Model-based meta-analysis for development of a population-pharmacokinetic (PPK) model for Vitamin D3 and its 25OHD3 metabolite using both individual and arm-level data

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• 作者：Alanna S. Ocampo-Pelland ; Marc R. Gastonguay…
• 关键词：Vitamin D3 ; 25 ; Hydroxyvitamin D3 ; Pharmacokinetics ; Nonlinear mixed effects model ; Meta ; analysis ; 25OHD3 assays
• 刊名：Journal of Pharmacokinetics and Pharmacodynamics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：43
• 期：2
• 页码：191-206
• 全文大小：720 KB
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• 作者单位：Alanna S. Ocampo-Pelland (1) (3)
  Marc R. Gastonguay (1) (2) (3)
  Jonathan F. French (2)
  Matthew M. Riggs (2)
  
  1. Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
  3. Metrum Institute, Tariffville, CT, USA
  2. Metrum Research Group LLC, Tariffville, CT, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Pharmacology and Toxicology
  Pharmacy
  Veterinary medicine
  Biomedical Engineering
  Biochemistry
  
• 出版者：Springer Netherlands
• ISSN：1573-8744

文摘

Clinical studies investigating relationships between D3 and 25OHD3 vary in dosing regimen, assays, demographics, and control of exogenous D3. This leads to uncertain and conflicting exposure-related associations with D3 and 25OHD3. To elucidate this parent-metabolite system, a PPK model was developed to predict mean D3 and 25OHD3 exposure from varied doses and administration routes. Sources of exposure variability related to metabolite baseline, weight, and assay type were explored. Specific search criteria were used in PUBMED to identify public source PK data pertaining to D3 and 25OHD3 in healthy or osteoporotic populations. Overall 57 studies representing 5395 individuals were selected, including 25 individual-level profiles and treatment-arm data. IV, oral, single and multiple dose data were used, with D3 and 25OHD3 dosing. A nonlinear mixed effects model was developed to simultaneously model PK dispositions of D3 and 25OHD3 (NONMEM v7.2), which were described by 2-compartment models with nonlinear and linear clearances, respectively. Proportional and additive assay variances were included on the 25OHD3 prediction. Unit-level random effects were weighted by treatment-arm size. D3 model estimates, relative to bioavailability were: maximum rate of metabolism (\(V{\rm max}\), 1.62 nmol/h), Michaelis–Menten constant (\(Km\), 6.39 nmol/L), central volume of distribution (\(Vc\), 15.5 L), intercompartmental clearance (\(Q\), 0.185 L/h), peripheral volume of distribution (\(Vp\), 2333 L/h), and baseline concentration (\({DBASE}\), 3.75 nmol/L). For 25OHD3 (\(m\) = metabolite): \(CLm\) = 0.0153 L/h, \(Vcm\) = 4.35 L, \(Vpm\) = 6.87 L, \(Qm\) = 0.0507 L/h. Simulations of 25OHD3 concentration indicated an inverse relationship between 25OHD3 baseline and response, as well as a less than proportional 25OHD3 response. Estimation of assay bias parameters suggested that HPLC–MS and RIA produced similar measurement results, whereas CPBA and CHEMI are over-predictive of 25OHD3 concentration, relative to HPLC–MS.