Building a Business Case for More in silico Modelling in the Pharmaceutical Industry

1. Building a Business Case for Morein silico Modelling in the Pharmaceutical Industry A. L. Eiden, G. Lever, J. Loh and A. Nicolas CUTEC advisor: P. Zulaica MedImmune Mentor: B. Agoram

2. Building a Business Case for Morein silico Modelling in the Pharmaceutical Industry A. L. Eiden, G. Lever, J. Loh and A. Nicolas CUTEC advisor: P. Zulaica MedImmune Mentor: B. Agoram

3. R&D Today: Escalating Costs, Need for Reshaping • Evolution of New Molecular Entities (NME) cost • Reasons for costs • more stringent regulation • tougher science • economic change • Over 90% of compounds entering the first stage of clinical trials fail to become a product B. Munos, Nature Reviews Drug Discovery, (2009), 8, 959

4. R&D Today: Escalating Costs, Need for Reshaping • Evolution of New Molecular Entities (NME) cost • Reasons for costs • more stringent regulation • tougher science • economic change • Over 90% of compounds entering the first stage of clinical trials fail to become a product • Predicted NME Cost

5. R&D Today: Escalating Costs, Need for Reshaping • Reduction of costs • use of simulations • more efficient selection of projects • dramatically reduce attrition rates

6. Presentation Outline • Drug discovery • In silico modelling: the state of the art • 3 proposed strategies for implementation of in silico modeling in the pharmaceutical industry • Collaborations between Industry and Academia • In-House simulation and development team • Acquisition of existing companies • Conclusions

7. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years

8. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years

9. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years

10. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years

11. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years Clinical Trials 6 Years

12. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years Clinical Trials 6 Years

13. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years Clinical Trials 6 Years

14. Time and Processes Involved in Drug Discovery Target Discovery 2.5 Years Pre-clinical Development 4 Years Clinical Trials 6 Years FDA Approval 1.5 Years

15. Current Estimates of Timelines and Costs $598 M $220 M $30 M $26 M Target Discovery 2.5 Years Pre-clinical Development 4 Years Clinical Trials 6 Years FDA Approval 1.5 Years

16. Predicted Savings from in silico Modelling $ 48 M (indirectly) • Simulation Filter • in silico savings $ 27 M $ 6 M $550 M $193 M $26 M $24 M Target Discovery 2 years Pre-clinical Development 3.5 years Clinical Trials 5 years FDA Approval 1.5 Years Projections taken from predictions in Indian Institute of Technology, Delhi 2005 study and Cambridge University Medicinal BioChem lecture notes

17. State of the Art • Airlines • ~ $100 million per airline per year* • $7.9 billion average per company revenue† • Semiconductor Industry • ~ $1 billion per company per year* • $9.9 billion average top 20 company revenue‡ * Horst D. Simon - Deputy Director Lawrence Berkeley National Laboratory †iSuppli Corporation supplied rankings for 2010 (Preliminary) ‡US DOT Form 41 via BTS, Schedule P12

18. State of the Art Length scale Molecules Cells, tissues and organs Proteins (Signalling pathways) Test animals Humans - Density Functional Theory - Molecular Dynamics - Coarse Grained Models - QSAR, Semi-Empirical Methods Theoretical approaches - Statistical and Empirical methods - ONETEP Accelrys - SYBYL-X Tripos - PathwayLab InNetics AB - Pharsight - Simcyp Limited - Physiome Project - PhysioLab Entelos - Living Human Project Available software (company)

19. Successful Case Studies - Pharma in silico Savings • Virtual Patients: Entelos and Johnson & Johnson • Design Phase I trial of novel treatment, simulated effects of various dosing levels. • Results: trial redesigned, with: • 40% time saving • 66% saving in number of patients • Real-life trial confirmed the simulation result. Pharma 2020: Virtual R&D, PricewaterhouseCoopers (2007). John Gartner, Wired (20 May 2005) ErbB 1 ErbB 2 ErbB 3 ErbB 4 • ErbB protein Family • Simulations required less than 24 hours on a desktop computer, data from experiment required weeks at the bench This image has been released into the public domain by its author, K.murphy at the wikipedia project. This applies worldwide. B.S. Hendriks et. al. IEE Proc. Syst. Biol. (2006) 153, 22–33

20. Academic Collaboration • Sponsor PhD projects at the University of Cambridge • Harness the expertise available in academia • Reciprocate the success found in similar endeavours

21. In-house simulation and development teams • 20-50 percent savings • Simulations cut down on trial and error • Software created became industry standard • There is not a single microelectronics company in the world that doesn’t use their technique

22. Acquisition of Existing Companies Acquisition of firms with in-silico modelling capabilities to enrich pipeline Examples including: • Ready-to-use methods & models Skilled manpower No conflict of interest

23. Strategy Evaluation

24. Conclusions Need for radical changes in R&D More predictive power in future methods Filter out unreliable new drugs before costly clinical trials Prompt more ideas for target-specific NMEs Invest in in silico modelling Acknowledgements: MedImmune Mentor: B. Agoram CUTEC advisor: P. Zulaica

25. Be green... Save mice... Invest in in silico modelling in the pharmaceutical industry !

26. Conclusions Need for radical changes in R&D More predictive power in future methods Filter out unreliable new drugs before costly clinical trials Prompt more ideas for target-specific NCEs Invest in in silico modelling Acknowledgements: MedImmune Mentor: B. Agoram CUTEC advisor: P. Zulaica Any Questions ?

27. Conclusions Need for radical changes in R&D More predictive power in future methods Filter out unreliable new drugs before costly clinical trials Prompt more ideas for target-specific NCEs Invest in in silico modelling Acknowledgements: MedImmune Mentor: B. Agoram CUTEC advisor: P. Zulaica Any Questions ?

28. Reviews of Problems • A PricewaterhouseCoopers study form 2008 identified some key areas in which the pharma industry could become more innnovative thus reducing its R&D costs • These included developing a comprehensive understanding of how the human body works at the molecular level along with a much greater use of new technologies in order to “virtualise” the research process thereby accelerating clinical development Source: FDA CDER, PhRMA and PricewaterhouseCoopers analysis • The virtual vermin* implementation, allowing researchers studying Type I diabetes to simulate the effects of new medicines including different dosing levels and regimens on different therapeutic targets * Developed by The American Diabetes Association and US Biopharma company Entelos

29. Current Ideas for types of modelling • Bioinformatics creating the “virtual man” but requiring a significant global effort comparable to that of the Human Genome Project • Parametric empirical models based on existing experimental data such as the virtual vermin, or the creation of 3D images from experimental data to enable closer scrutiny. A study conducted at the Supercomputing Facility for Bioinformatics & Computational Biology Indian Institute of Technology, Delhi in 2005 concluded that in silico intervention in drug discovery can save up to ~ 15% of time and cost which could be significant for life threatening diseases.

30. The Current Research Process • The Step Consortium - investigating the human body as a single complex system • The Living Human Project - an in silico model of the human musculoskeletal apparatus • The Physiome Project - a computational framework toward understanding the integrative function of cells, organs and organisms • Model Trial - Entelos have developed their virtual research laboratory Source: PricewaterhouseCoopers

31. Airline design in silico Boeing invested more than $1 Billion (and insiders say much more) in CAD infrastructure for the design of the Boeing 777. Boeing reaped huge benefits from design automation. The more than 3 million parts were represented in an integrated database that allowed designers to do a complete 3D virtual mock-up of the vehicle. They could investigate assembly interfaces and maintainability using spatial visualizations of the aircraft components to develop integrated parts lists and detailed manufacturing process and layouts to support final assembly. The consequences were dramatic. In comparing with extrapolations from earlier aircraft designs such as those for the 757 and 767, Boeing achieved : • Elimination of > 3000 assembly interfaces, without any physical prototyping • 90% reduction in engineering change requests (6000 to 600) • 50% reduction in cycle time for engineering change request • 90% reduction in material rework • 50x improvement in assembly tolerances for fuselage

32. Simulation: The Third Pillar of Science • Traditional scientific and engineering paradigm: • Do theory or paper design. • Perform experiments or build system. • Limitations: • Too difficult -- build large wind tunnels. • Too expensive -- build a throw-away passenger jet. • Too slow -- wait for climate or galactic evolution. • Too dangerous -- weapons, drug design, climate experimentation. • Computational science paradigm: • Use high performance computer systems to simulate the phenomenon • Base on known physical laws and efficient numerical methods.

33. Industries Making Use of Simulation • BusinessFinancial and economic modelingTransaction processing, web services • Search enginesDefenseNuclear weapons -- test by simulationsCryptography • Science • Global climate modeling • Astrophysical modeling • Biology: genomics; protein folding; drug design • Computational Chemistry • Computational Material Sciences and Nanosciences • Engineering • Crash simulation • Semiconductor design • Earthquake and structural modeling • Computational fluid dynamics • Combustion

34. The Future for Predictive Rigorous Accurate Quantum Mechanics based Methods

35. State of the Art - The Pharmaceutical Industry Length scale Molecules Cells, tissues and organs Proteins (Signalling pathways) Test animals Humans - DFT - MD - Coarse Grained Models - Less accurate MD - QSAR Theoretical approaches - Finite Element Analaysis - ONETEP (Accelrys) - SYBYL-X (Tripos) - PhysioLab (Entelos) - SRS (BioWisdom) - PathwayLab (InNetics AB) - HepatoSys - Pharsight - PhysioLab (Entelos) - Simcyp Limited - Physiome Project - BioSim - SysMo Available software (company) - PhysioLab (Entelos) - STEP Consortium, Visual Physiological Human - Living Human Project

36. State of the Art - Other Industries Airlines ~ $100 million per airline per year Semiconductor Indsutry ~ $1 billion per company per year Securities Indsutry ~ $15 billion per year for U.S. home mortgages Automotive Design ~ $1 billion per company per year