Normal fat mass cannot be reliably estimated in typical pharmacokinetic studies

Eur J Clin Pharmacol. 2021 May;77(5):727-733. doi: 10.1007/s00228-020-03042-4. Epub 2020 Nov 18.

Abstract

Purpose: An influential covariate for pharmacokinetics is (body) size. Recently, the method of estimation of normal fat mass (NFM) has been advocated. Here, the relative contribution of fat mass, estimated as a fraction fat (Ffat), is used to explain differences in pharmacokinetic parameters. This concept is more and more applied. However, it remains unclear whether NFM can be reliably estimated in these typical studies.

Methods: We performed an evaluation of the reliability of NFM estimation in a typical study size (n = 30), otherwise best-case scenario, by means of a pharmacokinetic simulation study. Several values of Ffat were investigated.

Results: In a typical pharmacokinetic study, high imprecision was observed for NFM parameter estimates over a range of scenarios. For example, in a scenario where the true value of Ffat on clearance was 0.5, we found a 95% confidence interval of - 0.1 to 2.1, demonstrating a low precision. The implications for practice are that one could conclude that fat-free mass best describes the relationship of the pharmacokinetics with body size, while the true relationship was between fat-free mass and total body weight. Consequently, this could lead to incorrect extrapolation of pharmacokinetics to extreme body sizes.

Conclusion: In typical pharmacokinetic studies, NFM should be used with caution because the Ffat estimates have low precision. The estimation of Ffat should always be preceded by careful study design evaluation before planning a study, to ensure that the design and sample size is sufficient to apply this potentially useful methodology.

Keywords: Fat-free mass; Ffat; Non-linear mixed-effects modeling; Normal fat mass; Pharmacokinetic modeling; Population pharmacokinetics.

MeSH terms

  • Body Composition / physiology*
  • Body Mass Index
  • Body Weight / physiology*
  • Computer Simulation
  • Half-Life
  • Humans
  • Metabolic Clearance Rate
  • Models, Biological
  • Pharmacokinetics*
  • Reproducibility of Results
  • Sex Factors