ArticleJournal of pharmacokinetics and pharmacodynamics2012
A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies.
Article in Journal of pharmacokinetics and pharmacodynamics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Remedial Dosing Recommendations for Sirolimus Delayed or Missed Dosages Caused by Poor Medication Compliance in Pediatric Tuberous Sclerosis Complex Patients.Current pharmaceutical design · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalog of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical population pharmacokinetic study, and compare our results with those obtained via a simulation method from the literature.
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Registered trials
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