May 7, 2013 Conrad Housand chousand@aegistg admewb

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 PharmSci (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 • Example • PhRMA data set • Allometry • CL, VDSS predicted using simple allometry

14. Example • 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!