Dynamic modeling of methylprednisolone effects on body weight and glucose regulation in rats

Effects of grape seed proanthocyanidin extract on side effects of high-dose methylprednisolone administration in male rats

In this study, we investigated the effects of grape seed proanthocyanidin extract (GSPE) against the side effects of high-dose administration of methylprednisolone (MP) in male rats. A total of 32 adult Wistar male albino rats were divided into four groups: (1) control (CON), received standard food only; (2) MP, received standard food + intraperitoneal injection of 60 mg/kg MP on day 7; (3) GSPE, received standard food + 200 mg/kg/day GSPE; and (4) MP + GSPE, received standard food + 200 mg/kg/day of GSPE + intraperitoneal injection of 60 mg/kg MP on day 7. All animals in the GSPE and GSPE + MP groups were treated once a day by oral gavage for 14 consecutive days. The feed intake of rats in the MP and MP + GSPE groups decreased significantly by 24.14% and 13.52%, respectively (p < 0.05). Administration of MP resulted in significant increases in serum concentrations of blood urea nitrogen (p < 0.001), glucose (p < 0.01), alkaline phosphatase, and adrenocorticotropic hormone (p < 0.05). High-dose MP administration significantly reduced catalase (p < 0.001) and glutathione peroxidase (p < 0.05) concentrations in the liver and kidney tissues of rats, while glutathione concentrations were only reduced in liver tissue (p < 0.05). The expression levels of Bcl-2 and TNF-α in liver, kidney, and testicular tissue were significantly increased, while the expression levels of caspase-3 were reduced (p < 0.001). Furthermore, sperm concentration was significantly affected by GSPE in rats induced by high-dose MP, and sperm loss was significantly reduced in MP + GSPE (p < 0.05). These findings suggest that GSPE could be useful as a supplement to alleviate MP-induced toxicity in rats.

The brief methylprednisolone administration is crucial to mitigate cardiac dysfunction after myocardial infarction

Acute myocardial infarction (AMI) is one of the major causes of heart failure and mortality. Glucocorticoids administration post-infarction has long been proposed, but it has shown conflicting results so far. This controversy may be associated with the glucocorticoid type and the period when it is administered. To elucidate these, the present aims to evaluate if the brief methylprednisolone acetate administration is determinant for heart adaptation after AMI. Male Wistar rats were divided into 3 groups: sham-operated (SHAM); infarcted (AMI); infarcted treated with methylprednisolone acetate (AMI+M). Immediately after surgery, the AMI+M group received a single dose of methylprednisolone acetate (40 mg/kg i.m.). After 56 days, the cardiac function was assessed and lungs, liver and heart were collected to determine rates of hypertrophy and congestion. Heart was used for oxidative stress and metalloproteinase activity analyses. Methylprednisolone acetate attenuated matrix metalloproteinase-2 activity, cardiac dilatation, and prevented the onset of pulmonary congestion, as well as avoided cardiac hypertrophy. Our data indicate that administration of methylprednisolone acetate shortly after AMI may be a therapeutic alternative for attenuation of detrimental ventricular remodeling.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Modeling energy intake and body weight effects of a long-acting amylin analogue

The inhibitory effect of anti-obesity drugs on energy intake (EI) is counter-acted by feedback regulation of the appetite control circuit leading to drug tolerance. This complicates the design and interpretation of EI studies in rodents that are used for anti-obesity drug development. Here, we investigated a synthetic long-acting analogue of the appetite-suppressing peptide hormone amylin (LAMY) in lean and diet-induced obese (DIO) rats. EI and body weight (BW) were measured daily and LAMY concentrations in plasma were assessed using defined time points following subcutaneous administration of the LAMY at different dosing regimens. Overall, 6 pharmacodynamic (PD) studies including a total of 173 rats were considered in this evaluation. Treatment caused a dose-dependent reduction in EI and BW, although multiple dosing indicated the development of tolerance over time. This behavior could be adequately described by a population model including homeostatic feedback of EI and a turnover model describing the relationship between EI and BW. The model was evaluated by testing its ability to predict BW loss in a toxicology study and was utilized to improve the understanding of dosing regimens for obesity therapy. As such, the model proved to be a valuable tool for the design and interpretation of rodent studies used in anti-obesity drug development.

Bayesian state space models for dynamic genetic network construction across multiple tissues

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.

Methylprednisolone Protects Cardiac Pumping Mechanics from Deteriorating in Lipopolysaccharide-Treated Rats

It has been shown that a prolonged low-dose corticosteroid treatment attenuates the severity of inflammation and the intensity and duration of organ system failure. In the present study, we determined whether low-dose methylprednisolone (a synthetic glucocorticoid) can protect male Wistar rats against cardiac pumping defects caused by lipopolysaccharide-induced chronic inflammation. For the induction of chronic inflammation, a slow-release ALZET osmotic pump was subcutaneously implanted to infuse lipopolysaccharide (1 mg kg(-1) d(-1)) for 2 weeks. The lipopolysaccharide-challenged rats were treated on a daily basis with intraperitoneal injection of methylprednisolone (5 mg kg(-1) d(-1)) for 2 weeks. Under conditions of anesthesia and open chest, we recorded left ventricular (LV) pressure and ascending aortic flow signals to calculate the maximal systolic elastance (E max) and the theoretical maximum flow (Q max), using the elastance-resistance model. Physically, E max reflects the contractility of the myocardium as an intact heart, whereas Q max has an inverse relationship with the LV internal resistance. Compared with the sham rats, the cardiodynamic condition was characterized by a decline in E max associated with the increased Q max in the lipopolysaccharide-treated rats. Methylprednisolone therapy increased E max, which suggests that the drug may have protected the contractile status from deteriorating in the inflamed heart. By contrast, methylprednisolone therapy considerably reduced Q max, indicating that the drug may have normalized the LV internal resistance. In parallel, the benefits of methylprednisolone on the LV systolic pumping mechanics were associated with the reduced cardiac levels of negative inotropic molecules such as peroxynitrite, malondialdehyde, and high-mobility group box 1 protein. Based on these data, we suggested that low-dose methylprednisolone might prevent lipopolysaccharide-induced decline in cardiac intrinsic contractility and LV internal resistance, possibly through its ability to reduce the aforementioned myocardial depressant substances. However, since our results were obtained in anesthetized open-chest rats, extrapolation to what may occur in conscious intact animals should be done with caution.

Modeling energy intake by adding homeostatic feedback and drug intervention

Energy intake (EI) is a pivotal biomarker used in quantification approaches to metabolic disease processes such as obesity, diabetes, and growth disorders. Eating behavior is however under both short-term and long-term control. This control system manifests itself as tolerance and rebound phenomena in EI, when challenged by drug treatment or diet restriction. The paper describes a model with the capability to capture physiological counter-regulatory feedback actions triggered by energy imbalances. This feedback is general as it handles tolerance to both increases and decreases in EI, and works in both acute and chronic settings. A drug mechanism function inhibits (or stimulates) EI. The deviation of EI relative to a reference level (set-point) serves as input to a non-linear appetite control signal which in turn impacts EI in parallel to the drug intervention. Three examples demonstrate the potential usefulness of the model in both acute and chronic dosing situations. The model shifts the predicted concentration-response relationship rightwardly at lower concentrations, in contrast to models that do not handle functional adaptation. A fourth example further shows that the model may qualitatively explain differences in rate and extent of adaptation in observed EI and its concomitants in both rodents and humans.

Meta-modeling of methylprednisolone effects on glucose regulation in rats

A retrospective meta-modeling analysis was performed to integrate previously reported data of glucocorticoid (GC) effects on glucose regulation following a single intramuscular dose (50 mg/kg), single intravenous doses (10, 50 mg/kg), and intravenous infusions (0.1, 0.2, 0.3 and 0.4 mg/kg/h) of methylprednisolone (MPL) in normal and adrenalectomized (ADX) male Wistar rats. A mechanistic pharmacodynamic (PD) model was developed based on the receptor/gene/protein-mediated GC effects on glucose regulation. Three major target organs (liver, white adipose tissue and skeletal muscle) together with some selected intermediate controlling factors were designated as important regulators involved in the pathogenesis of GC-induced glucose dysregulation. Assessed were dynamic changes of food intake and systemic factors (plasma glucose, insulin, free fatty acids (FFA) and leptin) and tissue-specific biomarkers (cAMP, phosphoenolpyruvate carboxykinase (PEPCK) mRNA and enzyme activity, leptin mRNA, interleukin 6 receptor type 1 (IL6R1) mRNA and Insulin receptor substrate-1 (IRS-1) mRNA) after acute and chronic dosing with MPL along with the GC receptor (GR) dynamics in each target organ. Upon binding to GR in liver, MPL dosing caused increased glucose production by stimulating hepatic cAMP and PEPCK activity. In adipose tissue, the rise in leptin mRNA and plasma leptin caused reduction of food intake, the exogenous source of glucose input. Down-regulation of IRS-1 mRNA expression in skeletal muscle inhibited the stimulatory effect of insulin on glucose utilization further contributing to hyperglycemia. The nuclear drug-receptor complex served as the driving force for stimulation or inhibition of downstream target gene expression within different tissues. Incorporating information such as receptor dynamics, as well as the gene and protein induction, allowed us to describe the receptor-mediated effects of MPL on glucose regulation in each important tissue. This advanced mechanistic model provides unique insights into the contributions of major tissues and quantitative hypotheses for the multi-factor control of a complex metabolic system.