Efficient Pharmacokinetic Modeling Workflow With the MonolixSuite: A Case Study of Remifentanil

Traynard, Pauline; Ayral, Géraldine; Twarogowska, Monika; Chauvin, Jonathan

doi:10.1002/psp4.12500

Cited by 45 publications

(55 citation statements)

References 9 publications

(11 reference statements)

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“…The conditional distribution of the vector of individual parameters was estimated for each patient using the Metropolis-Hastings algorithm and was used to calculate the 95% predictive interval of the viral load curve in Fig 1 . The mixed-model approach is becoming more common in longitudinal viral load data analysis [26,33], because it can capture the heterogeneity in virus dynamics, and parameter estimation is feasible even for those with limited data. Fitting was performed using MONOLIX 2019R2 (www.lixoft.com) [34]. To account for data points under the detection limit (see the red dots in S1 Fig), the likelihood function reflected the likelihood that the…”

Section: Parameter Estimation With the Nonlinear Mixed-effects Modelmentioning

confidence: 99%

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

et al. 2021

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Background Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. Methods and findings A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d−1 (95% CI: 1.06 to 1.27 d−1), 0.777 d−1 (0.716 to 0.838 d−1), and 0.450 d−1 (0.378 to 0.522 d−1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. Conclusions In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

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Section: Parameter Estimation With the Nonlinear Mixed-effects Modelmentioning

confidence: 99%

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

et al. 2021

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“…Antony, France: Lixoft SAS, 2019.). Population PK parameters were estimated by maximum likelihood using Stochastic Approximation Expectation-Maximization (SAEM) algorithm [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…The basic population PK model included a combination of structural and statistical models. The structural PK models consisted of one- and two-compartment systems with first-order elimination, whereas the statistical PK models consisted of systems where individual PK parameters were assumed to follow log-normal distributions [ 27 ] and where exponential random effects were applied for inter-individual variabilities as followed: Pi = P × e ηPi …”

Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetics and Dose Optimization of Ceftazidime and Imipenem in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease

et al. 2021

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Background: Ceftazidime and imipenem have been increasingly used to treat Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) due to their extended-spectrum covering Pseudomonas aeruginosa. This study aims to describe the population pharmacokinetic (PK) and pharmacodynamic (PD) target attainment for ceftazidime and imipenem in patients with AECOPD. Methods: We conducted a prospective PK study at Bach Mai Hospital (Viet Nam). A total of 50 (ceftazidime) and 44 (imipenem) patients with AECOPD were enrolled. Population PK analysis was performed using Monolix 2019R1 and Monte Carlo simulations were conducted to determine the optimal dose regimen with respect to the attainment of 60% and 40% fT>MIC for ceftazidime and imipenem, respectively. A dosing algorithm was developed to identify optimal treatment doses. Results: Ceftazidime and imipenem PK was best described by a one-compartment population model with a volume of distribution and clearance of 23.7 L and 8.74 L/h for ceftazidime and 15.1 L and 7.88 L/h for imipenem, respectively. Cockcroft–Gault creatinine clearance represented a significant covariate affecting the clearance of both drugs. Increased doses with prolonged infusion were found to cover pathogens with reduced susceptibility. Conclusions: This study describes a novel and versatile three-level dosing algorithm based on patients’ renal function and characteristic of the infective pathogen to explore ceftazidime and imipenem optimal regimen for AECOPD.

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“…This method overcomes the phenomenon of shrinkage, which can affect EBEs when the data are sparse. It will also avoid bias due to shrinkage and will produce more reliable diagnostic plots in the later stage of model development 34 . Residual unexplained variability (RUV) was evaluated using additive, proportional or combined (additive and proportional) error models.…”

Section: Methodsmentioning

confidence: 99%

Population pharmacokinetic modelling of intravenous immunoglobulin in patients with predominantly antibody deficiencies

Lee

Saffian

Makmor‐Bakry

et al. 2021

Brit J Clinical Pharma

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Aims There is considerable interpatient variability in the pharmacokinetics (PK) of intravenous immunoglobulin G (IVIG), causing difficulty in optimizing individual dosage regimen. This study aims to estimate the population PK parameters of IVIG and to investigate the impact of genetic polymorphism of the FcRn gene and clinical variability on the PK of IVIG in patients with predominantly antibody deficiencies. Methods Patients were recruited from four hospitals. Clinical data were recorded and blood samples were taken for PK and genetic studies. Population PK parameters were estimated by nonlinear mixed‐effects modelling in Monolix®. Models were evaluated using the difference in objective function value, goodness‐of‐fit plots, visual predictive check and bootstrap analysis. Monte Carlo simulation was conducted to evaluate different dosing regimens for IVIG. Results A total of 30 blood samples were analysed from 10 patients. The immunoglobulin G concentration data were best described by a one‐compartment model with linear elimination. The final model included both volume of distribution (Vd) and clearance (CL) based on patient's individual weight. Goodness‐of‐fit plots indicated that the model fit the data adequately, with minor model mis‐specification. Genetic polymorphism of the FcRn gene and the presence of bronchiectasis did not affect the PK of IVIG. Simulation showed that 3–4‐weekly dosing intervals were sufficient to maintain IgG levels of 5 g L−1, with more frequent intervals needed to achieve higher trough levels. Conclusions Body weight significantly affects the PK parameters of IVIG. Genetic and other clinical factors investigated did not affect the disposition of IVIG.

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Efficient Pharmacokinetic Modeling Workflow With the MonolixSuite: A Case Study of Remifentanil

Cited by 45 publications

References 9 publications

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Population Pharmacokinetics and Dose Optimization of Ceftazidime and Imipenem in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Population pharmacokinetic modelling of intravenous immunoglobulin in patients with predominantly antibody deficiencies

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