A Population Pharmacokinetic Model with Time-Dependent Covariates Measured with Errors

Quantification of Radiation Injury on Neutropenia and the Link between Absolute Neutrophil Count Time Course and Overall Survival in Nonhuman Primates Treated with G-CSF

Purpose

To model absolute neutrophil count (ANC) suppression in response to acute radiation (AR) exposure and evaluate ANC time course as a predictor of overall survival (OS) in response to AR exposure with or without treatment with granulocyte colony-stimulating factor in nonhuman primates.

Methods

Source data were obtained from two pivotal studies conducted in rhesus macaques exposed to 750 cGy of whole body irradiation on day 0 that received either placebo, daily filgrastim, or pegfilgrastim (days 1 and 8 after irradiation). Animals were observed for 60 days with ANC measured every 1 to 2 days. The population model of ANC response to AR and the link between observed ANC time course and OS consisted of three submodels characterizing injury due to radiation, granulopoiesis, and a time-to-event model of OS.

Results

The ANC response model accurately described the effects of AR exposure on the duration of neutropenia. ANC was a valid surrogate for survival because it explained 76% (95% CI, 41%–97%) and 73.2% (95% CI, 38.7%–99.9%) of the treatment effect for filgrastim and pegfilgrastim, respectively.

Conclusion

The current model linking radiation injury to neutropenia and ANC time course to OS can be used as a basis for translating these effects to humans.

Electronic supplementary material

The online version of this article (10.1007/s11095-020-02839-3) contains supplementary material, which is available to authorized users.

Practical recommendations for population PK studies with sampling time errors

PURPOSE

Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time variable. The goal of this study was to investigate the effects on model parameters when a scheduled time is used instead of the actual blood sampling time, and to propose ME correction methods.

METHODS

Simulation studies were conducted based on two major factors: the curvature in PK profiles and the size of ME. As ME correction methods, transform both sides (TBS) models were developed with application of Box-Cox power transformation and Taylor expansion. The TBS models were compared to a conventional population PK model using simulations.

RESULTS

The most important determinant of bias due to time ME was the degree of curvature (nonlinearity) in PK profiles; the smaller the curvature around sampling times, the smaller the associated bias. The second important determinant was the magnitude of ME; the larger the ME, the larger the bias. The proposed TBS models performed better than a conventional population PK modeling when curvature and ME were substantial.

CONCLUSIONS

Time ME in sampling time can lead to bias on the parameter estimators. The following practical recommendations are provided: 1) when the curvature of PK profiles is small, conventional population PK modeling is robust to even large ME; and 2) when the curvature is moderate or large, the proposed methodology reduces bias in parameter estimates.

A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.

Mixed models for ordinal data: a pharmacokinetic study on the effectiveness of drug for the reduction of epileptic seizures

Population pharmacokinetics (PK)/pharmacodynamics analysis is considered as an important component of drug development process in these days. Owing to different standardization techniques in different laboratories, the drug concentrations in blood are reported in intervals in which they lie. For obvious reason the outcomes are then recorded as multiple discrete indicators with natural ordering. But in view of the reduced amount of information, the modeling of such data becomes intrinsically more difficult than that of continuous data. The primary concern in this study is to analyze a one-compartmental PK model for ordinal data that can be used to describe the change in the concentration of drug over time. A flexible Monte Carlo EM-based two-step estimation method is proposed. Such a method is, in general, capable of analyzing a reasonably wide class of nonlinear mixed models. The modeling strategy is applied to a Phase I study of the drug Divalproex and its metabolite, developed for the treatment of epilepsy. The model's performance is evaluated through simulation.

Drug-drug interaction prediction: a Bayesian meta-analysis approach

In drug-drug interaction (DDI) research, a two drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor/inducer or substrate's PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. In this paper, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The first level model is a study-specific sample mean model; the second level model is a random effect model connecting different PK studies; and all priors of PK parameters are specified in the third level model. A Monte Carlo Markov chain (MCMC) PK parameter estimation procedure is developed, and DDI prediction for a future study is conducted based on the PK models of two drugs and posterior distributions of the PK parameters. The performance of Bayesian meta-analysis in DDI prediction is demonstrated through a ketoconazole-midazolam example. The biases of DDI prediction are evaluated through statistical simulation studies. The DDI marker, ratio of area under the concentration curves, is predicted with little bias (less than 5 per cent), and its 90 per cent credible interval coverage rate is close to the nominal level. Sensitivity analysis is conducted to justify prior distribution selections.