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Implementing PRED Subroutine of NONMEM for Versatile Pharmacokinetic Analysis Using Fast Inversion of Laplace Transform (FILT).
Jin, Ryota; Hisaka, Akihiro.
Affiliation
  • Jin R; Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University.
  • Hisaka A; Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University.
Chem Pharm Bull (Tokyo) ; 68(9): 891-894, 2020 Sep 01.
Article in En | MEDLINE | ID: mdl-32611991
ABSTRACT
In pharmacokinetic (PK) analysis, conventional models are described by ordinary differential equations (ODE) that are generally solved in their Laplace transformed forms. The solution in the Laplace transformed forms is inverse Laplace transformed to derive an analytical solution. However, inverse Laplace transform is often mathematically difficult. Consequently, numerical inverse Laplace transform methods have been developed. In this study, we focus on extending the modeling functions of Nonlinear Mixed Effect Model (NONMEM), a standard software for PK and population pharmacokinetic (PPK) analyses, by adding the Fast Inversion of Laplace Transform (FILT) method, one of the representative numerical inverse Laplace transform methods. We implemented PREDFILT, a specialized PRED subroutine, which functions as an internal model unit in NONMEM to enable versatile FILT analysis with second-order precision. The calculation results of the compartment models and a dispersion model are in good agreement with the ordinary analytical solutions and theoretical values. Therefore, PREDFILT ensures enhanced flexibility in PK or PPK analyses under NONMEM environments.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Pharmacokinetics / Pharmaceutical Preparations / Models, Biological Type of study: Prognostic_studies Language: En Journal: Chem Pharm Bull (Tokyo) Year: 2020 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Pharmacokinetics / Pharmaceutical Preparations / Models, Biological Type of study: Prognostic_studies Language: En Journal: Chem Pharm Bull (Tokyo) Year: 2020 Document type: Article
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