1. Worldwide Antimalarial Resistance Network (WWARN) Centre for Tropical Medicine & Global Health University of Oxford APPMG meeting January 20 th, 2015

2. Summary of presentation Background to drug resistance The Worldwide Antimalarial Resistance Network (WWARN) Key project examples Capacity building in endemic countries External quality assurance Drug Quality What next with resistance?

3. Risk factors driving drug resistance Pregnant Women & Infants Drug interactions Poor Quality Comorbidities Malnutrition Adherence Bio-availalibility

4. Revisiting history Response to the first waves of resistance o Slow and inadequate risk assessment o High economic cost o Millions of deaths Adapted from Carter & Mendis, 2002 2010 1970 2020

5. Drug development pipeline: Existing drugs o Affordable and available o Adaptation with new evidence New drugs Pipeline is long and complex Feed learning from existing medicines DiscoveryClinical phasesRegistration Change of National policy Scaling production Suppliers Training Access Flegg et al. American Journal of Tropical Medicine & Hygiene 2013

6. “The spirit of collaboration is permeating every institution and all of our lives… Learning to collaborate enables you to be more effective, problem solve, innovate and develop your knowledge throughout your life…” Don Tapscott

7. Phnom Penh WWARN: Collaboration 230 partners 100,000 clinical trial (patient) results Two thirds of all ACT clinical data published since 2000 Data pooled and analysed to identify failure risks

8. Policy - Data – Funding – Change?

9. WWARN strategy Collate/Collect Address heterogeneity Innovate

10. Building bio-informatics and technical framework Secure process to share and store data Data curation o Check data quality Data standardisation Data analysis Data visualisation o Maps o Reports

11. WWARN Data Centre

12. An innovative model WWARN Data Centre o Develop a scientific and ethical rationale o Provide long term data storage Translating science into public health action o Enhance the value of existing data o Ensure efficacy of existing and new drugs Providing accurate and useful intelligence o Share evidence to guide policy: control and elimination strategies

13. Optimising treatment for artemisinin resistant strains “TRAC” experimental regimen 3 days artesunate + 3 days ACTs Cure rate at Day42 in Cambodia -97.7% [95%CI 90.9 to 99.4] E Ashley, NJ White et al. New England Journal of Tropical Medicine July 2014

14. Maintain the useful life of existing, valuable ACTs – the power of pooled data Is AS+AQ (30% of ACT in Africa) a failing combination? Is dosing of lumefantrine (AL) in infants or pregnant women inadequate ? Can we validate molecular markers for current ACT partner drugs?

15. Global access to information Interactive format Interrogate data from anywhere Data visualisation

16. Targeted, pro-active surveillance Guiding surveillance: hotspots Concentrate effort and investment: high risk areas Proof of concept with drug cobinations e.g. o seasonal malaria chemoprevention o Malaria in Pregnancy (MiPC)

17. It’s not just about gathering the data…. Drug Quality? Quality of trial data? Retrospective v prospective?

18. WWARN Toolkit: quality management

19. External Quality Assurance Programme

20. Antimalarial quality FalsifiedSubstandard Degraded Result from negligent factory error Intentional fraudulent production Leave factory good quality but degrade due to heat, humidity

21. Drug Quality e.g. Africa 1.4 Million Coartem® seized Angola, Cameroon, DRC, Benin and Nigeria

22. Preventing the global spread of ACT (ART) resistance: what next? o Improve quality and leverage value of existing data o Preserve efficacy of current antimalarials o Optimise treatment o Detect and manage resistance spread/emergence o Adaptive regimen in target areas o Smart surveillance (timely, accurate) o Feed learning into development of new drugs Regime n Target Risk Factors

23. Thank you Visit: wwarn.org Follow: @WWARN Andrea Stewart, Head of Advocacy & Communications APPMG meeting. January 20 th, 2015

24. Each year increase of > than 116,000 deaths Excess of $32 million in healthcare costs >$385 million productivity losses due to extended patient illness Cost of inaction? 1970 Scenario model: 30% ACTs fail and treatment for severe cases of malaria is reverted to quinine Lubell et al. Malaria Journal 2014

25. www.wwarn.org/aqsurveyor