Limited-Sampling Strategy for the Prediction of Boosted Hard-Gel Saquinavir Exposure at a Dosage of 1000/100 mg Twice Daily in Human Immunodeficiency Virus-Infected Individuals

Abstract

Area under the concentration time curve (AUC) over a dosing interval is considered to be the best estimate of drug exposure in a patient. However, determination of this parameter is costly and often impractical, requiring multiple samples and a great deal of time and resources. A limited-sampling strategy (LSS) may overcome some of these issues, making pharmacokinetic studies easier to perform, particularly in a limited-resource setting. The aim of this work was to develop and validate a pragmatic LSS for the accurate and precise prediction of boosted saquinavir AUC0-12 (AUC over the 12-hour dosing interval) at a dosage of 1000/100 mg twice daily. Pharmacokinetic data were obtained from 34 human immunodeficiency virus (HIV)-infected individuals stable on saquinavir/ritonavir-containing therapy, randomly split into two sets (n = 17 per set). One set was used to construct prediction models using univariate and multivariate analysis (development set), and the second was used to determine the predictive performance of the models (validation set). For single samples, 6- and 10-hour concentrations correlated best with saquinavir AUC0-12 (r2: 0.913 and 0.911, respectively), yet all single samples failed to produce precise and unbiased predictions. However, combinations at 2, 6; 0, 2, 6; 0, 4, 10; 0, 4, 12; and 2, 4, 6 hours achieved good predictive performances, and both precise [root mean squared relative prediction error (%RMSE): 6.4% to 11.9%] and unbiased [mean relative prediction error (%MPE), 95% CI: -2.7%, (-0.8)-2.7 to 1.6%, (-1.8)-4.7] estimations of saquinavir AUC0-12. Of these models, concentrations obtained at 0, 2, 6 and 2, 4, 6 hours are more practical in a clinical setting and are therefore the LSS with most potential. Provided that the technique is validated in specific patient populations, an LSS approach is a potentially useful tool to evaluate the AUC0-12 of saquinavir in resource-limited settings, reducing both costs and volumes of blood taken. It may also aid the choice of sampling times for population analysis.

Limited Sampling Strategies for the Estimation of Raltegravir Daily Exposure in HIV‐Infected Patients

“…This pharmacokinetic parameter can be predicted, however, by limited sampling strategies aimed at minimizing the number of blood samples needed 16 . Algorithms to define limited blood sampling strategies to ensure reliable AUC predictions have been proposed previously for monitoring of nevirapine, nelfinavir, and saquinavir 13 – 15 . No similar studies were available for RAL.…”

“…16 Algorithms to define limited blood sampling strategies to ensure reliable AUC predictions have been proposed previously for monitoring of nevirapine, nelfinavir, and saquinavir. [13][14][15] No similar studies were available for RAL. Here we developed for the first time LSS based on multiple regression analysis to predict RAL exposure using daily pharmacokinetic profiles from HIV patients given the drug in combination with atazanavir or with etravirine.…”

“…A suitable alternative approach would be the use of algorithms that allow the estimation of total AUC based on few blood samples. [13][14][15][16][17] These limited sampling strategies (LSS) have already been proposed for some antiretroviral agents including nevirapine, nelfinavir, and saquinavir, [13][14][15] whereas no data are available for RAL.…”

Limited sampling strategies for the estimation of atazanavir daily exposure in HIV‐infected patients

“…A suitable and alternative approach would be the use of algorithms that allow the estimation of total AUC based on few blood samples. These limited sampling strategies (LSS) have already been proposed for some antiretroviral agents including nevirapine, nelfinavir and saquinavir [16–18], whereas no data are available for ATV.…”

Limited-Sampling Strategies for Anti-Infective Agents: Systematic Review

“…23,24 Table 2 summarizes the characteristics of the included studies [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] according to their levels of evidence. The following information was extracted from each study: level of evidence, the antiinfective agent, the population for derivation of the limitedsampling strategy, the sampling times investigated, the suggested timed samples, the equation(s) for the limited-sampling strategy, r (the correlation coefficient) or r 2 , the percent bias for the validation group, the percent precision, and additional comments.…”