Dataset Information

Modeling inter-sex and inter-individual variability in response to chronopharmacological administration of synthetic glucocorticoids.

ABSTRACT: Endogenous glucocorticoids have diverse physiological effects and are important regulators of metabolism, immunity, cardiovascular function, musculoskeletal health and central nervous system activity. Synthetic glucocorticoids have received widespread attention for their potent anti-inflammatory activity and have become an important class of drugs used to augment endogenous glucocorticoid activity for the treatment of a host of chronic inflammatory conditions. Chronic use of synthetic glucocorticoids is associated with a number of adverse effects as a result of the persistent dysregulation of glucocorticoid sensitive pathways. A failure to consider the pronounced circadian rhythmicity of endogenous glucocorticoids can result in either supraphysiological glucocorticoid exposure or severe suppression of endogenous glucocorticoid secretion, and is thought be a causal factor in the incidence of adverse effects during chronic glucocorticoid therapy. Furthermore, given that synthetic glucocorticoids have potent feedback effects on the hypothalamic-pituitary-adrenal (HPA) axis, physiological factors which can give rise to individual variability in HPA axis activity such as sex, age, and disease state might also have substantial implications for therapy. We use a semi-mechanistic mathematical model of the rodent HPA axis to study how putative sex differences and individual variability in HPA axis regulation can influence the effects of long-term synthetic exposure on endogenous glucocorticoid circadian rhythms. Model simulations suggest that for the same drug exposure, simulated females exhibit less endogenous suppression than males considering differences in adrenal sensitivity and negative feedback to the hypothalamus and pituitary. Simulations reveal that homeostatic regulatory variability and chronic stress-induced regulatory adaptations in the HPA axis network can result in substantial differences in the effects of synthetic exposure on the circadian rhythm of endogenous glucocorticoids. In general, our results provide insight into how the dosage and exposure profile of synthetic glucocorticoids could be manipulated in a personalized manner to preserve the circadian dynamics of endogenous glucocorticoids during chronic therapy, thus potentially minimizing the incidence of adverse effects associated with long-term use of glucocorticoids.

SUBMITTER: Scherholz ML

PROVIDER: S-EPMC7054177 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Natural glucocorticoids, a class of cholesterol-derived hormones, modulate an array of metabolic, anti-inflammatory, immunosuppressive and cognitive signaling. The synthesis of natural glucocorticoids, largely cortisol in humans, is regulated by the hypothalamic-pituitary-adrenal (HPA) axis and exhibits pronounced circadian variation. Considering the central regulatory function of endogenous glucocorticoids, maintenance of the circadian activity of the HPA axis is essential to host survival and chronic disruption of such activity leads to systemic complications. There is a great deal of interest in synthetic glucocorticoids due to the immunosuppressive and anti-inflammatory properties and the development of novel dosing regimens that can minimize the disruption of endogenous activity, while still maintaining the pharmacological benefits of long-term synthetic glucocorticoid therapy. Synthetic glucocorticoids are associated with an increased risk of developing the pathological disorders related to chronic suppression of cortisol rhythmicity as a result of the potent negative feedback by synthetic glucocorticoids on the HPA axis precursors. In this study, a mathematical model was developed to explore the influence of chronopharmacological dosing of exogenous glucocorticoids on the endogenous cortisol rhythm considering intra-venous and oral dosing. Chronic daily dosing resulted in modification of the circadian rhythmicity of endogenous cortisol with the amplitude and acrophase of the altered rhythm dependent on the administration time. Simulations revealed that the circadian features of the endogenous cortisol rhythm can be preserved by proper timing of administration. The response following a single dose was not indicative of the response following long-term, repeated chronopharmacological dosing of synthetic glucocorticoids. Furthermore, simulations revealed the inductive influence of long-term treatment was only associated with low to moderate doses, while high doses generally led to suppression of endogenous activity regardless of the chronopharmacological dose. Finally, chronic daily dosing was found to alter the responsiveness of the HPA axis, such that a decrease in the amplitude of the cortisol rhythm resulted in a partial loss in the time-of-day dependent response to CRH stimulation, while an increase in the amplitude was associated with a more pronounced time-of-day dependence of the response.

Project description:PurposeInter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.MethodsPBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.ResultsConsidering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.ConclusionsThe presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

Project description:IntroductionIntravenous lipid emulsions (ILE) have been credited for successful resuscitation in drug intoxication cases where other cardiac life-support methods have failed. However, inter-individual variability can function as a confounder that challenges our ability to define the scope of efficacy for lipid interventions, particularly as relevant data are scarce. To address this challenge, we developed a quantitative systems pharmacology model to predict outcome variability and shed light on causal mechanisms in a virtual population of rats subjected to bupivacaine toxicity and ILE intervention.Materials and methodsWe combined a physiologically based pharmacokinetic-pharmacodynamic model with data from a small study in Sprague-Dawley rats to characterize individual-specific cardiac responses to lipid infusion. We used the resulting individual parameter estimates to posit a population distribution of responses to lipid infusion. On that basis, we constructed a large virtual population of rats (N = 10,000) undergoing lipid therapy following bupivacaine cardiotoxicity.ResultsUsing unsupervised clustering to assign resuscitation endpoints, our simulations predicted that treatment with a 30% lipid emulsion increases bupivacaine median lethal dose (LD50) by 46% when compared with a simulated control fluid. Prior experimental findings indicated an LD50 increase of 48%. Causal analysis of the population data suggested that muscle accumulation rather than liver accumulation of bupivacaine drives survival outcomes.ConclusionOur results represent a successful prediction of complex, dynamic physiological outcomes over a virtual population. Despite being informed by very limited data, our mechanistic model predicted a plausible range of treatment outcomes that accurately predicts changes in LD50 when extrapolated to putatively toxic doses of bupivacaine. Furthermore, causal analysis of the predicted survival outcomes indicated a critical synergy between scavenging and direct cardiotonic mechanisms of ILE action.

Dataset Information

Modeling inter-sex and inter-individual variability in response to chronopharmacological administration of synthetic glucocorticoids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets