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Abstract
Model-informed precision dosing is being increasingly used to improve therapeutic drug monitoring. To meet this need, several tools have been developed, but open-source software remains uncommon. Posologyr is a free and open-source R package developed to enable Bayesian individual parameter estimation and dose individualization. Before using it for clinical practice, performance validation is mandatory. The estimation functions implemented in posologyr were benchmarked against reference software products on a wide variety of models and pharmacokinetic profiles: 35 population pharmacokinetic models, with 4.000 simulated subjects by model. Maximum A Posteriori (MAP) estimates were compared to NONMEM post hoc estimates, and full posterior distributions were compared to Monolix conditional distribution estimates. The performance of MAP estimation was excellent in 98.7% of the cases. Considering the full posterior distributions of individual parameters, the bias on dosage adjustment proposals was acceptable in 97% of cases with a median bias of 0.65%. These results confirmed the ability of posologyr to serve as a basis for the development of future Bayesian dose individualization tools.
Highlights
Model-informed precision dosing (MIPD) is an emerging dosing paradigm in which mathematical models are used for dose optimization, using individual information such as a patient’s age, organ function, and the results of therapeutic drug monitoring
Yij is the jth observation of subject i; N is the number of subjects; ni is the number of observations of subject i; f is the function defining the structural model; g is the function defining the residual error model; xij is the vector of regression variables; for subject i, the vector ψi is a vector of individual parameters: ψi = H (θ, ci, ηi )
Determination of the optimal dose to reach a concentration of 30 mg/L, 3 h after administration, with posologyr::poso_dose_conc; Determination of the optimal dose to achieve an AUC0-12h of 500 mg·h/L, with posologyr::poso_dose_auc; tion of individual parameters estimated using Monolix, and Outputalgorithm is the result of the same dose adjustment function based on the distribution of individual parameters estimated using either MCMC or Sequential Importance Resampling (SIR)
Summary
Model-informed precision dosing (MIPD) is an emerging dosing paradigm in which mathematical models are used for dose optimization, using individual information such as a patient’s age, organ function, and the results of therapeutic drug monitoring. Bayesian inference goes beyond point estimates: full posterior (conditional) distributions retain information about the uncertainty associated with individual parameter estimates. Taking this uncertainty into account helps to improve the quality of the predictions, and full Bayesian approaches have been shown superior to MAP-based approaches in dosing individualization [6]. The aim is for posologyr to be a reliable and open foundation for the development of future MIPD tools To this end, the extensive validation of the predictive performance of the various implemented functions was an essential requirement
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