1.
Henningsson, Anja, 1973-
(författare)
Mechanism-Based Pharmacokinetic and Pharmacodynamic Modelling of Paclitaxel
2005
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
Paclitaxel (Taxol®) is now widely used against breast, ovarian and non-small-cell lung cancer. Anticancer agents generally have narrow therapeutic indices, often with myelosuppression (mainly neutropenia) as dose-limiting side effect. A further complicating factor is that paclitaxel when given as Taxol® has a nonlinear pharmacokinetic (PK) behaviour in plasma. Identifying risk groups more sensitive to chemotherapy due to either a PK or pharmacodynamic (PD) interindividual variability is of importance. The aim of the thesis was to develop predictive mechanism-based PK and PD models applicable for paclitaxel. PK and PK/PD models were developed for patient data from studies with relatively frequent sampling or sparse sampling schedules. Population analyses were performed using the software NONMEM. A pharmacokinetic model describing unbound, total plasma and blood concentrations of paclitaxel from known binding mechanisms was developed and validated. The nonlinear PK in plasma could to a large extent be explained by the micelle forming vehicle Cremophor EL (CrEL) and the unbound drug showed linear PK. Besides a binding component directly proportional to concentrations of CrEL, the model included both linear and nonlinear binding components in plasma and blood. Further, relations between the PK parameters and different demographic factors, including polymorphisms in the cytochrome P450s involved in paclitaxel metabolism, were investigated. A semi-physiological PD model for chemotherapy-induced myelosuppression was developed and applied to different anticancer drugs. The model included a self-renewal for proliferating cells, transit compartments describing the delay in observed myelosuppression and a feedback parameter reflecting the effect on the bone marrow from growth factors that can result in an overshoot in white blood cells. The system-related parameters estimated showed consistency across drugs and the difference in the drug-related parameter reflected the relative bone marrow toxicity of the drugs. Relations between demographic factors and the PD parameters were identified. The developed mechanism-based models promote a better understanding of paclitaxel PK and PD and may be used as tools in dosing individualisation and in development of dosing strategies for new administration forms and new drugs in the same area.
2.
Zingmark, Per-Henrik, et al.
(författare)
Modelling a spontaneously reported side effect by use of a Markov mixed-effects model.
2005
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:2, s. 261-281
Tidskriftsartikel (refereegranskat) abstract
Aims: To present a method for analyzing side-effect data where change in severity is spontaneouslyreported during the experiment. Methods: A clinical study in 12 healthy volunteers aimed toinvestigate the concentration-response characteristics of a CNS-specific side-effect was conducted.After an open session where the subjects experienced the side-effect and where the individualpharmacokinetic parameters were evaluated they were randomized to a sequence of three differentinfusion rates of the drug in a double-blinded crossover way. The infusion rates were individualizedto achieve the same target concentration in all subjects and different drug input rates wereselected to mimic absorption profiles from different formulations. The occurrence of the specificside-effect and any subsequent change in severity was self-reported by the subjects. Severity wasrecorded as 0 = no side-effect, 1 = mild side-effect and 2 = moderate or severe side-effect.Results: The side-effect data were analyzed using a mixed-effects model for ordered categoricaldata with and without Markov elements. The former model estimated the probability of having acertain side-effect score conditioned on the preceding observation and drug exposure. The observednumbers of transitions between scores were from 0 ->1: 24, from 0 ->2: 11, from 1 ->2: 23, from2 ->1: 1, from 2 ->0: 32 and from 1 ->0: 2. The side-effect model consisted of an effect-compartmentmodel with a tolerance compartment. The predictive performance of the Markov model wasinvestigated by a posterior predictive check (PPC), where 100 datasets were simulated from thefinal model. Average number of the different transitions from the PPC was from 0 ->1: 26, from0 ->2: 11, from 1 ->2: 25, from 2 ->1: 1, from 2 ->0: 35 and from 1 ->0: 1. A similar PPCfor the model without Markov elements was at considerable disparity with the data. Conclusion:This approach of incorporating Markov elements in an analysis of spontaneously reported categoricalside-effect data could adequately predict the observed side-effect time course and could beconsidered in analyses of categorical data where dependence between observations is an issue.
3.
Zingmark, Per-Henrik, 1972-
(författare)
Models for Ordered Categorical Pharmacodynamic Data
2005
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.
