Nonlinear Mixed‐Effects Model Development and Simulation Using nlmixr and Related R Open‐Source Packages

Fidler, Matthew; Wilkins, Justin; Hooijmaijers, Richard; Post, Teun M.; Schoemaker, Rik; Trame, Mirjam N.; Xiong, Yuning; Wang, Wenping

doi:10.1002/psp4.12445

Cited by 56 publications

(58 citation statements)

References 36 publications

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“…We applied NLME modeling ( Fidler et al., 2019 ). We implemented a two-compartment model with first-order absorption and first-order elimination ( Strauss and Bourne, 1995 ), and initialized the parameters ( Lu et al., 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens

Lu¹,

Deng²,

Zhang³

et al. 2021

iScience

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Section: Methodsmentioning

confidence: 99%

Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens

Lu¹,

Deng²,

Zhang³

et al. 2021

iScience

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“…The Friberg population parameter estimates and distributions were used to identify the individual parameter estimates that best described each patient neutrophil profile. For that, the posthoc option of the nlmixr package in R was used 31,33 …”

Section: Methodsmentioning

confidence: 99%

“…For that, the posthoc option of the nlmixr package in R was used. 31,33 Docetaxel plasma concentrations were not available in the training dataset nor in the validation dataset. Therefore, the Fukae et al…”

Section: Neutrophil Metricsmentioning

confidence: 99%

Dose individualisation in oncology using chemotherapy‐induced neutropenia: Example of docetaxel in non‐small cell lung cancer patients

Lombard

Mistry

Aarons

et al. 2020

Brit J Clinical Pharma

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Aims: Chemotherapy-induced neutropenia has been associated with an increase in overall survival in non-small cell lung cancer patients. Therefore, neutrophil counts could be an interesting biomarker for drug efficacy as well as linked directly to toxicity. For drugs where neutropenia is dose limiting, neutrophil counts might be used for monitoring drug effect and for dosing optimisation. Methods: The relationship between drug effect on the first cycle neutrophil counts and patient survival was explored in a Phase III clinical trial where patients with nonsmall cell lung cancer were treated with docetaxel. Once the association has been established, dosing optimisation was performed for patients with severe toxicities (neutropenia) without compromising drug efficacy (overall survival). Results: After taking into account baseline prognostic factors, such as Eastern Cooperative Oncology Group performance status, smoking status, liver metastasis, tumour burden, neutrophil counts and albumin levels, a model-predicted drug effect on the first cycle neutrophil counts was strongly associated with patient survival (P = .005). Utilising this relationship in a dose optimisation algorithm, 194 out of 366 patients would have benefited from a dose reduction after the first cycle of docetaxel. The simulated 1-year survival probabilities associated with the original dose and the individualised dose were not different. Conclusion: The strong relationship between drug effect on the first cycle neutrophil counts and patient survival suggests that this variable could be used to individualise dosing, possibly without needing pharmacokinetic samples. The algorithm highlights that doses could be reduced in case of severe haematological toxicities without compromising drug efficacy. K E Y W O R D S chemotherapy-induced neutropenia, docetaxel, neutrophil counts, non-small cell lung cancer, precision dosing The authors confirm that the PI for this paper is Aurélie Lombard. This is a retrospective data analysis and there were no interventions performed or substances administered to patients.

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“…The model defined in Case 2 was used, and the number of subjects was set to 100. Expected aggregate data were simulated with FO, FOCE and MC approximations of aggregate data (equations [15][16][17][18]. Then, the Hessian of aggregate data log-likelihood as a function of parameters was calculated using each of the approximations.…”

Section: Case 2: Parameter Estimation Accuracymentioning

confidence: 99%

“…A further reason for individual-data FOCE being superior to aggregate-data FOCE is that when individual data are available, the log-likelihoods are calculated individually and only summed together at the end. On the other hand, when aggregate data are used, the expected mean vectorỹ and variance-covariance matrixṼ are calculated based on a set of quasi-random individual parameters (equations [15][16]. Then, the mean expectedỹ andṼ are used in the calculation of aggregate data log-likelihood.…”

Section: Limitationsmentioning

confidence: 99%

Pharmacometric estimation methods for aggregate data, including data simulated from other pharmacometric models

Välitalo

2021

J Pharmacokinet Pharmacodyn

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Lack of data is an obvious limitation to what can be modelled. However, aggregate data in the form of means and possibly (co)variances, as well as previously published pharmacometric models, are often available. Being able to use all available data is desirable, and therefore this paper will outline several methods for using aggregate data as the basis of parameter estimation. The presented methods can be used for estimation of parameters from aggregate data, and as a computationally efficient alternative for the stochastic simulation and estimation procedure. They also allow for population PK/PD optimal design in the case when the data-generating model is different from the data-analytic model, a scenario for which no solutions have previously been available. Mathematical analysis and computational results confirm that the aggregate-data FO algorithm converges to the same estimates as the individual-data FO and yields near-identical standard errors when used in optimal design. The aggregate-data MC algorithm will asymptotically converge to the exactly correct parameter estimates if the data-generating model is the same as the data-analytic model. The performance of the aggregate-data methods were also compared to stochastic simulations and estimations (SSEs) when the data-generating model is different from the data-analytic model. The aggregate-data FO optimal design correctly predicted the sampling distributions of 200 models fitted to simulated datasets with the individual-data FO method. Graphic abstract

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Nonlinear Mixed‐Effects Model Development and Simulation Using nlmixr and Related R Open‐Source Packages

Cited by 56 publications

References 36 publications

Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens

Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens

Dose individualisation in oncology using chemotherapy‐induced neutropenia: Example of docetaxel in non‐small cell lung cancer patients

Pharmacometric estimation methods for aggregate data, including data simulated from other pharmacometric models

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