Population Pharmacokinetics: Fundamentals, Methods and Applications

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.

Population Biokinetic Modeling of Thyroid Uptake and Retention of Radioiodine

The aim of this study was to build a population biokinetic model for the uptake and retention of radioiodine in the thyroid of hyperthyroid patients. At Malmö University Hospital (Malmö, Sweden), a database with detailed information from (131)I-iodide therapies has been created. The database consisted of uptake measurement from 422 patients with the diagnoses of diffuse goiter, multinodular goiter, adenoma, and undefined. In total, 2013 uptake measurements, performed from 3 hours to 9 days after intake of a test activity of (131)I, were included. Nonlinear mixed effect modeling (NLME) was used to model the population biokinetics of (131)I. The structural model was parameterized in terms of the level of uptake (A), the rate constant for the absorption to the thyroid (k(a)) and the rate constant for the output from the thyroid (k(e)). Interindividual and interoccasion variabilities were added to all three structural parameters. The following covariates were found to significantly affect the structural model parameters: Diagnosis on A, k(a) and K(e), volume of the thyroid on A and k(a), and age on k(a) and k(e). The interindividual and interoccasion variabilities were in the range of 8%-28%. The variance of the residual error, modeled as an additive, was low. This type of modeling is a powerful tool for biokinetic studies and should be used in other biokinetic areas of radioiodine therapy, such as radiation dosimetry.

The application of population pharmacokinetics to the drug development process

Population pharmacokinetics is playing an increasing role in clinical drug development. An overview of the population approach, including software and the advantages and limitations of the approach compared to the traditional approach to pharmacokinetic studies, is given. This paper also documents how the area has evolved over the past 15 years and addresses some of the issues that have arisen over the design and conduct of population studies. Finally, some alternative applications of the population approach are given for areas other than clinical drug development.