PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

Dumont, Cyrielle; Lestini, Giulia; Nagard, Hervé Le; Comets, Emmanuelle; Nguyễn, Thu Thủy

doi:10.1016/j.cmpb.2018.01.008

Cited by 31 publications

(25 citation statements)

References 45 publications

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“…For a satisfactory strategy, the predicted root mean square error for all fixed parameters had to be lower than 30%. Evaluation and optimization were performed using PFIM 4.0 software …”

Section: Methodsmentioning

confidence: 99%

Dose tailoring of human cell line‐derived recombinant factor VIII simoctocog alfa: Using a limited sampling strategy in patients with severe haemophilia A

Delavenne

Dargaud

Ollier

et al. 2019

Brit J Clinical Pharma

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Funding information OctapharmaAims: The use of factor VIII (FVIII) prophylaxis in haemophilia A is considered the standard of care, particularly in children. Despite adjustment of doses for body weight and/or age, a large pharmacokinetic (PK) variability between patients has been observed. PK-tailored prophylaxis may help clinicians adjust coagulation factor FVIII activity (FVIII:C) to the desired level, which may differ in individual patients. The objective was to develop a population PK model for simoctocog alfa based on pooled clinical trial data and to develop a Bayesian estimator to allow PK parameters in individual patients to be estimated using a reduced number of blood samples.Methods: PK data from 86 adults and 29 children/adolescents with severe haemophilia A were analysed. The FVIII data measured using 2 different assays (chromogenic and the 1-stage clotting assay) were fit to separate develop population PK models using nonlinear mixed-effect models. A Bayesian estimator was then developed to estimate the time above the threshold of 1%.Results: The PK data for chromogenic and the 1-stage clotting assays were both best described by a 2-compartment models. Simulations demonstrated good predictive capacity. The limited sampling strategy using blood sample at 3 and 24 hours allowed an accurate estimation of the time above the threshold of 1% FVIII:C (mean bias 0.01 and 0.11, mean precision 0.18 and 0.45 for 2 assay methods). Conclusion:In this study, we demonstrated that a Bayesian approach can help to reduce the number of samples required to estimate the time above the threshold of 1% FVIII:C with good accuracy.

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“…For a satisfactory strategy, the predicted root mean square error for all fixed parameters had to be lower than 30%. Evaluation and optimization were performed using PFIM 4.0 software …”

Section: Methodsmentioning

confidence: 99%

Dose tailoring of human cell line‐derived recombinant factor VIII simoctocog alfa: Using a limited sampling strategy in patients with severe haemophilia A

Delavenne

Dargaud

Ollier

et al. 2019

Brit J Clinical Pharma

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“…Optimal sampling times. Optimal times were estimated according to Doptimality criterion, using PFIM Interface 4.0 (25). The model employed in this process was designed to prioritize the correct adjustment of the active metabolite, 5-FU, using the same methodology as in the joint population PK model.…”

Section: Study Populationmentioning

confidence: 99%

Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

Oyaga-Iriarte¹,

Insausti²,

Bueno³

et al. 2019

J Pharm Pharm Sci

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Purpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases.

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“…R version 3.3.1 was used for calculations, data management and visualizations ( 18 ). For optimal design procedures of step 2 of the analytical workflow, the PFIM program 4.0 running in R was used ( 19 ). PBPK simulations were performed with Simcyp software (Simcyp, Sheffield, UK) V15.R1 to derive PBPK model parameters value for optimal design procedures in step 2 and the stochastic simulations and estimations in step 3.…”

Section: Methodsmentioning

confidence: 99%

“…The models, parameters and designs described above were implemented in PFIM 4.0 ( 19 ). Concentration-time profiles obtained for both drug A and B were simultaneously analysed to enable for the estimation of Θ and ω 2 for CLint ,u,WL and Qh.…”

Section: Methodsmentioning

confidence: 99%

Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory

et al. 2018

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PurposePhysiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint,u,WL) and hepatic blood flow (Qh) in children.MethodsFirst, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation.ResultsPrecise estimation of CLint,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty.ConclusionThe proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.Electronic supplementary materialThe online version of this article (10.1007/s11095-018-2487-1) contains supplementary material, which is available to authorized users.

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PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

Abstract: PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.

Cited by 31 publications

References 45 publications

Dose tailoring of human cell line‐derived recombinant factor VIII simoctocog alfa: Using a limited sampling strategy in patients with severe haemophilia A

Dose tailoring of human cell line‐derived recombinant factor VIII simoctocog alfa: Using a limited sampling strategy in patients with severe haemophilia A

Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory

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