title: [Modeling and simulation activities to design sampling scheme for population pharmacokinetic study on amlodipine].
abstract: Reasonable sampling scheme is the important basis for establishing reliable population pharmacokinetic model. It is an effective method for estimation of population pharmacokinetic parameters with sparse data to perform population pharmacokinetic analysis using the nonlinear mixed-effects models. We designed the sampling scheme for amlodipine based on D-optimal sampling strategy and Bayesian estimation method. First, optimized sample scenarios were designed using WinPOPT software according to the aim, dosage regimen and visit schedule of the clinical study protocol, and the amlodipine population model reported by Rohatagi et al. Second, we created a NONMEM-formatted dataset (n = 400) for each sample scenario via Monte Carlo simulation. Third, the estimation of amlodipine pharmacokinetic parameters (clearance (CL/F), volume (V/F) and Ka) was based on the simulation results. All modeling and simulation exercises were conducted with NONMEM version 7.2. Finally, the accuracy and precision of the estimated parameters were evaluated using the mean prediction error (MPE) and the mean absolute error (MAPE), respectively. Among the 6 schemes, schemes 6 and 3 have good accuracy and precision. MPE is 0.1% for scheme 6 and -0.6% for scheme 3, respectively. MAPE is 0.7% for both schemes. There is no significant difference in MPE and MAPE of volume among them. Therefore, we select scheme 3 as the final sample scenario because it has good accuracy and precision and less sample points. This research aims to provide scientific and effective sampling scheme for population pharmacokinetic (PK) study of amlodipine in patients with renal impairment and hypertension, provide a scientific method for an optimum design in clinical population PK/PD (pharmacodynamics) research.
authors.affiliation: The Third Xiangya Hospital of Central South University, Changsha 410013, China. zuoxc08@126.com
authors.forename: Xiao-Cong
authors.initials: XC
authors.lastname: Zuo
authors.forename: Hong
authors.initials: H
authors.lastname: Yuan
authors.forename: Bi-Kui
authors.initials: BK
authors.lastname: Zhang
authors.forename: Chee M
authors.initials: CM
authors.lastname: Ng
authors.forename: Jeff S
authors.initials: JS
authors.lastname: Barrett
authors.forename: Guo-Ping
authors.initials: GP
authors.lastname: Yang
authors.forename: Zhi-Jun
authors.initials: ZJ
authors.lastname: Huang
authors.forename: Qi
authors.initials: Q
authors.lastname: Pei
authors.forename: Ren
authors.initials: R
authors.lastname: Guo
authors.forename: Ya-Nan
authors.initials: YN
authors.lastname: Zhou
authors.forename: Ning-Ning
authors.initials: NN
authors.lastname: Jing
authors.forename: Wu
authors.initials: W
authors.lastname: Di
mesh_terms: D000328:Adult; D000367:Age Factors; D000410:Alanine Transaminase / Q000097:blood; D017311:Amlodipine / Q000493:pharmacokinetics* / Q000494:pharmacology; D000959:Antihypertensive Agents / Q000493:pharmacokinetics* / Q000494:pharmacology; D001499:Bayes Theorem; D001835:Body Weight; D002121:Calcium Channel Blockers / Q000493:pharmacokinetics* / Q000494:pharmacology; D006801:Humans; D006973:Hypertension / Q000378:metabolism*; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D051437:Renal Insufficiency / Q000378:metabolism; D012984:Software
chemical_list: D000959:Antihypertensive Agents; D002121:Calcium Channel Blockers; D017311:Amlodipine; D000410:Alanine Transaminase