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

Framing the Question

Q: Which human PK prediction

method should I use?

A: It depends…

Context

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

Context

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

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?

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)

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

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

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)

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

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

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

Example

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

Example

• Example– Mebendazole– PBPK

• ACAT• Unified algorithm• CL data from DrugBank

Roadmap

• 2013 Product Roadmap– Coming soon:

• Gut metabolism, transporters• Permeability-limited tissues

– Later this year:• DDI, mixtures, metabolites

Thank you

Questions?

Thank you!