Peter A Milligan 1 & Piet Van Der Graaf 2

1. 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. 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. Vision = Integration Pathway Target Drug Disease Systems Pharmacology Systems Biology Preclinical PKPD Clinical Pharmacology Pharmaco-metrics ‘Right pathway’ ‘Right target’ ‘Right molecule’ ‘Right dose’ ‘Right patients’

4. Van der Graaf & Danhof, 1998

5. 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 Pathology DISEASE Translational Sciences SYSTEM DRUG Translational Pharmacology Translational Physiology

6. Historical Reality

7. Approach Adopted to Raise Awareness

8. Evolution of Translational Sciences • ‘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 • 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 • Systems Pharmacology:Target selection • Understanding pathway • Focus on target identification and selection & disease

9. 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. Recommended dose of omalizumab (XolairR) obtained according to patient body weight and baseline total IgE • Dose (cost) • Dosing frequency • Untreated population From B.M. Prenner, J. Asthma 45, 429-436 (2008)

11. 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. Mager, JPKPD (2001) Meno-Tetang JPET (2005) Rin(t) Rin Kon MAb Target MAb Complex + Koff Kel_MAb Kel_target Kel_Complex Non-specific Internalisation Mechanistic model used to define relationship between in vitro affinity and clinical biomarker Non-specific and specific

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

16. Influencing Decision Making • Clear communication of project objectives to discovery team (with tabled assumptions) • Project guided by modelling and simulation in absence of in vivo models • Continuing expensive affinity maturation steps could be avoided: • No more than 2-2.5-fold dose reduction beyond 5-15-fold affinity increase • Model used to explore potential project opportunities beyond affinity improvement

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

19. 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 RG Lalonde et al, “Model-Based Drug Development”. CPT 2007;82(1):21-32 Kowalski KG et al “Model-Based Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15(2):2-22

20. Approach Adopted to Raise Awareness

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)