Circadian characteristics of permissive and suppressive effects of cortisol and their role in homeostasis and the acute inflammatory response

Abstract

In this work we explore a semi-mechanistic model that considers cortisol’s permissive and suppressive effects through the regulation of cytokine receptors and cytokines respectively. Our model reveals the proactive role of cortisol during the resting period and its reactive character during the body’s activity phase. Administration of an acute LPS dose during the night, when cortisol’s permissive effects are higher than suppressive, leads to increased cytokine levels compared to LPS administration at morning when cortisol’s suppressive effects are higher. Interestingly, our model presents a hysteretic behavior where the relative predominance of permissive or suppressive effects results not only from cortisol levels but also from the previous states of the model. Therefore, for the same cortisol levels, administration of an inflammatory stimulus at cortisol’s ascending phase, that follows a time period where cytokine receptor expression is elevated ultimately sensitizing the body for the impending stimulus, leads to higher cytokine expression compared to administration of the same stimulus at cortisol’s descending phase.

On the analysis of complex biological supply chains: From process systems engineering to quantitative systems pharmacology

“…By incorporating network models of the peripheral clocks, we were able to highlight the importance of the entrainer (cortisol) circadian characteristics in modulating the synchronization of circadian clocks in peripheral tissues (Mavroudis et al, 2012). We have also extended our model of circadian rhythms of cortisol to elucidate the observed time-of-day dependence in host susceptibility to inflammatory stimuli by incorporating a more detailed description of glucocorticoid modulation of the inflammatory response; whereby depending on its dynamic state, cortisol can both support and inhibit the inflammatory response (Mavroudis et al, 2014, 2015). …”

“…Seasonal variations in inflammatory components are risk biomarkers for cardiovascular (Douglas et al, 1995; Rocco et al, 1987) and inflammatory diseases (Haus et al, 2012; Schlesinger & Schlesinger, 2005) exhibiting seasonality with higher prevalence and aggravated symptoms occurring during the winter and spring months, as well diurnal activity fluctuations with symptom intensity aggravated in transition from the inactive to the active period. Our modeling work enabled us to recognize the importance of the “dynamics” of the regulatory elements since response to a disturbance (stress) is not only dependent on the level of the control (regulatory) elements, but also – and most likely even more so – on the dynamics of the element itself (Mavroudis et al, 2015). Undoubtably, seasonality affects a number of critical factors, including temperature and rainfall, however, photoperiod is most widely considered as the environmental signal that synchronizes physiological changes due to seasonality (Hazlerigg & Wagner, 2006).…”

Systems engineering meets quantitative systems pharmacology: from low‐level targets to engaging the host defenses

“…However, although it is well accepted that biological rhythmicity of defense mechanisms is likely an evolutionary trait to prepare the host to predictable environmental changes ( predictive homeostasis ) it is also recognized that this rhythmic dynamic of defense mechanisms improves the host response to unpredictable changes ( reactive homeostasis ) . Furthermore, evidence, experimental and theoretical, has indicated that oscillatory dynamics of defense mediators enhance efficiency, specificity and reactivity of the host . Therefore, the concept of health reflects the dynamic interactions of complex innate defense mechanisms which are engaged, altered or emerging as a result of a drug.…”

MatVPC: A User‐Friendly MATLAB‐Based Tool for the Simulation and Evaluation of Systems Pharmacology Models

“…Recently, a growing body of literature has been demonstrating the catalytic role of high complexity, systems‐level models in the pharmacology arena . Traditionally, the so‐called “QSP” models are not implemented in Monolix or NONMEM but in surrogate software tools, such as MATLAB or MATLAB‐based software .…”

“…Recently, a growing body of literature has been demonstrating the catalytic role of high complexity, systems-level models in the pharmacology arena. [19][20][21][22][23][24][25][26][27] Traditionally, the so-called "QSP" models are not implemented in Monolix or NONMEM but in surrogate software tools, such as MATLAB or MATLAB-based software. 8,[28][29][30][31][32][33][34][35][36][37] VPC plots that are generated with these tools are generally not as comprehensive 31,38 lacking important features, such as confidence intervals of simulation percentiles or data binning.…”