Different Methods to Calculate the Inhibitory Qoutient of Boosted Single Protease Inhibitors and Their Association With Virological Response

Winston, Alan MRCP*; Patel, Nimesh*; Back, David†; Khoo, Saye†; Bulbeck, Steve*; Mandalia, Sundhiya*; Pozniak, Anton L.*; Nelson, Mark*; Moyle, Graeme*; Gazzard, Brian*; Boffito, Marta*

JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/01.qui.0000209910.27997.d9
Letters to the Editor
Author Information

*St. Stephens Centre, Chelsea and Westminster Hospital, London, UK †Department of Pharmacology University of Liverpool, UK

Article Outline

To the Editor:

In individuals with incomplete suppression of HIV replication, a major clinical challenge is to establish which agents retain significant antiviral activity. Measurements of both plasma concentration and resistance of selected antiretroviral agents are widely available. Clinical studies have shown short-term virologic benefit associated with the use of resistance testing in drug-experienced patients; however, no clinical benefit has been observed with the use of plasma drug concentration monitoring in treated individuals.1

Inhibitory quotient (IQ), a measure of plasma drug exposure corrected by the degree of resistance, may enhance the result of HIV resistance tests by correcting for plasma drug exposure. Several methods to calculate IQ have been proposed including genotypic IQ (GIQ), virtual IQ (VIQ), and normalized IQ (NIQ), where the ratio of plasma drug exposure to a measure of genotypic resistance, virtual phenotypic resistance, or a calculated population IQ are calculated, respectively. Significant associations have been described between virologic response and the GIQ, VIQ, and NIQ.2-6 Despite these studies, there is a lack of standardization in the calculation of IQ, and there are no data on the benefit of 1 calculation compared with another which limits their use in clinical practice.

The aim of this study was to assess the association between virologic response in a cohort of HIV-1 treatment-experienced patients changing antiretroviral therapy because of virologic failure or intolerance within a prospective cohort and a range of covariates including the GIQ, VIQ, and NIQ.

The Therapeutic Drug Monitoring (TDM) Study is an ongoing prospective study assessing the usefulness of measuring protease inhibitor (PI) and nonnucleoside reverse transcriptase inhibitor trough plasma concentrations (Ctrough) in HIV-1 positive patients commencing new antiretroviral regimens at the Chelsea and Westminster Hospital, London, United Kingdom. Subjects recruited for the study underwent blood sampling to assess plasma PI and nonnucleoside reverse transcriptase inhibitor Ctrough at 4, 24, and 48 weeks. As part of a planned 24-week analysis, all antiretroviral-exposed subjects who had commenced a new ritonavir-boosted single PI regimen who enrolled in the TDM study were included in the current analysis.

All subjects had an HIV resistance test before commencing antiretroviral therapy and when changing therapy because of virologic failure. For the purpose of this analysis, the most recent HIV resistance test within 6 weeks of changing antiretroviral therapy was used. Fold change (FC) for each PI was determined from the predicted FC virtual phenotype report.

The Ctrough of the selected PI was determined using a validated highperformance liquid chromatography/ tandem mass spectrometry (HPLC-MS/MS) assay.7 The IQs were calculated as the ratio of the TDM study week 4 PI Ctrough to the following: (1) the number of major PI mutations for major genotypic IQ (majGIQ), (2) the number of total PI mutations (both major and minor mutations) for total genotypic IQ (totGIQ), (3) the virtual phenotype FC for VIQ, (4) the VIQ divided by population IQ for NIQ. For the calculation of both totGIQ and majGIQ, major and minor mutations in HIV protease on genotypic sequencing were considered significant as per International AIDS Society guidelines.8 A value of 1 was added to the number of PI mutations present to allow the subjects with no mutations to be included in the analysis. The FC to calculate VIQ for each PI was determined from the virtual phenotype result (Virco Type HIV-1 reports). The recently described 80% clinical cutoff9 and published population Ctrough10 levels were used to calculate population IQ for the correction of VIQ to NIQ.

Predictors of time-weighted change in HIV RNA for 48 weeks were determined using linear regression modeling. To allow comparisons of Ctrough, majGIQ, totGIQ, and VIQ for different PIs, these parameters were reported as the normal standard deviate (z transformation).

Between June 2004 and August 2005, 98 patients were enrolled in the TDM study. Fifty-three subjects were changing therapy to a ritonavir-boosted single PI regimen. Of the 53 patients, plasma HIV RNA was below 50 copies/mL in 18 (34%) and 3.68 log10 copies/mL (median) in the remaining patients. Mean number of previous antiretroviral regimens in this cohort was 5 (range, 4-8). The new PI is composed of atazanavir, amprenavir, fosamprenavir, lopinavir, and saquinavir in 18 (34%), 5 (9%), 4 (8%), 15 (28%), and 11 (21%) patients, respectively. No patient included in this analysis underwent any changes to antiretroviral therapy since enrollment or had withdrawn from the study at the time of analysis.

Despite the 32 patients (60%) in this cohort being exposed to PIs, the number of major and total PI mutations were low (median, 1; range, 0-8).

Mean time-weighted change in HIV RNA for 48 weeks was −1.82 log10 copies/mL. Factors associated with time-weighted change in HIV RNA are shown in Table 1. In a multivariate analysis, only baseline HIV RNA and NIQ were significantly associated with time-weighted change in HIV RNA.

In this cohort of treatment-exposed HIV-1 patients changing antiretroviral therapy to a boosted single PI regimen, baseline HIV RNA and NIQ were significantly associated with virologic response; whereas Ctrough, majGIQ, totGIQ, and VIQ were not significantly associated.

A significant reduction in virologic response has been described in patients harboring viral isolates with more than 6 PI-associated mutations to lopinavir2 and amprenavir3 and with more than 4 mutations to saquinavir4 when subjects were treated with these PIs boosted with ritonavir. In our study, we have not observed significant associations between majGIQ and virologic response. These differences may be explained by differences in PI resistance patterns between our cohort and other reports. Our cohort, although highly treatment-experienced (previous regimens median, 5), had lower number of patients with viral isolates harboring PI-associated mutations with only 10 (19%) and 24 (45%) patients with isolates carrying major or any PI mutations, respectively. Indeed, many subjects in our cohort had no PI-associated resistance observed. This may explain the lack of association between GIQ and VIQ with virologic response.

Our cohort is representative of patients undergoing changes in antiretroviral therapy because of intolerance or virologic failure and currently attending for care in a large UK HIV treatment center, and the associations observed in our cohort between different calculations for the IQ and virologic response may be representative of patients changing therapy in a real life situation rather than in clinical trials.

The NIQ may be a more sensitive marker in our cohort because of the correction of the IQ using the 80% clinical cutoffs FC based on clinical outcome. The recently described 80% clinical cutoffs are the FC associated with an 80% reduction in expected virologic response from wild type based on the results of more than 13,000 viral isolates.

Being corrected by population parameters, NIQ can be compared across several PIs without mathematical correction, whereas other IQ calculations require correction before comparisons can be made (normal standard deviate used in our study).

In summary, we have found NIQ to be associated with virologic response in treatment-exposed patients commencing single-boosted PI regimen within a clinical setting, whereas other parameters used to calculate the IQ were not significantly associated. Further prospective studies assessing the use of NIQ are warranted.

Alan Winston, MRCP*

Nimesh Patel*

David Back†

Saye Khoo†

Steve Bulbeck*

Sundhiya Mandalia*

Anton L. Pozniak*

Mark Nelson*

Graeme Moyle*

Brian Gazzard*

Marta Boffito*

*St. Stephens Centre, Chelsea

and Westminster Hospital

London, UK

†Department of Pharmacology

University of Liverpool, Liverpool,UK

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1. Durant J, Clevenbergh P, Halfon P, et al. Drug-resistance genotyping in HIV-1 therapy: the VIRADAPT randomised controlled trial. Lancet. 1999;353:2195-2199.
2. Gonzalez de Requena D, Gallego O, Valer L, Jimenez-Nacher I, Soriano V. Prediction of virological response to lopinavir/ritonavir using the genotypic inhibitory quotient. AIDS Res Hum Retroviruses. 2004; 20: 275-278.
3. Marcelin AG, Lamotte C, Delaugerre C, et al. Genotypic inhibitory quotient as predictor of virological response to ritonavir-amprenavir in human immunodeficiency virus type 1 protease inhibitor-experienced patients. Antimicrob Agents Chemother. 2003;47:594-600.
4. Valer L, De Mendoza C, De Requena DG, et al. Impact of HIV genotyping and drug levels on the response to salvage therapy with saquinavir/ritonavir. AIDS. 2002;16:1964-1966.
5. Shulman N, Zolopa A, Havlir D, et al. Virtual inhibitory quotient predicts response to ritonavir boosting of indinavir-based therapy in human immunodeficiency virus-infected patients with ongoing viremia. Antimicrob Agents Chemother. 2002;46:3907-3916.
6. Castagna A, Gianotti N, Galli L, et al. The NIQ of lopinavir is predictive of a 48-week virological response in highly treatment-experienced HIV-1-infected subjects treated with a lopinavir/ritonavir-containing regimen. Antivir Ther. 2004;9: 537-543.
7. Dickinson L, Robinson L, Tjia J, et al. Simultaneous determination of HIV protease inhibitors amprenavir, atazanavir, indinavir, lopinavir, nelfinavir, ritonavir and saquinavir in human plasma by high-performance liquid chromatography-tandem mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2005;829(1-2):82-90.
8. Johnson VA, Brun-Vezinet F, Clotet B, et al. Update of the drug resistance mutations in HIV-1: 2004. Top HIV Med. 2004;12:119-124.
9. Bacheler LT, Winters B, Nauwelaers D, et al. Estimation of phenotypic clinical cutoffs for VirtualPhenotype through meta analyses of clinical trial and cohort data. Antivir Ther. 2004;9:Abstract 138.
10. Montaner J, Hill A, Acosta E. Practical implications for the interpretation of minimum plasma concentration/inhibitory concentration ratios. Lancet. 2001;357:1438-1440.
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