Optimal Design in Population Kinetic Experiments by Set-Valued Methods

Gennemark, Peter; Danis, Alexander S.; Nyberg, Joakim; Hooker, Andrew C.; Tucker, Warwick

doi:10.1208/s12248-011-9291-8

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…An overview of the theoretical concepts, methods, and software programs available for optimization of design variables can be found elsewhere. [25][26][27][28][29] Prospective Clinical Study in Children < 6 Years of Age A prospective study has been proposed to characterize the pharmacokinetics of deferiprone in children. The protocol is part of a pediatric investigational plan and will involve a multicenter randomized, single-blind, and single-dose study in patients affected by transfusiondependent hemoglobinopathies younger than 6 years old.…”

Section: Methodsmentioning

confidence: 99%

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Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With β‐Thalassemia

Bellanti¹,

Iorio²,

Danhof³

et al. 2016

The Journal of Clinical Pharma

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Despite wide clinical experience with deferiprone, the optimum dosage in children younger than 6 years remains to be established. This analysis aimed to optimize the design of a prospective clinical study for the evaluation of deferiprone pharmacokinetics in children. A 1-compartment model with first-order oral absorption was used for the purposes of the analysis. Different sampling schemes were evaluated under the assumption of a constrained population size. A sampling scheme with 5 samples per subject was found to be sufficient to ensure accurate characterization of the pharmacokinetics of deferiprone. Whereas the accuracy of parameters estimates was high, precision was slightly reduced because of the small sample size (CV% >30% for Vd/F and KA). Mean AUC ± SD was found to be 33.4 ± 19.2 and 35.6 ± 20.2 mg · h/mL, and mean Cmax ± SD was found to be 10.2 ± 6.1 and 10.9 ± 6.7 mg/L based on sparse and frequent sampling, respectively. The results showed that typical frequent sampling schemes and sample sizes do not warrant accurate model and parameter identifiability. Expectation of the determinant (ED) optimality and simulation-based optimization concepts can be used to support pharmacokinetic bridging studies. Of importance is the accurate estimation of the magnitude of the covariate effects, as they partly determine the dose recommendation for the population of interest.

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Section: Methodsmentioning

confidence: 99%

“…A detailed description of the different steps and procedures is provided below. An overview of the theoretical concepts, methods, and software programs available for optimization of design variables can be found elsewhere …”

Section: Methodsmentioning

confidence: 99%

Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With β‐Thalassemia

Bellanti¹,

Iorio²,

Danhof³

et al. 2016

The Journal of Clinical Pharma

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“…They often require distributional assumptions and model linearization. They may encounter issues with local minima and underdetermined problems [ 124 ]. They are based solely on the plasma concentrations obtained from individual patients and applied directly to PK equations [ 123 ].…”

Section: Pharmacometrics Tools: Significance and Challenges In Precis...mentioning

confidence: 99%

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Marques,

Costa,

Pereira

et al. 2024

Pharmaceutics

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The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient’s uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the “five rights”: the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug–drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

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Optimal Design in Population Kinetic Experiments by Set-Valued Methods

Cited by 2 publications

References 23 publications

Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With β‐Thalassemia

Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With β‐Thalassemia

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

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