OBJECTIVES: This study was designed to identify common HIV-1 mutation complexes affecting the slope of inhibition curve, and to propose a new parameter incorporating both the IC50 and the slope to evaluate phenotypic resistance. METHODS: Utilizing site-directed mutagenesis, we constructed 22 HIV-1 common mutation complexes. IC50 and slope of 10 representative approved drugs and a novel agent against these mutations were measured to determine the resistance phenotypes. The values of new parameter incorporating both the IC50 and the slope of the inhibition curve were calculated, and the correlations between parameters were assessed. RESULTS: Depending on the class of drug, there were intrinsic differences in how the resistance mutations affected the drug parameters. All of the mutations resulted in large increases in the IC50s of nucleoside reverse transcriptase inhibitors. The effects of the mutations on the slope were the most apparent when examining their effects on the inhibition of non-nucleoside reverse transcriptase inhibitors and protease inhibitors. For example, some mutations, such as V82A, had no effect on IC50, but reduced the slope. We proposed a new concept, termed IIPatoxic, on the basis of IC50, slope and the maximum limiting concentrations of the drug. The IIPatoxic values of 10 approved drugs and 1 novel agent were calculated, and were closely related to the IIPmax values (r > 0.95, p < 0.001). CONCLUSIONS: This study confirms that resistance mutations cannot be accurately assessed by IC50 alone, because it tends to underestimate the degree of resistance. The slope parameter is of very importance in the measurement of drug resistance and the effect can be applied to more complex patterns of resistance. This is the most apparent when testing the effects of the mutations on protease inhibitors activity. We also propose a new index, IIPatoxic, which incorporates both the IC50 and the slope. This new index could complement current IIP indices, thereby enabling predict the efficacy of pre-clinical drugs for which human pharmacokinetic is not available.
OBJECTIVES: This study was designed to identify common HIV-1 mutation complexes affecting the slope of inhibition curve, and to propose a new parameter incorporating both the IC50 and the slope to evaluate phenotypic resistance. METHODS: Utilizing site-directed mutagenesis, we constructed 22 HIV-1 common mutation complexes. IC50 and slope of 10 representative approved drugs and a novel agent against these mutations were measured to determine the resistance phenotypes. The values of new parameter incorporating both the IC50 and the slope of the inhibition curve were calculated, and the correlations between parameters were assessed. RESULTS: Depending on the class of drug, there were intrinsic differences in how the resistance mutations affected the drug parameters. All of the mutations resulted in large increases in the IC50s of nucleoside reverse transcriptase inhibitors. The effects of the mutations on the slope were the most apparent when examining their effects on the inhibition of non-nucleoside reverse transcriptase inhibitors and protease inhibitors. For example, some mutations, such as V82A, had no effect on IC50, but reduced the slope. We proposed a new concept, termed IIPatoxic, on the basis of IC50, slope and the maximum limiting concentrations of the drug. The IIPatoxic values of 10 approved drugs and 1 novel agent were calculated, and were closely related to the IIPmax values (r > 0.95, p < 0.001). CONCLUSIONS: This study confirms that resistance mutations cannot be accurately assessed by IC50 alone, because it tends to underestimate the degree of resistance. The slope parameter is of very importance in the measurement of drug resistance and the effect can be applied to more complex patterns of resistance. This is the most apparent when testing the effects of the mutations on protease inhibitors activity. We also propose a new index, IIPatoxic, which incorporates both the IC50 and the slope. This new index could complement current IIP indices, thereby enabling predict the efficacy of pre-clinical drugs for which human pharmacokinetic is not available.
Authors: C J Petropoulos; N T Parkin; K L Limoli; Y S Lie; T Wrin; W Huang; H Tian; D Smith; G A Winslow; D J Capon; J M Whitcomb Journal: Antimicrob Agents Chemother Date: 2000-04 Impact factor: 5.191
Authors: Xiping Wei; Julie M Decker; Hongmei Liu; Zee Zhang; Ramin B Arani; J Michael Kilby; Michael S Saag; Xiaoyun Wu; George M Shaw; John C Kappes Journal: Antimicrob Agents Chemother Date: 2002-06 Impact factor: 5.191
Authors: A J Japour; D L Mayers; V A Johnson; D R Kuritzkes; L A Beckett; J M Arduino; J Lane; R J Black; P S Reichelderfer; R T D'Aquila Journal: Antimicrob Agents Chemother Date: 1993-05 Impact factor: 5.191
Authors: X Wei; S K Ghosh; M E Taylor; V A Johnson; E A Emini; P Deutsch; J D Lifson; S Bonhoeffer; M A Nowak; B H Hahn Journal: Nature Date: 1995-01-12 Impact factor: 49.962