HIV drug susceptibility and treatment response to mega-haart regimen in patients from the Frankfurt HIV cohort

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1 Antiviral Therapy 5: HIV drug susceptibility and treatment response to mega-haart regimen in patients from the Frankfurt HIV cohort Veronica Miller *, Alessandro Cozzi-Lepri 2, Kurt Hertogs 3, Peter Gute 4, Brendan Larder 5, Stuart Bloor 5, Stefan Klauke, Holger Rabenau 6, Andrew Phillips 2 and Schlomo Staszewski Klinikum der JW Goethe-Universität, Zentrum der Inneren Medizin, Frankfurt, Germany 2 Royal Free Centre for HIV Medicine and Deptartment of Primary Care & Population Sciences, Royal Free and University College Medical School, London, UK 3 Virco Laboratories, Mechelen, Belgium 4 Frankfurt, Germany 5 Virco Laboratories, Cambridge, UK 6 Institut für Medizinische Virologie, JW Goethe-Universität, Frankfurt, Germany *Corresponding author: Tel: ; Fax: ; millerv@hivcenter.de Objective: To assess the relationship between viral susceptibility at baseline and virological response in human immunodeficiency virus (HIV) infected patients treated with multi-drug salvage regimens after multiple previous treatment failures. Design: Retrospective analysis of 50 patients from the Frankfurt HIV cohort who had received treatment with a minimum of six drugs, and for whom a sample for baseline viral phenotyping was available. Methods: Viral drug susceptibility was measured retrospectively from stored samples using the Antivirogram, a recombinant virus assay based method. Virological response was defined as a viral load of <400 copies/ml at week 24. For analysis of treatment response, drop-outs were dealt with in two ways, either as failures (DAF) or censored (DAC). Several logistical regression models were applied to identify predictors of response, including baseline virus load, number of new drugs and phenotypic sensitivity scores. Results: At baseline, drug resistance was extensive: 96% of patients had viruses resistant to at least one drug class and 32% had viruses resistant to all three drug classes. In the DAF analysis, 39 patients experienced virological failure. In the DAC analysis, eight were censored and 3 patients experienced virological failure. In multivariate models that adjust for baseline viral load, the number of new drugs and total phenotypic sensitivity scores, the baseline viral load and phenotypic sensitivity score remained significantly associated with virological outcome, whereas in those adjusted for baseline viral load, the number of new drugs, NRTI phenotypic sensitivity score and PI phenotypic sensitivity score, only the latter remained significantly associated with virological outcome. Both the DAF and DAC analyses produced similar results. In all models used, virological failure was shown to be significantly associated with baseline viral load and phenotypic sensitivity score. Conclusions: In this retrospective analysis based on a small number of patients, viral drug susceptibility at baseline was strongly associated with virological outcome at 24 weeks, independent of covariates such as baseline viral load and treatment history. Baseline viral load also maintained a significant, independent association with virological outcome in most models. Introduction Current potent antiretroviral combination regimens may induce profound suppression of viral replication in patients with no or minimal antiretroviral pre-treatment [,2]. However, salvage regimens in patients having undergone multiple sub-optimal regimen have proven to be less effective [3 5]. HIV drug resistance has been associated with virological failure in pretreated patients in numerous studies [3,6 8]. Multi-drug resistance has been reported to occur frequently [9]. Drug resistance severely limits salvage treatment options. It is possible that additional factors, such as cellular resistance developing over the past treatment periods also play a role in inferior responses to salvage regimen [0,]. Furthermore, patients with extensive pre-treatment may have developed toxicities that preclude the prescription of some medications. Thus, patients with multi-drug-resistant HIV are among the most difficult populations to treat and there 2000 International Medical Press /00/$

2 V Miller et al. Table. Study population: baseline characteristics Characteristic Median Range HIV viral load (log 0 copies/ml) CD4 cell count (cells/mm 3 ) Time on HAART (months) is a need to develop new treatment strategies for this population. We used the approach of combining the maximum number of tolerated drugs to treat patients with multiple previous treatment failures. This type of regimen has been called mega-haart, and we define it here as a regimen consisting of six or more individual drugs. We hypothesized that such a regimen may induce virological suppression in patients with drugresistant HIV, due to optimum antiretroviral activity resulting from an increase in the number of individual agents, as any synergistic activities and increased drug levels due to pharmacokinetic interactions [2 4]. Furthermore, we reasoned that if, due to virus population heterogeneity, individual resistance mutations are not genetically linked [5], it may be possible to inhibit the replication of HIV populations resistant to individual drugs with such a regimen. In a preliminary analysis of heavily pre-treated patients from the Frankfurt HIV cohort receiving mega-haart, we observed good virological and immunological responces (median maximum viral load reduction of 3.02 log 0 and a median CD4 cell increase, 95 cells/mm 3 ) [6], similar to data reported by others [7 9]. In a smaller subset of patients from this preliminary analysis, we had observed that the HIV susceptibility status at baseline appeared to be associated with better response rates [20]. We undertook this analysis in order to assess the relationship between viral susceptibility at baseline and virological response in patients treated with mega- HAART in a larger set of patients and in more detail. We also sought to determine whether the baseline viral phenotype provides information in addition to, and independent of, baseline viral load and pre-treatment history. Patients and Methods All patients from the Frankfurt HIV cohort meeting the standardized data analysis plan criteria [2] were included in this analysis if they received treatment with a minimum of six drugs and a sample for baseline viral phenotyping was available. Baseline was defined as the commencement date of mega-haart. Virological response was defined as having a viral load of <400 Table 2. Summary of previous ART* < week week year > year Drugs n % n % n % Zidovudine Zalcitabine Didanosine Stavudine Lamivudine Abacavir Loviride Tivirapine Nevirapine Saquinavir Indinavir Ritonavir Nelfinavir HAART *No previous exposure to delavirdine, efavirenz, saquinavir (soft-gel), and amprenavir in this patient population. copies at week 24 (or week 24 window, see standardized data analysis plan). Plasma viral load was measured using the Roche Amplicor Assay (Roche). Ritonavir was counted as an individual drug regardless of the dosage used. Viral drug susceptibility was measured using a recombinant virus assay based method, the Antivirogram [22]. Resistance was expressed as fold increase in IC 50 compared with the wild-type control for each experiment. Two cut-offs were used for the analyses: fourfold increase in IC 50 and 0-fold increase in IC 50. Viral drug susceptibility was measured retrospectively from stored plasma samples. Statistical analysis This is a retrospective analysis of a patient cohort with prospectively collected data. The standardized data analysis plan guidelines [2] were followed closely. Patients were considered not to be on the original study regimen if they stopped all drugs, or if they added at least one new drug to the original mega- HAART regimen. Patients who stopped all treatments or added one drug before 6 weeks were included in the failures (DAF) analysis (patients classified as having experienced virological failure).these patients were censored in the DAC analysis. Patients who reduced treatment but did not discontinue all drugs were retained in the study. Logistical regression models included the following variables: baseline viral load, number of new drugs in treatment regimen, total phenotypic sensitivity score (number of drugs in the regimen to which the virus population was susceptible at either the fourfold or 0-fold levels), nucleoside reverse transcriptase inhibitor (NRTI) sensitivity score, and protease inhibitor (PI) sensitivity score. The latter International Medical Press

3 Baseline susceptibility and response to mega-haart Figure. Baseline drug susceptibility: proportion of patients Table 3. Drugs received in the original mega-haart regimen with viruses resistant to each drug at the fourfold and 0- fold cut-off levels n % Number of drugs: Patients (%) two variables refer to the number of drugs within this class in a patient s regimen to which the virus population was sensitive. Patients whose regimen changed during follow-up were treated in two ways, according to the standardized data analysis plan: they were counted as having experienced virological failure (DAF) or they were censored (DAC). Results ZDV ddi d4t 3TC ABC NVP DLV EFV SQV RTV IDV NFV Antiretroviral drug Fourfold 0-fold ZDV, zidovudine; ddi, didanosine; d4t, stavudine; 3TC, lamivudine; ABC, abacavir; NVP, nevirapine; DLV, delavirdine; EFV, efavirenz; SQV, saquinavir; RTV, ritonavir; IDV, indinavir; NFV, nelfinavir. Study Population We identified 92 patients who started 6 drugs simultaneously. A total of 50 of these met the other criteria for inclusion: a viral load data point recorded within the week 24 window as required by the DAP and a stored baseline plasma sample for drug susceptibility analysis. The baseline characteristics are described in Table. The patient population was relatively advanced (median CD4 cell count, 95 cells/mm 3 ) with a median viral load of 5.52 log 0 copies/ml. Pre-treatment was extensive, with a median time on HAART (receiving three or more drugs simultaneously) of 8 months. Table 2 shows the previous antiretroviral treatments divided into no exposure (less than one week), minimal exposure ( week to year) and extensive exposure (more than year). The patient population was heavily pre-treated in all three drug classes. The treatment history reflects the calendar time of the treatments, with very few patients (6%) having received abacavir for more than week, whereas all patients had received lamivudine for more than week. There was no previous exposure to delavirdine, efavirenz, saquinavir (soft-gel) or amprenavir in this patient population. Six Seven Eight Nine 2.0 Drugs: Zidovudine Zalcitabine 22.0 Didanosine Stavudine Lamivudine Abacavir 22.0 Delavirdine Nevirapine Efavirenz 2.0 Saquinavir hard gel soft gel Indinavir Ritonavir Nelfinavir PI NNRTI Baseline drug susceptibility The proportion of patients with virus resistant to each drug at the fourfold and 0-fold cut-off level is shown in Figure. Of these, 65% of patients had viruses with resistance to zidovudine and 82% with resistance to lamivudine (fourfold cut-off). Cross-resistance to abacavir at the fourfold level was relatively frequent (54%), with 8% showing >0-fold resistance to abacavir. Resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) ranged from 40% to 70% (fourfold) and 35% to 70% (0-fold cut-off). For PIs, resistance at the fourfold level ranged from 52% to 66%, and at the 0-fold level, from 32% to 52%. Using a fourfold increase in IC 50 as a definition of resistance, nine (8%) patients had viruses that were resistant to one drug class (in their regimen), 23 (46%) had viruses that were resistant to two drug classes and 6 (32%) had viruses that were resistant to all three drug classes. If resistance was defined as a 0-fold increase in IC 50, the patient numbers were 5 (30%), 20 (40%) and 3 (26%), respectively. Of the 50 patients, 7 (34%) were treated with a maximum of one drug that their virus population was sensitive to (using the fourfold definition), 6 (32%) with two or three drugs, (22%) with four or five drugs and five with six or more drugs to which their virus population was sensitive. On-treatment drugs Table 3 lists the drugs included in the treatment Antiviral Therapy 5: 5

4 V Miller et al. regimen. 90% of the patients received six or seven drugs, and 0% received at least eight drugs. All patients received at least one PI and almost all patients (47/50 or 94%) received ritonavir. Ten patients received two, and 38 patients received three PIs. NNRTIs were prescribed for 60% of the patients. In terms of NRTIs, all patients received lamivudine, approximately half with zidovudine and half with stavudine. Didanosine was prescribed in 84% of cases. In terms of new drugs, six patients (2%) did not receive any new drugs. The number of patients receiving one new drug was 2 (24%), two new drugs was 5 (30%), three new drugs was eight (6%), four new drugs was six (2%) and five new drugs was three (6%). Follow-up The median length of follow-up on the original mega- HAART regimen was 3.4 weeks (range, ). Before week 6, 29 patients altered the mega-haart regimen. Eight patients met our definition of drop-out (four patients stopped all drugs and four added one drug) and were treated accordingly in the DAC and DAF analyses. The remaining 2 patients simplified their regimen without stopping all the drugs [the majority (4/2) dropping one drug] and they have been considered still on treatment at the time that week 24 viral load was measured. Treatment response The median maximum change in CD4 cell count was an increase of 07 cells/mm 3 (range, 0 389). CD4 cell counts were available for 25 patients within the week 24 window period, and the median change at this time point was an increase of 40 cells/mm 3 over baseline (range, to 20). The median maximum change in viral load was a decrease of 2.64 log 0 copies/ml (range, 5.37 to 0.79) compared to baseline. At week 24 (window period), the median overall change was.37 log 0 copies/ml (range, 5.37 to 0.79). Using the standardized data analysis plan definition, where drop-outs were classified as DAF, 39 patients experienced virological failure. In the analysis in which drop-outs were censored (DAC), eight patients were censored and 3 patients experienced virological failure. We considered several logistical regression models, including the confounding variables of baseline viral load and the number of new drugs in the regimen. Resistance was considered in terms of total phenotypic sensitivity score, as well as the NRTI sensitivity score or the PI sensitivity score. All models that included resistance were run using a cut-off definition of fourfold, and repeated using a cut-off definition of 0-fold. Figure 2 illustrates the odds ratios for each of the covariates in a univariate analysis. In the DAF analysis, Figure 2. Odds ratio for each of the covariates in a univariate analysis baseline viral load (OR 2.7, P=0.05), total phenotypic sensitivity score based on fourfold cut-off (OR 0.46, P=0.0002), total phenotypic sensitivity score based on 0-fold cut-off (OR 0.47, P=0.007), PI phenotypic sensitivity score based on fourfold cut-off (OR 0.55, P=0.04) and PI phenotypic sensitivity score based on 0-fold cut-off (OR 0.43, P=0.03), were significantly associated with virological failure at week 24. Very similar results were obtained if patients with changes in their treatment regimen were DAC. We next looked at four different multivariate models (Figures 3 and 4). The first two considered baseline viral load, number of new drugs and phenotypic sensitivity score. In the DAF analysis, baseline viral load remained significantly associated with virological failure after adjustment (OR 4.00, P=0.03 or OR 3.0, P=0.03), as did the total phenotypic sensitivity score, regardless of whether a fourfold (OR 0.35, P=0.0003) or 0-fold (OR 0.39, P=0.007) cut-off definition of resistance was used (Figure 3). These results did not change in the DAC analysis. In the next two models, we considered baseline viral load, number of new drugs, NRTI phenotypic sensitivity score and PI phenotypic sensitivity score, again using fourfold and 0-fold cut-off definitions of resistance. In these latter two models, only the PI phenotypic sensitivity score was significantly associated with virological failure, with an OR of 0.50 (P=0.03) using the fourfold cut-off and an OR of 0.39 (P=0.03) using the 0-fold cut-off definition (Figure 4). As with the other models, both DAF and DAC analyses yielded very similar results. Discussion New drugs New drugs Sens/0 Sens/0 NRTI NRTI PI PI NRTI Sens/0 NRTI Sens/0 PI Sens/0 PI Sens/0 Solid (black) symbols represent the DAF analysis; white symbols represent the DAC analysis. = baseline viral load; New drugs = number of new drugs in regimen; = total phenotypic sensitivity score at the fourfold level; Sens/0 = total phenotypic sensitivity score at the 0-fold level; NRTI = NRTI phenotypic sensitivity score at the fourfold level; NRTI Sens/0 = NRTI phenotypic sensitivity score at the 0-fold level; PI/Sens 4 = PI phenotypic sensitivity score at the fourfold level; PI/Sens 0 = PI phenotypic sensitivity score at the 0-fold level. In this retrospective analysis based on a relatively International Medical Press

5 Baseline susceptibility and response to mega-haart Figure 3. Multivariate analysis: odds ratio of virological failure considering baseline viral load (), number of new drugs (New drugs) and phenotypic sensitivity score (, Sens/0) Figure 4. Multivariate analysis: odds ratio of virological failure considering baseline viral load (), number of new drugs (New drugs), NRTI phenotypic sensitivity score (NRTI, NRTI Sens/0) and PI phenotypic sensitivity score (PI, PI Sens/0) 00 (a) (b) 00 (a) (b) New drugs New drugs Sens/0 0. New drugs NRTI PI New drugs NRTI Sens/0 PI Sens/0 (a) Fourfold cut-off level; (b) 0-fold cut-off level. Solid (black) symbols represent the DAF analysis; white symbols represent the DAC analysis. (a) Fourfold cut-off level; (b) 0-fold cut-off level. Solid (black) symbols represent the DAF analysis; white symbols represent the DAC analysis. small, extensively pre-treated patient population, viral susceptibility at baseline was strongly associated with virological outcome in a multi-drug salvage regimen. For each additional sensitive drug, the odds ratio of virological failure was reduced by approximately 60%, independent of baseline viral load and the number of new drugs in the treatment regimen. Thus, phenotypic resistance testing yielded prognostic information over and above that available through current standard of care (viral load, treatment history). This study is based on a cohort analysis, thus reflecting a clinical population as encountered in daily clinical practice. There were no restrictions as to prior treatments, nor restrictions on choice of follow-up medications as is frequently the case in patients meeting entry criteria for clinical trials. Thus, the distribution of covariates such as the number of new drugs allowed the incorporation of this parameter in the analyses. Baseline viral load also retained a significant, independent association with virological outcome in each of the models presented, whereas the number of new drugs was not significant in any of the models. The latter may be due to the fact that cross-resistance due to extensive pre-treatment played a strong role in the virological outcome within this patient population. It is of interest that definition of resistance as either a fourfold or a 0-fold increase in IC 50 did not make much difference to the outcome of the analysis. It should be noted that in this study, most patients received ritonavir in combination with other PIs, thus high plasma levels of PIs are to be expected (although this is one parameter that was not possible to investigate in this retrospective study) in comparison with single PI treatments. This may also explain in part why PI resistance and not NRTI resistance was associated with outcome when individual drug class susceptibility was adjusted for. In another study looking at response to ritonavir plus saquinavir, a fourfold increase in IC 50 to either drug appeared sufficient to compromise virological response [3], although a direct comparison with a 0-fold increase-based definition of resistance was not undertaken. Harrigan et al. also reported that resistance to PIs, but not NRTIs, was associated with virological failure. The lack of significant association between reduced susceptibility to the NRTI drug class and treatment outcome may also be related to factors such as longer pre-treatment periods with this class of drug and unexplained mechanisms of virological failure for some drugs, such as stavudine. Other studies have assessed resistance as defined by viral genotype and found significant associations with virological outcome, but few studies have compared both methods of assessing resistance [3,8,2]. Whether one is better suited than the other may depend on the extent of resistance (in other words, how complex the genotypic patterns are and the capacity to predict phenotypic resistance from the genotype). Patients with multiple treatment failures may have virus populations with very complex genotypic patterns, which may make the interpretation difficult. It will be of interest to analyse the genotypes in our patient population and to evaluate this method of resistance assessment using the method presented here. Mega-HAART regimens are not standardized, and although presented as possible options for management of patients with multiple treatment failure [23], they are complex to maintain. Pharmacokinetic interaction analysis of such multi-drug combinations have not been carried out, thus it is not possible to rule out the effect of positive and negative drug interactions. Toxicities need to be managed at an individual level, and regimen reductions are frequently required. In our study, 2 patients simplified the regimen by not stop- Antiviral Therapy 5: 53

6 V Miller et al. ping any drugs before week 6. However, the majority (n=4, 66.7%) dropped one drug, and as the proportion of patients experiencing virological failure was similar in patients who simplified compared to those who did not simplify (χ 2, P=0.26 in the DAF analysis and P=0.6 in the DAC analysis), this fact is not likely to negate our conclusions relating to the association of baseline resistance and treatment outcome. This being a retrospective analysis of a non-intervention based study, it does not absolutely prove the usefulness of resistance testing when used in patient management. However, several groups have recently presented data indicating that resistance testing, when incorporated into treatment management in addition to standard-of-care, may have positive influences on the virological response to new treatment regimen in pretreated patients [24,25]. Our data do, however, support the concept of using resistance testing in clinical practice. 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