1. Global Pharmacometrics Influencing Early Portfolio Decision Making Using Pre-Clinical M&S: how early is too early and when is it too late? Peter A Milligan 1 & Piet Van Der Graaf 2 1 Head of Pharmacometrics, 2 Pharmacokinetics, Dynamics & Metabolism, Pfizer

2. 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. 3/21 Vision = Integration Systems Biology Systems Pharmacology Preclinical PKPD Clinical Pharmacology Pharmaco- metrics PathwayTargetDrugDisease ‘Right molecule’‘Right target’‘Right pathway’‘Right dose’‘Right patients’

4. 4/21 Van der Graaf & Danhof, 1998

5. 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. 6/21 Historical Reality

7. 7/21 Approach Adopted to Raise Awareness

8. 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. 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 model Agoram, Martin & Van Der Graaf, Drug Discovery Today (2007) 12: 1018-24 Agoram, Martin & Van Der Graaf (2007) PAGE 16 Abstr 1089

10. 10/21 Recommended dose of omalizumab (Xolair R ) 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. 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 model Agoram, Martin & Van Der Graaf, Drug Discovery Today (2007) 12: 1018-24 Agoram, Martin & Van Der Graaf (2007) PAGE 16 Abstr 1089

12. 12/21 Mechanistic model used to define relationship between in vitro affinity and clinical biomarker MAb R in (t) K el_MAb Non-specific Target R in K el_targ et K el_Comple x MAb Complex + K on K off Internalisation Mager, JPKPD (2001) Meno-Tetang JPET (2005) Non-specific and specific

13. 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 and pharmacodynamics of omalizumab. British Journal of Clinical Pharmacology 63:5 548–561

14. 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 model Agoram, Martin & Van Der Graaf, Drug Discovery Today (2007) 12: 1018-24 Agoram, Martin & Van Der Graaf (2007) PAGE 16 Abstr 1089

15. 15/21 PFMAb (5x greater affinity) response at different fractions of OMZ clinical dose 0.1X 1.0X 0.5X

16. 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. 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. 18/21 “indication” level Vision = Integration Pre-Clinical FIH FIP POC P3 Registration P4 “mechanism” level“compound” level Relative Data Contribution to Models 3) Study Design => OCs Trial Conduct 2) Decision Criteria => 1) Define Questions Relevant Data Volume to Models PTS 4) Update models

19. 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. 20/21 Approach Adopted to Raise Awareness

21. 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)