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Overview 1 March 2011 1 September 2012 On behalf of the DDMoRe - PowerPoint PPT Presentation

Overview 1 March 2011 1 September 2012 On behalf of the DDMoRe consortium The Productivity Gap in Pharma R&D 60 $55 50 50 45 40 New Drug Approvals 40 Pharma R&D ($ billions) 35 30 30 25 20 20 15 10 10 5 0 0 92 92


  1. Overview 1 March 2011 – 1 September 2012 On behalf of the DDMoRe consortium

  2. The Productivity Gap in Pharma R&D 60 $55 50 50 45 40 New Drug Approvals 40 Pharma R&D ($ billions) 35 30 30 25 20 20 15 10 10 5 0 0 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 00 00 01 01 02 02 03 03 04 04 05 05 06 06 07 07 Source: Burrill & Company; US Food and Drug Administration . 2 WCoP-Seoul-2012 2

  3. Innovative Medicines Initiative: the Largest PPP in Life Sciences R&D  Key concepts • Open innovation • Pre-competitive research 2 Billion Euro The Four Pillars of the Innovative Medicines Initiative 1 Billion € 1 Billion € Public Private Partnership 3 WCoP-Seoul-2012 3

  4. Participants are a unique combination of model builders, model users, software developers and teachers 5 WCoP-Seoul-2012 5

  5. Participants are a unique combination of model builders, model users, software developers and teachers 6 WCoP-Seoul-2012 6

  6. DDMoRe – The Vision Major deliverables  Data contains raw information, which is difficult to share • IP, CDISC Modelling  Models Library • represent an interpretation, understanding of the data Shared knowledge (given experimental conditions) • allow to predict the future with uncertainty • are an intellectual container of the knowledge 8 WCoP-Seoul-2012 8

  7. DDMoRe – The Vision Major deliverables Standards for describing models, data and designs Modelling … has standards at the core of its Library strategy … Shared knowledge Model System Definition interchange Language standards 9 WCoP-Seoul-2012 9

  8. DDMoRe – The Vision Major deliverables Standards for describing models, data and designs Modelling Modelling Library Framework Shared knowledge Model System A modular platform Definition interchange for integrating and Language standards reusing models; shortening timelines by removing barriers … but the framework will put the system into life … 10 WCoP-Seoul-2012 10

  9. DDMoRe – The Vision Major deliverables Standards for describing models, data and designs Modelling Modelling Library Framework Shared knowledge Model System A modular platform Definition interchange for integrating and Language standards reusing models; Specific shortening timelines … PoC: implementation and by removing disease barriers evolution of DA models models Examples from high priority areas 11 WCoP-Seoul-2012 11

  10. DDMoRe – The Vision Major deliverables Standards for describing models, data and designs Modelling Modelling Library Framework Shared knowledge Model System A modular platform Definition interchange for integrating and Language standards reusing models; Specific shortening timelines by removing disease barriers models Examples from high priority areas 12 WCoP-Seoul-2012 12

  11. Examples of models to be implemented Pr 00 of remaining Pr 11 of remaining non-responder responder Pr 10 of becoming responder 0 Rheumatoid Arthritis – ACR20 1 Non- Responder Pr 01 of becoming Responder non-responder Lacroix et al., CPT 2009  WBC 0 k prol =k tr Feedback= WBC(t) Pr 02 of Pr 12 of Non-mitotic cells dropping out dropping out 2 k tr k tr k tr Proliferative Dropped cells out Mean Transit Time (MTT) k tr = 4/k tr Slope (or Emax-model) Circulating Drug Glucose - Insulin WBC concentration Silber et al., JCP 2007 k tr Oncology – Myelosuppression Friberg et al., JCO, 2002 Drug Bacteria + Tumor growth – Xenografts k DA k DA k growth S+R + Dopamine Simeoni et al., 2004 Delayed effect k SR Agonist-antagonist - k e0 R interaction model S Conc Ce K in k out Prolactin 1 - Blood k death k death k e (=0) k e0 k drug + = E max · Ce  /(Ce  +Ec 50  ) Drug-specific parameter: Ki D 2 -receptor antagonists S=sensitive, R=resting, Bacteria kill of antibiotics Schizophrenia – Prolactin elevation proliferating insensitive Nielsen et al., AAC 2007 Friberg et al., CPT 2009 15 WCoP-Seoul-2012 15

  12. Model Exchange MONOLIX NONMEM MDL Model PK/PD Repository Model in MML MatLab R WinBUGS 19 19 WCoP-Seoul-2012

  13. Outcome libMML (WP 2.3: API) MML specification DDMoRe ML SED-ML PharML CellML NuML SBML 23 WCoP-Seoul-2012

  14. Developing the Specification MSSMml definition xml build prototype test cases expand implementation definition refine definition Code generator/tr anslator No tests work? Yes Executable Model 24 WCoP-Seoul-2012

  15. Model Exchange MONOLIX NONMEM MDL Model PK/PD Repository Model in MML MatLab R WinBUGS 29 29 WCoP-Seoul-2012

  16. MDL languages and objects Language MDL Sub-Language MCL TEL R TEL Objects Data Parameter Model Task Command Command  User defines new models using MCL  TEL provides script-based updating of MCL objects and execution settings, retrieval of task output 31 WCoP-Seoul-2012 31

  17. MCL objects and blocks Task Data Model Parameter HEADER PARAMETERS STRUCTURAL INPUT VARIABLES FILE VARIABILITY STRUCTURAL DATA PARAMETERS INLINE VARIABILITY ESTIMATE PARAMETERS RSCRIPT GROUP VARIABLES SIMULATE DESIGN RANDOM INDIVIDUAL VARIABLE VARIABLES DEFINIITON MODEL  Block structure separates structural ODE LIBRARY PREDICTION (fixed) and variability (random) parts of ESTIMATION a model SIMULATION  Modularity and use of sub-component OUTPUT VARIABLES models. 32 WCoP-Seoul-2012 32

  18. MCL – Examples NM-TRAN MCL Data Object $INPUT ID TIME AMT ODV INSU TOTG CMT OGTT_IGI_dat = dataobj{ BW EVID RATE DV TYPE OCC HEADER ;data contains dosing records, ID=list(type=categorical) glucose(1), hot glucose(3) and TIME=list(type=continuous, units="h") insulin(2) CMT=list(type=categorical) $DATA data_OGTT.csv IGNORE=@ ... BW=list(use=continuous, units="kg") ... OCC=list(type=categorical) # end HEADER FILE source="data_OGTT.csv" unjumble="NONMEM" ignore_char="@" # end FILE ) # end data object0 34 34 WCoP-Seoul-2012

  19. TEL Object Structure Principles Task object defines functions TEL Uses R language syntax May call target applications such as Task Command R Command NONMEM, Monolix, etc. through (Target Application) (R Environment) MCL task objects or pass commands to R environment 35 35 WCoP-Seoul-2012

  20. 45 WCoP-Seoul-2012

  21. Interoperability Framework 50 WCoP-Seoul-2012

  22. Interoperability Framework Data Flow 53 WCoP-Seoul-2012 53

  23. Model Repository in DDMoRe 58 WCoP-Seoul-2012 on behalf of the DDMoRe Consortium 58

  24. Functionalities (overview) 59 WCoP-Seoul-2012 on behalf of the DDMoRe Consortium 59

  25. A prototype of the CTS $DESCRIPTION PKPD model $PSI ka V Cl Imax IC50 Rin kout $EQUATION k=Cl/V E_0 = Rin/kout Cc = Ac/V DDT_Ad = -ka*Ad DDT_Ac = ka*Ad - k*Ac DDT_E = Rin*(1-Imax*Cc/(Cc+IC50)) - kout*E 64 WCoP-Seoul-2012 64

  26. Task 6.2 Adaptive Optimal Design  A survey among EFPIA participants has identified the level of current usage of optimal design software and the expectations of the industry for future capabilities.  Problems that will be studied: • Model robustness (model averaging/ selection) • Local versus Robust Optimal design (pros & cons) • How to include the previous information in the design calculations • Estimation • Effect of early cohorts as the driving force in an adaptive design • What to optimize? • How to optimize • Within subject adaptations vs. Between subject adaptations 65 WCoP-Seoul-2012 65

  27. Task 6.3 Diagnostic tools New diagnostic tools are necessary for model selection and model assessment. Specific areas of interest are: • New diagnostics for repeated time-to-event models • Optimal covariate model building strategies • Use of simulation techniques to assess various model diagnostics (VPC, NPC and NPDE of model diagnostics) • Fit output from NLME to simpler models to help in diagnosing and building • From visual diagnostic tools to inference and decision tools • Sampling from conditional distributions for model assessment 66 WCoP-Seoul-2012 66

  28. Training & Education (T&E) Objectives:  To create a landscape of technical and conceptual requirements in Drug/Disease Modelling and Simulation ( DD M&S )  To develop a Training and Education program incl. material in DD M&S on PhD and postdoc level F2F Diabetes F2F Onco, Beg. Mecha- F2F Survey Web- F2F Onco, Adv. nisms/ course based => requi- Process of rements curri- self-edu- F2F Oth., Beg. intern- culum/ cational for DD F2F Oth., Adv. ship material training M&S F2F Infect. F2F Safety 69 WCoP-Seoul-2012 69

  29. Website and Newsletter www.ddmore.eu 73 WCoP-Seoul-2012

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