Harma Ellens

Translating in vitro data into clinical project team discussions Harma Ellens

GlaxoSmithKline Pharmaceuticals

Dabrafenib, a CYP inhibition and induction case history

Dabrafenib Substrate of CYP2C8 and CYP3A4 Inhibitor of CYP2C8/9 and CYP3A4 Metabolism dependent inhibitor of CYP3A4 Inducer of CYP3A4 and CYP2B6 TRANSLATION

UNDERSTANDING POC through NDA:

CLINICAL STRATEGY

FTIH through POC: Clinical DDI studies

Discovery trough FTIH: In vitro studies Static model predictions

PBPK modeling: special populations, dose regimens, additional co-meds

CYP induction by dabrafenib

potent induction via PXR and CAR “inverted u shape”

Potential for autoinduction (CYP2C8 and CYP3A4)

indicator of cell health

Net Effect Static Mechanistic Model to Asses Risk of CYP3A4 perpetrator DDI AUC' po CL int, h F ' g 1 1   ( ) x( ) AUCpo CL' int, h Fg [ A x B x C ] x fm  (1  fm) [ X x Y x Z ] x(1  Fg )  Fg

CYP3A4 inactivation

CYP3A4 induction

CYP3A4 inhibition (including metabolite contributions)

A

k deg, h [ I ]h  kin a ct k deg, h  [ I ]h  KI

B  1

C

d   [ I ]h [ I ]h  EC 50, I 1

1 

[ I ]h Ki

X 

k deg, g [ I ]g  kina ct k deg, g  [ I ] g  KI

Y  1

Z

d   [ I ]g [ I ]g  EC 50, I

1 [ I ]g 1 Ki

Predicted fold change: 0.87 (net effect is induction) Since dabrafenib induces CYP3A4 and possibly CYP2C9, midazolam and warfarin DDI studies were recommended

Mechanistic Static Model to Asses Risk of Victim DDI (CYP3A4 and CYP2C9)

CYP 1A2 2C8 2C9 2C19 2D6 3A4

% CYP contribution in HLM 0 56 - 67 4 -10 1.5 - 5 0 23 - 24

Perpetrator Drug Gemfibrozil Ketoconazole

Predicted Increase in Exposure 1.6 1.5

Recommendation to perform gemfibrozil and ketoconazole DDI studies

‘Top Down’ PBPK Modeling Approach Clinical Data – Ketoconazole DDI result → fm CYP3A4

Clinical results to derive key parameters

– Gemfibrozil DDI result → fm CYP2C8 – Residual fm assigned to CYP2C9 – Midazolam DDI → CYP3A4 induction potency – Warfarin DDI → CYP2C9 induction potency

– Human ADME results for Cl and Vd

Further validations

Modeling Validation – SD and RD PK profiles for dabrafenib –

CYP2C8 induction potency assumed equal to CYP2C9

Predictions

Simulations to predict – Itraconazole DDI – Impact of ketoconazole dosing regimen Further applications • Victim DDI assessment for CYP3A4 and CYP2C8 • E.g., saquinavir, ritonavir • Perpetrator DDI assessment for CYP3A4 and CYP2C9 • E,g., erythromycin, simvastatin, saquinavir,

Model output: fm CYP3A4 and CYP2C8 Ratio obtained from PBPK model Perpetrator drug

Clinically observed ratio

AUC(0-t)

Cmax

AUC(0-t)

Cmax

gemfibrozil

1.52

1.33

1.49

0.98

ketoconazole

1.69

1.55

1.64

1.29

In vitro

PBPK model SD

PBPK model RD

fm CYP2C8

0.56 - 0.67

0.30

0.48

fm CYP3A4

0.23 – 0.24

0.48

0.21

In vitro fm CYP3A4 underestimated In vivo CYP2C8 activity induced more than CYP3A4 activity due to metabolism dependent inhibition of CYP3A4?

Model output: induction

Ratio obtained from PBPK model

Clinically observed ratio

Victim drug

AUC(0-∞)

Cmax

AUC(0-∞)

Cmax

midazolam

0.37

0.40

0.28

0.38

warfarin

0.54

0.87

0.64

1.18

CYP3A4 induction: required scaling factor of 10

Were in vitro Emax and EC50 for CYP3A4 captured correctly given observed toxicity?

PBPK Model for Dabrafenib

Model Application Model was used to answer regulatory question regarding impact of keto dose regimen on DDI

… and regarding impact of a another strong inhibitor such as itraconazole…

Learnings

– Measure induction of all enzymes significantly contributing to metabolism (i.e. CYP3A4 and CYP2C8) to address auto-induction potential – Reduce induction time to 24h to improve EC50/Emax parameters by reducing/avoiding toxicity – Measure induction of catalytic activity as well (at 48 h) (for inducers that are also inhibitors)

When static models indicate induction risk, use induction biomarker in FTIH study to confirm

Acknowledgements

– – – –

Guoying Tai Grant Generaux Aarti Patel Lauren Richards-Peterson