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
Novel Antiretrovirals in Clinical Development
MatureMature
TNX-355CCR5 antagonistsCXCR4 antagonists
Maturation inhibitorsBevirimat
Raltegravir Elvitegravir
Entry inhibitors
Reverse transcriptase
inhibitors
virus
PIsIntegrase inhibitors
TNX-355CCR5 antagonistsCXCR4 antagonists
Maturation inhibitorsBevirimat
Raltegravir Elvitegravir
Entry inhibitors
Reverse transcriptase
inhibitors
virus
PIsIntegrase inhibitors
Viral Entry
Virus-CellFusion
gp41
gp120
V3 loop
CD4Binding
CD4
CellMembrane
CoreceptorBinding
CCR5/CXCR4(R5/X4)
HIV-1 Entry Inhibitors
Virus-CellFusion
gp41
gp120
V3 loop
CD4Binding
CD4
CellMembrane
CoreceptorBinding
CCR5/CXCR4(R5/X4)
CCR5 antagonistsMaraviroc (Selzentry)
VicrivirocPRO140
INCB9471
Enfuvirtide TRI-999 TRI-1144TNX-355
CXCR4 antagonists AMD070
Nt
gp120
CD4
+ inhibitor
- inhibitor
stem V3
ECL2
CCR5
V1/V2crown V3
Mecanismo de Acción de los antagonistas de CCR5
Chemokine coreceptors antagonists
HIV-1 co-receptor usage
CXCR4 CCR5CD4
Lineas celulares T Linfocitos primarios Monocitos/macrofagos
R5(NSI)
X4(SI)
R5(NSI)
R5-TropicX4-Tropic
DM
HIV Tropism and Disease Progression
R5 Mixed/Dual
Imm
un
e fu
nct
ion
Time
Am
ou
nt o
f virus
Limit of tropism assay detection
X4↑
Dual-tropic HIV
R5-tropic
X4-tropic
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 < 10% at CD4+ cell count ≥ 200 cells/mm3 to > 50% at CD4+ cell count < 25 cells/mm3
• D/M or X4 virus progressively more likely in each lower CD4+ cell stratum
BL CD4+cell count, cells/mm3
R5 virus, %
D/M or X4 virus, %
> 500 93 7
350-499 91 9
200-349 91 9
100-199 72 28
50-99 74 26
25-49 69 31
< 25 46 54
Brumme ZL, et al. J Infect Dis. 2005;192:466-474.
Association between tropism and BL CD4+ confirmed
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
0 1 2 3 4 5 6 7 80.0
0.10.2
0.3
0.70.8
0.9
1.0
Pro
po
rtio
n A
IDS
fre
e
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.
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%
< 1%
< 1%
R5 D/M X4
HIV Tropism in Antiretroviral-Naive Populations
R5-only virus in 80% to 90% of patients, with D/M or X4 virus in remainder
50%
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
R5 D/M X4
HIV Tropism in Antiretroviral-Experienced Populations
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
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)
PhenoscriptTM (Eurofins-Viralliance, Kalamazoo, MI, USA) TrofileTM (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.
HIV-1 Coreceptor Tropism Assay
CD4+CCR5+
Infection
CD4+CXCR4+
++++
HIV genomicluc vector
HIV Envexpression
vector
Put into cell linewhere HIV can
replicate
Viral pseudotypes
Whitcomb J, et al. Antimicrob Agents Chemother. 2007;51:566-575.
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
99:1 95:5 90:10 50:50 10:90 5:95 1:99 controls
X4
R5
High sensitivity for detecting minoritary populations
1%
vira
l pro
duct
ion
(RLU
)
Tropism Report
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)
PhenoscriptTM (Eurofins-Viralliance, Kalamazoo, MI, USA) TrofileTM (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.
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.html http://www.geno2pheno.org http://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 usage11
25
Low et al. AIDS. 2007;21.
Set of 920 clinical samples HOMER cohort (drug-naïve patients)
195978 Phenoscript
Env AssayTM
8.95.185.9236083PSSM
243.871.8394483Geno2pheno
26.92.570.5404383 Webcat (SVM)
DiscordanceX4/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.
195978 Phenoscript
Env AssayTM
88.178.985.9236083PSSM
67.888.871.8394483Geno2pheno
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.
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 98.8 62.5
GENO2PHENO 91.2 86.6
PART 83.8 81.7
PSSM 82.5 97.5
C4.5 82.5 97.5
CHARGE RULE 75 90.8
C4.5+8-12 70 97.5
Chueca et al. 2007 (in press).
73887761907471.388.676.5SVMgeno2pheno
92.58491.238.410062.583.888.685.2PSSMsinsi
88928953906982.591.485.2PSSMX4R5
92929230704782.585.783.2Charge Rule
92808930905682.582.982.6PART
941009523302682.58081.7C4.5 only with p8-12
88969023503477.582.979.1C4.5
858083461006978.885.780.9SVM
Non-BBTotalNon-BBTotalNon-BBTotal
SpecificitySensitivityConcordanceBioinformatics method
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)
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
Chemokine coreceptors antagonists
Resistance to CCR5 antagonists
Outgrowth of X4 virus that pre-exits as a minority population below the level of assay detection.
Mutations 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
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
R5R5D/MD/MX4X4Nonfunctional cloneNonfunctional clone
0 100 200 300
TreatmentStart
R5 R5 D/M D/M D/M D/M D/M D/M R5 R5
Failure
TreatmentStop
Time since first administration (days)
Emergence of D/M Virus on CCR5 Antagonist Therapy
AgradecimientosHospital Carlos III:
Sección de Laboratorio:
Verónica Briz María del Mar González Carolina GarridoAngélica CorralNatalia ZahoneroCarmen de Mendoza
Sección Clínica:
Pablo Labarga Pilar García GascoFrancisco BlancoVicente SorianoJuan 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
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