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

Transcription

1 Thermo E Users Meeting, Somerset, J ctober 12, 2011, 1:50pm Comprehensive Metabolite Profiling in a Discovery Environment, Coupled Workflows Enabled by HRMS Jonathan L. Josephs 1, C. Emily Luk 1, Kate Comstock 2, Tim Stratton 2, Yingying Huang 2, and William G. Humphreys 1 1 Bristol-Myers Squibb, Hopewell, J 2 Thermo isher Scientific, San Jose, CA 1

2 Where we look and why Establishment of departments devoted to targeted quantitation The Success of SRM quantiation on Triple Quads has led to a mindset of targeted quantitation methods All quantitation done in a targeted manner 2

3 Where we look and why 3

4 A ew Mechanism of Selectivity with ull Scan HRMS

5 ull 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 aster structural assignments m/z Improved certainty of assignments Greater suitability for automated structural assignment approaches Integrated Metabolite ID/Quantitation datasets m/z 5

6 Where we look and why 6

7 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 IH studies Interpreted data streams normally integrated by a collaborative team Collection of integrated datasets increases the speed and quality of interpreted data acilitates the use of the combined knowledge in decision making 7

8 uture ADMET Process? Raw Data Acquisition HRMS Data Collection Lead Profiling/Bioanalytical /Analytical /Biotransformation Process/Interpret Data Parent PK Metabolite ID Metabolite Profiles Parent TK Biomarkers Convert Interpreted Data into Knowledge Drive Program Decisions DMPK Bioanalytical Biotransfromation BIL TX Analytical CHEM Discovery Working Group 8

9 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 ormally 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

10 HRMS Integrated Qual/Quan Approach * * Low substrate concentration in vitro incubation Metabolite Standards High substrate concentration in vitro incubation In vitro quantitation Parent and metabolites UV Corrected Area Buspirone Metabolite Time Course Accurate Mass LC/UV/MS/MS Calculated UV area In vitro metabolite ID In vivo quantitation Parent and metabolites Time (hr) **Yanou Yang, Mary. Grubb, Chiuwa E Luk, W. Griffith Humphreys and Jonathan L. Josephs, *Petia Shipkova, Jonathan Josephs, Rapid Mary Commun. Grubb, Robert Mass Spectrom. Langish, Weiqi 2011, Chen, 25, 3245 Mark Sanders, ASMS

11 HRMS Integrated Qual/Quan Approach +ADPH 30 µm t 0 t 45 = 2 Samples Dilute 1 compound = = 27 Samples 6 compounds = 27 x 6 = 162 Samples Analysis time = 162 x 10 = 1620 mins = 27 hrs = 1 Sample t 45/dil +ADPH 0.5 µm +ADPH 0.5 µm -ADPH 0.5 µm t 0 t 2.5 t 5 t 10 t 15 t 20 t 30 t 60 t 0 t 2.5 t 5 t 10 t 15 t 20 t 30 t 60 t 0 t 2.5 t 5 t 10 t 15 t 20 t 30 t 60 = 24 Samples

12 HRMS Integrated Qual/Quan Approach with Sample Pooling H 2 H H S H H S H High substrate concentration (30 µm) in vitro incubations t 0 t 45 t 0 t 45 t 0 t 45 t 0 t 45 t 0 t 45 t 0 t 45 Accurate Mass LC/UV/MS/MS RT-m/z MET ID Accurate Mass LC/UV/MS/MS RT-m/z MET ID Accurate Mass LC/UV/MS/MS RT-m/z MET ID Accurate Mass LC/UV/MS/MS RT-m/z MET ID Accurate Mass LC/UV/MS/MS RT-m/z MET ID Accurate Mass LC/UV/MS/MS RT-m/z MET ID Pooled Metabolite Standards 12 Qualitative samples generated 12

13 0.5 µm Parallel Incubations with Sample Pooling = 9 Samples Pooled Metabolite Standards t 60 t 60 t 30 t 30 t 20 t 20 t 15 t 15 t 10 t 10 t 5 t 5 t 2.5 t 2.5 t 0 t 0 13

14 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 = 37 total samples Analysis time = 37 x 10 = 370 mins 6 hrs total 1 hr/compound ull qualitative dataset 8 Time point t 1/2 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

15 Instrument requirements or quantitation High resolution (>30,000 WHM) High mass accuracy/stability ast scanning (> 3Hz at 30,000) Sensitive (detect metabolites at all time points in 6 x diluted 0.5 µm incubations) or structural elucidation High resolution (>30,000 WHM) 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 m/z 200) <5ppm external <1 ppm internal +/- switching within 1 sec

16 Samples for pooled analysis H S Chemical ormula: C 17 H S Exact Mass: Esomeprazole H S Chemical ormula: C 16 H S Exact Mass: Lansoprazole Chemical ormula: C 20 H 21 2 Exact Mass: Citalopram H H H Chemical ormula: C 18 H 20 3 Exact Mass: Paroxetine H 2 Chemical ormula: C 15 H Exact Mass: luvoxamine Chemical ormula: C 21 H Exact Mass: Buspirone

17 Esomeprazole RT: uau

18 0.5 µm Incubation 0 min Esomeprazole XICs +/- 5ppm 4.24 L: 2.48E8 m/z= : TMS + p ESI ull ms MS _Esomeprazole_ RLM_HL_MQ_ L: 1.11E6 m/z= : TMS + p ESI ull ms MS _Esomeprazole_ RLM_HL_MQ_ L: 4.35E6 m/z= : TMS + p ESI ull ms MS _Esomeprazole_ RLM_HL_MQ_

19 0.5 um Incubation 0 min Esomeprazole Theoretical bserved Error Mass (ppm) Variation from mean (ppm) Average STD STD ppm sec 12 scans/3s 4Hz L: 2.48E8 m/z= : TMS + p ESI ull ms MS _Esomeprazol e_rlm_hl_mq_

20 0.5 µm Incubation 0 min Esomeprazole [M+H] R= C 17 H S = ppm R = C CH S = ppm R = [M+H H 2 ] R=56547 [M+a] m/z [2M+H] m/z

21 0.5 µm Incubations Esomeprazole Half-Life of Esomeprazole in RLM %Remaining Half-Life of Esomeprazole in RLM Metabolite Profile of in %-ADPH Expon. (%Remaining) 100 Pooled Analysis %Remaining %-ADPH Expon. (%Remaining) %Remaining Corrected Met Area Corrected Met Area t 1/2 4.2 min y = e x R² = Metabolite Profile of Esomeprazole in RLM 2.96_ _ _ _362 %Remaining Corrected Met Area t 1/2 4.6 min 2.96_ _ _ _362 y = e x R² = Metabolite Profile of Esomeprazole in RLM Pooled Analysis 2.96_ _ _ _ Time 60 (min)

22 0.5 µm Incubations luvoxamine %Remaining Half-Life of luvoxamine in RLM y = 117.8e x R² = %Remaining %-ADPH Expon. (%Remaining) t 1/2 =6.66 min %Remaining Half-Life of luvoxamine in RLM Pooled analysis %Remaining y = e x R² = %-ADPH t 1/2 =6.36 min Expon. (%Remaining) Corrected Met Area Metabolite Profile of luvoxamine in RLM _ _ Corrected Met Area Metabolite Profile of luvoxamine in RLM Pooled Analysis _ _

23 0.5 µm Incubations Buspirone Corrected Met Area %Remaining Half-Life of Buspirone in RLM y = e x R² = %Remaining t 1/2 3.2 min Metabolite Profile of Buspirone in RLM _ _ _ _ _ _ _ _402 %Remaining Corrected Met Area Half-Life of Buspirone in RLM Pooled Analysis y = e x R² = %Remaining %-ADPH Expon. (%Remaining) t 1/2 3.3 min Metabolite Profile of Buspirone in RLM Pooled analysis _ _ _ _ _ _ _ _402

24 Conclusion Hybrid Quadrupole-rbitrap platform (Q Exactive) is well suited to integrated Qual/Quan workflows High resolution (>30,000 WHM) m/z 346 High mass accuracy/stability < 57,000 for mass m/z 346 ast scanning (> 3Hz at 30,000) 4 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 24

25 Beyond the horizon

26 Beyond the horizon

27 Beyond the horizon DWG Scientists ADME Scientists Recommendation of Structural Solutions In silico prediction of rate/site of metabolic stability Automated Determination of Relationship of Primary and Secondary Metabolites Building of Metabolic Softspot SAR by Data Mining of Quan/Qual Datasets Automated Structural Assignment of Metabolites Automated t 1/2 Determination Automated Calculation of Initial Rate of ormation of Primary Metabolites In In Vitro Vitro Incubations and and HRMS HRMS Data Data Collection Cloud Lead Instrumentation Profiling In In Vivo Vivo Samples and and HRMS HRMS Data Data Collection Cloud Instrumentation PC ADME Scientists 27

28 2020 A Clearer Understanding of ADMET Raw Data Acquisition HRMS Data Collection Lead Profiling/Bioanalytical/ Analytical/Biotransfromation Process/Interpret Data Parent PK Metabolite ID Metabolite Profiles Parent TK Biomarkers Convert Interpreted Data into Knowledge MAP Bioanalytical Biotransformation TX Analytical Drive Program Decisions BIL CHEM Discovery Working Group 28

29 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 isher Scientific Bjoern Rose Markus Kellmann Kevin Cook Mark Sanders 29