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Analysis of recent trials/cohort on resistance in Africa

Analysis of recent trials/cohort on resistance in Africa. Annemarie Wensing, MD, PhD. Disclaimer. 10 minutes presentation Not a complete overview on development of resistance in Africa Highlight a selection of interesting data & developments.

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Analysis of recent trials/cohort on resistance in Africa

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  1. Analysis of recent trials/cohort on resistance in Africa Annemarie Wensing, MD, PhD

  2. Disclaimer • 10 minutes presentation • Not a complete overview on development of resistance in Africa • Highlight a selection of interesting data & developments

  3. Virological efficacy and emergence of drug resistance in rural Tanzania Johannessenet al. BMC 2009 Overall the reported resistance rate does not appear to exceed earlier rates reported in industrialised countries (9% after 2 yrs of HAART Phillips et al. AIDS 2005) 212 patients (74.5% women),median follow-up of 22.3 months 88.2% HIV-RNA <400 copies/ml , 23 therapy failures: HIV-RNA> 1000 Dual-class resistance, (NRTIs and NNRTIs) was found in 64%, raising concerns about exhaustion of future antiretroviral drug options

  4. HAART in a rural district of Malawi:Ferradini et al. Lancet 2006 • Programme in Chiradzulu: 1308 patients • 84% (334/398) viral load <400 cp/mL Cross-sectional survey • Self-reported poor adherence (<80%) in the past 4 days best predictor of detectable VL (OR: 5·4, 95% CI 1·9–15·6). • 52 patients VL > 1000 copies • GT analysis: subtype C • 6% no mutations, • 84% NRTI, 76% 184V, 10% 65R • 94% NNRTI:44%103N, 28% 181C, 18% 190A, 14% 106M 84% of patients with HIV-RNA >1000 double class R Subtype C Mean 9 months on therapy, first VL determination: 10% mutation 65R

  5. Cohort data: Ndlovu care group in South Africa, Barth AIDS 2008 • 3 sites in Limpopo: data Elandsdoorn • Ca 2000 patients on therapy: NNRTI + 2 NRTIs • Criteria: CD4 <200/WHO stage 4, therapy-buddy, attending of 3 appointments, distance • Frequent monitoring CD4 count and plasma HIV-RNA: • bsl, 6 wks, 3, 6 ,9,12 m and thereafter every 6 months • Virological failure: after initial suppression <50, rebound >1000 copies/ml • Evaluation of therapy response in the first year: 313 patients

  6. Resistance data from Elanddoorn: Subtype C • Viral suppression was achieved in 73% (230/313) of pts • Virological failure in 13%: High level of NNRTI mutations: 58%: 2 NNRTI 26%: 3NNRTI in absence of NRTI related TAMS or 65R

  7. Harvard PEPFAR Nigeria • Through Bill & Melinda Gates funding, Harvard has been working with multiple hospitals and prevention programs in Nigeria since 2000 • Started PEPFAR ART activities at 6 tertiary hospitals in 2004 and will expand activities to a total of 36 sites throughout the country by early 2009

  8. Etravirine Resistance Among Failures of first-line NNRTI-based ART in Nigeria • Genotypic testing has been performed routinely in patients failing first-line NNRTI based ART in the Harvard PEPFAR Program, Nigeria since 2005. • 331 HIV-1 ART treated patients (≥ 18 years of age) in virologic failure, defined as viral load >1,000 c/mL after at least 6 months of ART. The most common ETR-mutations were: • Y181C, G190A, A98G and K101E • Schema T (Tibotec) or M (Monogram) – predicted suboptimal efficacy in 62.5% with 94% concordance B. Taiwo, et al. 2009

  9. PREPFAR Nigeria: Subtype Distribution in Patients on ART Other Maiduguri (n=40) A G’ G’ CRF06 Other A CRF02 A Other CRF06 CRF06 G’ G G Ibadan (n=60) Jos (n=160) CRF02 G A CRF02 Other A G’ CRF06 CRF06 G G Lagos (n=98) CRF02 Maiduguri Jos Ibadan Lagos

  10. 02 A M41L E44D K65R D67N K70R L74V Y115F V118I M184V/I L210W T215Y/F K219Q/E Tenofovir K65R A62V V75I F77L F116Y Q151M M41L A62V 69ins K70R L210W T215YF K219QE Most frequent - like subtype B - no subtype differences A62V V75I F77L F116Y Q151M Mutations Associated with NRTI Resistance by Subtype Multi-nRTI Resistance M41L E44D D67N K70R V118I L210W T215YF K219QE Zidovudine M41L E44D D67N K70R V118I L210W T215YF K219QE Stavudine M41L E44D K65R D67N K70R V118I L210W T215YF K219QE Didanosine K65R L74V Abacavir K65R Y115F M184V L74V Lamivudine K65R M184VI Emtricitabine K65R M184VI Multi-NRTI Resistance Ins. Complex Multi-NRTI Resistance 151 Complex

  11. A098 L100I K103N V106M/A V108I Y181C/I Y188C/L G190A/S P225H M230L P236L V90 Nevirapine Delavirdine Efavirenz Multi-NNRTI Resistance Etraverine Multi-NNRTI Resistance: Accumulation Of Mutations Most frequent - like subtype B - no subtype differences Mutations Associated with NNRTI Resistance by Subtype 02 G 02 L100I K103N V106AM V108I Y181CI Y188CLH G190A K103N V106M Y181C Y188L P236L L100I K103N V106M V108I Y181CI Y188L P225H G190SA K103N V106M Y188L V90I A98G L100I K101EP V106A G190SA Y181CI G190SA M230L L100I V106A Y181CI

  12. Resistance Analysis in Africa Resistance Analysis: expensive, resources prefenterially needed for roll-out of therapy Programs for development of less expensive genotypic resistance tools: ARTA Search for alternative possibilities to predict success of second line therapy is needed

  13. ROC curve for random forest model & GSS from common rules systems predicting VL<50 copies HIV Resistance Response Database Initiative RDI RF: AUC = 0.88 Accuracy = 82% RF GSS: AUC = 0.68-0.72 Accuracy = 63-68% Sensitivity 100-Specificity

  14. RF models predicting virological response without genotype Model 1 Model 2 (3 TH)(11 TH) AUC 0.879 0.878 Accuracy 82% 82% Sensitivity 77% 79% Specificity 86% 85%

  15. In silico analysis • Models were programmed to predict responses to multiple alternative 3-drug regimens using baseline data from the cases where the new treatment failed using two drug lists: • ‘Old’ PIs only (IDV/r, SQV/r, LPV/r, NFV) • Including ‘newer’ PIs ((fos-)APV/r, ATZ/r, DRV/r)

  16. Conclusions • Frequency of particular mutations seem to differ between subtypes • But there is limited knowledge on the relationship between time at a failing regimen and the appearance of specific mutations • Generally mutational patterns are similar between subtypes • With more data becoming available computational modelling has potential for optimising antiretroviral therapy in resource-poor countries

  17. Acknowledgements South Africa: Roos E. Barth, Annemarie M. Wensing, Hugo A.Tempelman, Robert Moraba, Rob Schuurman andAndy I. Hoepelman Tanzania: Asgeir Johannessen , Ezra Nama , Sokoine L Kivuyo , Mabula J Kasubi, Mona Holberg-Petersen , Mecky I Matee, Svein G Gundersen and Johan N Bruun Nigeria: P. Kanki, C. Hawkins, B Taiwo Malawi: Laurent Ferradini, Arnaud Jeannin, Loretxu Pinoges, Jacques Izopet, Didakus Odhiambo, Limangeni Mankhambo, Gloria Karungi,Elisabeth Szumilin, Serge Balandine, Gaëlle Fedida, M Patrizia Carrieri, Bruno Spire, Nathan Ford, Jean-Michel Tassie, Philippe J Guerin, Chris Brasher RDI: AD Revell, D Wang, R Harrigan, J Gatell, L Ruiz, S Emery, MJ Pérez-Elías, C Torti, J Baxter, F de Wolf, B Gazzard, AM Geretti, S Staszewski, R Hamers, AMJ Wensing, J Lange, JM Montaner, BA Larder

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