2010 - Berlin - Germany

PAGE 2010: Lewis Sheiner Student Session
Lay Ahyoung Lim

Dose-Response-Dropout Analysis for Somnolence in Pregabalin-treated Patients with Generalized Anxiety Disorder

Lay Ahyoung Lim (1), Raymond Miller (2), Kyungsoo Park (1)

(1) Department of Pharmacology, College of Medicine, Yonsei University, Seoul, Korea (2) Pfizer Global R&D, New London, CT, USA

Background: Pregabalin (Lyrica®) is a voltage-gated calcium channel α2-δ ligand for the treatment of partial seizure, neuropathic pain and generalized anxiety disorder (GAD). It is reported that dizziness and somnolence are the most common adverse events (AEs) in pregabalin treatments. These AEs might be among the major reasons that cause people to drop out of the treatment. Quantitative understanding of such AEs, in terms of incidence and severity over the course of study, therefore would provide the better treatment guideline for patients. With this as a background, this study was designed to analyze daily somnolence scores collected from 6 randomized, double-blind, multiple-dose, placebo-controlled, parallel-group studies in patients with GAD. Treatment was up to five to seven weeks and ranged from the dose of 150 to 600 mg/day given as BID or TID regimen with a one-week dose titration and a one-week taper period.

Objectives: This study aimed to investigate the dose-AE(somnolence)-dropout relationship of pregabalin, in terms of incidence and severity, following oral doses given in patients with GAD.  

Methods: The relationship of dose-AE-dropout was modeled using the two-part mixture AE model in which separate models were developed for the incidence of AE and for the severity of AE given that an AE has occurred [1], [2]. The data were analyzed using NONMEM 7.

Incidence model: A logistic regression model was used to describe the incidence data where the logit was described as a sum of baseline and drug effect. No interindividual random effect was considered because each subject had only one incidence record of either "occurred (AE=1)" or "not occurred (AE=0)". Several types of models for drug effect were tested such as linear, Emax, and sigmoid Emax models. In each model, the resulting predicted incidence was compared by dose, and 95% confidence intervals (CI) were calculated by a nonparametric-bootstrap method (n=1000).

Conditional severity model: A longitudinal proportional odds model [3] was used to describe the relationship between the probability of daily AE scores measured by the ordered categorical scale (none, mild, moderate, and severe) and pregabalin exposure (titrated daily dose). The logit was described as a sum of baseline parameters, placebo and drug effects, with interindividual random effects being included. Several drug effect models including linear, Emax and sigmoid Emax models were tested, considering time-dependent effects of drug exposure and exponential attenuation of AE. The model was further elaborated by incorporating a first-order Markov model [3], [4] to account for the correlation between adjacent observations, in which the prediction was assumed dependent on the previous observation.

Unconditional severity probability: The incidence and the conditional severity probabilities were then multiplied each other to obtain the joint probability for the incidence and the severity of AE. The joint probabilities were summed over the possible outcomes for AE status (i.e., AE = 0 and 1) to obtain the marginal (unconditional) severity probability.

Dropout model: To explore the influence of AE on the patient withdrawal status, the dropout model was incorporated into both the incidence and the conditional severity model. For the incidence model, the dropout likelihood was estimated by dose, then the overall likelihood was obtained by multiplying the incidence likelihood and the dropout likelihood for each dose. For the conditional severity model, the time to dropout was treated as a survival variable where the hazard of dropout was assumed constant at each severity level, with no interindividual variation included. The overall likelihood was obtained as the severity likelihood multiplied by the dropout likelihood for each severity level [5].

Results: The dataset consisted of 47,218 observations collected from 1,630 patients. For the incidence model, the drug effect in the logit was adequately described by the Emax model. The predicted mean (95% CI) incidence was 24.6% (20.2-29.5%) at the dose of 150 mg/day, which was about 2-fold higher compared to the placebo group of 11.8% (8.9-14.8%).The predicted incidence tended to increase with dose, reaching 32.4% (28.8-36.5%) at the dose of 600 mg/day. For the conditional severity model, a monoexponential function was chosen for the placebo effect in the logit, and the Emax model for the drug effect, in which both time-dependent effects of drug exposure and attenuation of AE significantly improved the model fit. Adding a Markov component further improved the model, yielding the rate constants (half-life) for the placebo effect, time-dependent drug exposure effect, and attenuated AE effect of 3/day (0.23 day), 0.689/day (1 .01 days), and 0.102/day (6.8 days), respectively. The visual inspection of unconditional severity probability versus time computed from the above choice of model revealed that after reaching the peak probability in about 5 days the incidence and the severity of AE declined over 3-4 weeks, as expected from the estimated half-life of attenuation effect of 6.8 days. For the incidence-dropout model, the predicted dropout rate matches well with the observed dropout rate, with placebo and drug effect parameters being almost identical to the case not modeling dropout events. For the severity-dropout model, the predicted dropout rate was lowest for patients who experienced no AE and abruptly increased for those with severe somnolence. It was predicted that the probability of dropout for no AE was as high as for the mild AE partly because other kinds of AEs such as dizziness have occurred to these patients, which might have acted as other sources of dropout.

Conclusions: This study showed that the probability of somnolence incidence increases with the dose in pregabalin treatments. A combined model of the proportional odds model and the Markov model well described the time course of AE rates where time-dependent effects of drug exposure and attenuation of AE were found significant. Including a dropout model did not improve the model fit, indicating no significant dropout effect present. A further study will be needed to validate the proposed model. 

[1] Ito K, Hutmacher MM, Liu J, Qiu R, Frame B, Miller R. Exposure-response Analysis for Spontaneously Reported Dizziness in Pregabalin-treated Patient With Generalized Anxiety Disorder; Clinical Pharmacology & Therapeutics (2008); 84(1): 127-135
[2] Kowalski KG, McFadyen L, Hutmacher MM, Frame B, Miller R. A two-Part Mixture Model for Longitudinal Adverse Event Severity Data; Journal of Pharmacokinetics and Pharmacodynamics (2003); 30(5): 315-336
[3] Ette EI, Transition Models in Pharmacodynamics. In Ette EI and Williams PJ (Eds.),Pharmacometrics; 2007 John Wiley & Sons, Inc.; 689-698.
[4] Zingmark PH, Kagedal M, Karlsson MO. Modelling a Spontaneously Reported Side Effect by Use of a Markov Mixed-Effects Model; Journal  of Pharmacokinetics and Pharmacodynamics, (2005); 32(2): 261-281.
[5] Ette EI, Roy A, Nandy P. Population Pharmacokinetic/Pharmacodynamic Modeling of Ordered Categorical Longitudinal Data. In Ette EI and Williams PJ (Eds.), Pharmacometrics; 2007 John Wiley & Sons, Inc.; 655-688.

Reference: PAGE 19 (2010) Abstr 1703 [www.page-meeting.org/?abstract=1703]
Oral Presentation : Lewis Sheiner Student Session
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