Culture Hepatocytes in Human Plasma to Count the free Concentration of Drug in Evaluation of Drug-drug Interaction. Chuang Lu

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1 Culture Hepatocytes in Human Plasma to Count the free Concentration of Drug in Evaluation of Drug-drug nteraction Chuang Lu Millennium, The Takeda Oncology Company Cambridge, MA, USA DD 205, Seattle, 6/29/205 Outline The challenges in current approaches of DD prediction The hepatocyte / plasma incubation that enables avoidance of using the [] / Ki ratio The prediction of ketoconazole and fluconazole mediated DD on marketed compounds The DD prediction and clinical results on an investigational compound 2

2 Current [l] / Ki based DD model AUC AUC CL CL F g, F g fm 3A4 + [ + Ki fm 3A4 ] Challenges: Trying to find out the true in vivo [] (Rostami and Tucker, 2004; to 2005, Bachman 2006, Obach 2006, Galetin and Houston 2006) Uncertainty in assessing the Ki value 3 Limitation of current DD approaches define the true [] Bachman

3 Limitation of the current DD approaches determining the Ki The Ki value determination could be a challenge because of the: ) Protein binding effect 2) Substrate used 3) n vitro systems involved Ki values for ketoconazole ranges: µm in literature (Thummel and Wilkinson 998) µm in the FDA DD draft guidance (Sept. 2006) 5 Relationship Between []/Ki and AUC High variability in ([] / fu, plasma) / (Ki / fu, mic) High variability in predicted AUC change 6 3

4 For predicting DD Can we get around the need to know the true [] and Ki values? 7 Yes. Create an in vitro model that mimics the in vivo situation for the inhibitor concentration at the hepatic enzyme level. Model: Suspension of human hepatocytes in human plasma 8 4

5 n Vivo at Steady State Liver / Hepatocytes n Vitro ncubations Hepatocytes Blood / Plasma Plasma C C plasma, invivo hepatocytes, invivo C C plasma, invitro hepatocytes, invitro 9 Comparison of hepatocytes in the liver (in vivo) and in suspension (in vitro) Cryopreserved hepatocytes are known to preserve phase enzymes and the majority of phase enzymes Cryopreserved hepatocytes are known to preserve uptake transporters, such as OATP and NTCP Efflux transporters (such as Pgp and BCRP) in the hepatocyte suspension are internalized, but they are also inhibited by ketoconazole or fluconazole in vivo Thus, Hepatocytes in vitro can mimic hepatocytes in vivo, especially in the ketoconazole or fluconazole DD study 0 5

6 Noncompetitive inhibition binds to both E and ES complex Steady state assumption (same as Michaelis-Menten) fa (enzyme activity remaining) enzyme velocity in the presence of an inhibitor (v ) over its control (v) regardless the substrate concentration v fa v K m Vmax[ S] (+ ) + [ S](+ Ki V [ S] K max m + [ S] Ki ) + ki Enzyme Kinetics, Segel 974 Competitive inhibition binds to E only Steady state assumption (same as Michaelis-Menten) K v v m Vmax[ S] (+ ) + [ S] ki Vmax[ S] K K + [ S] m m Km + [ S] (+ ) + [ S] ki When [S] 0, the v / v reflects the true enzyme activity remaining because substrate is not interfering the inhibitor v fa v + ki Enzyme Kinetics, Segel

7 Our DD model AUC AUC CL CL f m, hep [ fm fa3a 4+ fm fa2c 9... ] + fotherfaother 3A4 2C9 fa 3A4 v / v / (+ / Ki 3A4 ) for noncompetitive and competitive ([S] 0) inhibitors Thus DD could be predicted using measurable terms: ) f m - fraction of contribution of each CYPs (reactive phenotyping) 2) fa -fa is the fraction of enzyme activity remaining in the presence of an inhibitor 3 Determination of enzyme activity remaining in the presence of an inhibitor (fa) Hepatocytes were mixed with ketoconazole or fluconazole under room temperature for 20 min, then warmed up to 37 o C for 0 min before adding the CYP substrates for activity determination. This provides time for the inhibitor to be equilibrated in hepatocytes At the same time, hepatocytes were separated from the media. Keto/Fluconazole concentrations in the media were determined with standard curves. 4 7

8 Extra-hepatocyte concentration determination 0.5 ml ketoconazole solution ml hepatocyte suspension ncubated in 37 o C water bath 00 ul hepatocytes + ketoconazole Aliquot at different time points Oil, d.0 Centrifuge 0 sec Cut centrifuge tube 5N NaCl+ 0.2% saponin Hepatocytes were separated from their ketoconazole solution LC/MS/MS analyses for both top and bottom samples 5 CYP activities of human hepatocytes in the presence of ketoconazole in human plasma Total Keto in incubation Keto extracellular CYPA2 CYP2C9 CYP2C9 CYP2D6 CYP3A4 (um ) (um ) % % % % %

9 CYP activities of human hepatocytes in the presence of ketoconazole in KHB Total Keto in incubation Keto extracellular CYPA2 CYP2C9 CYP2C9 CYP2D6 CYP3A4 (um ) (um ) % % % % % CYP activities of human hepatocytes in the presence of fluconazole in human plasma Total Fluco Fluco in incubation extracellular CYPA2 CYP2C9 CYP2C9 CYP2D6 CYP3A4 (um) (um) (% activity)

10 Determination of f m in the DD model AUC AUC CL CL f m, hep [ fm fa3a 4+ fm fa2c 9... ] + fotherfaother 3A4 2C9 9 Substrate specificity of prototypical CYP inhibitors in human liver microsomes CYPA2 CYP2C9 CYP2C9 CYP2D6 CYP3A4 CYP nhibitor Phenacetin Tolbutamide S-mephenytoin Dextromethorphan Testosterone MLM3897 (Average % nhibition) a A2 Furafylline C9 Sulfaphenazole C9 Benzylnirvanol D6 Quinidine A4 Azamulin

11 Calculation of the relative contributions of CYPA2, 2C9, 2C9, 2D6, and 3A4 to the metabolism of MLN xA xB xC xD xE xA xB xC xD + 0.5xE xA xB xC xD xE xA xB xC xD xE xA xB xC xD xE Where A, B, C, D, and E are the percent contributions of CYPA2, 2C9, 2C9, 2D6, and 3A4, respectively, to the metabolism of MLN Relative contributions of CYPA2, 2C9, 2C9, 2D6, and 3A4 to the metabolism of MLN3897 in human liver microsomes CYPA2 CYP2C9 CYP2C9 CYP2D6 CYP3A4 fm (%) a a: f m (%) fractional contribution of specific CYP to the metabolism of MLN

12 Relative contributions of CYPA2, 2C9, 2C9, 2D6, and 3A4 to the metabolism of marketed compounds in human liver microsomes Drug CYPA2 CYP2C9 CYP2C9 CYP2D6 CYP3A4 Relative contribution (fm, %) a Budesonide Buprenorphine Docetaxel Loratadine Methylprednisolone Sirolimus Tacrolimus a : fm, % relative contributions of the CYP toward the metabolism of drug Lu et al., 2008a 23 Put Both f m and fa in the DD model to make prediction for an unknown compound AUC AUC CL CL f m, hep [ fm fa3a 4+ fm fa2c 9... ] + fotherfaother 3A4 2C9 24 2

13 Drug - ketoconazole interaction prediction using the hepatocytes in plasma model (Fold of AUC change) Compound Predicted a Observed f renal b Reference Theophylline Obach 2006 c, d, e Desipramine Obach 2006 c, d, e Midazolam <0.0 Tolbutamide Omeprazole <0.0 Loratadine 2.48 g 3.47 g negligible Cyclosporine <0.0 Alprazolam Budesonide negligible Buprenorphine negligible Docetaxel Loratadine negligible Methylprednisolone Sirolimus negligible Tacrolimus < Obach 2006 d Galetin 2006 e Obach 2006 d Soars 2003 e U of Washington DD database d Soars 2003 e U of Washington DD database d Galetin 2006 e, f U of Washington DD database d Galetin 2006 e, f U of Washington DD database d Galetin 2006 e, f U of Washington DD database d This study e U of Washington DD database d This study e U of Washington DD database d This study e U of Washington DD database d This study e U of Washington DD database d This study e U of Washington DD database d This study e U of Washington DD database d This study e 25 Prediction of Clinical DD Using n Vitro Phenotyping Data 20 y 0.93x R Predicted fold change in AUC Clinical observed fold change in AUC 26 3

14 Marketed compounds - Fluconazole DD prediction using the hepatocytes in plasma model (Fold of AUC change) Prediction Compound Predicted a Observed error (%) f renal b Reference Theophylline Obach 2006 c, d Tolbutamide Omeperazole <0.0 S-Wafarin <0.02 Phenytoin Midazolam <0.0 Sirolimus negligible Cyclosporine <0.0 Tacrolimus <0.0 Obach 2006 d Soars 2003 e Obach 2006 d Soars 2003 e to 2005, Obach 2006 c, d to 2005 d Pea 200 e Obach 2005 d Galetin 2006 e to 2005 d This work e to 2005 d Galetin 2006 e Osowaski d This work e a: Predictions are based on our model assuming 200 mg BD dose of flutoconazole have plasma C max of 34.6 µm and linear PK. Prediction error is expressed as 00 x (predicted value-observed value) / observed value. b: nformation on renal clearance was from Hardman et al. 200 c: Assume theophylline and S-wafarin are selective substrates for CYPA2 and CYP2C9, respectively d: References for observed AUC changes e: References of f m 27 Prediction of Clinical DD Using n Vitro Phenotyping Data Observed clinical fold of AUC changes Omeprazole y.6x R Predicted fold of AUC changes 28 4

15 DD prediction and clinical studies of ketoconazole, fluconazole and MLN Prediction of Clinical DD of MLN3897 from n Vitro Model. Ketoconazole interaction with MLN3897 Predicted AUC 8.33 folds 2. Fluconazole interaction with MLN3897 Predicted AUC 3.26 folds 30 5

16 Mean Trough Plasma Concentration versus Time Profile following 0 mg Dose of MLN3897 and MLN Ketoconazole Mean Trough MLN3897 Plasma Concentration (ng/ml) Mean MLN3897 Mean MLN3897 Female Mean MLN3897 Male Time (Day) 3 Mean Plasma Concentration versus Time Profile following 0 mg Dose of MLN3897 and MLN Ketoconazole Mean MLN3897 Plasma Concentration (ng/ml) Mean MLN3897 Mean MLN Ketoconazole Mean MLN3897 Female Mean MLN3897 Male Mean MLN Ketoconazole Female Mean MLN Ketoconazole Male Time (h) 32 6

17 Clinical DD Study Design of MLN3897 Effect on Midazolam and Fluconazole Effect on MLN3897 Days 0 Days Days 2 26 Day Days 2 26 Midazolam dosing Midazolam dosing 2 mg QD 2 mg QD MLN3897 dosing 30 mg QD MLN3897 dosing 0 mg QD Fluconazole dosing 400 mg QD 33 Mean Plasma Concentration versus Time Profile following 0 mg Dose of MLN3897 and MLN Fluconazole Mean MLN3897 plasma concentration (ng/ml) 00 0 Mean MLN3897 Mean MLN3897+Fluconazole Time (h) 34 7

18 Comparison of DD: in vitro predictions vs. clinical observations of MLN3897. Ketoconazole interaction with MLN3897 Observed AUC Predicted AUC 8.28 folds 8.33 folds 2. Fluconazole interaction with MLN3897 Observed AUC Predicted AUC 3.90 folds 3.26 folds 35 Conclusions The new in vitro method of human hepatocytes suspended in plasma is a viable model that closely mimics the in vivo conditions for the prediction of magnitude of DD between CYP substrates and CYP inhibitors The model can effectively replace the current paradigm of using the ambiguous []/Ki ratio for DD predictions The method is validated by retrospective prediction of DDs for several marketed drugs and prospectively for MLN3897 The model is extremely useful to estimate DD parameters (ki, K inact, k ) for highly bound compounds without determining the fu, plasma and fu, mic separately. t also allow long-term hepatocyte culture and provide a venue of determining CYP induction in a more relevant conditions 36 8