Comparison of methods of carbamazepine dosage, individualization in epileptic patients

Detoxication treatment for carbamazepine and lithium overdose

This article reports detoxication treatments of a case of combined overdose of carbamazepine and lithium in a 38-year-old female with bipolar disorder. She was brought to the emergency unit after the family found her unresponsive and lying near empty packages for carbamazepine (corresponded to 7.7 g) and lithium carbonate (corresponded to 6.6 g) tablets. On admission, her blood pressure, heart rate and respiratory rate were 80/55 mmHg, 90 per minute and 13 per minute, respectively. Her GCS was 3 (E1, M1, V1). She received gastric lavage after intratracheal intubation, followed by administration of activated charcoal via gastric tube, and a large volume (800 ml/h) of lactate Ringer's solution by intravenous infusion. The serum levels of carbamazepine and lithium approximately 5 h after ingestion were 56.0 mug/ml and 3.56 mEq/l, respectively. The carbamazepine overdose was mainly treated by a 3 h charcoal hemoperfusion (CHP). The CHP treatment decreased serum carbamazepine levels by approximately 30-40% as compared with the levels simulated by Bayesian analysis using 1-point or 2-points serum level(s) (without detoxication treatment). For lithium overdose continuous infusion of Ringer's solution was effective, which increased serum sodium gradually and facilitated the elimination of lithium. In conclusion, the treatments with CHP and continuous infusion of Ringer's solution were considered to be effective for detoxification of carbamazepine and lithium overdose, respectively, when compared with those drug levels without detoxication treatment that simulated by Bayesian analysis method.

Bayesian estimation of six different sets of carbamazepine pharmacokinetic parameters in Egyptian adult epileptic patients

The individualization of carbamazepine (CBZ) dosage regimen based on estimation of pharmacokinetic (PK) parameters and measurement of serum drug concentration in epileptic patients can help to control epilepsy. In a retrospective study, the predictive performance of six different sets of CBZ PK parameters selected according to the literature was evaluated in 60 adult epileptic patients. Patients were administered controlled release CBZ (dose range: 200-1200 mg day(-1)) as monotherapy and one steady state serum concentration of the drug was available for each patient. The Bayesian Program of Abbott (PKS system; Abbott Laboratories, Wiesbaden, Germany) was used in the prediction process. Predictive measures included estimation of mean prediction error (mpe) for bias, mean squared prediction error (mspe) and root mean squared prediction error (rmspe) for precision. The analysis showed that three of the investigated six sets achieved the best predictive performance in Egyptian patients and consequently, the PK parameters of any of these three sets can be used by the Bayesian approach as prior information for CBZ dose optimization among the Egyptian adult population.

Predictive capacity of carbamazepine pharmacokinetic parameters in a Portuguese outpatient population

The individualization of anticonvulsant therapy regimens can contribute to the implementation of appropriate carbamazepine (CBZ) maintenance doses in epileptic patients. An accurate method for the prediction of concentrations based on a determination of parameters and serum concentrations could be of clinical relevance in the management of epilepsy. In this study, we retrospectively evaluated the predictive performance in an adult outpatient population of six different methods, representing six sets of CBZ pharmacokinetic parameters selected according to the literature using a Bayesian computer program (PKS System; Abbott Laboratories, Abbott Park, IL, USA). The study involved 50 patients with two or more available concentrations selected under several inclusion criteria. The patients were taking CBZ (between 200 and 1600 mg/d) in monotherapy or polytherapy regimens and had no hepatic or renal disease. Steady state concentrations were predicted according to the use of prior information and using one and two feedback patient concentrations. Accuracy and precision were assessed by mean prediction error (ME), mean squared prediction error (MSE) and root mean squared prediction error (RMSE). The analysis showed CL = 0.067 L/hour/kg and Vd = 1.19 L/kg as the most accurate and precise set of pharmacokinetic parameters, presenting the highest percentage of clinically acceptable estimates (error < 2 microg/mL). Additionally, predictions based on one measured feedback concentration were found to be more accurate and precise than prior population-based predictions; the use of two previous patient concentrations further improved predictive capacity but failed to show a significant difference when compared with predictions based on one measured feedback concentration. In conclusion, the adoption of the previously mentioned set of parameters as population estimates and the use of at least one feedback concentration through the Bayesian approach seems to be essential for a better CBZ use in clinical practice. Finally, despite the obtained results, we believe that the Portuguese pharmacokinetic parameter determination of antiepileptics should be carried out to improve the rationale and cost-effectiveness of anticonvulsant therapy.

Kinetic profile of carbamazepine in an adult Portuguese outpatient population

OBJECTIVE

The aim of our work was to define the kinetic profile of carbamazepine (CBZ), in order to improve on dosing schedules through a Bayesian approach.

METHOD

Carbamazepine dose/steady-state trough concentrations data pairs and associated information were collected retrospectively on a population of adult epileptic patients.

RESULTS

Fifty patients (index population) with two or more available concentrations (total of 174 determinations) met our inclusion criteria. Patients were taking CBZ (200-1800 mg/day) in mono- or polytherapy regimens. The analysis assumed a one-compartmental model with first-order absorption and elimination. Due to the data source (only trough concentrations were measured as part of hospital routine), the volume of distribution was fixed at 1.19 l/kg. The final estimates for CL were: 0.075 +/- 0.027 (mono- and polytherapy), 0.069 +/- 0.020 (monotherapy), and 0.106 +/- 0.037 l/h/kg (polytherapy). In order to validate these results, we assessed their predictive capacity using 18 new patients (validation population), submitted to the same inclusion criteria and using Prediction-Error analysis. The results suggested a different CL value for our population compared to earlier published clearance values. The results also pointed to an increased metabolic rate associated with polytherapy. The prediction capacity of the optimization method derived from a Portuguese population made in an a priori evaluation indicated a low error (-0.04 microg/ml), close to the theoretical zero value.

CONCLUSION

Our results provide specific data on CBZ disposition in a Portuguese population and given the wide variability in the literature values, our data may help improve dosing of CBZ in Portuguese patients.

A limited sampling method for the estimation of AUC and Cmax of carbamazepine and carbamazepine epoxide following a single and multiple dose of a sustained-release product

AIMS

The objectives of this study are to develop a model to predict area under the curve (AUC) and maximum plasma concentration (Cmax) of carbamazepine (CBZ) and its active metabolite carbamazepine epoxide (CBZE) following single and multiple dose of CBZ using one or two samples in volunteers.

METHODS

Limited sampling models (LSM) were developed for CBZ and CBZE following 200-800 mg single oral dose and 400-800 mg twice daily dose for 14 days of a sustained-release product (CBZ-SR) to estimate AUC and Cmax. The LSM was developed from a training data set of 15 subjects using one blood sample taken at 48 h following a single dose. The model was validated on 60 subjects who received different doses of CBZ. Following multiple dosing, the LSM was developed from a training data set of 10 subjects using the steady state Cmin (plasma concentration obtained 5 min before the last CBZ-SR dose).

RESULTS

The model provided good estimates of AUC and Cmax for CBZ and CBZE. The bias and the precision of the predicted AUC and Cmax for CBZ and CBZE were less than 10% and 15%, respectively. Similar results were obtained when CBZ was given as multiple dose.

CONCLUSIONS

The method described here may be used to estimate AUC and Cmax for CBZ and CBZE without detailed pharmacokinetic studies following single or multiple dose of CBZ.

Predictions of carbamazepine concentrations using a Bayesian program (PKS System, Abbott): a retrospective evaluation in an outpatient population

This work evaluates the performance of a Bayesian program (PKS System, Abbott) for predicting carbamazepine concentrations in an outpatient population. The retrospective study involved 20 epileptic patients (12 adults and 8 children) receiving carbamazepine monotherapy orally. The program was used to predict measured serum levels after feedback of 0, 1 or 2 steady-state concentrations. A significant negative prediction bias was observed when no feedback concentration was used for estimation. However, the prediction bias (mean prediction error; m.e.) decreased as soon as one feedback concentration was used for estimation. Precision (mean absolute prediction error; m.a.e.) was significantly improved with one feedback concentration and was even better with two concentrations. Likewise, r.m.s.e. (root mean squared error; composite of bias and precision) regularly decreased when the number of feedback concentrations used was increased. Eleven percent of the estimates were unacceptable clinically (prediction error > 2 mg L-1) when 1 feedback concentration was used; less than 3% were unacceptable when two concentrations were used. Thus the performance of the Bayesian dosing program is acceptable when two feedback concentrations are known, and seems able to help the clinician adjust carbamazepine dosage in an outpatient population.

Bayesian parameter estimation and population pharmacokinetics

The widespread application of Bayesian parameter estimation in the area of therapeutic drug monitoring (TDM) has prompted the need for well conducted population studies to obtain relevant prior pharmacokinetic parameter estimates. In many cases the population has consisted of a relatively small number of subjects. This may be unavoidable for drugs used in cancer chemotherapy or in small, specific populations of patients. In contrast, information about drugs which are used extensively, such as the aminoglycosides, can be obtained by population studies which involve a large number of individuals. Indeed, this technique has proved particularly useful for determining parameter estimates which can be employed in neonatal TDM. Bayesian parameter estimation has been most frequently used for drugs with narrow therapeutic ranges such as the aminoglycosides, cyclosporin, digoxin, anticonvulsants (especially phenytoin), lithium and theophylline. However, the technique has now been extended to cytotoxic drugs, Factor VIII and warfarin. Bayesian methods have also been used to limit the number of samples required in more conventional pharmacokinetic studies with new drugs. Further advances in the use of these methods are likely to include measures of drug response and toxicity requiring population studies which also include relevant pharmacodynamic information.

Advanced computer programs for drug dosing that combine pharmacokinetic and symbolic modeling of patients

In this paper, we describe our design for advanced drug dosing programs that "reason" using a combination of Bayesian pharmacokinetic modeling and symbolic modeling of patient status and drug response. Our design is similar to the design of the Digitalis Therapy Advisor program, but extends this previous work by incorporating a Bayesian pharmacokinetic model, performing a "meta-level" analysis of drug concentrations to identify sampling errors and changes in pharmacokinetics, and including the results of this analysis in reasoning for dosing and therapeutic monitoring recommendations. The design has been implemented in a program for aminoglycoside antibiotics called Aminoglycoside Therapy Manager. The program is user-friendly and runs on low-cost general-purpose hardware. The initial validation study showed that the program was as accurate in predicting future drug concentrations as an expert using commercial Bayesian forecasting software and that its dosing recommendations were similar to those of an expert.