1. WWARN Perspective and Progress RBM Case Management Working Group Philippe Guerin Geneva 8 July 2009

2. WWARN History Planning for over 4 years –Core group of > 50 individuals –28 malaria endemic countries Support from the Bill and Melinda Gates Foundation -Starting 30 January 2009 Collaboration between WHO and WWARN / Oxford University -MOU signed June 2009

3. WWARN objectives Develop a global network of scientists involved in antimalarial drug resistance work Central database of information from malaria- endemic countries on drug resistance Shared efforts with WHO –Surveillance data by NMCPs on global antimalarial therapeutic efficacy Collate data from additional sources

4. WWARN Scientific Aims Support standardisation of antimalarial resistance indicators –Standard data formats allow analysis of data from diverse studies Test utility of proxy markers Provide spatio temporal evidence on drug efficacy –Early WWARNing System  Provide evidence base for policy markers

5. Where do we stand with resistance data globally? Absence of data –Geographical gap Poor quality Absence of standardisation –Data collection, analyze Good quality but delay accessing data –Publication years after data collection –Unpublished data Used “only” for surveillance or registration purposes Not see as a priority, lack of resources

6. Historical data on resistance DrugsIntroductionReported resistance Difference (Years) QuinineXVIII cent.1910100? Chloroquine1945195712 Proguanil194819491 Sulfadoxine- pyrimethamine 1967 0 Mefloquine197719825 Atovaquone1996 0 Artemisinin19732005?32? Adapted from Wongsrichanalai et al. LID 2002

7. Resistance spread: chloroquine and SP

9. Drug quality Counterfeit drugs –Investigation South East Asia in 2007 195 counterfeit drugs out of 391 samples Little or no artesunate –Lots of potentially dangerous products (metamizole, safrole, ecstasy) Suboptimal concentration of drugs –Very limited information on Africa “Pre-qualified” drugs and others Drug used –Storage –“Fixed dose versus blister combination” Newton et al. PlosMed 2008;5(2):e32

10. Resistance data format Four angles to look at resistance –Clinical efficacy –Clinical pharmacology –In Vitro susceptibility –Molecular markers 4 core modules of WWARN WHO in vivo study protocol

11. http://www.wwarn.org

12. Clinical Efficacy Optimise analytical methodologies Facilitate the conduct and analysis of clinical trials Technical support, tools, CRFs, syntax or Do files Process own data online: Cleaning and standard report Collate current knowledge of antimalarial efficacy Correlate with in vitro, molecular, and pharmacokinetic data

13. Clinical Pharmacology Measuring drug concentrations essential to define resistance accurately and inform optimal dosing regimens Clinical pharmacology module activities include: Guidelines & Technical support Study design, sample assay and PK-PD analysis QA programme Greater assay accuracy/comparability Tools for data cleaning and PK analysis Analysing pooled data Define therapeutic drug concentrations Inform optimal dosing regimens – most important / vulnerable target populations

14. In-Vitro Susceptibility Clinical failure may be the result of factors other than resistance and even resistant parasites may be cleared with drug therapy Pilot protocols will be developed on website Fresh patient isolates Culture time, media, parasitemia, hematocrit Standardized quality control measures Positive & negative controls Standard reference clones Standard drugs

15. Molecular Markers Molecular information in the WWARN database linked to clinical outcomes and in vitro susceptibility results Surveillance with defined molecular markers of drug resistance Single nucleotide polymorphisms (SNPs) Copy number variation Regional and global patterns of emergence and spread Track trends Detect new patterns of rising or falling resistance Identification and validation of markers to ACT resistance Critical mass of data

16. Informatics We aim to create a web platform which provides Secure environment Easy-to-use tools to manage and analyse their data Statistical algorithms for analysis of complex patterns and trends Accessible data summaries for different user groups

17. WWARN Stakeholders Patients Policy makers National Malaria Control Programmes WHO RBM NGOs Scientific community Drug developers Funding agencies …

18.  Regional / Global analysis Stakeholder Groups  National Policy Makers  Field Researchers

19. - Library of Protocols - SOPs for Data Formats - Analytical Tools Online protocols  WHO/WWARN Detailed procedures  SOPs  Quality Control Standards Standardised Analysis  Acceptable methodology Follow up trends  Stakeholder Groups

20.  National Policy Makers  Field Researchers - Spatio-temporal Description of Drug Efficacy - Evidence to Inform Policy Makers Updated Geographic Data Drug Quality Information  Data on Available supply Accessible to Public Health Professionals  Stakeholder Groups

21.  Regional / Global analysis  National Policy Makers  Field Researchers - Regional Analysis of Drug Resistance Trends - Evidence to Inform International Policy: Proactive Strategy - Evidence to Inform Drug Developers Provide Early Warning Inform Global Policy Makers  Global Fund, PMI, World Bank, UNITAID... Guide Drug Development  Stakeholder Groups

22.  Regional / Global analysis WWARN Targets  National Policy Makers  Field Researchers NMCP NGOs Scientific community  NMCP MoH WHO  MoH Policy Makers Drug developers Patients 

23. Process and Data Access Individual data collection –Limitation of aggregated data Secured process –Data upload –Data consistency –Data analysis –Data output Improve access of data –Data sharing should not undermine publication –Speed up publication Web based access Comprehensive, up to date, quality-assured information

25. Structure Board Executive Team DATA PLATFORM Clinical Efficacy U. Oxford / Darwin Ric Price Molecular Markers Maryland U. Chris Plowe Phenotyping IMEA – CNR palu J. Le Bras Pharmacology Cape Town U. Mahidol U. Karen Barnes Integrating Database U. Oxford Dominic Kwiatkowski Scientific Advisory Committee Stakeholders Regional sites East Africa Leader & Team West Africa Leader & Team Asia Leader & Team Latin America Leader & Team

26. Country View

27. Clinical: single study detail Implements standard analysis methods Tools available for anyone who’d like to use them Storing raw data gives flexibility to analyse data in numerous ways

28. Clinical study: risk of failure Thai-Burmese Border Nosten et al.

29. Clinical study: risk of failure Thai-Burmese Border

31. Molecular: frequency of resistance markers Data courtesy of Cally Roper

32. Geomaps – historical summaries

33. Pharmacology: drug concentration