Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations

Deng, Chonghai; Plan, Elodie L.; Karlsson, Mats O.

doi:10.1007/s10928-016-9473-1

Cited by 12 publications

(12 citation statements)

References 14 publications

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“…The effect of corticosteroid use at baseline on the infliximab volume of distribution might reflect the physician's anticipation of higher disease activity (leaky gut) in selected patients . Furthermore, higher CRP and lower ALB reflect higher disease activity, possibly explaining the increased infliximab elimination by a higher target load . Although CRP is often in the normal range (≤5 mg/L) in patients with active UC, it was elevated in 112/204 (55%) patients in our cohort.…”

Section: Discussionmentioning

confidence: 76%

“…It is well known that the pharmacokinetics of infliximab varies between and within patients . Part of this between‐ and within‐subject variability might be explained by variables such as C‐reactive protein (CRP), albumin (ALB), antidrug antibody titres and faecal calprotectin . In a small cohort study, Brandse et al observed lower infliximab exposure during the first 6 weeks of therapy in patients with high CRP at baseline …”

Section: Introductionmentioning

confidence: 99%

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Optimising infliximab induction dosing for patients with ulcerative colitis

Dreesen

Faelens

Assche

et al. 2019

Brit J Clinical Pharma

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Aims: The therapeutic failure of infliximab therapy in patients with ulcerative colitis remains a challenge even 2 decades after its approval. Therapeutic drug monitoring (TDM) has shown value during maintenance therapy, but induction therapy has still not been explored. Patients may be primary nonresponders or underexposed with the standard dosing regimen. We aimed to: (i) develop a population pharmacokineticpharmacodynamic model; (ii) identify the best exposure metric that predicts mucosal healing; and (iii) build an exposure-response (ER) model to demonstrate model-based dose finding during induction therapy with infliximab. Methods: Data were retrospectively collected from a clinical database. A total of 583 samples, from 204 patients, was used to develop a population pharmacokinetic model to generate exposure metrics for subsequent ER modelling. A subset of 159 patients was used to develop a logistic regression ER model, describing the relationship between infliximab exposure and ordered transitions between Mayo endoscopic subscore (MES) 3, 2 and ≤1 (baseline to post-induction).Results: A 1-compartment population pharmacokinetic model with interindividual and interoccasion variability was found to fit the data best. Covariates influencing exposure were C-reactive protein, albumin, baseline MES, fat-free mass, concomitant corticosteroid use and pancolitis. The cumulative area under the infliximab concentration-time curve until endoscopy (CAUC endoscopy ) was found to be the best exposure metric for predicting mucosal healing (baseline MES >1 and post-induction MES ≤1). The model predicted that 70% of patients will attain mucosal healing with infliximab administered at days 0, 14 and 42 and a target CAUC endoscopy of 3752 mg/L*day at day 84.Conclusions: TDM-based dose individualisation targeting CAUC endoscopy has the potential to improve the effectiveness of infliximab during induction therapy.

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Section: Discussionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Optimising infliximab induction dosing for patients with ulcerative colitis

Dreesen

Faelens

Assche

et al. 2019

Brit J Clinical Pharma

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“…longitudinal age information or yes/no information of an interacting drug). If the individual parameters change systematically over time due to unknown reasons, such trends can be modelled using alternative approaches such as the dynamic IOV or stochastic differential equations …”

Section: Discussionmentioning

confidence: 99%

Handling interoccasion variability in model‐based dose individualization using therapeutic drug monitoring data

Abrantes

Jönsson

Karlsson

et al. 2019

Brit J Clinical Pharma

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Aims: This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.Methods: We assessed 5 model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV;and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) reestimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error percentiles.Results: When IOV was lower than IIV, the accuracy was good for all approaches (50 th percentile of the prediction error [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.Conclusions: Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualization is to include IOV in the generation of the EBEs but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

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“…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,…”

Section: Modelsmentioning

confidence: 99%

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

Tharayil

Chiang

Moss³

et al. 2017

Epilepsia

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OBJECTIVE Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure recording paradigms. Most existing seizure count models do not include children and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS Using data from SeizureTracker.com, a patient-reported seizure diary tool with over 1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian Information Criteria. Generalized linear mixed-effects models were used to account for demographic and etiological covariates and for autocorrelation structure. Hold-out cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS For both adults and children, we found that a negative binomial model with autocorrelation over one day was optimal. Using hold-out cross validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to 4 seizures per day. SIGNIFICANCE The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

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Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations

Cited by 12 publications

References 14 publications

Optimising infliximab induction dosing for patients with ulcerative colitis

Optimising infliximab induction dosing for patients with ulcerative colitis

Handling interoccasion variability in model‐based dose individualization using therapeutic drug monitoring data

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

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