Anumber of clinical trials have demonstrated

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1 IMPROVING THE UTILITY OF PHENOTYPE RESISTANCE ASSAYS: NEW CUT-POINTS AND INTERPRETATION * Richard Haubrich, MD ABSTRACT The interpretation of a phenotype assay is determined by the cut-point, which defines whether the patient s virus is susceptible or has reduced susceptibility to the drug being studied. More recently, biologically defined cut-points, derived from the range of drug susceptibilities in therapy-naive isolates, have helped to define the fold change in inhibitory concentration of 50% (IC 50 ) that can be expected to reduce drug susceptibility. Virtual phenotype (VIRCO) assays have also been used. The best way to identify cut-points is to relate the susceptibility of a drug to the virological response to a regimen that contains that drug. Cut-points defined by this method are referred to as clinical cut-points. The CCTG 575 investigators examined phenotypic cut-points to determine the correlation of phenotype with drug susceptibility and virologic response in a group of patients who failed on at least 1 previous regimen. A phenotype, determined at baseline, was used to evaluate the number of susceptible drugs by class (nucleoside reverse transcriptase inhibitors [NRTIs], nonnucleoside reverse transcriptase inhibitors [NNRTIs], and protease inhibitors [PIs]) in the regimen. Investigators concluded that phenotypic susceptibility to PIs and NNRTIs were strong predictors of response when using a cut-point of 2.5, even after correction for baseline CD4 and HIV RNA. (Advanced Studies in Medicine 2001;1(12): ) *This article is based in part on a presentation given by Dr Haubrich at the 1st International AIDS Society Conference on HIV Pathogenesis and Treatment. Correspondence to Richard Haubrich, MD, Associate Professor of Medicine, Division of Infectious Diseases, University of California San Diego, 150 West Washington Street, Suite 100, San Diego, California Anumber of clinical trials have demonstrated that resistance testing, used to select drugs for the next regimen when a patient is failing his or her current therapy, can result in improved clinical outcome, which has been defined as short-term (12- to 24-week) suppression of HIV RNA levels. However, not all of the studies have revealed a positive impact of resistance testing, including 2 recent studies that failed to show better virologic suppression in the resistance testing arms compared to patients treated without resistance testing. The variable success of prospective trials of phenotype-resistance guided therapy initially considered to yield results that were easier to interpret may have been due to difficulty of interpreting HIV-resistance tests. This difficulty has been attributed to the lack of clinical guidelines for interpretation of the data from these assays. The interpretation of a phenotype assay is determined by the cut-point, which defines whether the patient s virus is susceptible or has reduced susceptibility to the drug being studied. It is presumed that use of more susceptible drugs in a treatment regimen will be more likely to result in virologic suppression than use of less susceptible drugs. Phenotype assays initially used cut-points based on assay reproducibility, the retesting of a sample multiple times to determine the range of assay variability. For the ViroLogic phenotype assay, if the fold change in inhibitory concentration of 50% (IC 50 ) of the patient s virus varied more than 2.5-fold from control, it could be assumed that the virus was less susceptible to the drug than a wild-type (nonresistant) virus. More recently, biologically defined cut-points, derived from the range of drug susceptibilities in therapy-naïve isolates, have helped to define the fold change in IC 50 that can be expected to reduce drug susceptibili- 486 Vol. 1, No. 12 December 2001

2 ty. To determine the biological cut-point, a large number of phenotype values are evaluated from treatment-naïve patients with no prior therapy. The range of susceptibilities for these patients is then examined, and a metric, such as the mean +2 standard deviations, is used to define the upper range of susceptibility for the assay. Patient samples with a fold change in IC 50 beyond this upper range would be defined as having reduced susceptibility. Although phenotype cut-points for RTV boosted PI are not yet available for all the drugs, there are a number of ongoing studies designed to help improve this knowledge base. In one study of 240 treatment-naïve patients, the 95% confidence interval (the mean +2 standard deviations) for a cut-point was examined for the ViroLogic PhenoSense assay. The upper limit of the confidence interval for the nucleoside reverse transcriptase inhibitor (NRTI) drugs is shown in Table 1. In naïve patients, there is little variation in susceptibility for stavudine (d4t) and didanosine (ddi); for d4t and ddi, most of the samples were <1.5, while for zidovudine (AZT), normal values can range as high as 1.9. Similar analyses have been conducted using VIRCO assays to determine the biologic cut-point for that assay. In Table 2, the old and new cut-points for the VIRCO assay are listed for selected drugs. The percentages of samples with resistance, defined by the old and new cut-points from 5000 samples in the VIRCO database, are presented. Investigators defined the new cut-points for the assay by testing a large number of clinical samples (not the samples included in Table 2) from treatment-naïve patients. These cut-points differ from those of the ViroLogic assay due to intrinsic differences in the way the assays are conducted. In the case of VIRCO, the original cut-point for all of the nucleosides based on assay reproducibility was 4.0. Table 2 illustrates the impact of the new biologic cutpoints. For example, the previous cut-point for d4t was 4.0. Using this value, 7% of the 5000-patient sample would have been classified as resistant, while with the new cut-point of 3.0, 12% of the patients would be considered resistant. As can be seen by comparing Tables 1 and 2, cut-points are different for the 2 different assays. Clinicians must be aware of which assay has been performed and recognize the different cut-points for each assay when selecting drugs for a regimen. Unfortunately, cut-points derived from biological variation are not optimal. The best way to identify cutpoints is to relate the susceptibility of a drug to the virological response to a regimen that contains that drug. Cut-points defined by this method are referred to as clinical cut-points. One of the most important issues in resistance testing is therefore the identification of phenotypic breakpoints for individual drugs that are based on therapeutic response to a regimen. Table 1. Biologic Cut-offs for PhenoSense Assay Vary by Drug Drug 95th Percentile susceptibility fold change AZT 1.9 3TC 1.5 d4t 1.5 ddl 1.5 ddc 1.6 ABC 1.7 Variability of drug susceptibility in virus from treatment-naïve patients. AZT = zidovudine; 3TC = lamivudine; d4t = stavudine; ddi = didanosine; ddc = zalcitabine; ABC = abacavir. Table 2. Effect of New Cut-offs: 5000 VIRCO Clinical Samples Old % New % Drug Cut-point Resist Cut-Point Resist AZT d4t ABC EFV APV AZT = zidovudine; d4t = stavudine; ABC = abacavir; EFV = efavirenz; APV = amprenavir. Advanced Studies in Medicine 487

3 Few studies that differentiate clinical cut-points have been conducted. One recent meta-analysis of 4 early trials of therapeutic intensification with abacavir (ABC) that evaluated the addition of ABC to a failing regimen identified 2 clinically relevant breakpoints using the PhenoSense assay. Since the only drug that was changed at baseline was ABC, the change in HIV RNA from baseline would be due to the effects of ABC and this response to ABC is related to the phenotypic susceptibility to ABC at baseline. In this analysis, a 4.5-fold reduction in susceptibility to ABC marked the point at which the likelihood of a plasma HIV-1 RNA decrease to <400 copies/ml was significantly reduced. Patients with a 6.5-fold reduction were not likely to have significant viral load reductions. Phenotypic analysis of samples from older, monotherapy trials can also help define assay breakpoints. One study evaluated patients treated with AZT monotherapy who then received d4t. Phenotypic evaluation of AZTtreated patients showed very small differences in d4t susceptibility. Virologic response (>-0.3 log 10 reduction in HIV RNA) was seen for patients with median d4t susceptibility of 1.0. Non-responders had an average fold change of 1.6 to d4t. Another analytical method to define phenotypic cutpoints is to relate virologic response to baseline phenotype in patients receiving different baseline regimens. This presents a greater analytic challenge because of the impact of a variety of different regimens. The CCTG 575 investigators have examined phenotypic cut-points to determine the correlation of phenotype with drug susceptibility and virologic response in a group of patients who failed on at least 1 previous regimen (Table 4). Virologic success was defined as having HIV RNA <400 copies/ml at 6 or 12 months. Patients had an average of 36 months of prior therapy and switched to different regimens at baseline. A phenotype, determined at baseline, was used to evaluate the number of susceptible drugs by class (NRTIs, nonnucleoside reverse transcriptase inhibitors [NNRTIs], and protease inhibitors [PIs]) in the regimen. Susceptibility in the initial models was defined using a 2.5-fold cut-point. In the multivariate models, logistic regression was used, controlling for baseline CD4 and HIV RNA. Changes in odds ratios or P-value were evaluated for differences in NRTI cut-points. The Figure shows the distribution of susceptibility to ddi in the CCTG 575 sample population of more than 200 experienced patients with an average of 36 Table 3. Predictors of Virological Response to Stavudine (d4t) After Zidovudine (AZT) Monotherapy [ACTG 302] " Efficacy of d4t monotherapy in monotherapy-experienced patients (genotyped, n=31 & phenotyped, n=24) " Small decreases in d4t phenotypic susceptibility were associated with nonresponse " The clinically relevant phenotypic cut-off for d4t in this study was <1.5 fold " No patient with >1 thymidine analogue mutation (TAM) had a significant reduction in viral load (> -0.3 Log 10 ) with d4t therapy Shulman et al. Genotypic Predictors of Virologic Response to Stavudine after Zidovudine monotherapy (ACTG 302). Presented at: 8th Conference on Retroviruses and Opportunistic Infections; February 4-8, 2001; Chicago, IL. Abstract 437. d4t = stavudine; AZT = zidovudine. Table 4. CCTG 575 Statistical Methods " Virologic outcome: % HIV RNA <400 copies/ml at months 6 and 12 " Baseline regimen evaluated for the number of susceptible drugs; all drugs and by ARV class (NRTI, PI, and NNRTI) " Definitions of susceptibility to a drug: " FC <2.5 for all drugs " FC <2.0 or 1.5 for ddl and d4t " substitution of AZT FC for d4t; ABC FC for ddl ARV = antiretroviral; NRTI = nucleoside reverse transcriptase inhibitor; PI = protease inhibitor; NNRTI = nonnucleoside reverse transcriptase inhibitor; ddi = didanosine; d4t = stavudine; AZT = zidovudine; FC = fold change; ABC = abacavir. 488 Vol. 1, No. 12 December 2001

4 months of nucleosides, 45% of whom had prior ddi. Only 13 patients had any level of reduced susceptibility when the cut-point for the drug was at 2.5. A similar result was seen with d4t, demonstrating that a cut-point of 2.5 for ddi or d4t may not be a sensitive indicator for determining which patients are likely to respond to either of those drugs. Virologic response, based on the number of susceptible drugs in the baseline regimen, demonstrated that for patients whose viral isolates had 3 susceptible drugs in their baseline regimen, 53% responded versus 32% of patients who had <3 susceptible drugs. If d4t and ddi are excluded from the analysis and not counted among susceptible drugs, almost the same result occurs, suggesting that d4t and ddi do not add to the predictive value of the baseline phenotype relating to virologic success. A multivariate model was then constructed to define the relative contribution of the various drug classes in predicting response. If the baseline regimen contained a PI to which the patient s virus was susceptible, the patient was twice as likely to reach undetectable viral load levels. A susceptible NNRTI in the baseline regimen improved the chances of having HIV RNA < 400 copies/ml at month 6 by 3-fold. Susceptible NRTIs did not appear to add to the predictive value of the phenotype. In subsequent multivariate models, the cutpoint of 1.5 was used for d4t and ddi while the cut-point for the other drugs was maintained at 2.5. In these models, susceptibility to both PIs and NNRTIs remained important predictors. The predictive value of susceptibility to NRTIs did improve with the new cut-points, but not to statistically significant levels. CCTG 575 investigators concluded that phenotypic susceptibilities to PIs and NNRTIs were strong predictors of response when using a cut-point of 2.5, even after correction for baseline CD4 and HIV RNA. By lowering the phenotypic cut-points to 1.5 for d4t and ddi, the predictive value of using NRTIs in multivariate models was improved. One question remains: why did the NRTIs fail to be predictive in these models? One explanation is that a cut-point of 2.5 for d4t and ddi is not the optimal predictor of response. Also, many of the patients in the study had received prior HIV therapy with dual NRTIs for approximately 2 years before their first PI regimen was initiated. This previous drug experience might have diminished the effectiveness of the NRTIs in the subsequent baseline regimen. Given the low potency of these agents in the CCTG 575 population, the statistical power to discern the impact of an NRTI in a regimen with more potent agents (NNRTIs and PIs) was reduced. It is hoped that by using new cut-points, the usefulness of phenotype testing will be improved. DISCUSSION PERIOD Dr Miller*: Is it possible that resistance to NRTIs or the inability to predict susceptibility might be related to assay technology, which requires further improvement? Or is cross-resistance among the classes so much higher than we expected? Figure. Low Prevalence of ddl Resistance in Experienced Patients *Veronica Miller, MD, JW Goethe Universitaet, Klinkum der JW Goethe Universitaet, Theordor Stern Kai 7, Frankfurt, Germany. ddi = didanosine; FC = fold change. Advanced Studies in Medicine 489

5 Dr Haubrich: I think it is probably a combination of both. Both genotype and phenotype assays have difficulty defining resistance to d4t and ddi. Using cutpoints of 2.5, very few individuals had d4t or ddi resistance, even with extensive prior treatment with the nucleosides. Cross-resistance among the NRTIs is now better defined. There is increasing evidence that many of the so-called AZT mutations probably affect d4t as well. Since the range of phenotypic susceptibilities for d4t and ddi is narrow, an assay with the least amount of intra-assay variation should be used in order to discern subtle differences between viral isolates that are susceptible from those that are not. Dr Miller: How would you use these assays in the care of your patients? Dr Haubrich: I think resistance testing is indicated for all patients failing any highly active antiretroviral therapy regimen. The biologic and clinical cutpoints of each drug will continue to be refined. This in turn will improve our use of assays. For example, both VIRCO and ViroLogic have defined new cutpoints for the nucleosides. As I mentioned for d4t and ddi, the virologic assay uses 1.7. With abacavir, we have a clinical cut-point of 4.5. Consequently, using these new cut-points will improve our ability to select a new regimen. The wide range of cross-resistance that develops limits treatment options. If a patient shows cross-resistance to all drugs which is becoming more and more common for patients who have cycled through 3, 4, and 5 regimens accurately defining this resistance does not improve treatment options. However, it may help increase the likelihood of achieving some reduction in the viral load and maintain T cell counts even in the absence of complete virologic suppression. Suggested Reading 1. Costagliola D, Descamps D, Calvez V, et al. Presence of thymidine-associated mutations and response to d4t, abacavir and ddi in the control arm of the Narval ANRS 088 trial. Presented at: 8th Conference on Retroviruses and Opportunistic Infections; February 4-8, 2001; Chicago, IL. Abstract Shulman N, Shafer R, Winters M, et al. Genotypic predictors of virologic response to stavudine after zidovudine monotherapy (ACTG 302). Presented at: 8th Conference on Retroviruses and Opportunistic Infections; February 4-8, 2001; Chicago, IL. Abstract Lanier ER, Hellmann N, Scott J, et al. Determination of a clinically relevant phenotypic resistance cutoff for abacavir using the PhenoSense assay. Presented at: 8th Conference on Retroviruses and Opportunistic Infections; February 4-8, 2001; Chicago, IL. Abstract Haubrich R, Keiser P, Vitt M, et al. CCTG 575: a randomized, prospective study of phenotype testing versus standard of care for patients failing antiretroviral therapy. Antiretroviral Ther. 2001;6:63. Abstract Vol. 1, No. 12 December 2001