Exposure-response analysis of pregabalin add-on treatment of patients with refractory partial seizures

Miller, Raymond; Frame, Bill; Corrigan, Brian; Burger, Paula; Bockbrader, Howard N.; Garofalo, Elizabeth; Lalonde, Richard L.

doi:10.1016/s0009-9236(03)00049-3

Cited by 46 publications

(33 citation statements)

References 19 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several count data models have been proposed, including a mixture of two Poisson models [33] and a hidden Markov model. [34] Such models assume that the probability function of the number of seizures is piecewise-constant over time.…”

Section: Epileptic Seizure Countsmentioning

confidence: 99%

Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

Mbogning

Bleakley

Lavielle

2014

Journal of Statistical Computation and Simulation

View full text Add to dashboard Cite

We propose a nonlinear mixed-effects framework to jointly model longitudinal and repeated time-to-event data. A parametric nonlinear mixed-effects model is used for the longitudinal observations and a parametric mixed-effects hazard model for repeated event times. We show the importance for parameter estimation of properly calculating the conditional density of the observations (given the individual parameters) in the presence of interval and/or right censoring. Parameters are estimated by maximizing the exact joint likelihood with the stochastic approximation expectation-maximization algorithm. This workflow for joint models is now implemented in the Monolix software, and illustrated here on five simulated and two real datasets.

show abstract

Section: Epileptic Seizure Countsmentioning

confidence: 99%

Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

Mbogning

Bleakley

Lavielle

2014

Journal of Statistical Computation and Simulation

View full text Add to dashboard Cite

show abstract

“…Once baseline model was developed and evaluated, development of the placebo and drug model followed. Placebo effect (d [1][2][3][4] ) was introduced as a step function after the treatment onset on all four parameters defining the seizures baseline. Similarly, gabapentin drug effect was introduced as a linear function of dose (c 1-4 Dose i ) on log or logit transformations of all four parameters of the baseline model.…”

Section: Mixed Hidden Markov Modelmentioning

confidence: 99%

“…The count data analysis for repeated measurements has been most commonly used methodology for the analysis of seizure data. Miller et al [1] used Mixed Poisson model to describe exposure response relationship of pregabalin. Similar was the case in the work by Snoeck and Stockis [2], describing dose response relationship for levetiracetam.…”

Section: Introductionmentioning

confidence: 99%

Analysis of exposure–response of CI-945 in patients with epilepsy: application of novel mixed hidden Markov modeling methodology

Delattre

Savic

Miller

et al. 2012

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

We propose to describe exposure-response relationship of an antiepileptic agent, using mixed hidden Markov modeling methodology, to reveal additional insights in the mode of the drug action which the novel approach offers. Daily seizure frequency data from six clinical studies including patients who received gabapentin were available for the analysis. In the model, seizure frequencies are governed by underlying unobserved disease activity states. Individual neighbouring states are dependent, like in reality and they exhibit their own dynamics with patients transitioning between low and high disease states, according to a set of transition probabilities. Our methodology enables estimation of unobserved disease dynamics and daily seizure frequencies in all disease states. Additional modes of drug action are achievable: gabapentin may influence both daily seizure frequencies and disease state dynamics. Gabapentin significantly reduced seizure frequencies in both disease activity states; however it did not significatively affect disease dynamics. Mixed hidden Markov modeling is able to mimic dynamics of seizure frequencies very well. It offers novel insights into understanding disease dynamics in epilepsy and gabapentin mode of action.

show abstract

“…Analyzing counts of the events occurring during predefined time intervals is an alternative method more commonly used for the analysis of epileptic seizures Miller et al, 2003;Trocóniz et al, 2009). Count models often contain a low number of parameters and are easily used for simulations, but they may involve simplification of the data, with potential impact depending on the length of the time interval.…”

Section: Introductionmentioning

confidence: 99%

Transient Lower Esophageal Sphincter Relaxation Pharmacokinetic-Pharmacodynamic Modeling: Count Model and Repeated Time-To-Event Model

Plan

Ma²,

Någård³

et al. 2011

J Pharmacol Exp Ther

View full text Add to dashboard Cite

Transient lower esophageal sphincter relaxation (TLESR) is the major mechanism for gastroesophageal reflux. Characterizations of candidate compounds for reduction of TLESRs are traditionally done through summary exposure and response measures and would benefit from model-based analyses of exposure-TLESR events relationships. Pharmacokinetic (PK)-pharmacodynamic (PD) modeling approaches treating TLESRs either as count data or repeated time-to-event (RTTE) data were developed and compared in terms of their ability to characterize system and drug characteristics. Vehicle data comprising 294 TLESR events were collected from nine dogs.-yl]-1-naphthalenylmethanone mesylate (WIN55212-2)] data containing 66 TLESR events, as well as plasma concentrations, were obtained from four dogs. Each experiment lasted for 45 min and was initiated with a meal. Counts in equispaced 5-and 1-min intervals were modeled based on a Poisson probability distribution model. TLESR events were analyzed with the RTTE model. The PK was connected to the PD with a one-compartment model. Vehicle data were described by a baseline and a surge function; the surge peak was determined to be approximately 9.69 min by all approaches, and its width in time at half-maximal intensity was 5 min (1-min count and RTTE) or 10 min (5-min count). TLESR inhibition by WIN55212-2 was described by an I max model, with an IC 50 of on average 2.39 nmol ⅐ l Ϫ1. Modeling approaches using count or RTTE data linked to a dynamic PK-PD representation of exposure are superior to using summary PK and PD measures and are associated with a higher power for detecting a statistically significant drug effect.

show abstract

Exposure-response analysis of pregabalin add-on treatment of patients with refractory partial seizures

Cited by 46 publications

References 19 publications

Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

Analysis of exposure–response of CI-945 in patients with epilepsy: application of novel mixed hidden Markov modeling methodology

Transient Lower Esophageal Sphincter Relaxation Pharmacokinetic-Pharmacodynamic Modeling: Count Model and Repeated Time-To-Event Model

Contact Info

Product

Resources

About