1. Determinación del tropismo celular en la selección terapéutica. Aplicaciones clinicas de la investigación de correceptores. Eva Poveda Servicio de Enfermedades Infecciosas Hospital Carlos III, Madrid

2. Novel Antiretrovirals in Clinical Development Mature

3. Viral Entry Virus-Cell Fusion gp41 gp120 V3 loop CD4 Binding CD4 Cell Membrane Coreceptor Binding CCR5/CXCR4 (R5/X4)

4. HIV-1 Entry Inhibitors Virus-Cell Fusion gp41 gp120 V3 loop CD4 Binding CD4 Cell Membrane Coreceptor Binding CCR5/CXCR4 (R5/X4) CCR5 antagonists Maraviroc (Selzentry) Vicriviroc PRO140 INCB9471 Enfuvirtide TRI-999 TRI-1144 TNX-355 CXCR4 antagonists AMD070

5. Nt gp120 CD 4 + inhibitor - inhibitor stem V3 ECL2 CCR5 V1/V2 crown V3 Mecanismo de Acción de los antagonistas de CCR5 Chemokine coreceptors antagonists

6. HIV-1 co-receptor usage CXCR4 CCR5 CD4 Lineas celulares T Linfocitos primariosMonocitos/macrofagos R5 (NSI) X4 (SI) R5 (NSI) R5-TropicX4-Tropic DMDM

7. HIV Tropism and Disease Progression R5-tropic X4-tropic

8. Cross-sectional Canadian study of 979 patients beginning triple therapy Strong association between presence of D/M or X4 virus and baseline CD4+ cell count Proportion of D/M and X4 virus ranging from 50% at CD4+ cell count < 25 cells/mm 3 D/M or X4 virus progressively more likely in each lower CD4+ cell stratum BL CD4+ cell count, cells/mm 3 R5 virus, % D/M or X4 virus, % > 500937 350-499919 200-349919 100-1997228 50-997426 25-496931 < 254654 Brumme ZL, et al. J Infect Dis. 2005;192:466-474. Association between tropism and BL CD4+ confirmed

9. Tropism confirmed as a marker of HIV progresion 0.6 0.4 0.5  Kaplan-Meier curves showing progression to AIDS for patients with R5 or D/M virus BL tropism measured in 126 children and adolescents D/M virus at BL associated with lower BL CD4+ cell count and higher VL BL D/M virus associated with 3.8-fold higher risk of progression to AIDS R5 virus D/M virus 012345678 0.0 0.1 0.2 0.3 0.7 0.8 0.9 1.0 Proportion AIDS free Time, years Daar ES, et al. ICAAC 2003. Abstract 1722c. Daar ES, et al. Clin Infect Dis. 2007;45:643-649. Conclusion: coreceptor tropism independently influences natural history of HIV disease.

10. 1. Brumme ZL, et al. J Infect Dis. 2005;192:466-474. 2. Moyle GJ, et al. J Infect Dis. 2005;191:866-872. 3. Demarest J, et al. ICAAC 2004. Abstract H-1136. 4. Coakley E, et al. International Workshop on Targeting HIV Entry 2006. Abstract 8. 82% 81% 88% 85% HOMER cohort [1] (N = 979) Chelsea and Westminster cohort [2] (N = 402) Demarest et al. [3] (N = 299) MERIT cohort [4] (N = 1428) 18% 19% 12% 15% < 1% R5D/MX4 HIV Tropism in Antiretroviral- Naive Populations  R5-only virus in 80% to 90% of patients, with D/M or X4 virus in remainder

11. 50% 59% 56% TORO 1 and 2 ENF trials [1] (N = 612) ACTG A5211 [2] (N = 391) SCOPE cohort [3] (N = 186) 48% 46% 39.5% 41% 4% 0.5% 1. Melby J, et al. J Infect Dis.2006;194:238-246. 2. Wilkin TJ, et al. Clin Infect Dis. 2007;44:591-595. 3. Hunt PW, et al. J Infect Dis. 2006;194:926-930. 4. Coakley E, et al. International Workshop on Targeting HIV Entry 2006. Abstract 8. MOTIVATE 1 and 2 MVC trials [4] (N = 2560) 2% 3%  R5-only virus in 50% to 60% of patients, with D/M or X4 virus in remainder R5D/MX4 HIV Tropism in Antiretroviral- Experienced Populations

12. MVC fase 2b/III para estudiar la eficacia y la eficiencia de MVC en pacientes pre-tratados infectados con variantes D/M Mayer et al. XVI International AIDS Conference. Toronto, 2006 [THLB0215] Chemokine coreceptors antagonists +62+60+36CD4 change from baseline 30.8 26.9 24.6 21.1 24.1 15.5 HIV RNA <400 (%) HIV RNA <50 (%) -1.20-0.91-0.97 Mean decrease in HIV-1 RNA (log) MVC BID +OBT n=52 MVC QD + OBT n=57 Placebo+OBT n= 58 Treated patients with D/M-tropic HIV-1

13. Few data that relate to virtual and real phenotype Idenfification of residues in V3 that strongly influence the viral co-receptor usage. V3 phenotype prediction Threshold for detection of X4 viruses in mixed population (R5+X4) Phenoscript TM (Eurofins- Viralliance, Kalamazoo, MI, USA) Trofile TM (Monogram Biosciences, San Francisco, CA, USA) Recombinant viruses Different levels of co- receptors between cell lines and natural targets of HIV Ability of primary or recombinant virus isolates to replicate in cell lines that express CCR5 or CXCR4 receptors on their surface Cell lines To obtain viral stocksAbility of virus isolates to form syncitia in MT-2 cells MT-2 LimitationsMethodologyAssays Tools for viral tropism determination Poveda et al. AIDS. 2006;20:1359-1367.

14. HIV-1 Coreceptor Tropism Assay CD4+ CCR5+ Infection CD4+ CXCR4+ ++ HIV genomic luc vector HIV Env expression vector Put into cell line where HIV can replicate Viral pseudotypes Whitcomb J, et al. Antimicrob Agents Chemother. 2007;51:566-575.

15. 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000 2000000 99:195:590:1050:5010:905:951:99controls X4 R5 High sensitivity for detecting minoritary populations 1% viral production (RLU)

16. Tropism Report

17. Few data that relate to virtual and real phenotype Idenfification of residues in V3 that strongly influence the viral co-receptor usage. V3 phenotype prediction Threshold for detection of X4 viruses in mixed population (R5+X4) Phenoscript TM (Eurofins- Viralliance, Kalamazoo, MI, USA) Trofile TM (Monogram Biosciences, San Francisco, CA, USA) Recombinant viruses Different levels of co- receptors between cell lines and natural targets of HIV Ability of primary or recombinant virus isolates to replicate in cell lines that express CCR5 or CXCR4 receptors on their surface Cell lines To obtain viral stocksAbility of virus isolates to form syncitia in MT-2 cells MT-2 LimitationsMethodologyAssays Tools for viral tropism determination Poveda et al. AIDS. 2006;20:1359-1367.

18. Predicción del tropismo en base a la secuencia genética de V3 Identificación de residuos de aa críticos relacionados con el tropismo Websites de acceso público que predicen el uso del correceptor a partir de la secuencia de nucleótidos o aa de V3:  http://genomiac2.ucsd.edu:8080/wetcat/v3.htmlhttp://genomiac2.ucsd.edu:8080/wetcat/v3.html  http://www.geno2pheno.orghttp://www.geno2pheno.org  http://ubik.microbiol.washington.edu/computing/pssmhttp://ubik.microbiol.washington.edu/computing/pssm Loop V3 11/25 Rule: basic aa (R or K) at 11 or 25 position of the V3 region ------ CXCR4 co-receptor usage. Net Charge Rule: (K+R) – (D+E)  5 ------- CXCR4 co- receptor usage 11 25

19. Low et al. AIDS. 2007;21.  Set of 920 clinical samples HOMER cohort (drug-naïve patients)

20. 195978 Phenoscript Env Assay TM 8.95.185.9236083PSSM 243.871.8394483 Geno2pheno 26.92.570.5404383 Webcat (SVM) Discordance X4/R5 † (%) Discordance R5/X4* (%) Concordance (%) X4R5No.Predictor *Samples informed as R5 by genotypic methods and X4 by phenotype (Phenoscript Env AssayTM) †Samples informed as X4 by genotypic methods and R5 by phenotype. Prediction of HIV-1 co-receptor use using genotypic and phenotypic methods Poveda et al. AIDS. 2007;21:1487-1489.

21. 195978 Phenoscript Env Assay TM 88.178.985.9236083PSSM 67.888.871.8394483 Geno2pheno 64.489.470.5404383 Webcat (SVM) Specificity (%)Sensitivity (%) Concordance (%) X4R5No.Predictor *Samples informed as R5 by genotypic methods and X4 by phenotype (Phenoscript Env AssayTM) †Samples informed as X4 by genotypic methods and R5 by phenotype. Prediction of HIV-1 co-receptor use using genotypic and phenotypic methods Poveda et al. AIDS. 2007;21:1487-1489.

22. Combination of bioinformatic tools to infer HIV-1 co-receptor usage may be used as a screening strategy to determine tropism  Set of 200 samples from several database: 60% R5-tropic, 40 % X4-tropic Sensitivity (%) Specificity (%) SVM 98.862.5 GENO2PHENO91.286.6 PART83.881.7 PSSM82.597.5 C4.582.597.5 CHARGE RULE7590.8 C4.5+8-127097.5 Chueca et al. 2007 (in press).

23. Garrido et al. J Clin Virol. 2007 (in press) Evaluation of eight different bioinformatics tools to predict HIV-1 tropism in different subtypes  Set of 150 clinical samples: 115 non-B subtypes (54.3% ARV-experienced) 35 B subtypes (82.9% ARV-experienced)

24. Skrabal et al, J Clin Microbiol 2007; 45:279-84. Degree of correlation between two phenotypic assays (Trofile vs. Phenoscript®ENV assays)  Set of 74 clinical samples  Degree of concordance 85.1% between both phenotypic assays 86.5% between Trofile/SVM 79.7% between Phenoscript®ENV assay/SVM

25. Chemokine coreceptors antagonists Resistance to CCR5 antagonists  Outgrowth of X4 virus that pre-exits as a minority population below the level of assay detection. rMutations in the HIV-1 gp120 molecule that allow the virus to bind to R5 receptors in the presence of drug. Mutations in V3 loop of gp120 associated with MVC resistance but different pattern of amino acid changes between patients Nelson et al, 14th CROI, 2007. R5 ------> DM or X4 64% 5% MVC Placebo

26.  Clonal and phylogenic analyses of 20 pts (16 MVC, 4 placebo) suggest D/M virus predominantly from preexisting population Clinical implications remain to be fully defined R5 D/M X4 Nonfunctional clone 0 100 200 300 Treatment Start R5 R5 D/M D/M D/M D/M D/M D/M R5 R5 Failure Treatment Stop Time since first administration (days) Emergence of D/M Virus on CCR5 Antagonist Therapy

27. Agradecimientos Hospital Carlos III: Sección de Laboratorio: Verónica Briz María del Mar González Carolina Garrido Angélica Corral Natalia Zahonero Carmen de Mendoza Sección Clínica: Pablo Labarga Pilar García Gasco Francisco Blanco Vicente Soriano Juan González-Lahoz Eurofins-Viralliance, MI, USA Katharina Skrabal Vanessa Roulet Jean-Louis Faudon Hospital Univ. San Cecilio, Granada Natalia Chueca Federico García Hosp. Xeral, Santiago de Compostela Antonio Aguilera