A pharmacodynamic Markov mixed-effect model for the effect of temazepam on sleep

Karlsson, Mats O.; Schoemaker, Rik C.; Kemp, B.; Cohen, Adam F.; Gerven, Joop M. A. van; Tuk, B.; Peck, Carl C.; Danhof, Meindert

doi:10.1067/mcp.2000.108669

Cited by 51 publications

(57 citation statements)

References 12 publications

(19 reference statements)

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“…More expansive nonlinear mixed-effects models have previously been developed to estimate the transition probabilities to different sleep states [15][16][17][18][19] ; however, for this work, phase advanced sleep data were dichotomized (awake and asleep) to form the framework to investigate D-optimality for non-homogeneous (over time) discrete responses.…”

Section: Methodsmentioning

confidence: 99%

“…Estimation of the probability of transitioning to another state, or transition probability (TP), from each particular sleep stage to another was obtained through the implementation of a non-homogeneous Markovchain model, using a nonlinear mixed-effect approach, similar to that previously reported 16,17 . If Yi = (Yi1, Yi2… Yin) is the vector of observations for the i th subject, then the probability that Yit is equal to the stage m (m=0 or 1) at epoch= t, given that the preceding observation was k (k ≠ m), has the following general structure:…”

Section: Dichotomous Markov Mixed-effect Sleep Modelmentioning

confidence: 99%

“…As these models describe the individual likelihood of observing a discrete response, a higher order linearization (than the first order) is typically required due to the non-linear random effects 16,17 . Therefore, parameter estimation was performed using NONMEM 7.1.2 ® using the LAPLACE option 24 .…”

Section: Dichotomous Markov Mixed-effect Sleep Modelmentioning

confidence: 99%

“…The NLMEMs created to describe the sleep architecture for this type of polysomnography (PSG) data are partitioned into separate, smaller models (sub-models) based on the previous sleep state (Markov-chain) and estimate the transition probabilities to different sleep states [15][16][17][18][19] . To account for non-homogeneity over time, temporal dependence is implemented using piecewise linear functions.…”

Section: Introductionmentioning

confidence: 99%

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Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model

Ernest

Nyberg

Karlsson

et al. 2014

J Pharmacokinet Pharmacodyn

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Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model. Pharmacokinetics and Pharmacodynamics, 41(6) Journal of Abstract.Objectives: D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the Fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. Methods:The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM total ). The reference designs were placebo, 0.1, 1-, 6-, 10-and 20-mg dosing for a 2-to 6-way crossover study in 6 dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE).Results: Contrary to expectations, the predicted parameter uncertainty obtained via FIM total was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM total . Conclusion: Through the use of an approximate analytic solution and weighting schemes, the FIM total for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased NLMEM parameter precision.

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Section: Methodsmentioning

confidence: 99%

Section: Dichotomous Markov Mixed-effect Sleep Modelmentioning

confidence: 99%

Section: Dichotomous Markov Mixed-effect Sleep Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model

Ernest

Nyberg

Karlsson

et al. 2014

J Pharmacokinet Pharmacodyn

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“…We internally validated the final model against the original dataset using two traditional visual diagnostics for categorical data: the visual predictive check (VPC) (8) and a check closely related to posterior predictive check, already implemented in (5) and called here simplified posterior predictive check (sPPC). In addition, a new diagnostic was introduced in this work to assess the accuracy and precision of model parameter estimates through a graphic description of transition probability time courses: the visual estimation check (VEC).…”

Section: Introductionmentioning

confidence: 99%

Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture: Internal and External Validation and Covariate Analysis

Bizzotto

Zamuner

Mezzalana

et al. 2011

AAPS J

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Abstract. Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model's predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.

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Pharmacokinetic–pharmacodynamic modeling of fostamatinib efficacy on ACR20 to support dose selection in patients with rheumatoid arthritis (RA)

Maringwa¹,

Kågedal

Martín

et al. 2014

The Journal of Clinical Pharma

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R788 (fostamatinib) is an oral prodrug that is rapidly converted into a relatively selective spleen tyrosine kinase (SYK) inhibitor R406, evaluated for the treatment of rheumatoid arthritis (RA). This analysis aimed at developing a pharmacodynamic model for efficacy using pooled ACR20 data from two phase II studies in patients with rheumatoid arthritis (TASKi1 and TASKi2), describing the effect of fostamatinib as a function of fostamatinib exposure (dose, R406 plasma concentration) and other explanatory variables. The exposure-response relationship of fostamatinib was implemented into a continuous time Markov model describing the time course of transition probabilities between the three possible states of ACR20 non-responder, responder, and dropout at each visit. The probability of transition to the ACR20 response state was linearly (at the rate constant level) related to average R406 plasma concentrations and the onset of this drug effect was fast. Further, increases of fostamatinib dose resulted in increased dropout and subsequent loss of efficacy. This analysis provided an increased understanding of the exposure-response relationship, and provided support for fostamatinib 100 mg BID an appropriate dose regimen for further clinical evaluation.

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A pharmacodynamic Markov mixed-effect model for the effect of temazepam on sleep

Abstract: By the development of a Markov model for these non-ordered six categorical data, the effect of temazepam on the sleep-wake status could be interpreted in terms of known mechanisms for sleep generation and benzodiazepine pharmacology.

Cited by 51 publications

References 12 publications

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model

Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture: Internal and External Validation and Covariate Analysis

Pharmacokinetic–pharmacodynamic modeling of fostamatinib efficacy on ACR20 to support dose selection in patients with rheumatoid arthritis (RA)

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