title: Population pharmacokinetics of lopinavir/ritonavir (Kaletra) in HIV-infected patients.
abstract: BACKGROUND
  A relationship between plasma concentrations and viral suppression in patients receiving lopinavir (LPV)/ritonavir (RTV) has been observed. Therefore, it is important to increase our knowledge about factors that determine interpatient variability in LPV pharmacokinetics (PK).
  
  
  METHODS
  The study, designed to develop and validate population PK models for LPV and RTV, involved 263 ambulatory patients treated with 400/100 mg of LPV/RTV twice daily. A database of 1110 concentrations of LPV and RTV (647 from a single time-point and 463 from 73 full PK profiles) was available. Concentrations were determined at steady state using high-performance liquid chromatography with ultraviolet detection. PK analysis was performed with NONMEM software. Age, gender, height, total body weight, body mass index, RTV trough concentration (RTC), hepatitis C virus coinfection, total bilirubin, hospital of origin, formulation and concomitant administration of efavirenz (EFV), saquinavir (SQV), atazanavir (ATV), and tenofovir were analyzed as possible covariates influencing LPV/RTV kinetic behavior.
  
  
  RESULTS
  Population models were developed with 954 drug plasma concentrations from 201 patients, and the validation was conducted in the remaining 62 patients (156 concentrations). A 1-compartment model with first-order absorption (including lag-time) and elimination best described the PK. Proportional error models for interindividual and residual variability were used. The final models for the drugs oral clearance (CL/F) were as follows: CL/F(LPV)(L/h)=0.216·BMI·0.81(RTC)·1.25(EFV)·0.84(ATV); CL/F(RTV)(L/h) = 8.00·1.34(SQV)·1.77(EFV)·1.35(ATV). The predictive performance of the final population PK models was tested using standardized mean prediction errors, showing values of 0.03 ± 0.74 and 0.05 ± 0.91 for LPV and RTV, and normalized prediction distribution error, confirming the suitability of both models.
  
  
  CONCLUSIONS
  These validated models could be implemented in clinical PK software and applied to dose individualization using a Bayesian approach for both drugs.
authors.affiliation: Pharmacy Service, University Hospital of Salamanca, Salamanca, Spain.
authors.forename: Elena
authors.initials: E
authors.lastname: López Aspiroz
authors.forename: Dolores
authors.initials: D
authors.lastname: Santos Buelga
authors.forename: Salvador
authors.initials: S
authors.lastname: Cabrera Figueroa
authors.forename: Rosa María
authors.initials: RM
authors.lastname: López Galera
authors.forename: Esteban
authors.initials: E
authors.lastname: Ribera Pascuet
authors.forename: Alfonso
authors.initials: A
authors.lastname: Domínguez-Gil Hurlé
authors.forename: María José
authors.initials: MJ
authors.lastname: García Sánchez
mesh_terms: D000328:Adult; D019380:Anti-HIV Agents / Q000493:pharmacokinetics* / Q000627:therapeutic use; D004338:Drug Combinations; D016903:Drug Monitoring; D005260:Female; D015658:HIV Infections / Q000097:blood / Q000188:drug therapy* / Q000378:metabolism; D006801:Humans; D061466:Lopinavir / Q000493:pharmacokinetics* / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015203:Reproducibility of Results; D019438:Ritonavir / Q000493:pharmacokinetics* / Q000627:therapeutic use
chemical_list: D019380:Anti-HIV Agents; D004338:Drug Combinations; C558899:lopinavir-ritonavir drug combination; D061466:Lopinavir; D019438:Ritonavir