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Best practices and challenges for robust Quantitative proteomics of DMEs
- 1. Best Practices for Robust LC-MS/MS Quantification of Drug Metabolizing Enzymes and Transporters to Predict Inter-Individual Variability: Case Examples of Hepatic Cytosolic ADHs and ALDH1A1 Dr. Deepak Kumar Bhatt Senior Postdoctoral Fellow Dr. Bhagwat Prasad’s Lab bhatt81@uw.edu 1
- 2. LC-MS/MS Quantification of DMEs and transporters • Applications: • To predict interindividual variability (effect of age, genotype, gender or disease condition) • To extrapolate clearance from in vitro to in vivo (IVIVE) (cell lines or microsomes to organs) Quantification of unique surrogate peptide(s) Group Age (years) Neonatal 0.01 to 0.05 (4) Infancy 0.08 to 0.92 (17) Toddler/Early Childhood 1 to <6 (30) Middle Childhood 6 to <12 (38) Adolescence 12 to 18 (48) Adulthood 19 To 87 (57) • University of Washington liver bank • Children’s Mercy-Kansas City hospital liver bank 2
- 3. Goal: To address technical variability over biological (e.g., interindividual) variability in LC-MS/MS protein quantification Challenges Surrogate Peptide Selection Optimum Stability, Solubility, No PTMs, No Variants (SNPs) Peptide Qualification Removing ghost peaks Sample Preparation Trypsin digestion efficiency and sample loss in processing LC-MS/MS analysis Matrix effect/Ion suppression Data Analysis Removing artifacts, peak integration, data normalization and absolute quantification 3
- 4. Surrogate peptide selection Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions •Uniqueness •Optimum peptide length (6-22) •No transmembrane regions •No posttranslational modification (PTM) •No non-synonymous single nucleotide polymorphism (SNP) •Avoid unstable residue (e.g.- C, M, W) •No splice variants •No ragged end (RR, KK, RK, KR) •No missed cleavage sites •Optimum hydrophobicity (45-155) MDR3_HUMAN Unique and stable entry identifier A compendium of targeted proteomics assays PROTTER MS-Homology 4 Prasad B. and Unadkat J., AAPS J, 16, 1-15 (2014)
- 5. Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions 5 0 50 100 150 200 250 300 350 0 50 100 150 NYLEDSLLK SVINDPVYK UGT2B15 (D85Y) YY DD DY Y- Tyrosine D- Aspartic acid
- 6. Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions Surrogate peptide qualification • Superimposability of multiple transitions • Elution at predicted retention time • Validate light transitions with heavy transitions TVLAVFGK_Light TVLAVFG(K)_Heavy 6
- 7. Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions Sample preparation Individual Sample + albumin* Desalting, enrichment Trypsin digestion Reaction quenching, addition of heavy internal standard LC-MS sample (multiple fragments and multiple peptides)$ Denaturation, reduction, alkylation *Use albumin as a protein internal standard $Use pooled QC samples in each batch of LC-MS samples 7
- 8. Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions Ion suppression TVLAVFGK 2 fold decrease in the peak height Internal standard in sample matrixInternal standard in buffer 8
- 9. Data analysis Sample Name GAIFGGFK. +2y6.light GAIFGGFK.+ 2y5.light GAIFGGFK. +2y4.light GAIFGGFK. +2y5.heavy GAIFGGFK. +2y4.heavy Light (avg) Heavy (avg) Area ratio BSA AR RoR Average QC % Area ratio (Normalized to QCs) HL_102_1 8030000 21800000 9470000 8120000 3200000 13100000 5660000 2.31 4.31 0.54 155 HL_103_1 5440000 15100000 6380000 7960000 3120000 8973333 5540000 1.62 4.27 0.38 110 HL_104_1 8140000 23100000 9360000 7760000 3150000 13533333 5455000 2.48 4.40 0.56 163 HL_105_1 4600000 13100000 5460000 6540000 2680000 7720000 4610000 1.67 4.79 0.35 101 HL_106_1 5010000 14100000 5650000 6650000 2540000 8253333 4595000 1.80 4.86 0.37 107 QC1_1 4590000 13000000 5230000 6990000 2730000 7606667 4860000 1.57 4.41 0.35 0.35 102 QC2_1 4750000 13900000 5910000 8200000 3210000 8186667 5705000 1.43 4.24 0.34 98 QC3_1 4160000 12000000 5270000 7490000 2840000 7143333 5165000 1.38 4.01 0.35 100 R² = 0.98 0.E+00 5.E+06 1.E+07 2.E+07 2.E+07 3.E+07 3.E+07 4.E+07 4.E+07 5.E+07 5.E+07 0.E+00 5.E+06 1.E+07 2.E+07 2.E+07 ADH1C.GAIFGGFK.+2y5.light ADH1C.GAIFGGFK.+2y6.light ADH1C.GAIFGGFK_Light R² = 0.9565 0.00E+00 1.00E+06 2.00E+06 3.00E+06 4.00E+06 5.00E+06 6.00E+06 0.00E+00 5.00E+06 1.00E+07 1.50E+07 ADH1C.GAIFGGFK.+2y4.heavy ADH1C.GAIFGGFK.+2y5.heavy ADH1C.GAIFGGFK_Heavy Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions 9
- 10. Inconsistent protein digestion/sample loss during sample processing 0 10 20 30 40 50 60 70 HL_102 HL_103 HL_104 HL_105 HL_106 HL_108 HL_109 HL_111 HL_112 HL_113 HL_114 HL_115 HL_118 HL_119 HL_120 HL_121 HL_124 HL_125 HL_126 HL_127 %CoefficientofVariation(CV) ADH1C.INEGFDLLR BSA.LVNELTEFAK ADH1C/BSA Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions 10
- 11. Correlation between two peptides R² = 0.9673 0 50 100 150 200 250 0 50 100 150 200 250 ADH1A.GAILGGFK ADH1A.NDVSNPQGTLQDGTSR Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions 11
- 12. Correlation of expression and activity Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions 0 20 40 60 80 100 120 0 1000 2000 3000 4000 5000 6000 RelativeCYP3A4 Activity (pmol/min/mg) OH-MDZ formation r2 =0.96 12
- 13. Case example: Age-dependent expression of ADH1A, ADH1B, ADH1C and ALDH1A1 in human liver cytosol samples 13 Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions
- 15. Clinical applications 15 Chen et. al., Drug Metab Dispos, 25, 544–51 (1997) Thomas et. al., Cancer Research, 53, 5629-37 (1993)
- 16. Addition of heavy peptide internal standard • Denaturation, reduction, alkylation • Desalting, enrichment • Trypsin digestion 4: MS ionization and measurement reproducibility 2: Trypsin digestion reproducibility 6: Validation of quantifiable peptides to establish overall precision Addition of protein internal standard (e.g., BSA) 3: Inter-day reproducibility Analysis of 3 replicates of a pooled HLM sample with each set of samples 5: Validation of quantifiable fragments Robustness parameters Normalize individual means and standard deviation by QC pool Do multiple peptides of a protein correlate? Average of multiple peptide Yes No Select alternate peptides Do multiple fragments of a peptide correlate? No Average of multiple fragments Yes Select alternate fragments Day 1 Day 2 Day 3 Individual sample processed and analyzed on three different days Fixed total starting protein concentration Conclusions 1: Inter-day assay variability Surrogate Peptide Selection Peptide Qualification Sample Preparation LC-MS/MS analysis Data AnalysisCase Example Conclusions 16
- 17. Acknowledgements University of Washington • Prasad Lab • Bhagwat Prasad, Ph.D. • Neha Saxena, Ph.D. • Meijuan Xu, Ph.D. • Abdul Basit Shaikh, Ph.D. • Marc Vrana • Haeyoung Zhang • Kenneth Thummel, Ph.D. • UW SOP Mass Spec Center • Funding • NIH/NICHD; 1R01HD081299-01A1 • Department of Pharmaceutics, UW Children’s Mercy Hospital, Kansas City • J. Steven Leeder, Ph.D. • Andrea Gaedigk, Ph.D. • Robin E. Pearce, Ph.D. Special thanks to Division for Drug Metabolism, ASPET for awarding travel grant 17
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