Translation from mouse to human of pharmacokinetic-pharmacodynamic modelling of biomarker response –learnings from the AstraZeneca Oncology portfolio

Rhys Owen Jones3rd ICPAD Workshop – Amsterdam November 8th & 9th

Improving Phase II success rate5Rs framework and application of PKPD to predict human target engagement

• Right Tissue: Demonstrate adequate

exposure, and PKPD pre-clinically and

clinically in order to

• Build confidence molecule has PKPD

properties to reach sufficient levels of target

engagement (TE) to viably test the clinical

hypothesis

• Predict a PD active dose & establish a dose /

schedule model to help guide clinical study

design and input to set criteria for PoM

• Benchmark emerging data from dose

escalation studies against pre-clinical model

5Rs

framework

Pre-clinical data and predictive pharmacokinetic-pharmacodynamic

modelling is the cornerstone to predict human doseWhat is the evidence for pre-clinical data being adequate to predict human dose?

3

Predicted human exposure

Good understanding of translation of model

systems to predict human PK

PK is well predicted

Predicted human active dose

Translation of pre-clinical model systems to humanin vivo drug potency for

target engagement

Limited demonstration on how well we do this

Predicted human dose for optimal efficacy

Limited validated pre-clinical model systems

available

Attrition rate in late phases continues to be high, often

due to lack of efficacy

DosePlasma /

Tissue PKTarget

OccupancyTarget

EngagementPathway

ModulationPhenotypic Response

Efficacy /

Toxicity

Pre-clinical data and predictive pharmacokinetic-pharmacodynamic

modelling is the cornerstone to predict human doseWhat is the evidence for pre-clinical data being adequate to predict human dose?

4

Predicted human exposure

Good understanding of translation of model

systems to predict human PK

PK is well predicted

Predicted human active dose

Translation of pre-clinical model systems to humanin vivo drug potency for

target engagement

Limited demonstration on how well we do this

Predicted human dose for optimal efficacy

Limited validated pre-clinical model systems

available

Attrition rate in late phases continues to be high, often

due to lack of efficacy

DosePlasma /

Tissue PKTarget

OccupancyTarget

EngagementPathway

ModulationPhenotypic Response

Efficacy /

Toxicity

5

What comparison can we make comparing mouse to

human PKPD?

PK

Surrogate PD

Tumour PD

PK

Surrogate PD

Tumour PDDetailed articulation to define

PKPD relationship

Rarely sufficient data to build

PKPD relationship

Less emphasis & rarely

explored

When biologically feasible often

offers greater depth of data to

build PKPD relationship

Fully characterised Fully characterised

Minimal requirements

• Availability of biomarker pre-clinically and clinically

• Tissue and assays may differ mouse to patient

• Quantitative pre-clinical models and minimally, clinical data to overlay

6

What comparison can we make comparing mouse to

human PKPD?

Tumour PD Surrogate PD

Case example 1

• Exposure vs. biomarker modulation

explored in mouse tumour (CDX

model) and human PBMC

• Datasets provide comparisons of

derived EC50

• Good agreement between mouse and

human

Mouse tumour EC50 = 10 nM Human PBMC EC50 = 8 nM

0

20

40

60

80

100

120

140

160

0 4 8 12 16 20 24

pSe

r2 (

%b

asel

ine

at T

=0)

Time (hours)

Patient101 Patient106

Patient110 Patient111

Case example 2: Early insight builds confidence in model based approach to

prioritise dose / schedule

7

• PK and pProtein (in PBMC) data for 4 patients available to assess pre-clinical (mouse CDX

model) to clinical translation of PKPD relationship

0

50

100

150

200

250

300

0 4 8 12 16 20 24

Pla

sma

con

cen

trat

ion

(n

g/m

L)

Time (hours)

Patient101

Patient106

Patient110

Patient111

PK pProtein (PBMCs)

0

20

40

60

80

100

10 100 1000 10000

pSe

r2 (

%b

asel

ine

at T

=0)

Plasma concentration (ng/mL)

Patient101

Patient106

Patient110

Patient111

pProtein vs [plasma]

Lines show individual model fit

to patient PKPreclinical PD model used to

predict pProtein timecourse (lines)

in patients

IC50 from xenograft model

IC50 from

fitting to

clinical

data

IC50 curve from CDX studies

consistent with IC50 curve derived

by fitting model to clinical data

8

Case example 3: Clinical data and benchmarking against pre-clinical

requirements for efficacy suggest insufficient target engagement at a tolerated

dose

Biomarker EC50

Mouse (tumour) 15 nM

Human (PRP) 72 nM

• Clinical PD explored in

platelet rich plasma (PRP)

assay

• High degree of variability

observed across patients

• Population mean EC50 in

patients 5-fold higher than

mouse tumour

Phospho protein

Simulation of biomarker time course for

suppression on repeat dosing mouse and human

Human

Mouse

Patient exposure vs. response

relationship for biomarker in PRP

9

Case example 4: Inadequate clinical PD to derive quantitative clinical relationship

• Exposure vs. response defined in mouse across 3 cell-line xenograft models (one shown).

• Peripheral blood data from 4 patients available with multiple samples – inadequate to derive IC50

• Data overlaid onto Emax relationships derived from 3 mouse CDX models

Pe

rce

nt p

ho

sp

ho

pro

tein

re

lative

to

to

tal

Plasma concentration ng/ml

Imax = ~72%; IC50 = 100 ng/ml

Exposure vs. response in cell-line 1

Mouse Human

Degree of inhibition seen in 3 patients out of 4 consistent

with that predicted from mouse PKPD model

Clinical PD overlayed onto predictions from multiple mouse CDX models

Case example 5: Using pre-cinical PKPD predictions when clinical PD

is not available during dose escalation

• 2500 simulated tumor PD profiles

created by combining 500 virtual

patient PK profiles with 5 different

mouse PD models (derived from

NSCLC CDX / PDX models).

• Percentage of virtual patient tumours

achieving ≥ 50% PD knockdown was

calculated at each dose level tested

• Modelling used to guide dose

requirement for of ≥ 50% PD

knockdown (POM criteria) in ≥ 50%

patients (when using PDX models)

Combining clinical PK variability and pre-clinical heterogeneity across CDX / PDX

models to predict target engagement

AZD4785

clinical

PopPK model

Simulate 500

virtual patient

PK profiles

at each dose

NCIH358

PKPD

PC9

PKPD

LXFA983

PKPD

H1437

PKPD

LXFA526

PKPD

Heterogeneity in PKPD

relationship (KRAS mRNA KD)

Count %individuals with

>50% maximum Kras KD

Calculate average

across 5 models

Count %individuals with

>50% maximum Kras KD

Count %individuals with

>50% maximum Kras KD

Count %individuals with

>50% maximum Kras KD

Count %individuals with

>50% maximum Kras KD

Variability in PK

BM KD

BM KD

BM KD

BM KD

BM KD

AZDxxxx

40

50

60

70

80

90

100

0 168 336 504

KR

AS

mR

NA

(%

bas

elin

e)

Time (hours)

840 mg dose; 1 hr inf; PopMean PK

H1437 PKPD

PC9 PKPD

NCI-H358 PKPD

LXFA526 PKPD

LXFA983 PKPD

Example: Predicted PD profiles for typical

patient at xx mg dose

Bio

mark

er

0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200

%P

atie

nts

wit

h m

ax

KR

AS

red

uct

ion

>5

0%

Dose (mg)

LXFA983 PKPD

LXFA526 PKPD

PC9 PKPD

H1437 PKPD

NCI-H358 PKPD

Average across 5models

Relationship between weekly dose and

estimated %patients achieving ≥ 50% PD

knockdownB

iom

ark

er

Retrospective analysis of preclinical predictions of

exposure-target engagement across multiple therapy areas

11

0.1

1

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Clin

ica

l /

Pre

-clin

icale

xp

osu

re

Compound no

• Oncology, Respiratory,

Inflamation & Autoimmune (RIA)

and Cardiovascular, Renal &

Metabolism (CVRM) therapy

areas covered

• 88 % predicted IC50 from pre-

clinical data / modelling within

2-fold of that observed in the

clinic

• 3 biomarkers had ~10-fold

difference in prediction

compared to observed

Learnings

12

• Pre-clinical tools offer a feasible way to quantitively predict human target engagement (TE)

• Enables the use of pre-clinical insights to benchmark (sparse) clinical data in an iterative

and rapid way as data emerges from clinical trials

• Further analysis is required to understand the variability of clinical data and the precision

with which the biomarker IC50 can be estimated – how does this impact the benchmarking /

calibration of a pre-clinical model?

• PD data from surrogate tissues is valuable to calibrate pre-clinical models and to

demonstrate duration of effect relative to PK. Tumour PD remains gold standard for PoM

• PKPD should be explored in multiple mouse CDX & PDX models rather than only the most

sensitive model

• Benchmarking and back-translation of compounds with the same mechanisms and already

in the clinic is a powerful opportunity to calibrate translational PKPD assumptions

Conclusions

13

• Builds confidence in the application of pre-clinical data to predict TE, an active dose in

human, and as an input to define G/NG criteria for early clinical trials (POM)

• Success at quantitatively translating PKPD is predicated on a sufficient level of

understanding of the biology, with appropriate pre-clinical models (in vitro, in vivo) that

enable the kinetics and dynamics of drug effects to be explored adequately

• Portfolios continually evolve to novel targets / mode of action with an increasing diversity

of drug modalities – continuous assessment of this kind is necessary

• The attrition rate in the clinic due to lack of efficacy is still significant and attention should

be directed to improve translational approaches that define the extent and duration of TE

required for optimal efficacy in patient populations – downstream pathway BM, cellular

effects

Acknowledgements

14

Co-authors: Simon Barry, Andrew Pierce & James Yates

Case example contributors: Frank Gibbons, Douglas Ferguson, Michael

Davies, Elizabeth Harrington, Tammie Yeh, Alexander MacDonald, Tarjinder

Sahota

Cross-TA: Markus Fridén, Rasmus Löfmark-Jansson

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