Limited sampling model for the estimation of pharmacokinetic parameters in children

Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies

BACKGROUND

The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam.

METHODS

A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision

RESULTS

The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction.

CONCLUSION

A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

Application of optimal design methodologies in clinical pharmacology experiments

Pharmacokinetics and pharmacodynamics data are often analysed by mixed-effects modelling techniques (also known as population analysis), which has become a standard tool in the pharmaceutical industries for drug development. The last 10 years has witnessed considerable interest in the application of experimental design theories to population pharmacokinetic and pharmacodynamic experiments. Design of population pharmacokinetic experiments involves selection and a careful balance of a number of design factors. Optimal design theory uses prior information about the model and parameter estimates to optimize a function of the Fisher information matrix to obtain the best combination of the design factors. This paper provides a review of the different approaches that have been described in the literature for optimal design of population pharmacokinetic and pharmacodynamic experiments. It describes options that are available and highlights some of the issues that could be of concern as regards practical application. It also discusses areas of application of optimal design theories in clinical pharmacology experiments. It is expected that as the awareness about the benefits of this approach increases, more people will embrace it and ultimately will lead to more efficient population pharmacokinetic and pharmacodynamic experiments and can also help to reduce both cost and time during drug development.

A systematic review of limited sampling strategies for platinum agents used in cancer chemotherapy

Despite evidence in the literature suggesting that a strong correlation exists between the pharmacokinetic parameters and pharmacodynamic effect of anticancer agents, many of these agents are still dosed by body surface area. Therapeutic drug monitoring with the aim of pharmacokinetic-guided dosing would not only maintain target concentrations associated with efficacy but may potentially minimise the likelihood of dose-related systemic toxicities. The pharmacokinetic parameter that displays the best correlation with the pharmacodynamics of anticancer drugs is the area under the plasma concentration-time curve (AUC). However, accurate determination of the AUC requires numerous blood samples over an extended interval, which is not feasible in clinical practice. Therefore, limited sampling strategies (LSSs) have been proposed as a means to accurately and precisely estimate pharmacokinetic parameters with a minimal number of blood samples. LSSs have been developed for many drugs, particularly ciclosporin and other immunosuppressants, as well as for certain anticancer drugs. This systematic review evaluates LSSs developed for the platinum compounds and categorises 18 pertinent citations according to criteria adapted from the US Preventive Services Task Force. Thirteen citations (four level I, six level II-1, three level II-2) pertained to LSSs for carboplatin, four citations (one level II-1, one level II-2, two level III) to cisplatin LSSs, and one citation (level II-2) to nedaplatin. Based on the current evidence, it appears that LSSs may be useful for pharmacokinetic-guided dosage adjustments of carboplatin in both adults and children with cancer. Although some validation studies suggest that LSSs can be extended to different cancer populations or different chemotherapy regimens, other studies dispute this finding. Although the use of LSSs to predict the pharmacokinetic parameters of cisplatin and nedaplatin appear promising, the quality of evidence from published studies does not support routine implementation at this time.LSSs represent one approach in which clinicians can make specific dosage adjustments for individual patients to optimise outcomes. However, the limitations of these strategies must also be taken into consideration. There is also a need for prospective studies to demonstrate that application of LSSs for platinum agents ultimately improves patient response and decreases systemic toxicities.

Development and validation of limited sampling models for topotecan lactone pharmacokinetic studies in children

PURPOSE

To develop and validate a pharmacokinetic limited sampling model (LSM) for intravenous and oral topotecan pharmacokinetic studies in children.

METHODS

Topotecan lactone concentration-time data from five trials were used to develop and validate LSM for intravenous and oral topotecan. Based on full sampling from one intravenous study (30 patients; 195 studies), a LSM for intravenous topotecan was determined using a modification of the D-optimality algorithm. For oral topotecan we used full sampling data from one oral topotecan study (27 patients; 47 studies) to develop an LSM. Accuracy and bias of each LSM were determined relative to the full sampling method. Predictive performance of the LSM was validated using additional data and Monte-Carlo simulations based on these data.

RESULTS

LSM for intravenous topotecan includes: 5 min, 1.5, and 2.5 h after the end of the 30 min infusion. The median accuracy (absolute predicted error) and bias (predicted error) are < or =8% and < or =6.1%, respectively. For oral topotecan, the optimal LSM includes: 15 min, 1.5, and 6 h. The median accuracy and bias are 6% and 4%, respectively.

CONCLUSIONS

Our results indicate that the optimal sampling times for the intravenous LSM for topotecan in children consist of: predose, and 5 min, 1.5, and 2.5 h after the end of infusion. For oral topotecan the sample times are predose, 15 min, 1.5, and 6 h after dose administration. These LSM are invaluable to children receiving topotecan because it minimizes inconvenience and blood collection.

Limitations in the Clinical Usefulness of Single-Dose Pharmacokinetic Studies of Drugs and a Bayesian Approach for the Estimation of Kinetic Parameters

This review presents several clinical examples indicating that physiological changes in the body dependent and/or independent of developmental age, genetic polymorphisms, different disease states, acute and/or chronic inflammations, and physicochemical properties of drugs as well as some environmental factors, such as viral infections, may exert a significant effect on the first-time assessment of kinetic parameters of drug absorption, disposition, metabolism, and excretion after a single-dose administration in children and adults. The available pharmacokinetic data in the literature suggest that one must be cautious in interpretation and practical use of pharmacokinetic variables derived from either single-dose studies or bayesian methods, especially in a pediatric population.

A Bayesian approach for the estimation of pharmacokinetic parameters in children

A Bayesian method is proposed for the first-time assessment of pharmacokinetic parameters (area under the curve [AUC], maximum concentration [C(max)], half-life [T(1/2)]) in children after a single oral dose. Pharmacokinetic parameters were compared using sparse sampling (three blood samples) with extensive sampling (nine blood samples). Two different therapeutic classes of drugs were evaluated in this study. The population pharmacokinetic parameters and the blood sampling time points were obtained from adult data. The results indicate that the Bayesian approach can be used to estimate pharmacokinetic parameters in children with fair degree of accuracy. The Bayesian method described here can be used to assess pharmacokinetic parameters in children, provided there is a prior knowledge of the pharmacokinetics of a drug in adult population.