1. Best practices in human PK prediction: which method should I use? (An introduction to ADME WorkBench) May 7, 2013 Conrad Housand chousand@aegistg.com www.admewb.com

2. Framing the Question Q: Which human PK prediction method should I use? A: It depends…

3. Context What exactly do we need to predict? –NCA descriptors? –PK parameters? –Plasma concentration profiles? –Tissue cell or interstitia concentrations?

4. Context What data do we have with which to make predictions? –Preclinical species in vivo? –Physicochemical parameter values? –In vitro values?

5. Context What predicive accuracy do we require? –Plasma AUC and AUMC within 3-fold error for 75% of drug-like compounds? –Accurate prediction of curve shape for a small set of compounds? –Within 50% of observed values for a single chemical?

6. Best Practices PhRMA CPCDC Initiative on Predictive Models of Human PK –Working group comprised of representative of 12 PhRMA member companies –Goal: “to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates” –Findings published in series of five articles in J Pharm Sci (2011)

7. Best Practices PhRMA Initiative study components –Assembly of a diverse data set 108 compounds IV, PO PK data in humans and preclinical species In vitro and physchem data –Assessment of predictive methods based on this data set Methods for predicting human CL, VDSS Wajima (allometric) approach Physiologically-based (PBPK) approach

8. Prediction Methods Prediction of human CL –Evaluated 29 different methods including allometric and IVIVE techniques –In vivo performed slightly better than in vitro –FCIM and two-species allometry performed best among in in vivo methods –IVIVE using hepatocyte data w/o binding and microsomal data with plasma and mic binding performed best among IVIVE methods

9. Prediction Methods Prediction of human Vdss –Evaluated 24 methods including empirical, semi- mechanistic and mechanistic –No single method was better for all compounds, but limitations in data precluded thorough evaluation of some methods –But methods based on in vivo preclinical data generally performed better –Best in vivo: Øie–Tozer, two-species scaling (rat/dog) and Arundel (lumped PBPK)

10. Prediction Methods Allometry (Wajima) –Uses CL and VDSS prediction techniques described above –Conc scaled by Css, time scaled by MRT Equivalently, can scale microconstants –Human Ka, Fabs predicted by averaging values from preclinical species (determined by comparmental PK analysis) –Predictions were within 3-fold error for IV compounds, but ability to predict PO parameters and overall curve shape was poor

11. Prediction Methods PBPK –Combinations of absorption, distribution and clearance models were evaluated Absorption: avg. preclinical, ACAT Distribution: Jansson, Arundel, tissue composition Clearance: IVIVE, in vivo allometric methods –Inputs based on in vitro and in vivo methods showed similar accuracy –In general, IV kinetics were predicted much more accurately than PO

12. Implementation in ADME WorkBench Models –CSL files, M language scripts Computational engines (acslX) –ODE solution, parameter estimation User Interface –Spreadsheet-based inputs –Tabular and graphical results –Interactive tools

13. Example –PhRMA data set –Allometry –CL, VDSS predicted using simple allometry

14. Example –Mebendazole –PBPK ACAT Unified algorithm CL data from DrugBank

15. Roadmap 2013 Product Roadmap –Coming soon: Gut metabolism, transporters Permeability-limited tissues –Later this year: DDI, mixtures, metabolites

16. Thank you Questions? Thank you!