Emerging patterns of drug resistance and viral tropism in cART-naïve and failing patients infected with HIV-1 subtype C
Thumbi Ndung’u, BVM, PhDAssociate Professor
Director, HIV Pathogenesis ProgrammeDoris Duke Medical Research InstituteNelson R. Mandela School of Medicine
University of KwaZulu-Natal
100
100
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100
100
100
100
100
100100
100
100
100
100
100
100
H
C
F1
F2
K
D
B
J
G
A1
A2
N-group
O-groupCPZ ANT
CPZ GABCPZ US
M-group
5%
HIV-1 Phylogeny
47.2%
27.0%
12.3%
Phenotypic Classification of HIV-1
• Slow/low versus rapid/high
• Syntitium-inducing (SI) versus NSI
• Slow/Low = NSI (Early, slow progression)
• Rapid/High = SI (Late, rapid progression)
CD4 CD4 CD4
CCR5
CCR5
CXCR4
CXCR4
M-tropic Dual tropic T-tropicVirus Variants
HIV-1 coreceptor usage and viral tropism
Target Cell Types
Macrophage Primary T cell
T-cell line
>25 years of HIV/AIDS>25 years of HIV/AIDS
> 33
For every 2 people put on treatment, 5 others are infected
Treatment begins
Selection of resistant quasispecies
Incomplete suppression•Inadequate potency•Inadequate drug levels•Inadequate adherence•Pre-existing resistance
Selection of Resistant strains
Time
Vira
l loa
d
Drug-susceptible quasispecies
Drug-resistant quasispecies
Study rationale
Background:• Relatively limited information on coreceptor usage by
HIV-1 subtype C isolates, particularly in children. However, most studies suggest very rare CXCR4 usage
• Some reports suggest increasing X4 usage (in adults) eg. Johnston et. al. (n=28), 50% using X4 among ART experienced viremic patients
• Previously used methods may be biased because they involved first generating viral isolates by co-culture
Study rationale
• ART may boost T-cell immune responses which have been shown to preferentially suppress X4 viruses. Thus partially effective therapy may select against X4 viruses (Deeks et al, JID 2004; Harouse et al, PNAS 2003)
• ART reduces CCR5 expression on T cells (due to reduction in T cell activation) potentially selecting for X4 viruses (Brumme et al, JID 2005; Anderson et al, AIDS 1998)
• Suboptimal drug metabolism (such as AZT) in the cellular reservoirs for X4 viruses has been suggested and could lead to selection for X4 viruses (Boucher et al, AIDS 1992)
Aims
Specific Aims:1) To determine the prevalence of major drug
mutations in ART-naïve and failing children and adults
2) Determine overall prevalence of X4 tropism among children and adults initiating and failing HAART
3) Compare prevalence of X4-utilizing viruses between ART-naïve and ART-experienced subjects with detectable viremia
4) Explore factors associated with viral tropism in HIV-1C infection
HIV-1 Genotyping Assay
plasma
Blood cells
centrifugation
RNA
cDNA
DNA
RT-PCR
PCR
Dye terminatorsPCR
A T G C
ATAGACCAG : consensus sequence I Q QATCGACCTG : patient sequence I Q *L
TT C
T C GT C G A
Software analysis
CMV pA
Env
5’LTR gagpol env
vif
vpr
vpu
tatrev
Luc
3’LTR
+
Trofile assay summary- for coreceptor usage
pcDNA-env
0.2µfilter
0.2µfilter
Luciferase assay
CCR5 cells CXCR4 cells293T cells
Table 1: Children Demographic and Clinical Characteristics
NOTE. Data are no. (%) of children unless otherwise indicated. For cases where the data is incomplete, the n value is indicated. Statistical tests: a Mann-Whitney U test and b Fisher’s exact test (for WHO stage analysis, stages I, II and III were grouped together).
Characteristics HAART-Failures (n=41) HAART-Naïve (n=40) P value
Age, median years (IQR) 7.9 (4.8-10.4) 0.9 (0.5-2.8) <0.0001a
Black Race 41 (100.0) 39 (97.5) 0.49b
Male Gender 24 (58.5) 18 (45.0) 0.27b
Nadir CD4%, median (IQR) 9.0 (3.1-13.5) (n=33) 14.0 (7.5-22.0) (n=37) 0.008a
Current CD4%, median (IQR) 18.0 (9.0-24.0) 14.0 (7.5-22.0) (n=37) 0.47a
Current CD8%, median (IQR) 51.0 (40.5-58.0) 48.0 (35.5-56.5) (n=37) 0.38a
Current CD3%, median (IQR) 72.0 (67.0-77.0) 66.0 (56.0-77.5) (n=37) 0.18a
Current plasma HIV-1 viral load,
median log 10 copies/ml (IQR)4.9 (4.4-5.4) 5.9 (5.6-6.8) <0.0001a
Current WHO Stage: (n=40)
I 1 (2.5) 0 (0.0)
0.003bII 15 (37.5) 1 (2.5)
III 18 (45.0) 20 (50.0)
IV 6 (15.0) 19 (47.5)
NOTE. Data are no. (%) of children unless otherwise indicated. For cases where the data is incomplete, the n value is indicated. Prior treatment indicated with underlined drug/s changed ● d4T, 3TC, ritonavir (n=1); * unknown; ○ d4T, 3TC, EFV (n=1) and AZT, 3TC, NVP (n=1); d4T, 3TC, kaletra; d4T, 3TC, EFV.Statistical tests: a Mann-Whitney U test and b Fisher’s exact test
Table 1: Patient Demographic and Clinical Characteristics Cont. Characteristics HAART-Failures (n=41) HAART-Naïve (n=40) P value
Current Drug regimen:
D4T, 3TC, EFV 25 (61.0)
D4T, 3TC, LPV/r ● 6 (14.6)
D4T, DDI, EFV * 1 (2.4)
AZT, 3TC, NVP 3 (7.3)
AZT, 3TC, EFV ○ 3 (7.3)
AZT, DDI, EFV 1 (2.4)
AZT, DDI, LPV/r 1 (2.4)
D4T, ABC, LPV/r * 1 (2.4)
Duration of HAART prior to study
recruitment, median months (IQR) 28.6 (19.7-37.5) (n=38)
History of single-dose NVP for PMTCT 10 (26.3) (n=38) 18 (47.4) (n=38) 0.09b
Frequency of drug resistance mutations and levels of resistance in HAART-failing children to the NRTIs (a) and NNRTIs (b)
58.5% had TAMs39% had ≥3 TAMs
• d4T/3TC/EFV (n=25)
– 3 patients have no DRMs (VLs are 617; 79,400; 228,000)
– 20 NRTI DRM
– 2 NNRTI DRM
(one patient had a PI DRM)
• d4T/3TC/kaletra (n=5)
– 3 patients have no DRMs (VLs are 143,000; 198,000; 4,410,000)
– 1 patient has 1 NRTI DRM (M184V) only
– 1 patient has 1 NRTI (M184V) and 1 NNRTI DRM (Y181C)
Average no. of major mutation in patients failing standard first line treatment (n=30)
How many major mutations compromise the standard second line treatment?
d4T/3TC/EFV (n=25) → AZT/ddI/Kaletra• 3 patients susceptible to all drugs – no change needed• All patients susceptible to kaletra• 3 patients susceptible to 3 drugs in standard second line tx.
AZT Resistance ddI ResistanceSusceptible (n=2) High-Level (n=2)
Low level (n=5)
Potential low-level (n=2)
Low-level (n=1)
Intermediate (n=2)
Intermediate (n=8)
Potential low-level (n=2)
Low-level (n=3)
Intermediate (n=2)
High-level (n=1)
High-Level (n=4)Intermediate (n=2)
High-Level (n=2)
• Overall, 13 of 25 (52%) patients will have some degree of resistance (low to high) to two of the three drugs in their new regimen (excluding potential low-level resistance)
d4T/3TC/kaletra (n=5) → AZT/ddI/(NVP/EFV)
• 4 of 5 patients are susceptible to all second line drugs
• 1 patient had intermediate resistance to EFV (3.7 yrs old)(Y181C)
Note: Overall better if not changed
• All still susceptible to PIs and d4T with 3 patients still susceptible to 3TC
[2 high-level resistance to 3TC (M184V)]
90.6%
9.4%
HAART-Naïve
R5-tropic
D/M-tropic
45.7%
11.4%
42.9%
HAART-Failures
R5-tropic
X4-tropic
D/M-tropic
p<0.0001
45.7%
11.4%
42.9%
HAART-Failures
R5-tropic
X4-tropic
D/M-tropic
Comparison of coreceptor usage in HAART-failing and HAART-naïve children
Evaluation of Several Genotypic Tools for the Prediction of CXCR4-usage
a A total of 52 pure subtype C isolates with both phenotypic and genotypic data were included in this analysis. bA false positive rate of 10% was used. c A combination of the first four genotypic tools were used where the majority prediction was considered as the final genotype prediction (n=47).
Genotypic ToolPrediction of CXCR4-usagea
Sensitivity (%) Specificity (%) PPV (%) NPV (%)
11/25 charge rule 30.0 96.9 83.0 74.0
Net V3 charge rule 65.0 78.1 59.0 82.0
C-PSSMsinsi 75.0 87.5 75.0 88.0
Geno2pheno[coreceptor]b 60.0 87.5 70.0 82.0
Combined Rulesc 63.2 100.0 100.0 85.0
C4.5 25.0 100.0 100.0 73.0
C4.5 positions 8-12 25.0 100.0 100.0 73.0
PART 30.0 100.0 100.0 75.0
SVMwetcat 40.0 96.9 86.0 77.0
Patient Characteristic
HAART-Experienced Patients failing
Treatment (n=45)
HAART-Naïve Patients (n=45)
p-value
Age, median years (Q1-Q3)
36 (24-51)
36 (20-78)
0.65
Gender: Female 28 (65%) 27 (60%)
Black race 45 (100%) 45 (100%)
CD4 count, median cells/mm3 (Q1-Q3) Current Nadir
174 (9-718)57 (3-197)
123 (8-660) 0.0360.0004
Vial load, median copies/ml
6, 653 (225-220,010)
44,042(1,702-1,167,759)
0.001
WHO stage at visit I-III IV
32 (71 %)13 (29 %)
9 (20 %)36 (80%)
Adult patient information
Patterns of drugresistance
• What is the outcome of patients failing if started on the standard second line of treatment without having genotypic data?
• d4T/3TC/ (EFV/NVP) (n=16) (Note: 2 on NVP)– No major PI mutations
– 1.75 NRTI DRM
– 1.69 NNRTI DRM
Average no. of major mutation in patients failing standard first line treatment (n=16)
How many compromise the standard second line treatment?
d4T/3TC/ (EFV/NVP) (n=16) → AZT/ddI/LPV/r• All patients susceptible to kaletra (LPV/r)• 6 patients susceptible to all 3 drugs in standard second line tx.
AZT Resistance ddI Resistance
Susceptible (n=4)Potential low-level (n=3)
High-Level (n=1)
Potential low-level (n=2)Susceptible(n=1)
Low-level (n=1)
Low-level (n=2) Low-level (n=2)
Intermediate (n=2) Low-level (n=2)
• 4 of 16 (25%) patients will have some degree of resistance (low to intermediate) to two of the three drugs in their new regimen (excluding potential low-level resistance).
• 6 of 16 (37.5%) will have some degree of resistance (low to high) to one of the three drugs in their new regimen (excluding potential low-level resistance).
High levels of CXCR4 viruses in patients failing therapy- limited salvage options
Method % of sequences correctly predicted
% of R5 sequences correctly predicted
% of X4/D/M sequences correctly predicted
11/25 78 90 55Overall net V3 charge 75 71 81C-PSSM 81 85 72Geno2Pheno 84 86 82Combined algorithm* 87 90 80
*In the combined algorithm, concordant results from at least 3 of 4 methods (i.e. the amino acids at positions 11 and/or 25, the overall net V3 charge, C-PSSM prediction and Geno2Pheno prediction) were used.
V3 loop-based methods for coreceptor usage prediction
Conclusions• Virologic failure is mainly due to DRMs• High levels of TAMs is source of concern- suggests
subpotimal adherence and need for intensive monitoring
• Higher levels of CXCR4 using viruses among HAART experienced patients- need to explore CCR5 antagonists as part of first-line/early treatment
• Collectively, these data highlight the need for intensified adherence counselling and better HAART monitoring to maximize benefits.
AcknowledgementsUKZN• Taryn Green• Ashika Singh• Mohendran Archary• Michelle Gordon• Raziya Bobat• Hoosen Coovadia
McCord Hospital• Henry Sunpath• Richard Murphy
Monogram Biosciences• Jacqueline Reeves• Yolanda Lie• Elizabeth Anton
Harvard University• Daniel Kuritzkes• Bruce Walker
Funding• IMPAACT Network, NIH• Harvard University CFAR• South African DST/NRF• Hasso Plattner Foundation