8. Baseline Genotypes, Thomas Street clinic 1999 2003-04 Sample size 44 40 CD4 288 (6-904) 274 (9-727) Time since HIV diagnosis (mo.) 17 (0-144) 39 (1-236) NRTI mutations 2 (4.5) 2 (5) NNRTI mutants 1 (2.3) 2 (5) PI 1 (2.3) 1 (2.5) Total mutants 4 (9.1) 5 (12.5)
9. Increasing Primary Drug Resistance: CDC Survey 1. Bennett D, et al. CROI 2002. Abstract 372. 2. Bennett D, et al. CROI 2005. Abstract 674. Prevalence of Drug Resistance, % 1998 [1] (n = 257) 1999 [1] (n = 239) 2000 [1] (n = 299) 2003-2004 [2] (n = 787) Any drug 5.5 8.8 10.7 14.5 NRTI 5.1 7.1 7.7 7.1 NNRTI 0.4 2.1 1.7 8.4 PI 0 0.8 3.0 2.8 ≥ 2 drug class 0 1.3 1.3 3.1
10.
11.
12.
13. Viral Resistance is the Outcome of Viral Replication, Mutations and Selection Pressure Original Virus Quasispecies Selection Pressure exerted by Drugs HIV RNA Level New Virus Quasispecies Resistant clone Resistant clones Time
24. Discordance due to mixtures Result: Phenotype reads “sensitive” because of presence of wild-type virus in the mixture But Genotype predicts “resistant” due to presence of mutant virus in the mixture
25. Incomplete Rules may lead to discordance Result: Genotype predicts resistance based on rules-based algorithm, but Phenotype reads “sensitive” due to interactions of complex mutation patterns PT GT NET
37. Mutations Selected by nRTIs Multi-nRTI Resistance: 69 Insertion Complex (affects all nRTIs currently approved by the US FDA) Multi-nRTI Resistance: 151 Complex (affects all nRTIs currently approved by the US FDA except tenofovir) Multi-nRTI Resistance: Thymidine Analogue-Associated Mutations (TAMs; affect all nRTIs currently approved by the US FDA)
42. Mutations Selected by PIs Atazanavir +/-ritonavir Darunavir/ ritonavir Fosamprenavir/ ritonavir Indinavir/ ritonavir Lopinavir/ ritonavir
43. Mutations Selected by PIs (cont) Nelfinavir Saquinavir/ ritonavir Tipranavir /ritonavir
44.
45.
46. Darunavir response by DRV score A wrinkle: The common PI mutation at 82A may actually have a positive effect on viral response to darunavir 0-2 mutations 3 mutations 4 mutations 50% 22% 10% Patients (%) with HIV-1 RNA <50 copies/mL at Week 24 Number of Darunavir mutations at baseline
47. Interpreting Phenotypes Cutoffs differ for each drug Probability of response Fold Change “ Zone of Intermediate Response” Lower clinical cutoff Response is significantly reduced Upper clinical cutoff Response is unlikely
51. Impact of BL Resistance in DUET: The Number of Baseline ETV RAMs Correlated with the Virologic Response (<50 copies/mL) TTCA0066-07332-29UN Number of ETV RAMs present at baseline Placebo + OBT (n=414) ETV + OBT (n=406) 0 2 3 4 1 7/28 3/18 25 17 25 41 6/24 13/32 25 58 17/68 37/64 38 59/157 73/121 60 64/147 121/161 75 44 Patients with confirmed viral load <50 HIV-1 RNA copies/mL (%) 0 20 40 60 80 Vingerhoets J , et al. 11th EACS 2007. Abstract P7.3/05 7/28 3/18 6/24 13/32 17/68 37/64 59/157 73/121 64/147 121/161 3 or more ETV associated mutations give a reduced response to ETV
52. Updated List of INTELENCE RAMs: Weight Factors for 2008 INTELENCE RAMs TTCA0100-08173-8UN a Median (Q1–Q3) FC for all isolates was 3.0 (1.1–9.3); b V179F was never present as single INTELENCE RAM (always with Y181C) Median Q1–Q3 n Y181I 1.5 42.0 23.2–129.7 34 12.5 High 3 Y181V 0.9 10.4 3.9–60.6 28 17.4 High 3 K101P 2.6 22.3 5.6 – 42.9 65 6.2 High 2.5 L100I 8.4 6.7 2.7–17 264 1.8 Medium 2.5 Y181C 32.0 4.4 2.1 – 11.6 552 3.9 Medium 2.5 M230L 1.1 4.3 2.7 – 10.5 20 3.4 High 2.5 E138A 2.5 2.9 1.4 – 10.6 44 2.0 Medium 1.5 V106I 4.4 2.6 1.4 – 5.2 63 NA Low 1.5 G190S 3.7 0.8 0.6 – 1.7 32 0.2 Low 1.5 V179F b 0.7 – – 0 0.1 Medium 1.5 V90I 6.8 2.0 0.8 – 3.6 97 1.5 Low 1 V179D 2.1 1.7 1.0 – 4.7 33 2.6 Low 1 K101E 9.9 1.5 0.8 – 2.5 24 1.7 Low 1 K101H 2.2 1.1 0.6 – 2.8 8 1.3 Low 1 A98G 9.5 1.0 0.5 – 1.9 127 2.5 Low 1 V179T 0.6 0.9 0.7 – 1.2 2 0.8 Low 1 G190A 23.3 0.8 0.5 – 1.5 226 0.8 Low 1 Effect on FC in linear model Weight factor INTELENCE FC in the subset of HIV-1 clinical isolates with 1 INTELENCE RAM (n=1,619), regardless of the presence of other NRTI or NNRTI RAMs a Prevalence (%) in the panel of 4,248 HIV-1 clinical isolates Mutation INTELENCE FC in a single SDM Vingerhoets J, et al. IHDRW 2008; Abstract 24 Mutation not in table is scored 0; RAMs, resistance-associated mutations; FC, fold change
53.
54. Mutations in the Integrase Gene Associated With Resistance to Integrase Inhibitors Raltegravir
Newly diagnosed patients not necessarily newly infected and those with genotypes were not a random sample but selected for resistance testing by their treating provider which introduces possibility of selection bias in the results. None the less this is the largest and most geographic diverse sample we have in the US population and levels remain high.
Transmission of drug resistant virus is becoming a significant public health concern.
8
Phenotypic testing directly measures the amount of drug necessary to inhibit or suppress viral replication in vitro . The assays use recombinant virus composed of the person’s viral PR and RT genes inserted into a standard reference strain of virus Drug susceptibility results are reported as the amount of drug required to inhibit virus replication by 50% (50% inhibitory concentration, IC 50 ). The change in susceptibility is measured by comparing the IC value of the person’s virus to that of the reference control. As the person’s virus becomes more resistant, the susceptibility curve shifts to the right. This means that a greater amount of drug is required in order to inhibit the same (50%) amount of viral replication. The proportion of IC 50 of the mutant (patient) virus and the IC 50 of the reference control is the fold resistance. It is a quantitative measurement of change in susceptibility. Examples: Antivirogram (Virco) and PhenoSense (ViroLogic) An advantage of testing for resistance with a phenotypic assay is that nearly any drug, even new ones, can be tested The major limitation of a phenotypic assay is how to clinically interpret these results. These are in vitro data of single drugs and they do not account for the numerous complex drug-drug interactions that can occur with combinations of ARVs
2 nd failure; VL 8500; >12 months on latest regimen: d4T, 3TC, NVP Next slide: Types of discordance: Pheno-S Geno-R no mixtures
Slide . Virco’s Databases This slide shows the composition of Virco’s databases as of December 2008. These databases are not static and continue to grow using samples from routine clinical testing, clinical trials, and research collaborations. Virco’s correlative database of genotypes and corresponding phenotypes on the same clinical samples is the key to the FC calculations. The Clinical Outcomes Database is the key for determining CCOs. The correlative database includes genotypic data on >373,000 samples and phenotypic data on >93,000 samples. As of December 2008, there were >58,000 correlated GP pairs in the database. The Virtual Phenotype ™ -LM engine can generate a calculated FC in IC 50 for any sequence that is submitted for analysis using a multiple linear regression modeling approach. The Clinical Outcomes Database includes 21,781 patient records, from which 8,849 treatment regimens were used to determine CCO values.
Slide : Page 1: Summary Report (1) The Summary Report page includes the following: [Circle Number 1 with rows] Antiretroviral drug classes and the publicly recognized mutations associated with resistance to those drug classes detected in the individual sample. [Green column header: Drugs, first 2 columns] The brand and generic names of the antiretroviral agents are listed in the first two columns. Recently, Intelence ® (etravirine) was added to the report under the NNRTI class. [Circle number 2; green column header: Fold Change; third column] The hall mark of the virco ® TYPE HIV-1 report is the calculated fold change in wild type IC 50 using the Virtual Phenotype™-LM approach. These are found in the third column marked by the circle number 2. [Circle number 3; green column header: Cut-Off; fourth column] The next column, marked by circle number 3, lists the cut-off values (either biological or clinical). [Circle number 4; green column header: Resistance Analysis; fifth column] The resistance analysis based on the calculated fold-change for that sample and cut-off values appears in this column. [Circle number 5; green column header: Clinical Notes; sixth column] The clinical notes column will guide readers to the second page (ie, Detailed Report page) for cases where clinically relevant information can be deduced from the viral genotype that may not be captured fully in the calculated phenotype
Slide 22: Correlation Between Predicted and Measured FC Values This slide shows the correlation between measured and predicted FC values for the 7 clinical isolates analyzed. All 16 drugs and viruses were plotted together. The left panel shows the correlation and R value obtained between a randomly selected conventional phenotype measurement and the corresponding predicted FC value for the same isolates. As shown , there is very good correlation with an R=0.93. However, if the mean of multiple conventional phenotype measurements is correlated with the corresponding predicted values (right panel) the correlation is even better (R=0.97) therefore suggesting that the Virtual Phenotype TM approach provides values that are comparable to the mean of multiple conventional phenotype measurements. In other words, the virtual phenotype approach eradicates much of the assay variation intrinsic to the Antivirogram ® assay.
11
This slide summarizes NNRTI resistance patterns. There is a clustering of mutations between codons 100 and 106 and codons 181 and 190, with several others occurring in the low 200s. There is broad cross-resistance in these patterns, although subtle differences can be seen. 1 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
This slide summarizes NNRTI resistance patterns. There is a clustering of mutations between codons 100 and 106 and codons 181 and 190, with several others occurring in the low 200s. There is broad cross-resistance in these patterns, although subtle differences can be seen. 1 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
This slide summarizes NNRTI resistance patterns. There is a clustering of mutations between codons 100 and 106 and codons 181 and 190, with several others occurring in the low 200s. There is broad cross-resistance in these patterns, although subtle differences can be seen. 1 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
Many mutations in the protease gene, as shown in this recent representation, 1 confer significant cross-resistance across the entire class. The mutations highlighted in yellow generally develop in patients who receive the individual PIs for the first time with or without nucleosides. Indinavir—46 and 82 Ritonavir—84 and 82 Saquinavir—48 and 90 Nelfinavir—30 and 90, occasionally 88 Amprenavir—50, 54, and 84 Lopinavir/ritonavir—It is unclear which mutation develops first in patients, but the mutations shown all contribute to lopinavir resistance, based on phenotypic and genotypic analyses of clinical isolates. It has been suggested that as few as 4 mutations may be associated with high-level resistance to lopinavir/ritonavir. Although L63P causes no appreciable increase in IC 50 , it is shown for only lopinavir/ritonavir because, along with other mutations, it predicts a lack of viral load response to regimens containing this agent. 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
Many mutations in the protease gene, as shown in this recent representation, 1 confer significant cross-resistance across the entire class. The mutations highlighted in yellow generally develop in patients who receive the individual PIs for the first time with or without nucleosides. Indinavir—46 and 82 Ritonavir—84 and 82 Saquinavir—48 and 90 Nelfinavir—30 and 90, occasionally 88 Amprenavir—50, 54, and 84 Lopinavir/ritonavir—It is unclear which mutation develops first in patients, but the mutations shown all contribute to lopinavir resistance, based on phenotypic and genotypic analyses of clinical isolates. It has been suggested that as few as 4 mutations may be associated with high-level resistance to lopinavir/ritonavir. Although L63P causes no appreciable increase in IC 50 , it is shown for only lopinavir/ritonavir because, along with other mutations, it predicts a lack of viral load response to regimens containing this agent. 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
A supportive analysis of POWER 1, 2, and 3, which will be discussed in detail in this presentation, showed that the proportion of patients achieving viral load <50 copies/mL at week 24 was 50%, 22%, and 10% when baseline genotype had 0-2, 3, and 4 or more RAMs, respectively.
This slide summarizes NNRTI resistance patterns. There is a clustering of mutations between codons 100 and 106 and codons 181 and 190, with several others occurring in the low 200s. There is broad cross-resistance in these patterns, although subtle differences can be seen. 1 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
This slide summarizes NNRTI resistance patterns. There is a clustering of mutations between codons 100 and 106 and codons 181 and 190, with several others occurring in the low 200s. There is broad cross-resistance in these patterns, although subtle differences can be seen. 1 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
This slide summarizes NNRTI resistance patterns. There is a clustering of mutations between codons 100 and 106 and codons 181 and 190, with several others occurring in the low 200s. There is broad cross-resistance in these patterns, although subtle differences can be seen. 1 1. D’Aquila RT, Schapiro JM, Brun-Vezinet F, et al. Drug resistance mutations in HIV-1. Top HIV Med . 2002;10:11-15. ´
![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)