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

Abstract

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.

A big data approach to the development of mixed‐effects models for seizure count data

“…1 We extend these models to account for patient-level demographic and etiologic covariates, effects unexplored in the literature. 1,2,11,12,[24][25][26][27][28][29][30] Our ZINB model is similar to our NB model, but for a certain percentage of days, denoted by p, seizure number is set to zero regardless of probabilities given by the NB formula. Formally,…”

Individualized Dosing Algorithms and Therapeutic Monitoring for Antiepileptic Drugs

“…It also shows how clinical decision criteria and therapeutic guidelines can benefit from quantitative clinical pharmacology methods. We anticipate that as the relationships between AED exposure and efficacy become elucidated, [26][27][28][29][30] this approach may be further refined by targeting individualized plasma concentrations to account for variability in pharmacodynamics. In any case, the assumption that standard doses and dosing regimens, whether or not corrected empirically by body weight or other covariate factors, is no longer defendable for AEDs.…”

Modeling and Simulation of Count Data

“…In a hidden Markov model, the latent system transitions between hidden states, with the state where it is positioned at a certain time altering the probabilities for the observations at that time. This methodology was implemented for mixed‐effects count models 41 …”

“…This methodology was implemented for mixed-effects count models. 41 Markov models are integrated as components to probability models through functions of a prior observation affecting the probability of a current observation (Eq. 12).…”