Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Nyberg, Joakim; Bazzoli, Caroline; Ogungbenro, K; Aliev, Alexander; Leonov, Sergei; Duffull, Stephen B.; Hooker, Andrew C.; Mentré, France

doi:10.1111/bcp.12352

Cited by 73 publications

(87 citation statements)

References 38 publications

(62 reference statements)

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“…This package currently uses PopED (28,29), NONMEM (26), PsN (30), and R (31) to handle the various tasks inherent to simulating and evaluating the MBAOD process. Parallel computational processes available in the MBAOD R-package and compilation in Clanguage of the scripts were also used to shorten runtimes.…”

Section: Tools Usedmentioning

confidence: 99%

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

Pierrillas

Fouliard

Chenel

et al. 2018

AAPS J

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Abstract.Design of phase 1 combination therapy trials is complex compared to single therapy trials. In this work, model-based adaptive optimal design (MBAOD) was exemplified and evaluated for a combination of paclitaxel and a hypothetical new compound in a phase 1 study to determine the best dosing regimen for a phase 2 trial. Neutropenia was assumed as the main toxicity and the dose optimization process targeted a 33% probability of grade 4 neutropenia and maximal efficacy (based on preclinical studies) by changing the dose amount of both drugs and the dosing schedule for the new drug. Different starting conditions (e.g., initial dose), search paths (e.g., maximal change in dose intensity per step), and stopping criteria (e.g., B3 + 3 rule^) were explored. The MBAOD approach was successfully implemented allowing the possibility of flexible designs with the modification of doses and dosing schedule throughout the trial. The 3 + 3 rule was shown to be highly conservative (selection of a dosing regimen with at least 90% of the possible maximal efficacy in less than 21% of the cases) but also safer (selection of a toxic design in less than 2% of the cases). Without the 3 + 3 rule, better performance was observed (>67% of selected designs were associated with at least 90% of possible maximal efficacy) while the proportion of DLTs per trial was similar. Overall, MBAOD is a promising tool in the context of dose finding studies of combination treatments and was showed to be flexible enough to be associated with requirements imposed by clinical protocols.

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Section: Tools Usedmentioning

confidence: 99%

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

Pierrillas

Fouliard

Chenel

et al. 2018

AAPS J

Self Cite

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“…A more detailed discussion of optimal design, including the advantages and disadvantages associated with different methods and software programs, can be found in detailed reviews by Roberts et al . and Nyberg et al 17, 18…”

Section: Optimal Sampling Timesmentioning

confidence: 88%

Use of Modeling and Simulation in the Design and Conduct of Pediatric Clinical Trials and the Optimization of Individualized Dosing Regimens

Barrett

Roberts

Sherwin

2015

CPT Pharmacometrics Syst. Pharmacol.

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Mathematical models of drug action and disease progression can inform pediatric pharmacotherapy. In this tutorial, we explore the key issues that differentiate pediatric from adult pharmacokinetic (PK) / pharmacodynamic (PD) studies, describe methods to calculate the number of participants to be enrolled and the optimal times at which blood samples should be collected, and therapeutic drug monitoring methods for individualizing pharmacotherapy. The development of pediatric‐specific drug dosing dashboards is also highlighted, with an emphasis on clinical‐relevance and ease of use.

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“…The predicted M F after linearisation is a block matrix with a block corresponding to derivatives of the log-likelihood with respect to the fixed effects, a block for derivatives with respect to the standard derivation terms and a block containing mixed derivatives with respect to all parameters. In our work, the block of mixed derivatives was set to 0 for linearisation, based on publications showing the better performance of the block diagonal expression compared with the full one (Mielke and Schwabe, 2010;Fedorov and Leonov, 2014;Nyberg et al, 2014).…”

Section: Fisher Information Matrixmentioning

confidence: 99%

Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature

Nguyễn

2014

Computational Statistics & Data Analysis

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. Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature. Computational Statistics and Data Analysis, Elsevier, 2014, 80, pp.57 -69 AbstractNonlinear mixed effect models (NLMEM) are used in model-based drug development to analyse longitudinal data. To design these studies, the use of the expected Fisher information matrix (M F ) is a good alternative to clinical trial simulation. Presently, M F in NLMEM is mostly evaluated with first-order linearisation. The adequacy of this approximation is, however, influenced by model nonlinearity. Alternatives for the evaluation of M F without linearisation are proposed, based on Gaussian quadratures. The M F , expressed as the expectation of the derivatives of the log-likelihood, can be obtained by stochastic integration. The likelihood for each simulated vector of observations is approximated by Gaussian quadrature centred at 0 (standard quadrature) or at the simulated random effects (adaptive quadrature). These approaches have been implemented in R. Their relevance was compared with clinical trial simulation and linearisation, using dose-response models, with various nonlinearity levels and different number of doses per patient. When the nonlinearity was mild, three approaches based on M F gave correct predictions of standard errors, when compared with the simulation. When the nonlinearity increased, linearisation correctly predicted standard errors of fixed effects, but over-predicted, with sparse designs, standard errors of some variability terms. Meanwhile, quadrature approaches gave correct predictions of standard errors overall, but standard Gaussian quadrature was very time-consuming when there were more than two random effects. To conclude, adaptive Gaussian quadrature is a relevant alternative for the evaluation of M F for models with stronger nonlinearity, while being more computationally efficient than standard quadrature.

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Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Cited by 73 publications

References 38 publications

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

Use of Modeling and Simulation in the Design and Conduct of Pediatric Clinical Trials and the Optimization of Individualized Dosing Regimens

Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature

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