Sorzano Sánchez, Carlos Óscar

Introduction: Macrophages are a heterogeneous population of innate immune cells that support tissue homeostasis through their involvement in tissue development and repair, and pathogen defense. Emerging data reveal that metabolism may control macrophage polarization and function and, conversely, phenotypic polarization may drive metabolic reprogramming. Methods: Here we use biochemical analysis, correlative cryogenic fluorescence microscopy and cryo-focused ion-beam scanning electron microscopy. Results: We demonstrate that growth hormone (GH) reprograms inflammatory GM-CSF-primed monocyte-derived macrophages (GM-MØ) by functioning as a metabolic modulator. We found that exogenous treatment of GM-MØ with recombinant human GH reduced glycolysis and lactate production to levels similar to those found in anti-inflammatory M-MØ. Moreover, GH treatment of GM-MØ augmented mitochondrial volume and altered mitochondrial dynamics, including the remodeling of the inner membrane to increase the density of cristae. Conclusions: Our data demonstrate that GH likely serves a modulatory role in the metabolism of inflammatory macrophages and suggest that metabolic reprogramming of macrophages should be considered as a new target to intervene in inflammatory diseases.

The connection between pharmacokinetic models and system theory has been established for a long time. In this approach, the drug concentration is seen as the output of a system whose input is the drug administered at different times. In this article we further explore this connection. We show that system theory can be used to easily accommodate any therapeutic regime, no matter its complexity, allowing the identification of the pharmacokinetic parameters by means of a non-linear regression analysis. We illustrate how to exploit the properties of linear systems to identify non-linearities in the pharmacokinetic data. We also explore the use of bootstrapping as a way to compare populations of pharmacokinetic parameters and how to handle the common situation of using multiple hypothesis tests as a way to distinguish two different populations. Finally, we demonstrate how the bootstrap values can be used to estimate the distribution of derived parameters, as can be the allometric scale factors.