Presentation on theme: "Emerging patterns of drug resistance and viral tropism in cART-naïve and failing patients infected with HIV-1 subtype C Thumbi Ndung’u, BVM, PhD Associate."— Presentation transcript:
Emerging patterns of drug resistance and viral tropism in cART-naïve and failing patients infected with HIV-1 subtype C Thumbi Ndung’u, BVM, PhD Associate Professor Director, HIV Pathogenesis Programme Doris Duke Medical Research Institute Nelson R. Mandela School of Medicine University of KwaZulu-Natal
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 CCR5 CXCR4 M-tropic Dual tropic T-tropic Virus Variants HIV-1 coreceptor usage and viral tropism Target Cell Types Macrophage Primary T cell T-cell line
>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 Viral load 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 terminators PCR A T G C ATAGACCAG : consensus sequence I Q Q ATCGACCTG : patient sequence I Q *L T T C T C G T CGA Software analysis
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).
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.
Frequency of drug resistance mutations and levels of resistance in HAART-failing children to the NRTIs (a) and NNRTIs (b) 58.5% had TAMs 39% 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 ResistanceddI Resistance Susceptible (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)]
p<0.0001 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. b A 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).
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: Female28 (65%)27 (60%) Black race45 (100%) CD4 count, median cells/mm 3 (Q1-Q3) Current Nadir 174 (9-718) 57 (3-197) 123 (8-660)0.036 0.0004 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 drug resistance
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 ResistanceddI 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) 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 11/25789055 Overall net V3 charge757181 C-PSSM818572 Geno2Pheno848682 Combined algorithm*879080 *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.
Acknowledgements UKZN 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