16 research outputs found
Development of a new version of the Liverpool Malaria Model. I. Refining the parameter settings and mathematical formulation of basic processes based on a literature review
The impact of composite AUC estimates on the prediction of systemic exposure in toxicology experiments
Assessment of asymptomatic Plasmodium spp. infection by detection of parasite DNA in residents of an extra-Amazonian region of Brazil
Steady-State Imatinib Trough Levels as Well as Dose Interruptions Are Associated with Clinical Response (CCyR and MMR) and Adverse Events (AEs) in Patients with Chronic Myeloid Leukemia (CML) Receiving IM as Frontline Therapy
Non-compartmental estimation of pharmacokinetic parameters in serial sampling designs.
Pharmacokinetic studies are commonly analyzed using a two-stage approach where the first stage involves estimation of pharmacokinetic parameters for each subject separately and the second stage uses the individual parameter estimates for statistical inference. This two-stage approach is not applicable in sparse sampling situations where only one sample is available per subject. Nonlinear models are often applied to analyze pharmacokinetic data assessed in such serial sampling designs. Modelling approaches are suitable provided that the form of the true model is known, which is rarely the case in early stages of drug development. This paper presents an alternative approach to estimate pharmacokinetic parameters based on non-compartmental and asymptotic theory in the case of serial sampling when a drug is given as an intravenous bolus. The statistical properties of the estimators of the pharmacokinetic parameters are investigated and evaluated using Monte Carlo simulations