AbstractsBiology & Animal Science

Individualization of fixed-dose combination regimens; Individualisering av design och dosering av kombinationstabletter

by Gunnar Yngman

Institution: Uppsala University
Year: 2015
Keywords: pharmacometrics; pharmacokinetics; PK; population pharmacokinetics; optimal design; FDC; fixed-dose combination; pediatrics; tuberculosis; rifampicin; pyrazinamide; isoniazid; ethambutol; modeling; simulation; estimation; allometric scaling; stochastical simulation and estimation; NONMEM; MATLAB; FO; FOCE; Expectation-Maximization algorithm; importance sampling; Nelder-Mead method; logistic function; discontinuity; euclidean distance; Sammon mapping; global minimum; local minima; Medical and Health Sciences; Basic Medicine; Pharmaceutical Sciences; Medicin och hälsovetenskap; Medicinska grundvetenskaper; Farmaceutisk vetenskap; Natural Sciences; Computer and Information Science; Bioinformatics (Computational Biology); Naturvetenskap; Data- och informationsvetenskap; Bioinformatik (beräkningsbiologi); Master of Science Programme in Pharmacy; Apotekarprogrammet; Pharmacokinetics; Farmakokinetik
Record ID: 1365297
Full text PDF: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242059


<strong>Introduction:</strong> No Fixed-Dose Combination (FDC) formulations currently exist for pediatric tuberculosis (TB) treatment. Earlier work implemented, in the software NONMEM, a rational method for optimizing design and individualization of pediatric anti-TB FDC formulations based on patient body weight, but issues with parameter estimation, dosage strata heterogeneity and representative pharmacokinetics remained. <strong>Aim:</strong> To further develop the rational model-based methodology aiding the selection of appropriate FDC formulation designs and dosage regimens, in pediatric TB treatment. <strong>Materials and Methods:</strong> Optimization of the method with respect to the estimation of body weight breakpoints was sought. Heterogeneity of dosage groups with respect to treatment efficiency was sought to be improved. Recently published pediatric pharmacokinetic parameters were implemented and the model translated to MATLAB, where also the performance was evaluated by stochastic estimation and graphical visualization. <strong>Results:</strong> A logistic function was found better suited as an approximation of breakpoints. None of the estimation methods implemented in NONMEM were more suitable than the originally used FO method. Homogenization of dosage group treatment efficiency could not be solved. MATLAB translation was successful but required stochastic estimations and highlighted high densities of local minima. Representative pharmacokinetics were successfully implemented. <strong>Conclusions:</strong> NONMEM was found suboptimal for the task due to problems with discontinuities and heterogeneity, but a stepwise method with representative pharmacokinetics were successfully implemented. MATLAB showed more promise in the search for a method also addressing the heterogeneity issue.