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Challenges of transporter IVIVE, Digoxin example

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Presented by Mailys De Sousa Mendes at the PBPK Symposium
Paris - 04/04/2019

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Challenges of transporter IVIVE, Digoxin example

  1. 1. Challenges of transporter IVIVE Digoxin example Maïlys De Sousa Mendes 04/04/2019 2019 PBPK Symposium - Paris
  2. 2. © Copyright 2019 Certara, L.P. All rights reserved. What do Transporters do? High  Effect of transporters CELLExtracellular space [Cu] [Cu] Passive permeability Equilibrative transport e.g. SLC (ENTs) [Cu] [Cu] Concentrative Uptake transport e.g. SLC (PEPTs, OCTs, OATs, OATPs) CL, V, tissue levels [Cu] [Cu] Efflux transport e.g. ABC (P-gp, BCRP, MRPs) and some SLC (OATPs, MATEs) tissue levels Low  Effect of transporters  X Passive membrane permeability 0.0 0.5 1.0 0.01 0.1 1 10 100 1000 fa Dose [mg] Intestinal uptake example 0.0 0.5 1.0 0.01 1 100 10000 fa Dose [mg] Efflux
  3. 3. © Copyright 2019 Certara, L.P. All rights reserved. Clinical Effects of Transporters: Non-Linear Absorption Non-Linear Clearance and Vss Drug-Drug Interactions (Inhibition and Induction) Species Differences Pharmacodynamics Toxicity Why are Transporters Important? 3
  4. 4. © Copyright 2019 Certara, L.P. All rights reserved. In Vitro - In Vivo Extrapolation (IVIVE) of Transporter In vitro assays In vitro parameters In vivo parameters In vivo PK Analyse of the raw data: What are the assumptions IVIVE depends on the parameter and the organ PBPK model Physchem Distribution Metabolism solubility Verification: Saturation? DDI? Genetic? 4 Which in-vitro assay? Optimal design?
  5. 5. © Copyright 2019 Certara, L.P. All rights reserved. In-Vitro Parameters The key parameters we need from in vitro assays to describe the effects of transporters are: Passive: Papp, CLPD or PS Active: Km, Jmax or CLint, T Analysis of in vitro data is complex and it may be necessary to use modelling and simulation to account for phenomena not seen in standard in vitro metabolic systems (e.g. disappearance CLint) We assume saturable Michaelis-Menten kinetics:  Is this the correct assumption for all transporters?  Is this correctly built into the model, i.e. has the passive permeation been accounted for as well. 5
  6. 6. © Copyright 2019 Certara, L.P. All rights reserved. Things to Consider with in vitro Transporter Data Membrane Transporter Transport direction C initial, donor Concentration not relevant Relevant Concentration! b) How to measure intracellular concentrations (and/or fucell) for efflux transporters? Things to consider: c) Inside-out vesicles? Need to maintain sink conditions. d) Ratio of inside-out versus right-site out vesicles in the assay a) Where and what is the relevant concentration? 6
  7. 7. © Copyright 2019 Certara, L.P. All rights reserved. Transwell Assays: Conventional analysis (to get P-gp Jmax and Km) AB BA Apical Basolateral Apparent passive permeability (Papp) dt dA J  Where dA/dt is the amount transported per time interval and represents the slope of the conc.-time curve.Flux [S]SA J appP  Where J is calculated via linear regression analysis of cumulative drug permeation as a function of time. Apparent permeability 92 94 96 98 100 102 1 10 100 1000 Papp,AB Apical Concentration Papp,max Papp, min Kmapp 92 94 96 98 100 102 1 10 100 1000 Papp,AB Apical Concentration
  8. 8. © Copyright 2019 Certara, L.P. All rights reserved. Transwell Assays • Conventional analyse assumptions o Kinetic parameters are determined on the basis of media concentrations. • Wrong concentration for active efflux which is dependent on binding within the cell. o Sink conditions are assumed, therefore need to be maintained experimentally o Passive permeability not separately accounted for in the analysis • Consequences: example of effect of changing transporter expression on Km (Tachibana et al., 2010) Apparent correlation between P-gp protein expression and Km(app) value observed for three P-gp substrates (Shirasaka et al., 2008) quinidine verapamil vinblastine Reanalysis of the same experimental data using mathematical model: Km values defined for the intracellular concentration were almost the same among the cells expressing different P-gp levels 8
  9. 9. © Copyright 2019 Certara, L.P. All rights reserved. How is the Km influenced? Passive permeation! concentrationKm concentration Km not changed concentration Km change Concentration in the donor wellConcentration at the ‘transporting’ binding side! Jmax(pmol/min/mg) Jmax Changed Depending on the data analysis techniques used, different conclusions can be drawn from the same data
  10. 10. © Copyright 2019 Certara, L.P. All rights reserved. • Modelling can help make the most out of the data obtained from in vitro systems used to study transporters. • Know your in vitro system: • Modelling of the ‘relevant’ concentration is necessary • Transporter abundance and activity • Fixed parameters (system parameters: Hepatocyte volume per well, No. hepatocytes per well, Volume incubation media) • Be aware of what parameters should be fitted: • Passive permeability, Km, CLint,T, fucell, etc. Modelling of In Vitro Assays Helps!
  11. 11. © Copyright 2019 Certara, L.P. All rights reserved. Three compartment model – Transwell® Apparatus Bidirectional passive permeability across apical and basolateral membranes Active apical efflux driven by drug concentration in cell Drug concentration dynamically modelled (dC/dt) in each compartment Green = Drug parameters Blue = System parameters Substrate in apical media [S]ap, Vap Passive Permeability Substrate in basolateral media [S]bl, Vbl Substrate in cell [S]cell, Vcell Active Efflux 11 Simcyp In Vitro Analysis (SIVA) toolkit
  12. 12. © Copyright 2019 Certara, L.P. All rights reserved. Digoxin example 12 • Drug properties o Digoxin is a probe P-gp substrate o MW = 780 g/mol o Neutral within the physiological pH range, logP = 1.26 o Low metabolism o Elimination: renal and biliary excretion • 3 in vitro datasets: o Troutman and Thakker 2003 o Neuhoff 2005 o Meng et al. 2016
  13. 13. © Copyright 2019 Certara, L.P. All rights reserved. Troutman and Thakker 2003 13 Experimental details: • Caco-2 cells 12 wells • 8 concentrations (0.1-500 uM) • Papp calculated when linear rate and sink conditions are respected
  14. 14. © Copyright 2019 Certara, L.P. All rights reserved. Troutman and Thakker 2003 – SIVA analysis Conventional Absorptive (AB) Conventional Secretory (BA) SIVA analysis Mean ± SD Mean ± SD Mean (CI) PPD (106 cm/s) 7.74 ± 0.56 6.49 ± 1.3 20.6 (19.2-22.1) Km (µM) 1150 ± 179 177 ± 9.2 50.7 (2-99) Jmax (pmol/min) 718 ± 2.38 434 ± 97.4 570 (388-752) CLint,T (µL/min) 0.62 2.45 11.24 14 • Lower Km obtained when fitting the in vitro data • Higher CLint,T Passive diffusion Active
  15. 15. © Copyright 2019 Certara, L.P. All rights reserved. Neuhoff 2005 (PhD thesis) Experimental details: • Caco-2 cells, 12 wells • 4 concentrations (0.059, 1, 10, 100 µM) • Apical and basolateral volumes of 0.5 and 1.5 mL • Raw data available • Samples collected at 5,15, 25, 50, 80, and 120 min • Sampling of A-B experiments was conducted by moving the Transwell insert to a new well containing blank buffer and retaining the previous well • Sampling of B-A experiments was conducted by replacement of 400 µl of apical buffer. • The impact of sampling on the concentrations measured was accounted for in the model. 15
  16. 16. © Copyright 2019 Certara, L.P. All rights reserved. Neuhoff 2005- Estimation of km using SIVA 16 Note: Km estimated closed to the maximal intracellular estimated Estimate 5th 95th PPD (10^-6 cm/sec) 34.1 33.34 34.87 Km,u (µM) 24.97 0 66 Jmax (pmol/min) 404.46 0 816.5 AIC 817.8
  17. 17. © Copyright 2019 Certara, L.P. All rights reserved. Neuhoff 2005- fixed km 17 Km fixed to 50.7 µM Good confidence interval for the other parameter and good fit of the data Estimate 5th 95th PPD (10^-6 cm/sec) 33.87 33.12 34.6 Km,u (µM) 50.7* - - Jmax (pmol/min) 766.8 678 855 AIC 826.6
  18. 18. © Copyright 2019 Certara, L.P. All rights reserved. Meng et al. 2016 Kinetic of different P-gp substrate in Caco-2 cells Active transporter fitted (T0) Modelling approach: -the drug in the membrane interact with P-gp -the drug in the membrane is estimated with a coefficient partition -basolateral (and additional uptake transporter) are assumed bidirectional Digoxin: concentrations 0.1, 0.3, 1, 3, and 10 µM They found out that digoxin needed a basolateral transporter (bidirectional) to fit the data P-gp: Other transporters: 18
  19. 19. © Copyright 2019 Certara, L.P. All rights reserved. Meng et al. 2016 – SIVA Estimation of Km 19 Reasonably well fitted but CI out! Rq: Km obtained by Meng et al. 1.18 uM Km estimated close to the maximal intracellular estimated Estimate 5th 95th PPD (10^-6 cm/sec) 29.9 28.2 31.6 Km,u (µM) 2.1 0 429.3 Jmax (pmol/min) 13724.2 0 2.75E+06 AIC 440.6
  20. 20. © Copyright 2019 Certara, L.P. All rights reserved. Fixing Km to 50.7 µM obtained fromTroutman and Thakker 2003 20 Concentrations too low to saturate the transporter Good fit, reasonable CI Lower AIC (better fit of the data) A basolateral transporter doesn’t seem needed Estimate 5th 95th PPD (10^-6 cm/sec) 39.7 38.0 41.3 Km,u (µM) 50.7* - - Jmax (pmol/min) 645.1 569.3 720.9 AIC 352.2
  21. 21. © Copyright 2019 Certara, L.P. All rights reserved. Summary- In vitro parameter Troutman and Thakker 2003 Neuhoff 2005 Meng et al. 2016 PPD (10-6 cm/s) 20.6 34.1 34.6 29.9 39.7 Jmax (pmol/min) 570 404.46 766.8 13724 645.12 Km (µM) 50.7 24.97 50.7* 2.1 50.7* CLint,T (uL/min) 11.24 16.19 15.12 6535 12.72 Transport expression method WB, REF (2.03) to jejunum Abundance per well (pmol) 0.0353 0.95 Jmax (pmol/min/pmol Transporter) 16147 14600 686.29 CLPD (mL/min/million cells) 0.023072 0.0072 0.00953 21 *Fixed / Abundance per well xSA/cell density x60/1000000
  22. 22. © Copyright 2019 Certara, L.P. All rights reserved. In vitro parameter - conclusion • In vitro setting: o Several concentrations with at least 2 concentration above the Km (100, 200 µM and 300 µM) o Too high concentration will lead to solubility issue and should be avoid (below 500-800 µM) • Next: o Meng et al. paper was the only one with direct measurement of absolute abundance o The concentrations used were to low to estimate areliable Km value therefore the Km was fixed based on Troutman and Thakker dataset o No saturation is seen in vivo and the simulated intracellular concentration are below 0.01µM 22
  23. 23. © Copyright 2019 Certara, L.P. All rights reserved. In Vitro - In Vivo Extrapolation (IVIVE) of Transporter In vitro assays In vitro parameters In vivo parameters In vivo PK Analyse of the raw data: What are the assumptions IVIVE depends on the parameter and the organ PBPK model Physchem Distribution Metabolism solubility Verification: Saturation? DDI? Genetic? 23 Which in-vitro assay? Optimal design?
  24. 24. © Copyright 2019 Certara, L.P. All rights reserved. In Vitro - In Vivo Extrapolation (IVIVE) transporter scalars Relative Scalars Activity-Abundance Organ-Based Relative Expression/Activity Factor (REF/RAF) •Protein* (relative, absolute) •mRNA •In Vivo/In Vitro Expression or Activity Ratio - CLint-T, Jmax Inter-System Extrapolation Factor- Transporters (ISEF-T) Disconnection between the in-vitro and in-vivo abundance-activity correlation •Sequestration/redundancy •In vitro-In vivo milieu disconnection •Hepatocytes per gram of liver (HPGL) •Total Membrane Protein Per Intestine (TMePPI) •Proximal tubule cells per gram of kidney (PTCPGK) •Human Brain Microvessels Per Gram of Brain (H-BMvPGB) Harwood et al., 2013 Biopharm Drug Dispos., 34(1):2-28 Variability * Neuhoff et al., 2013
  25. 25. © Copyright 2019 Certara, L.P. All rights reserved. Scaling to in vivo: Gut 25 Caco-2, MDCK- II, LLC-PK1 etc. Jmax/Km or CLuint T CLuint, T In Jejunum I Intestine ISEF,TJejunum I Abundance pmol transporter per filter Correction pmol/mg total membrane protein Absolute scaling approach Scaling needs to account for: - Transporter expression/activity in vitro - Transporter expression/activity in vivo - Regional differences
  26. 26. © Copyright 2019 Certara, L.P. All rights reserved. Input units: Scaling to in-vivo : Liver HEK-293, HepaRG, Oocytes Jmax/Km CLuint T Scaling Factor 2 HHEP CLuint, T CLuint, T per g Liver Scaling Factor 3 Scaling Factor 4 pmol transporter ? ? ? µL.min-1 pmol transporter 106 hepatocytes • Scaling via absolute abundance HPGL Hepatocytes per gram of liver Liver Weight Cluint, T per Liver ? ISEF, THHEP Scaling Factor 1 CLuint, T 106HEP P-gp: 0.201 pmol/106 hepatocytes 0 50 100 150 25 45 65 HPGL(x106cells.g-1) Age (years) 0 10 20 30 40 50 1 25 45 65 MPPGL(mg.g-1) Age (years) BSA (m2) Johnson et al., 2005 Barter et al., 2007 Burt HJ et al., Drug Metab Dispos. 2016 Absolute transporter protein abundance data from a meta-analysis of 9 published studies that used LC-MS/MS or quantitative immunoblotting methods.
  27. 27. © Copyright 2019 Certara, L.P. All rights reserved. 𝐶𝐿𝑖𝑛𝑡 = 𝑗=1 𝑛 𝐼𝑆𝐸𝐹, 𝑇𝑗 ∙ 𝐽max,𝑗 ∙ 𝑋𝑗 𝐾 𝑚,𝑗 ∙ 𝐻𝑃𝐺𝐿 ∙ 𝐿𝑖𝑣𝑒𝑟 𝑊𝑒𝑖𝑔ℎ𝑡 IVIVE with abundance data for transporters: Absolute transporter abundance in the target population pmol transporterj per million hepatocytes j transporter Scalars in units of pmol of transporter (as for CYP and UGT scaling) Rate of transport pmol/min/pmol transporterj
  28. 28. © Copyright 2019 Certara, L.P. All rights reserved. Scaling to in-vivo: Kidney HEK-293, CHO etc. Jmax/Km CLuint T SF 1 HPTC CLuint, T CLuint, T per g Kidney SF 2 SF 3 CLuint, T per Kidney REF/RAFPTC PTCPGK Proximal tubule cells per gram of kidney Kidney WeightInput Units So far fitted IVIVE scalars: Metformin OCT2 = 3 Metformin MATEs = 3 Cimetidine OCT2 = 3 Cimetidine MATEs = 3 Burt et al., 2016 Only relative scaling approach available REF of 3 used for P-gp as well
  29. 29. © Copyright 2019 Certara, L.P. All rights reserved. PBPK model input Organ/Tissue Gut Jmax (pmol/min/pmol Transporter) 686.290 Km (µM) 50.700 ISEF,T 1.000 Ptrans,0 (10-6 cm/s) 39.7 Organ/Tissue Liver Jmax (pmol/min/pmol Transporter) 686.290 Km (µM) 50.700 System User ISEF,T 1.000 CLPD(mL/min/106 cells) 0.00953 Organ/Tissue Kidney Jmax (pmol/min/million cells) 645.120 Km (µM) 50.700 System User RAF/REF 3.000 CLPD basal (blood-to-cell) (mL/min/106 ) 0.00953 29 Transporter inputs from Meng et al., the rest is the Simcyp digoxin PBPK model values No change in the transporter activity was assumed for the gut and the liver Assumes that the permeability per million cells is the same in the caco-2 cells, liver and the kidney
  30. 30. © Copyright 2019 Certara, L.P. All rights reserved. In Vitro - In Vivo Extrapolation (IVIVE) of Transporter In vitro assays In vitro parameters In vivo parameters In vivo PK Analyse of the raw data: What are the assumptions IVIVE depends on the parameter and the organ PBPK model Physchem Distribution Metabolism solubility Verification: Saturation? DDI? Genetic? 30 Which in-vitro assay? Optimal design?
  31. 31. © Copyright 2019 Certara, L.P. All rights reserved. Performance verification 31 Ritonavir Inhibits P-gp Kirby et al., 2012 DDI –P-gp Inhibition Values above bars 90% CI Geomean 0 0.5 1 1.5 2 CmaxRatio(Geomean) Observed Predicted PK profile 0 5 10 15 20 25 30 35 40 0 20 40 60 SystemicConcentration (ng/mL) Time (h) Infusion 1mg Johnson et al. 1987 0 1 2 3 4 5 6 7 0 4 8 12 16 20 24 SystemicConcentration (ng/mL) Time (h) Hayward et al. 1978, Oosterhuis et al. 1991 0.1 1 10 100 0 20 40 60 SystemicConcentration (ng/mL) Time (h) Infusion 1mg 0.1 1 10 0 4 8 12 16 20 24 SystemicConcentration (ng/mL) Time (h) Oral dose 1mg Oral dose 1mg 0 0.5 1 1.5 2 AUCRatio Observed Predicted
  32. 32. © Copyright 2019 Certara, L.P. All rights reserved. Performance verification Rifampicin Induces P-gp (x3.5) Greiner et al., 1999 DDI –P-gp Induction Turnover data for transporter are sparse Intestinal P-gp was induced by 3.5 fold after rifampicin administration (Westernblot of biopsy) 32Taipalensuu et al., 2004 P-gp in Caco-2 using Digoxin: Linear correlation between activity and abundance? Limits? REF of 3.5 to simulate induction
  33. 33. © Copyright 2019 Certara, L.P. All rights reserved. Conclusion • Knowing your in vitro system is important • Be aware of the assumptions made when analysing your data • Modelling your in vitro data can help • Each transporter needs to be scaled according to its location and function • Absolute abundance of transporter is available in certain tissues and can help the IVIVE 33 IVIVE of transporter is still a work in progress and understanding better their mechanisms as well as the difference between in vitro and in vivo system, the impact of a disease or the interspecies difference is required
  34. 34. © Copyright 2019 Certara, L.P. All rights reserved. Thanks • Sibylle Neuhoff • Matthew Hardwood • Howard Burt 34
  35. 35. © Copyright 2019 Certara, L.P. All rights reserved. Questions?Questions? 35
  36. 36. © Copyright 2019 Certara, L.P. All rights reserved. Appendix 1: P-gp Ki shift PAGE 2017 In-vitro P-gp inhibition data required an average fold decrease of 94-fold (19.5 fold considering only lowest in vitro values for each inhibitor) to recover the invivo interaction with digoxin. 36

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