Circulating cell-free DNA is considered as one of the major breakthroughs in the field of innovative diagnosis, used as a liquid biopsy. The kinetic parameters of a biomarker are mandatory to assess its usefulness as a diagnostic tool. Obtaining precise mathematical values for the kinetic parameters (e.g., half-life) is then crucial because it could be used for therapeutic monitoring as a prognostic factor. However, little is known about the intrinsic properties of circulating cell-free DNA, more especially, its kinetic properties within the organism. We summarized the basic principles that may affect the kinetics of circulating cell-free DNA within the organism in the light of biological and clinical evidence. We also meta-analyzed the reported data in the literature and the methodologies that have been used to study the kinetic parameters of human circulating cell-free DNA in vivo.
Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).
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