title: Development of a model based on body composition to predict drug kinetics-1 evaluation in healthy volunteers.
abstract: A kinetic model for widely used drugs, based on body composition analysis, was developed and evaluated in young and elderly healthy individuals. Body composition was studied by body impedance analysis (BIA). Antipyrine, amlodipine, digoxin and tobramycin kinetics, liver microsomal activity enzyme (lidocaine/MEGX test), and appropriate clinical and laboratory tests were carried out. Major variables (V(d), AUC, t(1/2), C(max), Cl) for these drugs were calculated, and the possible relationships with the other clinical and biochemical data were analyzed by the Pearson's moment correlation, forecasting models being then obtained by a multiple linear regression method. Major kinetic parameters, particularly for the mixed elimination drugs (liver/renal), i.e. digoxin and amlodipine, proved to be well correlated to data collected during the study, in particular with body structure parameters. Results were less satisfactory in the case of the mainly renally handled tobramycin. Mathematical models to forecast the kinetic behaviors of the three chosen drugs, using readily accessible data, showed both in the young and the elder, as well as in the whole examined population, very satisfactory correlations in the case of digoxin (R(2) ranging from 0.89 to 0.85) and amlodipine (R(2) between 0.81 and 0.91), less satisfactory (with a wide range of R(2), from 0.65 to 0.94), in the case of tobramycin.
authors.affiliation: Department of Pharmacological Sciences, Grossi Paoletti Center, University of Milano, Via Balzaretti 9, 20133 Milan, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Pazzucconi
authors.forename: S
authors.initials: S
authors.lastname: Ferrara
authors.forename: A
authors.initials: A
authors.lastname: Bondioli
authors.forename: F
authors.initials: F
authors.lastname: Zoppi
authors.forename: R
authors.initials: R
authors.lastname: Yeates
authors.forename: C
authors.initials: C
authors.lastname: De Rosa
authors.forename: G
authors.initials: G
authors.lastname: Mombelli
authors.forename: L
authors.initials: L
authors.lastname: Calabresi
authors.forename: C R
authors.initials: CR
authors.lastname: Sirtori
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D017311:Amlodipine / Q000493:pharmacokinetics; D000779:Anesthetics, Local / Q000493:pharmacokinetics; D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics; D000959:Antihypertensive Agents / Q000493:pharmacokinetics; D019540:Area Under Curve; D001823:Body Composition / Q000502:physiology*; D001834:Body Water / Q000502:physiology; D002316:Cardiotonic Agents / Q000493:pharmacokinetics; D004077:Digoxin / Q000493:pharmacokinetics; D017097:Electric Impedance; D006207:Half-Life; D006801:Humans; D007424:Intracellular Fluid / Q000502:physiology; D008012:Lidocaine / Q000493:pharmacokinetics; D008954:Models, Biological; D010599:Pharmacokinetics; D014031:Tobramycin / Q000493:pharmacokinetics
chemical_list: D000779:Anesthetics, Local; D000900:Anti-Bacterial Agents; D000959:Antihypertensive Agents; D002316:Cardiotonic Agents; D017311:Amlodipine; D004077:Digoxin; D008012:Lidocaine; D014031:Tobramycin