Pregabalin add-on treatment demonstrates a dose-response relationship in 3 out of 4 patients with refractory partial seizures. A dose of 186 mg pregabalin daily is expected to decrease the seizure rate by 50% of maximum from baseline. Age, race, and menopausal status of women did not affect the dose-response relationship.
Noncompliance presents a persistent problem while analyzing PK data from outpatient clinical studies. Ignoring dose omission or making uninformed assumptions about patient drug intake history can prove detrimental to the objectives of the analysis (e.g. determining the PK model parameters or identifying covariates) and ultimately compromise the interpretation of the data. In order to overcome this problem, an alternative method of handling noncompliant data is evaluated in this report. The proposed approach is based on the principle of superposition and works by separating the estimation of the elimination rate from the model based steady-state PK concentration. Simulations implementing this method under different scenarios of noncompliance demonstrate that it performs better than the conventional method of analyzing population PK data when compared on the basis of bias and imprecision in parameter estimation and power (and type I error) for covariate detection. Overall, the new method exhibits great potential to address the issue of uncertain/unreliable dosing histories frequently encountered in clinical trials.
We fit a mixed effects logistic regression model to longitudinal adverse event (AE) severity data (four-point ordered categorical response) to describe the dose-AE severity response for an investigational drug. The distribution of the predicted interindividual random effects (Bayes predictions) was extremely bimodal. This extreme bimodality indicated that biased parameter estimates and poor predictive performance were likely. The distribution's primary mode was composed of patients that did not experience an AE. Moreover, the Bayes predictions of these non-AE patients were nearly degenerative, i.e., the predictions were nearly identical. To resolve this extreme bimodality we propose using a two-part mixture modeling approach. The first part models the incidence of AE's, and the second part models the severity grade given the patient had an AE. Unconditional probability predictions are calculated by mixing the incidence and severity model probability predictions. We also report results of simulation studies, which assess the predictive and statistical (bias and precision) performance of our approach.
This observational study explored the effects of demographics, sickness, and polypharmacy on the non-steady state population pharmacokinetics of intravenous phenytoin. One hundred fifteen patients were studied. Models were developed using the NONMEM program with hybrid first-order conditional estimation. A Michaelis-Menten model with delayed induction was preferred over a Michaelis-Menten model without induction, a Michaelis-Menten model with immediate induction, or a linear model with delayed induction. When the data were fit to a Michaelis-Menten model with delayed induction, the volume of distribution (Vd) was found to depend on weight and serum albumin. The Vd was estimated to be 0.95 l/kg, assuming an albumin level of 3 g/dl. The Michaelis-Menten constant (km) was estimated to be 7.9 mg/l. The baseline maximum metabolic rate was 580 mg/day for a 70-kg patient. The average time to onset of induction was 59.5 hours. If a fever developed after induction began, it increased the extent of induction. This model was evaluated retrospectively in 26 additional patients, yielding a mean prediction error of -0.4 mg/l (-3.0-2.2 mg/l) and a mean absolute prediction error of 4.7 mg/l (3.2-6.2 mg/l) based on two-level feedback. Given the large interindividual variances in maximum metabolic rate, phenytoin levels should be measured frequently.
Dizziness and drowsiness are cited as being predictors of dropout from clinical trials for the medicine pregabalin. These adverse events are typically recorded daily on a four point ordinal scale (0 = none, 1 = mild, 2 = moderate, 3 = severe), with most subjects never reporting either adverse event. We modeled the dizziness, drowsiness, and dropout associated with pregabalin use in generalized anxiety disorder using piecewise Weibull distributions for the time to first non-zero dizziness or drowsiness score, after which the dizziness or drowsiness was modeled with ordinal regression with a Markovian element. Dropout was modeled with a Weibull distribution. Platykurtosis was encountered in the estimated random effects distributions for the ordinal regression models and was addressed with dynamic John-Draper transformations. The only identified predictor for the time to first non-zero dizziness or drowsiness score was daily titrated dose. Predictors for dropout included creatinine clearance and maximum daily adverse event score. Tolerance to adverse events over time was modeled by including a non-stationary component for the dizziness ordinal Markov regression while the piecewise Weibull distributions allowed a change point in the median time to first non-zero dizziness or drowsiness score.
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