Predictability of individualized dosage regimens of carbamazepine and valproate mono- and combination therapy.
Summary What is known and Objective: Many investigators agree that appropriate rational utilization of therapeutic drug monitoring (TDM) with Bayesian feedback dosage adjustment facilitates epilepsy treatment with carbamazepine (CBZ) and/or valproate (VPA) by increasing the seizure control and safet...
| Publicado en: | Journal of Clinical Pharmacy & Therapeutics Vol. 36; no. 5; pp. 625 - 637 |
|---|---|
| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2011
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104685574&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104685574 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02694727 EV4 jtl: Journal of Clinical Pharmacy & Therapeutics issn: 02694727 maglogo: Y pubinfo: dt: Oct2011 vid: 36 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104685574 65430252 10.1111/j.1365-2710.2010.01215.x NLM21062333 104685574 ppf: 625 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Predictability of individualized dosage regimens of carbamazepine and valproate mono- and combination therapy. aug: au: Bondareva, I. B. Jelliffe, R. W. Andreeva, O. V. Bondareva, K. I. affil: Laboratory of Mathematical Modeling, The Research Institute of Physical-Chemical Medicine, Moscow, Russia sug: subj: Carbamazepine Administration and Dosage Valproic Acid Administration and Dosage Drug Monitoring Methods Epilepsy Drug Therapy Drug Therapy, Combination Carbamazepine Pharmacokinetics Valproic Acid Pharmacokinetics Models, Statistical Evaluation Data Analysis Software Russia Linear Regression Human Chromatography, High Pressure Liquid Retrospective Design Male Female Adult Middle Age Carbamazepine Blood Valproic Acid Blood Descriptive Statistics T-Tests Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Summary What is known and Objective: Many investigators agree that appropriate rational utilization of therapeutic drug monitoring (TDM) with Bayesian feedback dosage adjustment facilitates epilepsy treatment with carbamazepine (CBZ) and/or valproate (VPA) by increasing the seizure control and safety, as well as by reducing treatment costs. In previous works we have developed and used in clinical practice population pharmacokinetic (PK) models of different dosage forms for VPA and post-induction CBZ behaviour, as well as for combined therapy with CBZ plus another 'old' antiepileptic drug (AED). An important step of external validation is to evaluate how well a procedure of Bayesian individualizing AED dosage regimens based on a proposed population PK model and sparse TDM data 'works', and how helpful it is in real practical clinical settings. The aim of this study was to evaluate the predictability of individualized dosage regimens for monotherapy with CBZ in the post-induction period or with VPA, as well as for CBZ and VPA given as combination therapy based on TDM data of epileptic patients and the earlier developed population models. Methods: Four groups of TDM data were analysed using the USC*PACK software for PK/PD analysis: 556 predictions for adult epileptic patients on CBZ monotherapy, 662 predictions for VPA monotherapy, 402 predictions of CBZ serum levels and 430 predictions of VPA serum levels for adult epileptic patients on CBZ+VPA combination therapy. Statistical characteristics of the prediction errors (PE) and weighted PE were used to estimate bias and precision of predictions. Intraindividual and interoccasional variability of predictions were also estimated. Results and Discussion: This study demonstrated that in most cases of CBZ and VPA monotherapy and combination therapy, predictions of future AED concentrations based on the earlier developed population PK models, TDM data and patient-specific maximum a posteriori probability Bayesian posterior parameter values provided clinically acceptable estimates. Statistical analysis of the residuals demonstrated that the distributions of residual and weighted residual were close to the normal distribution (Kolmogorov-Smirnov test, P > 0·05) and their mean values did not differ statistically significant from zero (no statistically significant bias, P > 0·05) for all groups of predictions. The observed decreased quality of predictions of VPA concentrations during VPA+CBZ combination therapy, especially when CBZ dosages were changed, might well be explained by their PK interactions. For all groups, in linear regression analysis, the observed trend of decreasing of the prediction quality over various future prediction time horizons was considered statistically significant ( P < 0·05). Prediction of serum levels further in future was less precise than those closer to the present for a 1·5- to 3·5-year observation period. No bias in predictions was associated with the time horizons. What is new and Conclusion: Our validation results suggest good predictive performance of the population models developed earlier, and quite acceptable predictions of future AED serum levels for individualized dosage regimens of CBZ and VPA therapy in real clinical settings. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|