Proteomic Quantification of Kidney Transporters: Methodological Challenges, Interindividual Variability and Application in IVIVE

Transcription

1 Proteomic Quantification of Kidney Transporters: Methodological Challenges, Interindividual Variability and Application in IVIVE Bhagwat Prasad, Ph.D. University of Washington, Seattle, WA

2 Drug transporters in proximal renal tubules About 30% of approved drugs are predominantly (>50% of total body clearance) cleared by the kidneys Brater, 2002; Feng et al.,

3 Renal Transporters and Clinical DDIs Transporters Inhibitor Victim ROA Change Reference OATs Probenecid Furosemide Iv AUC; Clr Smith et al., 1980; p.o AUC; Cl Vree et al., 1995 OCT2, MATEs Probenecid Cephradine Iv AUC; Clr Roberts et al., 1981 Cimetidine Metformin p.o. AUC; Clr Somogyi et al., 1987 Cimetidine Ranitidine p.o. AUC; Clr van Crugten et al., 1986 Cimetidine Dofetilide p.o. AUC; Clr Abel et al., 2000 Cimetidine Pindolol p.o. AUC; Clr Somogyi et al., 1992 Trimethoprim Procainamide p.o. AUC; Clr Kosoglou et al., 1988 P-gp Ritonavir Digoxin i.v. AUC; Clt, Clr Ding et al., 2004 Quinidine Digoxin i.v. Cserum ; Clt,Clr Leahey et al., 1981; Fenster et al., 1982,

4 FDA Guidance on Renal Transporters Investigational drug Renal active secretion major? e.g., 25% of total Cl or unknown Determine whether investigational drug is an OAT1, OAT3 and/or OCT2 substrate in vitro PBPK modeling if an in vivo study is needed An investigational drug also should be evaluated to determine whether it inhibits OAT1, OAT3 and OCT2 MATE transporters should be considered when appropriate P-gp and BCRP are also investigated 4

5 Renal Transporters and Nephrotoxicity Need to predict tissue concentration 5

6 Knowledge gaps/challenges Good in vitro models are available However, conventional approaches to predict transporter mediated in vivo CL are not optimum lack of transporter protein abundance data interindividual variability in renal transporter activity is unknown 6

7 Challenges using alternate approaches 1. Poor correlation of mrna data with activity 2. Primary human proximal tubular cells (2D culture) tissue 7

8 Hypothesis Transporter quantification data can help predict transporter activity To translate in vitro transporter CL data to in vivo (IVIVE) To predict interindividual variability in renal transporter activity Transporter quantification approach Quantitative LC-MS/MS proteomics 8

9 Methodology: Quantitative Proteomics Surrogate peptide(s) generated from protein digestion is quantified LC-MS compatible sample LC-MS/MS Protein isolation Protein in solution Protein digestion (trypsin) Sample cleaning and enrichment MRM analysis of surrogate peptide Calibrator: Synthetic peptide standard Internal standard: Heavy labeled peptide standard

10 Is method development challenging? Not anymore QPrOmics TM ( *Poster (Prachi Jha) 10

11 Human kidney samples Non-cancerous portion of the human kidney cortex from nephrectomies UW medical center (n=20) Cortices of kidneys initially targeted for transplant purposes, but eventually not transplanted Ardea Biosciences (n=7) Newcastle University (n=14) 11

12 Renal transporters are homogenously expressed in kidney cortex 12

13 Transporter expression (pmol/mg membrane protein) Renal transporter expression and inter-individual variability OAT1 OAT3 OAT2 OAT4 OCT2 OCTN1 OCTN2 P-gp MRP2 MRP4 MATE1 SGLT2 13

14 Renal Transporter Pie 3% 2% 2% OCT2 OAT1 4% 3% MATE1 26% 5% OAT3 P-gp 7% MRP2 OCTN1 12% 18% OAT2 MRP4 OCTN2 18% OAT4 14

15 Pmol/mg total membrane protein Correlation of renal transporters 15

16 Pmol/mg total membrane protein SGLT2 expression correlates with majority of renal drug transporters 16

17 Application of transporter expression data to predict metformin secretory CL Excretion Urine (>90%) CL(Renal) CL (secretory) 552 ml/min 432 ml/min 17

18 OCT2 expression in human kidney cortex and in OCT2 expressing HEK and MDCKII cells Data generated by Vineet Kumar (Unadkat Lab) 18

19 Transporter expression based scaling factor CL = V max K m and V max = K cat * [T] Scaling Factor = [T] in vivo [T] in vitro OCT2 Expressing Cells OCT2 Expression (fmol/µg protein) [ 14 C] Metformin Uptake Activity (pmol/min/mg protein) HEK ± MDCKII 19.0 ± HEK293/MDCKII Data generated by Vineet Kumar (Unadkat Lab) in collaboration with Dr. Joanne Wang and Jia Yin 19

20 IVIVE of metformin CL Sec Human kidney weight (g) 150 Cortex WT/kidney (70% of total kidney weight) (g) 105 OCT2 expression in cells (fmol/µg Protein) (HEK293) 19 (MDCKII) OCT2 expression in Kidney (fmol/µg Protein) 7.6 In-vitro Clearance (µl/mg protein/min) 36.7 (HEK293) 2.0 (MDCKII) Scaling factor (SF) 0.02 (HEK293) 0.40 (MDCKII) mg of protein per unit cortex weight 0.3 CL Sec = 2 x CLin vitro x E in vivo x protein per unit cortex weight x cortex weight E in vitro Metformin CL Sec (ml/min) 47.6 (HEK293) 50.4 (MDCKII) Renal secretory clearance of metformin: 432 ml/min Kumar et al. (manuscript) 20

21 % Expressed on plasma membrane A substantial % of OCT2 protein is localized in the intracellular (inactive) fractions HEK Cells MDCKII Cells OCT2 Na K ATPase Calreticulin HEK293 MDCKII Metformin CL Sec, ml/min (uncorrected) Metformin CL Sec, ml/min (Corrected for intracellular contamination) In vivo observed renal secretory clearance of metformin: 432 ml/min Kumar et al. (manuscript) 21

22 Other possible reasons of underprediction 1. Mechanisms of OC transport Proximal tubular cell Blood Plasma membrane potential in cell lines OC mv OCT2 OC + HEK293 cells: -19 to -27 mv MDCKII cells: -20 to -50 mv -Chien et al., DMD, Role of other (unknown?) transporters Kumar et al. (manuscript) 22

23 Conclusions OCT2, OAT1, OAT2 and MATE1 are the major drug transporters in human kidneys A significant protein-protein correlation was observed between renal transporters The transporter protein expression and correlation will be useful for the PBPK prediction of renal drug disposition including predicting tissue concentration A significant fraction of transporters can be present in the intracellular pools in the over-expressing cell lines The predicted metformin CL Sec is 30% of the observed in vivo Cl sec using the transporter expression based scaling (when correlated for intracellular contamination) Importance of mechanism of drug transport in IVIVE 23

24 Acknowledgements University of Washington, Seattle, WA Jashvant Unadkat, Ph.D. Jonathan Himmelfarb, M.D. Ed Kelly, Ph.D. Joanne Wang, Ph.D. Other significant contributors Vineet Kumar Katherine Johnson Sarah Billington, Ph.D. Jia Yin Gabby Patilea-Vrana Newcastle University, UK Colin Brown, Ph.D Ardea Bio Caroline Lee, Ph.D Grants UH2TR DA Ardea Bio UWRAPT 24

25 UWRAPT Drs. Cornelis Hop (Marcel) & Laurent Salphati Genentech Drs. Raymond Evers & Xiaoyan Chu Merck Drs. Guangqing Xiao & Chuang Lu Biogen Drs. Yurong Lai and Wenying Li BMS Drs. Donavon McConn and Mingxiang Liao Takeda Drs. Brian Kirby, Adrian Ray and Anita Mathias Gilead Drs. Caroline Lee & Ravindra Alluri Ardea Bio & AZ Dr. Anshul Gupta, AZ 25