

ADME

Scientists

ADME

Scientists

In Vivo Samples and HRMS Data Collection “Cloud Instrumentation”

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Parent PK

TOX

Parent TK Biomarkers

Biotransformation

Metabolite Profiles

Metabolite ID

2020 A Clearer Understanding of ADMET

MAP Bioanalytical Analytical

BIOL CHEM

Raw Data Acquisition

Discovery Working Group

Process/Interpret Data

Convert Interpreted Data into Knowledge

Drive Program Decisions

28

HRMS Data Collection Lead Profiling/Bioanalytical/ Analytical/Biotransfromation

Acknowledgements • Bristol-Myers Squibb

Bruce Car David Rodrigues Adrian Tymiak Harold Weller Mark Hillman Wilson Shu Ragu Ramanathan Silvi Chacko Yanou Yang Mary Grubb Hong Cai Kim Johnson Ben Johnson Yue-Zhong Shu Qin Ruan

• Thermo Fisher Scientific Bjoern Rose Markus Kellmann Kevin Cook Mark Sanders

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Comprehensive Metabolite Profiling in a Discovery...

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