The document discusses the challenges of predicting drug success based on preclinical data and high attrition rates in drug development. It argues that improving predictability, which is determined by the predictivity of efficacy, safety and compound properties, could significantly lower attrition. However, understanding of these predictivities is currently limited. Coordinated efforts across the industry are needed to better quantify predictivities, standardize methods and data collection, and apply systems modeling approaches to improve prediction and lower attrition in drug development.

1. Does Pharmaceutical Predictivity Translate To Productivity in Drug Development? If So, How? Thorir D. Bjornsson, MD, PhD Saint Davids, Pennsylvania May-2010

2. The Art of Making Predictions “ It’s hard to make predictions, especially about the future.” Lawrence Peter Berra ("Yogi" Berra) American Baseball Legend (born 1925)

4. From Preclinical to Clinical Development Discovery & Preclinical Efficacy Safety Compound Properties Efficacy Safety Compound Properties Clinical Development DATA DATA PLANNING

8. Clinical Development Plans Discovery & Preclinical Efficacy Safety Compound Properties Efficacy Safety Compound Properties Clinical Development PREDICTIVITY DATA DATA

11. Attrition is a Key Challenge Attrition Rates Vary Depending on Different Attributes Vary by therapeutic areas Vary by phase of development Vary depending on targets Small Molecules > Biopharmaceuticals > Vaccines

12. 90 - 95% Average attrition rate across the industry Rate of Attrition: Unacceptably High

13. Compound Terminations Clinical safety Lack of efficacy Formulation PK/bioavailability Commercial Toxicology Cost of goods Unknown/other Kola & Landis, Nature Review Drug Discovery, 3:711-715, 2004 Commonly Cited Causes

14. Attrition Categories* Scientific Reasons Technical Reasons Commercial Reasons Regulatory Reasons Preclinical and Clinical Efficacy Preclinical and Clinical Safety Preclinical and Clinical Pharmacokinetics Bioavailability Formulation Issues Patent Issues Cost of Goods Budget/Resource Constraints Portfolio Rationalization Potential Value Regulatory Hurdles Regulatory Requirements Regulatory Decisions * CMR Categories

15. Fundamental Causes of Termination (Scientific) Bjornsson et al., Pharmaceutical Predictivity (msc), 2010 * Compound properties are defined as determinants and descriptors of acceptable exposure, including variability and time course Efficacy Safety Compound Properties*

17. Need For New Designs “ When things aren’t working the way they should be, you have the makings of a great design project.” Bruce Mau, design thinker

18. Pharmaceutical Predictivity P T = (1 – A T ) p i = [1 – (a i x A T )] P T = p e x p s x p c Just a Few Equations ….. Total predictivity, P T , is derived from total attrition, A T Total predictivity equals the product of the individual three key predictivities, ie, of efficacy, safety and compound properties, p e , p s and p c , respectively, and assumes these are independent of each other Each individual predictivity, p i , is related to the proportion of that attrition, a i, relative to total attrition Bjornsson et al., Pharmaceutical Predictivity (msc), 2010

19. Pharmaceutical Predictivity P T = p e x p s x p c Scientific Determinants of Success Rates What Do We Know About These Individual Predictivities?

26. Examples of different likelihoods of success depending on different mix of predictivities of efficacy, safety and compound properties in man Pharmaceutical Predictivity Is this what we are talking about on average ? Maybe, but need data 0.5 0.5 0.2 0.050 (5.0%) 0.6 0.6 0.3 0.108 (10.8%) 0.5 0.5 0.5 0.125 (12.5%) 0.67 0.67 0.67 0.30 (30.0%) p c p s p e P T

31. In Conclusion Predictivity = Productivity

32. A Quote on Predictivity “ No branch of science can be called truly mature until it has developed some form of predictive capability.” Sir Peter Medawar (1915 - 1987) Nobel Laureate in Physiology and Medicine, 1960