OBJECTIVES: The aim of this study was to develop and validate a population pharmacokinetic model in order to describe ritonavir-boosted saquinavir concentrations dosed twice and once daily in human immunodeficiency virus (HIV)-infected patients from the UK, Uganda and Thailand and to identify factors that may influence saquinavir pharmacokinetics. METHODS: Pharmacokinetic data from 10 clinical studies were combined. Non-linear mixed effects modelling (NONMEM version V) was applied to determine the saquinavir pharmacokinetic parameters, interindividual/interoccasion variability (IIV/IOV) and residual error. Various covariates potentially related to saquinavir pharmacokinetics were explored, and the final model was validated by means of 95% prediction interval and testing the predictive performance of the model with data not included in the model-building process. RESULTS: Ninety-seven patients were included from the UK (n = 52), Uganda (n = 18) and Thailand (n = 27), contributing 347 saquinavir profiles (1-14 profiles per patient). A one-compartment model with zero-order absorption and lag-time best described the data with IIV/IOV on apparent oral clearance (CL/F) and volume of distribution (V/F) and with IIV on duration and absorption lag-time. The ritonavir area under the curve over the dosing interval was significantly associated with saquinavir CL/F and V/F. A typical patient from the UK had approximately 1.5- and 3-fold higher saquinavir CL/F compared with patients from Uganda (89.0 versus 49.8 L/h) and Thailand (89.0 versus 26.7 L/h), respectively. CONCLUSIONS: A model to characterize ritonavir-boosted saquinavir pharmacokinetics in HIV-infected adults has been developed and validated. The model could be used for dosage adaptation following therapeutic drug monitoring and to assess patients' suitability for once-daily boosted saquinavir therapy.
OBJECTIVES: The aim of this study was to develop and validate a population pharmacokinetic model in order to describe ritonavir-boosted saquinavir concentrations dosed twice and once daily in humanimmunodeficiency virus (HIV)-infectedpatients from the UK, Uganda and Thailand and to identify factors that may influence saquinavir pharmacokinetics. METHODS: Pharmacokinetic data from 10 clinical studies were combined. Non-linear mixed effects modelling (NONMEM version V) was applied to determine the saquinavir pharmacokinetic parameters, interindividual/interoccasion variability (IIV/IOV) and residual error. Various covariates potentially related to saquinavir pharmacokinetics were explored, and the final model was validated by means of 95% prediction interval and testing the predictive performance of the model with data not included in the model-building process. RESULTS: Ninety-seven patients were included from the UK (n = 52), Uganda (n = 18) and Thailand (n = 27), contributing 347 saquinavir profiles (1-14 profiles per patient). A one-compartment model with zero-order absorption and lag-time best described the data with IIV/IOV on apparent oral clearance (CL/F) and volume of distribution (V/F) and with IIV on duration and absorption lag-time. The ritonavir area under the curve over the dosing interval was significantly associated with saquinavir CL/F and V/F. A typical patient from the UK had approximately 1.5- and 3-fold higher saquinavir CL/F compared with patients from Uganda (89.0 versus 49.8 L/h) and Thailand (89.0 versus 26.7 L/h), respectively. CONCLUSIONS: A model to characterize ritonavir-boosted saquinavir pharmacokinetics in HIV-infected adults has been developed and validated. The model could be used for dosage adaptation following therapeutic drug monitoring and to assess patients' suitability for once-daily boosted saquinavir therapy.
Authors: David Back; Giorgio Gatti; Courtney Fletcher; Rodolphe Garaffo; Richard Haubrich; Richard Hoetelmans; Michael Kurowski; Andrew Luber; Concepta Merry; Carlo-Federico Perno Journal: AIDS Date: 2002-03 Impact factor: 4.177
Authors: Peter G Cardiello; Tarkika Monhaphol; Apicha Mahanontharit; Rolf P van Heeswijk; David Burger; Andrew Hill; Kiat Ruxrungtham; Joep M Lange; David A Cooper; Praphan Phanuphak Journal: J Acquir Immune Defic Syndr Date: 2003-04-01 Impact factor: 3.731
Authors: Marta Boffito; Laura Dickinson; Andrew Hill; David Back; Graeme Moyle; Mark Nelson; Chris Higgs; Carl Fletcher; Sundhiya Mandalia; Brian Gazzard; Anton Pozniak Journal: Antivir Ther Date: 2004-06
Authors: Laura Dickinson; Marta Boffito; Saye H Khoo; Malte Schutz; Leon J Aarons; Anton L Pozniak; David J Back Journal: J Antimicrob Chemother Date: 2008-05-07 Impact factor: 5.790
Authors: Courtney V Fletcher; Hongyu Jiang; Richard C Brundage; Edward P Acosta; Richard Haubrich; David Katzenstein; Roy M Gulick Journal: J Infect Dis Date: 2004-03-16 Impact factor: 5.226
Authors: H Knechten; C Stephan; F A Mosthaf; H Jaeger; A Carganico; T Lutz; K Schewe; C Mayr; E Wolf; E Wellmann; A Tappe Journal: Eur J Med Res Date: 2011-03-28 Impact factor: 2.175
Authors: Mona Rafik Loutfy; Sharon Lynn Walmsley; Marina Barbara Klein; Janet Raboud; Alice Lin-In Tseng; Sandra Lauren Blitz; Neora Pick; Brian Conway; Jonathan Benjamin Angel; Anita Rochelle Rachlis; Kevin Gough; Jeff Cohen; David Haase; David Burdge; Fiona Mary Smaill; Alexandra de Pokomandy; Hugues Loemba; Sylvie Trottier; Charles Jean la Porte Journal: BMC Infect Dis Date: 2013-06-03 Impact factor: 3.090