Optimal sampling strategies for darunavir and external validation of the underlying population pharmacokinetic model.

Purpose: A variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external...

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Publicado en:European Journal of Clinical Pharmacology Vol. 77; no. 4; pp. 607 - 617
Autores principales: Stillemans, Gabriel, Belkhir, Leila, Vandercam, Bernard, Vincent, Anne, Haufroid, Vincent, Elens, Laure
Formato: equations & formulas research tables/charts Journal Article
Publicado: Springer Nature Apr2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2021
      vid: 77
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s00228-020-03036-2
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        atl: Optimal sampling strategies for darunavir and external validation of the underlying population pharmacokinetic model.
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          Stillemans, Gabriel
          Belkhir, Leila
          Vandercam, Bernard
          Vincent, Anne
          Haufroid, Vincent
          Elens, Laure
        affil: Integrated PharmacoMetrics, PharmacoGenomics and PharmacoKinetics, Louvain Drug Research Institute, Université catholique de Louvain, Avenue E. Mounier 72, B01.72.0, Brussels, Belgium
      sug:
        subj:
          HIV Infections Drug Therapy
          HIV Protease Inhibitors Pharmacokinetics
          Ritonavir Pharmacokinetics
          Acids, Acyclic Pharmacokinetics
          Drug Combinations Pharmacokinetics
          Prediction Models
          Population Surveillance
          Human
          Validation Studies
          Drug Monitoring Methods
          Drug Hypersensitivity
          HIV Protease Inhibitors Administration and Dosage
      ab: Purpose: A variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible. Methods: A validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations. Results: External validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly. Conclusions: Optimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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