GlobalPharmacometrics

Influencing Early Portfolio Decision Making Using Pre-Clinical M&S:

how early is too early and when is it too late?

Peter A Milligan1 & Piet Van Der Graaf2

1Head of Pharmacometrics, 2Pharmacokinetics, Dynamics & Metabolism, Pfizer

2/21

Overview

• How early is too early and when is it too late?– For what?

• Vision– What does success look like?

• Historical Reality

• Current Reality

3/21

Vision = Integration

Systems Biology

Systems Pharmacology

Preclinical PKPD

Clinical Pharmacology

Pharmaco-metrics

Pathway Target Drug Disease

Time

Eff ect

Concen

tration

PD

PK

PKPD

‘Right molecule’‘Right target’‘Right pathway’ ‘Right dose’ ‘Right patients’

4/21

DRUG

Affinity

IntrinsicEfficay

SYSTEM

Emax

N

EC50

Tissue

Species

Gender

Age

Disease

Chronic treatment

R0, ke, Em, n

Drug concentration-effect relationships depend on the drug and the biological system

Van der Graaf & Danhof, 1998

5/21

Translational Sciences and R&D

• Across species• Within an indication

– across molecules– across mechanisms– across response

instruments etc.– across volunteers to

patients– across patient sub

populations• Across indications

Translational Pharmacology

Translational Physiology

Translational Pathology

DRUG SYSTEM

DISEASE

Translational Sciences

6/21

Historical Reality

7/21

Pfizer Internal Use Only 6

5 Levels of PKPD understanding

Dose-response

Exposure-Response

Time-course of response

Multiple dose levelsModel-derived description and prediction

Benchmarked against comparators

Linked to clinical model

0

1

2

3

4 5

Observational Descriptive Predictive

Approach Adopted to Raise Awareness

8/21

Evolution of Translational Sciences

1. ‘Classical’ PKPD: Compound selection– Understanding time-concentration-effect relationship– Focus on dose predictions, TI, design and interpretation of in

vivo studies, in vitro-in vivo correlations– Data driven

2. Mechanism-based PKPD: Target validation– Understanding target pharmacology– Focus on lab objectives, biomarker selection, translational

strategy– Replaces (in part) requirement to generate (in vivo) data

3. Systems Pharmacology: Target selection– Understanding pathway– Focus on target identification and selection & disease

9/21

Decisions Taken During Selection of Monoclonal Antibody for Asthma

• IgE-induced mast cell degranulation exacerbates allergic asthma and rhinitis

• Anti IgE antibody, omalizumab, approved for severe asthma• Treatment success defined by reduction of free IgE levels to < 25 ng/mL• Limited treated population due to high dose and associated cost/delivery

limitations

• Challenge:– What is the impact of increasing in vitro affinity on the required clinical dose?– Sufficient probability (> 0.7) to support treatment of a wider population with a

50% lower dose than established agent– Follow-on candidates lack cross reactivity in preclinical species

• Opportunity:– Clinical data and mechanistic PKPD model available for omalizumab

• Solution:– In silico selection of candidate attributes (affinity and disposition) based on

trial simulations using mechanistic PKPD modelAgoram, Martin & Van Der Graaf, Drug Discovery Today (2007) 12: 1018-24

Agoram, Martin & Van Der Graaf (2007) PAGE 16 Abstr 1089

10/21

Recommended dose of omalizumab (XolairR) obtained according to patient body weight and baseline total IgE

From B.M. Prenner, J. Asthma 45, 429-436 (2008)

1. Dose (cost)

2. Dosing frequency

3. Untreated population

11/21

Decisions Taken During Selection of Monoclonal Antibody for Asthma

• IgE-induced mast cell degranulation exacerbates allergic asthma and rhinitis

• Anti IgE antibody, omalizumab, approved for severe asthma• Treatment success defined by reduction of free IgE levels to < 25 ng/mL• Limited treated population due to high dose and associated cost/delivery

limitations

• Challenge:– What is the impact of increasing in vitro affinity on the required clinical dose?– Sufficient probability (≥ 0.7) to support treatment of a wider population with a

50% lower dose than established agent– Follow-on candidates lack cross reactivity in preclinical species

• Opportunity:– Clinical data and mechanistic PKPD model available for omalizumab

• Solution:– In silico selection of candidate attributes (affinity and disposition) based on

trial simulations using mechanistic PKPD modelAgoram, Martin & Van Der Graaf, Drug Discovery Today (2007) 12: 1018-24

Agoram, Martin & Van Der Graaf (2007) PAGE 16 Abstr 1089

12/21

Mechanistic model used to define relationship between in vitro affinity and clinical biomarker

MAb

Rin(t)

Kel_MAb

Non-specific

Target

Rin

Kel_target Kel_Complex

MAb Complex+

Kon

Koff

Internalisation

Mager, JPKPD (2001)

Meno-Tetang JPET (2005)

Non-specific and specific

13/21

Collate existing data required to characterise translation: Omalizumab PKPD Profile allergic asthma patients

Hayashi et al. A mechanism-based binding model for the population pharmacokinetics andpharmacodynamics of omalizumab. British Journal of Clinical Pharmacology 63:5 548–561

14/21

Decisions Taken During Selection of Monoclonal Antibody for Asthma

• IgE-induced mast cell degranulation exacerbates allergic asthma and rhinitis

• Anti IgE antibody, omalizumab, approved for severe asthma• Treatment success defined by reduction of free IgE levels to < 25 ng/mL• Limited treated population due to high dose and associated cost/delivery

limitations

• Challenge:– What is the impact of increasing in vitro affinity on the required clinical dose?– Sufficient probability (≥ 0.7) to support treatment of a wider population with a

50% lower dose than established agent– Follow-on candidates lack cross reactivity in preclinical species

• Opportunity:– Clinical data and mechanistic PKPD model available for omalizumab

• Solution:– In silico selection of candidate attributes (affinity and disposition) based on

trial simulations using mechanistic PKPD modelAgoram, Martin & Van Der Graaf, Drug Discovery Today (2007) 12: 1018-24

Agoram, Martin & Van Der Graaf (2007) PAGE 16 Abstr 1089

15/21

PFMAb (5x greater affinity) response at different fractions of OMZ clinical dose

Time (days)

Fre

e I

gE

Co

nce

ntr

atio

n (

ng

/mL

)

0 20 40 60 80

15

10

50

10

05

00

0.1X

1.0X

0.5X

16/21

Influencing Decision Making 1. Clear communication of project

objectives to discovery team (with tabled assumptions)

2. Project guided by modelling and simulation in absence of in vivo models

3. Continuing expensive affinity maturation steps could be avoided:

• No more than 2-2.5-fold dose reduction beyond 5-15-fold affinity increase

4. Model used to explore potential project opportunities beyond affinity improvement

17/21

•HAE1 23-fold increased binding affinity to IGE compared to omalizumab

•‘Further increases in HAE1 dose beyond 180 mg were not expected to improve the response

18/21“indication” level

Vision = Integration

Pre-Clinical FIH FIP POC P3 Registration P4

“mechanism” level“compound” levelRe

lati

ve

Da

ta

Co

ntr

ibu

tio

n

to M

od

els

3) Study Design => OCs

Trial Conduct

2) Decision Criteria

=>

1) Define Questions

Re

lev

an

t D

ata

V

olu

me

to

Mo

de

ls

PTS

4) Update models

19/21

Decision Theoretic Models

• Quantifies the ability of protocol/program to meet stated objectives

• In-depth consideration of operating characteristics– decision criteria (aka Go/No Go rules)– based on the “truth” (“if we knew the truth, would we go/no go”)– based on the data in a trial (“given the data do we go/no go”) i.e.,

the data-analytic decision rule– focus on false positive and negative rates (when you should GO

or NO GO), probability of making a correct decision, probability of technical success

– maximise probability of achieving correct decision– PTS depends on precedence, portfolio, stage of development

• Beyond typical sample size methods– want to know something beyond properties of given design

Kowalski KG et al “Model-Based Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15(2):2-22

RG Lalonde et al, “Model-Based Drug Development”. CPT 2007;82(1):21-32

20/21

Pfizer Internal Use Only 6

5 Levels of PKPD understanding

Dose-response

Exposure-Response

Time-course of response

Multiple dose levelsModel-derived description and prediction

Benchmarked against comparators

Linked to clinical model

0

1

2

3

4 5

Observational Descriptive Predictive

Approach Adopted to Raise Awareness

21/21

Acknowledgements

• Steven W Martin, Thomas Kerbusch, Balaji Agoram, John Ward, Jonathan L French, Kenneth G Kowalski, Mike K Smith (Pfizer)

• Monica Simeoni & Maurizio Rocchetti (Accelera Nerviano Medical Sciences)

• Tom Sun (& Genentech Colleagues)

• Phil Lowe (& Novartis Colleagues)