Operational characteristics of full random effects modelling ('frem') compared to stepwise covariate modelling ('scm')

An adequate covariate selection is a key step in population pharmacokinetic modelling. In this study, the automated stepwise covariate modelling technique ('scm') was compared to full random effects modelling ('frem'). We evaluated the power to identify a 'true' covariate (covariate with highest correlation to the pharmacokinetic parameter), precision, and accuracy of the parameter-covariate estimates. Furthermore, the predictive performance of the final models was assessed. The scenarios varied in covariate effect sizes, number of individuals (n = 20-500) and covariate correlations (0-90% cov-corr). The PsN 'frem' routine provides a 90% confidence intervals around the covariate effects. This was used to evaluate its operational characteristics for a statistical backward elimination procedure, defined as 'frem_posthoc' and to facilitate the comparison to 'scm'. 'Frem_posthoc' had a higher power to detect the true covariate with lower bias in small n studies compared to 'scm', applied with commonly used settings (forward p < 0.05, backward p < 0.01). This finding was vice versa in a statistically similar setting. For 'frem_posthoc', power, precision and accuracy of the covariate coefficient increased with higher number of individuals and covariate effect magnitudes. Without a backward elimination step 'frem' models provided unbiased coefficients with highly imprecise coefficients in small n datasets. Yet, precision was superior to final 'scm' model precision obtained using common settings. We conclude that 'frem_posthoc' is also a suitable method to guide covariate selection, although intended to serve as a full model approach. However, a deliberated selection of automated methods is essential for the modeller and using those methods in small datasets needs to be taken with caution.

Modelling the dose-response relationship: the fair share of pharmacokinetic and pharmacodynamic information

AIMS

The aim of this paper is to investigate the role of drug concentration samplings in the modelling of the dose-response relationship.

METHODS

Using an initial PK/PD model, a reference dataset was simulated. PK and PD samples were extracted to create reduced datasets. PK/PD and K-PD models were fitted to theses reduced datasets. Post hoc estimates from both types of models were compared to the initial PK/PD model and performance was assessed.

RESULTS

K-PD models were largely biased when the drug has a nonlinear elimination. PK/PD models with 1 PK and 2 PD samples were superior to K-PD models with 3 PD samples. PK/PD models with 1 or 2 PK samples and 3 PD samples proved to be superior to K-PD models with 4 PD samples.

CONCLUSIONS

K-PD models should not be used when the drug has nonlinear elimination. K-PD models should not replace PK/PD modelling but are an alternative approach if the PD information is large enough.

Population pharmacokinetics of meropenem in elderly patients: dosing simulations based on renal function

OBJECTIVES

The aim of this study was to evaluate different dosage regimens of meropenem in elderly patients in relation with renal function using a population pharmacokinetic (popPK) model.

METHODS

The data of 178 elderly patients treated with meropenem was collected from different sources. A popPK model was developed by using NONMEM® and the influence of different covariates on meropenem CL and V₁ was observed. Monte Carlo dosing simulations were performed at steady state to observe the % T > MIC for targets of 40, 60 and 80% of dosage intervals at different levels of creatinine clearance (CL_CR).

RESULTS

The data was described by a two-compartment model and the values of parameter estimates for CL, V₁, Q and V₂ were 5.27 L/h, 17.2 L, 9.92 L/h and 10.6 L, respectively. The CL_CR, body weight and centre had a significant influence on meropenem CL while no direct influence of age was observed. Extended infusions had pharmacokinetic and pharmacodynamic (PK/PD) breakpoint one dilution greater than corresponding short infusion regimens for each target of % T > MIC.

CONCLUSION

Meropenem CL was significantly lower in the elderly compared to CL reported in younger patients due to the reduced renal function. An extended infusion of 1000 mg q8h can be considered for empirical treatment of infections in elderly patients when CL_CR is ≤ 50 mL/min. A continuous infusion of 3000 mg daily dose is preferred if CL_CR > 50 mL/min. However, a higher daily dose of meropenem would be required for resistant strains (MIC >8 mg/L) of bacteria if CL_CR is >100 mL/min.

Modeling Testosterone Circadian Rhythm in Hypogonadal Males: Effect of Age and Circannual Variations

The objective of this study was to characterize the baseline circadian rhythm of testosterone levels in hypogonadal men. A total of 859 baseline profiles of testosterone from hypogonadal men were included in this analysis. The circadian rhythm of the testosterone was described by a stretched cosine function. Model parameters were estimated using NONMEM^® 7.3. The effect of different covariates on the testosterone levels was investigated. Model evaluation was performed using non-parametric bootstrap and predictive checks. A stretched cosine function deeply improved the data goodness of fit compared to the standard trigonometric function (p < 0.001; ΔOFV = −204). The effect of the age and the semester, defined as winter and spring versus summer and fall, were significantly associated with the baseline levels of testosterone (p < 0.001, ΔOFV = −15.6, and p < 0.001, ΔOFV = −47.0). Model evaluation procedures such as diagnostic plots, visual predictive check, and non-parametric bootstrap evidenced that the proposed stretched cosine function was able to model the time course of the diurnal testosterone levels in hypogonadal males with accuracy and precision. The circadian rhythm of the testosterone levels was better predicted by the proposed stretched cosine function than a standard cosine function. Testosterone levels decreased by 5.74 ng/dL (2.4%) every 10 years and were 19.3 ng/dL (8.1%) higher during winter and spring compared to summer and fall.

Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer's subjects from the Alzheimer's Disease Neuroimaging Initiative

BACKGROUND

The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) scores. These were derived from the Alzheimer's Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer's disease and mild cognitive impairment patients who were followed for 2-3 years.

METHODS

The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications.

RESULTS

Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR-SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR-SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer's disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively.

CONCLUSIONS

In conclusion, this model describes disease progression in terms of CDR-SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.