OBJECTIVES: The protease inhibitor atazanavir (ATV) can be used either boosted by ritonavir (ATV300/r) or unboosted (ATV400). To date, however, genotypic resistance scores (GRSs) have been developed only for boosted-ATV. We have determined GRS associated with virologic response (VR) for both ATV300/r and ATV400 in highly pre-treated HIV-1 infected patients. PATIENTS AND METHODS: We analyzed the results of genotypic tests available 0-3 months before the initiation of an ATV-containing regimen in 159 patients with HIV-RNA >or= 500 copies/ml (ATV300/r group: 74; ATV400 group: 85) who were enrolled in the CARe study through an Early Access Program. The impact of baseline protease mutations on VR (>or= 1 log(10)copies/ml HIV-RNA decrease at 12-24 weeks) was analyzed using Fisher's exact test. Mutated protease amino acid positions (MPP) with p < 0.20 were retained for further analysis. The GRSs were determined by a step-by-step analysis using the chi(2) test for trend. RESULTS: The GRSs for ATV300/r and ATV400 revealed differing sets of mutations. For ATV300/r, 12 MPPs (10C/I/V + 32I + 34Q + 46I/L + 53L + 54A/M/V + 82A/F/I/T + 84V + 90M - 15E/G/L/V - 69K/M/N/Q/R/T/Y - 72M/ T/V; p = 1.38 x 10(-9)) were the most strongly associated with VR (VR: 100%, 78.3%, 83.3%, 75% and 0% of patients with a score of -2/-1, 0, 1, 2, and >or= 3, respectively); the last three MPPs (I15/H69/I72) were associated with a better VR. For ATV400, nine MPPs (16E + 20I/M/R/T/V + 32I + 33F/I/V + 53L/Y + 64L/M/ V + 71I/T/V + 85V + 93L/M; p = 9.42 x 10(-8)) were most strongly associated with VR (VR: 83.3%, 66.7%, 5.9%, 0% of patients with 0, 1/2, 3, and >or= 4 MPP, respectively). Differences between GRSs for ATV300/r and ATV400 may be due to different ATV drug levels (boosted vs unboosted), favoring different pathways of escape from antiviral pressure. CONCLUSIONS: Both GRSs were independent predictors of response in a multivariable logistic regression model. Nevertheless, cross-validation of these GRSs on different patient databases is required before their implementation in clinical practice.
OBJECTIVES: The protease inhibitor atazanavir (ATV) can be used either boosted by ritonavir (ATV300/r) or unboosted (ATV400). To date, however, genotypic resistance scores (GRSs) have been developed only for boosted-ATV. We have determined GRS associated with virologic response (VR) for both ATV300/r and ATV400 in highly pre-treated HIV-1 infectedpatients. PATIENTS AND METHODS: We analyzed the results of genotypic tests available 0-3 months before the initiation of an ATV-containing regimen in 159 patients with HIV-RNA >or= 500 copies/ml (ATV300/r group: 74; ATV400 group: 85) who were enrolled in the CARe study through an Early Access Program. The impact of baseline protease mutations on VR (>or= 1 log(10)copies/ml HIV-RNA decrease at 12-24 weeks) was analyzed using Fisher's exact test. Mutated protease amino acid positions (MPP) with p < 0.20 were retained for further analysis. The GRSs were determined by a step-by-step analysis using the chi(2) test for trend. RESULTS: The GRSs for ATV300/r and ATV400 revealed differing sets of mutations. For ATV300/r, 12 MPPs (10C/I/V + 32I + 34Q + 46I/L + 53L + 54A/M/V + 82A/F/I/T + 84V + 90M - 15E/G/L/V - 69K/M/N/Q/R/T/Y - 72M/ T/V; p = 1.38 x 10(-9)) were the most strongly associated with VR (VR: 100%, 78.3%, 83.3%, 75% and 0% of patients with a score of -2/-1, 0, 1, 2, and >or= 3, respectively); the last three MPPs (I15/H69/I72) were associated with a better VR. For ATV400, nine MPPs (16E + 20I/M/R/T/V + 32I + 33F/I/V + 53L/Y + 64L/M/ V + 71I/T/V + 85V + 93L/M; p = 9.42 x 10(-8)) were most strongly associated with VR (VR: 83.3%, 66.7%, 5.9%, 0% of patients with 0, 1/2, 3, and >or= 4 MPP, respectively). Differences between GRSs for ATV300/r and ATV400 may be due to different ATV drug levels (boosted vs unboosted), favoring different pathways of escape from antiviral pressure. CONCLUSIONS: Both GRSs were independent predictors of response in a multivariable logistic regression model. Nevertheless, cross-validation of these GRSs on different patient databases is required before their implementation in clinical practice.
Authors: B Gazzard; A J Bernard; M Boffito; D Churchill; S Edwards; N Fisher; A M Geretti; M Johnson; C Leen; B Peters; A Pozniak; J Ross; J Walsh; E Wilkins; M Youle Journal: HIV Med Date: 2006-11 Impact factor: 3.180
Authors: Margaret Johnson; Beatriz Grinsztejn; Claudia Rodriguez; Jeffrey Coco; Edwin DeJesus; Adriano Lazzarin; Kenneth Lichtenstein; Victoria Wirtz; Anna Rightmire; Linda Odeshoo; Colin McLaren Journal: AIDS Date: 2006-03-21 Impact factor: 4.177
Authors: G Sterrantino; M Zaccarelli; G Colao; F Baldanti; S Di Giambenedetto; T Carli; F Maggiolo; M Zazzi Journal: Infection Date: 2012-01-12 Impact factor: 3.553
Authors: David I Dolling; David T Dunn; Katherine A Sutherland; Deenan Pillay; Jean L Mbisa; Chris M Parry; Frank A Post; Caroline A Sabin; Patricia A Cane Journal: J Antimicrob Chemother Date: 2013-05-27 Impact factor: 5.790