Under prediction of hepatic clearance from in vitro studies: prospects for resolution. J Brian Houston

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1 DDI, Seattle, June 2018 Under prediction of hepatic clearance from in vitro studies: prospects for resolution J Brian Houston Centre for Applied Pharmacokinetic Research (CAPkR)

2 Scaling up in vitro parameters For capacity: established scalers hepatocellularity, microsomal yield, proteomic measurements For functional activity:? (mitigate under prediction) Empirical Scaling Factors (ESF) used as surrogate. Represents average bias. Can do better?

3 Use of Empirical Scaling Factors Clearance prediction to correct for bias Explored in different ways (with test sets of drugs) e.g. off set, regression method Works satisfactorily, use of calibrators PBPK modelling to recover the time profile ( model-based factors applied to individual drugs) Impact on predicting DDIs Extrapolation to patient populations (reverse translation)

4 Some Options to refine Empirical Scaling Factors Ideally need a mechanistic marker of functionality Cross-species Scaling Factor (sesf) may assist assumes the IVIVE disconnects are same in both species sesf av A systems factor, allows prospective use for human sesf sd Also assumes hepatic drug distribution/access of drug to proteins same in both species Probably as mechanistic as currently possible

5 Application of sesf in Hepatic Clearance 1: OATP hepatic transporters Lack of comprehensive kinetic data set in this area and a need for development of strategies for in vitro in vivo extrapolation 2: Hepatic metabolic clearance prediction Revisiting an extensive database for hepatocyte based prediction of metabolic clearance

6 9 selected drugs for transporter studies Bosentan, Cerivastatin, Fexofenadine, Pitavastatin, Pravastatin, Repaglinide, Rosuvastatin, Telmisartan, Valsartan Log D range (ECCS class 2 & 3) Varied metabolic stability Monolayer format with hepatocytes from human and cynomolgus monkey Explore suitable preclinical species and prospect of cross species scaling

7 Cynomolgus Monkey Study Manchester Pfizer collaboration

8 Cross species optimization of human predictions from human hepatocytes Bias correction based on residuals difference between predicted and observed. Investigated in two ways: 1. by the use of the gmfe from the relationship between in vivo clearance and in vitro predicted clearance (sesf av ) 2. by using individual drug empirical scaling factors for each specific drug obtained from the ratio of observed to predicted clearance (sesf sd )

9 Species Comparison of clearances and Kp uu for nine OATP substrates between cynomolgus monkey and human CL total CL active CL passive Kp uu 1, pravastatin; 2, fexofenadine; 3, rosuvastatin; 4, valsartan; 5, repaglinide; 6, pitavastatin; 7, cerivastatin; 8, telmisartan; 9, bosentan.

10 Species comparison of in vivo intrinsic hepatic clearance for nine drugs and correlation between observed and predicted clearance in cynomolgus monkeys 1, pravastatin; 2, fexofenadine; 3, rosuvastatin; 4, valsartan; 5, repaglinide; 6, pitavastatin; 7, cerivastatin; 8, telmisartan; 9, bosentan.

11 sesf from cynomolgus monkey sesfav from gmfe for cyno: 2.66 sesfsd Obs CL int,h cyno CL int,uptake cyno Rosuvastatin 2 Valsartan 1 Pitavastatin 1 Telmisartan 0 Fexofenadine 1 Repaglinide 2 Cerivastatin 4 Pravastatin 19 Bosentan 3

12 Improvement in human clearance prediction using sesf: correlations and residual plots Direct sesf av sesf sd Human gmfe , pravastatin; 2, fexofenadine; 3, rosuvastatin; 4, valsartan; 5, repaglinide; 6, pitavastatin; 7, cerivastatin; 8, telmisartan; 9, bosentan.

13 Impact of human donor on utility of sesf Direct 1, cerivastatin; 2, fexofenadine; 3, pitavastatin; 4, pravastatin; 5, repaglinide; 6, rosuvastatin; 7, telmisartan; 8, valsartan

14 SUMMARY Hepatic clearance for OATP transported drugs can be predicted from in vitro monolayer hepatocytes successfully Cross-species scaling using cynomolgus monkey data helps to capture some of the bias from in vitro human predictions Other pre-clinical species? sesf av or sesf sd best? Cross lab applicability?

15 Revisiting Met CL in light of transporter experience

16 Human in vivo CLint Species differences in CLint for drugs common to both hepatocyte databases common drugs Rank order and range similar Rat values approximately 10-fold higher than human Similar finding in vitro Rat in vivo CLint

17 CL dependence in Human (A) and rat (C) CLint predicted from hepatocyte studies and individual ESF n = 101 n = 128

18 sesf after zoning the observed clearances Observed CLint (ml/min/kg) Human Rat ESF n ESF n < , >10, All CLints

19 Predicted CL Predicted CL Importance of zoning for improving human CLint predictions (without cross species involvement) Using ESF from all data Using ESF from zoned data Observed CLint Observed CLint

20 Log predicted/observed CLint Log predicted/observed CLint Predicted CLint Predicted CLint Cross-species improvement in human CLint prediction using sesf from rat hepatocytes for 101 drugs Direct (gmfe 4.2) sesf av from respective zones (gmfe 2.8) Observed CLint Predicted CLint Observed CLint Predicted CLint

21 Log predicted/observed CLint Log predicted/observed CLint Predicted CLint Predicted CLint Cross-species improvement in human CLint prediction using sesf from rat hepatocytes for 23 drugs sesf av from respective zones sesf sd Observed CLint Predicted CLint Observed CLint Predicted CLint

22 Key observations Cross-species ESF assists prediction of metabolic Clearance (confirming small studies in early 2000s) Zoning according to CL value required Rat CL > human CL, so zones assigned in rat and human contain different drugs Improvement in CL from cross-species scaling not limited to use of same drugs in both species Cross labs usage acceptable

23 General Conclusions Prediction of transporter clearance comparable to metabolic clearance in terms of bias and precision Use of sesf helps as an independent measure of potential shortcomings of isolated hepatocyte system This refinement increases confidence in IVIVE for CL prediction. Could be used as alternative or supportive metrics to ESF Also may inform model-based factors required in PBPK models.

24 Acknowledgements Ayşe Ufuk, David Hallifax Pfizer Transporter group Other Member companies and colleagues in the CAPkR Consortium