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  • Population pharmacokinetic modelling of tramadol using inverse Gaussian function for the assessment of drug absorption from prolonged and immediate release formulations
    Brvar, Nina ; Mateović, Tatjana ; Grabnar, Iztok
    This study aimed to develop a population pharmacokinetic model for tramadol that combines different input rates with disposition characteristics. Data used for the analysis were pooled from two phase ... I bioavailability studies with immediate (IR) and prolonged release (PR) formulations in healthy volunteers. Tramadol plasma concentration-time data were described by an inverse Gaussian function to model the complete input process linked to a two-compartment disposition model with first-order elimination. Although polymorphic CYP2D6 appears to be a major enzyme involved in the metabolism of tramadol, application of a mixture model to test the assumption of two and three subpopulations did not reveal any improvement of the model. The final model estimated parameters with reasonable precision and was able to estimate the interindividual variability of all parameters except for the relative bioavailability of PR vs. IR formulation. Validity of the model was further tested using the nonparametric bootstrap approach. Finally, the model was applied to assess absorption kinetics of tramadol and predict steady-state pharmacokinetics following administration of both types of formulations. For both formulations, the final model yielded a stable estimate of the absorption time profiles. Steady-state simulation supports switching of patients from IR to PR formulation.
    Source: International journal of pharmaceutics. - ISSN 0378-5173 (Vol. 473, iss. 1-2, 2014, str. 170-178)
    Type of material - article, component part
    Publish date - 2014
    Language - english
    COBISS.SI-ID - 3687537

    Link(s):

    http://www.sciencedirect.com/science/article/pii/S0378517314005031#
    http://ac.els-cdn.com/S0378517314005031/1-s2.0-S0378517314005031-main.pdf?_tid=97c0b02c-0747-11e4-9c9f-00000aacb35d&acdnat=1404896659_3299ef189e3c0f66529a7ad31b7ae98a
    DOI

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