Although corticosteroids (CSs) affect gene expression in multiple tissues, the array of genes that are regulated by these catabolic steroids is diverse, highly tissue specific, and depends on their functions in the tissue. Liver has many important functions in performing and regulating diverse metabolic processes. Muscle, in addition to its mechanical role, is critical in maintaining systemic energy homeostasis and accounts for about 80% of insulin-directed glucose disposal. Consequently, a better understanding of CS pharmacogenomic effects in these tissues would provide valuable information regarding the tissue-specificity of transcriptional dynamics, and would provide insights into the underlying molecular mechanisms of action for both beneficial and detrimental effects.
We performed an integrated analysis of transcriptional data from liver and muscle in response to methylprednisolone (MPL) infusion, which included clustering and functional annotation of clustered gene groups, promoter extraction and putative transcription factor (TF) identification, and finally, regulatory closeness (RC) identification.
This analysis allowed the identification of critical transcriptional responses and CS-responsive functions in liver and muscle during chronic MPL administration, the prediction of putative transcriptional regulators relevant to transcriptional responses of CS-affected genes which are also potential secondary bio-signals altering expression levels of target-genes, and the exploration of the tissue-specificity and biological significance of gene expression patterns, CS-responsive functions, and transcriptional regulation.
The analysis provided an integrated description of the genomic and functional effects of chronic MPL infusion in liver and muscle.
liver; muscle; glucocorticoids; corticosteroids; gene expression; gene regulation; promoter analysis
Glucocorticoids are important regulators of metabolism and immune function. Synthetic glucocorticoids are extensively used for immunosuppression/anti-inflammatory therapy. Since the glucocorticoid receptor (GR) is central to most hormone effects; its in vivo regulation will influence hormone/drug action. An alternative splice variant, GRβ, is present in humans and may function as a dominant negative regulator of GR transcriptional activity. Recently, a similar splice variant was reported in mouse, although the mechanism of alternative splicing differs from that in humans. We present evidence that a splice variant of GR with an alternative C-terminus also occurs in the rat by a mechanism of intron inclusion. A highly quantitative qRT-PCR assay for the simultaneous measurement of both splice variants in a single sample was developed in order to accurately measure their regulation. We used this assay to assess the tissue specific expression of both mRNAs, and demonstrate that GRα is predominant in all tissues. In addition, the regulation of both GRα and GRβ mRNA by various physiological factors in rat liver was assessed. GRα showed a robust circadian rhythm, which was entrained with the circadian oscillation of the endogenous hormone. Time series experiments showed that both corticosteroids and LPS but not insulin dosing resulted in the transient down-regulation of GRα mRNA. LPS treatment also resulted in down-regulation of GRβ expression. A modest up-regulation in GRβ expression was observed only in animals having chronically elevated plasma insulin concentrations. However the expression of GRβ was significantly lower than that of GRα in all cases.
glucocorticoids; glucocorticoid receptor; GRβ; qRTPCR
The pharmacokinetics (PK) of salsalate (SS) and salicylic acid (SA) was assessed in normal Wistar and diabetic Goto-Kakizaki rats. Three PK studies were conducted: 1) PK of SA in normal rats after intravenous dosing of SA at 20, 40, 80 mg/kg. 2) PK of SS and SA in normal rats after oral dosing of SS at 28, 56, 112 mg/kg. 3) PK during 4 months feeding of SS-containing diet in both normal and diabetic rats. The disposition of SS and SA were simultaneously evaluated using a pharmacokinetic model comprised of several transit absorption steps and linear and nonlinear dual elimination pathways for SA. The results indicated that the nonlinear elimination pathway of SA only accounted for a small fraction of the total clearance (< 12%) at therapeutic concentrations. A flat profile of SA was observed after oral dosing SS, particularly at a high dose. The possible reasons for this flat profile were posed. During the SS-diet feeding, diabetic rats achieved lower blood concentrations of SA than normal rats with a higher apparent clearance (CL/F) possibly due to incomplete (47%) bioavailability. Such CL/F decreased with age in both diabetic and normal rats. The effect of diabetes on SA pharmacokinetics may necessitate increased dosing in future usage of SS in diabetes.
salsalate; salicylic acid; pharmacokinetics; diabetes
Adrenal suppression and lymphocytopenia are commonly monitored pharmacological responses during systemic exposure to exogenously administered corticosteroids. The pharmacodynamics of plasma corticosterone (CS) and blood lymphocytes were investigated in 60 normal rats which received either 50 mg/kg methylprednisolone (MPL) or vehicle intramuscularly. Blood samples were collected between 0.5 and 96 h following treatment. Plasma CS displayed a transient suppression with re-establishment of a normal circadian rhythm 24 h following drug treatment. An indirect response model with suppression of production well captured plasma CS profiles. An early stress-induced rise in CS was also factored into the model. Blood lymphocyte numbers exhibited a sharp decline and then returned to a new circadian rhythm which was half of the original baseline level. An integrated pharmacodynamic (PD) model with inhibition of lymphocyte trafficking from tissue to blood by both MPL and CS and induction of cell apoptosis by MPL reasonably captured this lymphocytopenia. Rats and humans differ in lymphocyte responses with humans showing full recovery of baselines. Modeling provides a valuable tool in quantitative assessment of dual, complex drug responses.
pharmacokinetics; pharmacodynamics; hormones; mathematical model; pharmacokinetic/pharmacodynamic models; corticosteroid; lymphocyte; cell trafficking; indirect response model; circadian rhythm
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.
Conventional mammillary models are frequently used for pharmacokinetic (PK) analysis when only blood or plasma data are available. Such models depend on the quality of the drug disposition data and have vague biological features. An alternative minimal-physiologically-based PK (minimal-PBPK) modeling approach is proposed which inherits and lumps major physiologic attributes from whole-body PBPK models. The body and model are represented as actual blood and tissue usually total body weight) volumes, fractions (fd) of cardiac output with Fick’s Law of Perfusion, tissue/blood partitioning (Kp), and systemic or intrinsic clearance. Analyzing only blood or plasma concentrations versus time, the minimal-PBPK models parsimoniously generate physiologically-relevant PK parameters which are more easily interpreted than those from mam-millary models. The minimal-PBPK models were applied to four types of therapeutic agents and conditions. The models well captured the human PK profiles of 22 selected beta-lactam antibiotics allowing comparison of fitted and calculated Kp values. Adding a classical hepatic compartment with hepatic blood flow allowed joint fitting of oral and intravenous (IV) data for four hepatic elimination drugs (dihydrocodeine, verapamil, repaglinide, midazolam) providing separate estimates of hepatic intrinsic clearance, non-hepatic clearance, and pre-hepatic bioavailability. The basic model was integrated with allometric scaling principles to simultaneously describe moxifloxacin PK in five species with common Kp and fd values. A basic model assigning clearance to the tissue compartment well characterized plasma concentrations of six monoclonal antibodies in human subjects, providing good concordance of predictions with expected tissue kinetics. The proposed minimal-PBPK modeling approach offers an alternative and more rational basis for assessing PK than compartmental models.
PBPK; Mammillary model; Pharmacokinetics; Compartmental analysis
Inflammation is an array of immune responses to infection and injury. It results from a complex immune cascade and is the basis of many chronic diseases such as arthritis, diabetes, and cancer. Numerous mathematical models have been developed to describe the disease progression and effects of anti-inflammatory drugs. This review illustrates the state of the art in modeling the effects of diverse drugs for treating inflammation, describes relevant biomarkers amenable to modeling, and summarizes major advantages and limitations of the published pharmacokinetic/ pharmacodynamic (PK/PD) models. Simple direct inhibitory models are often used to describe in vitro effects of anti-inflammatory drugs. Indirect response models are more mechanism based and have been widely applied to the turnover of symptoms and biomarkers. These, along with target-mediated and transduction models, have been successfully applied to capture the PK/PD of many anti-inflammatory drugs and describe disease progression of inflammation. Biologics have offered opportunities to address specific mechanisms of action, and evolve small systems models to quantitatively capture the underlying physiological processes. More advanced mechanistic models should allow evaluation of the roles of some key mediators in disease progression, assess drug interactions, and better translate drug properties from in vitro and animal data to patients.
inflammation; pharmacokinetics; pharmacodynamics; arthritis; modeling
Everolimus targets the mammalian target of rapamycin, a kinase that promotes cell growth and proliferation in pancreatic cancer. Sorafenib inhibits the Raf-mitogen-activated protein kinase, vascular endothelial growth factor, and platelet-derived growth factor pathways, thus inhibiting cell growth and angiogenesis. Combinations of these two agents are under evaluation for therapy of several cancers. This study examined the effects of everolimus and sorafenib on proliferation of the pancreatic cancer cell lines MiaPaCa-2 and Panc-1. Cell growth inhibition was evaluated in vitro for a range of concentrations of the drugs alone and in combination. Maximum inhibition capacity (Imax) and potency (IC50) were determined. The data were analyzed to characterize drug interactions using two mathematical analysis techniques. The Ariens noncompetitive interaction model and Earp model were modified to accommodate alterations in the inhibition parameters of one drug in the presence of another. Sorafenib alone inhibited growth of both cell lines completely (Imax = 1), with an IC50 of 5–8 μM. Maximal inhibition by everolimus alone was only 40% (Imax = 0.4) in both cell lines, with an IC50 of 5 nM. Slight antagonistic interaction occurred between the drugs; both analytic methods estimated the interaction term Ψ as greater than 1 for both cell lines. The in vitro data for two pancreatic cancer cell lines suggest that a combination of these two drugs would be no more efficacious than the individual drugs alone, consistent with the drug interaction analysis that indicated slight antagonism for growth inhibition.
everolimus; MiaPaCa-2; modeling interactions; Panc-1; sorafenib
This report describes a pharmacokinetic/pharmacodynamic model for pramlintide, an amylinomimetic, in type 1 diabetes mellitus (T1DM). Plasma glucose and drug concentrations were obtained following bolus and 2-h intravenous infusions of pramlintide at three dose levels or placebo in 25 T1DM subjects during the postprandial period in a crossover study. The original clinical data were reanalyzed by mechanism-based population modeling. Pramlintide pharmacokinetics followed a two-compartment model with zero-order infusion and first-order elimination. Pramlintide lowered overall postprandial plasma glucose AUC (AUCnet) and delayed the time to peak plasma glucose after a meal (Tmax). The delay in glucose Tmax and reduction of AUCnet indicate that overall plasma glucose concentrations might be affected by differing mechanisms of action of pramlintide. The observed increase in glucose Tmax following pramlintide treatment was independent of dose within the studied dose range and was adequately described by a dose-independent, maximum pramlintide effect on gastric emptying of glucose in the model. The inhibition of endogenous glucose production by pramlintide was described using a sigmoidal function with capacity and sensitivity parameter estimates of 0.995 for Imax and 23.8 pmol/L for IC50. The parameter estimates are in good agreement with literature values and the IC50 is well within the range of postprandial plasma amylin concentrations in healthy humans, indicating physiological relevance of the pramlintide effect on glucagon secretion in the postprandial state. This model may prove to be useful in future clinical studies of other amylinomimetics or antidiabetic drugs with similar mechanisms of action.
diabetes; glucose; pharmacodynamics; pharmacokinetics; pramlintide
analytical methodology; biomarkers; limit of quantitation; pharmacokinetics
Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expressed in the tissue, not regulated by drug, or did not meet defined quality control standards. These filters eliminated 14,890 probe sets (94%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series datasets. The remaining data can then be further analyzed by clustering and mathematical modeling. Initial analysis of this filtered dataset identified a group of genes whose pattern of regulation was highly correlated with prototype corticosteroid enhanced genes. Twenty genes in this group, as well as selected genes exhibiting either downregulation or no regulation, were analyzed for 5′ GRE half-sites conserved across species. In general, the results support the hypothesis that the existence of conserved DNA binding sites can serve as an important adjunct to purely analytic approaches to clustering genes into groups with common mechanisms of regulation. This dataset, as well as similar datasets on liver and muscle, are available online in a format amenable to further analysis by others.
data mining; gene arrays; glucocorticoids; pharmacogenomics; evolutionary conservation
This report generates efficient experimental designs (dose, sampling times) for parameter estimation for four basic physiologic indirect pharmacodynamic response (IDR) models. The principles underlying IDR models and their response patterns have been well described. Each IDR model explicitly contains four parameters, kin (production), kout (loss), Imax/Smax (capacity) and IC50/SC50 (sensitivity). The pharmacokinetics of an IV dose of drug described by a monoexponential function of time with two parameters, V and kel, is assumed. The random errors in the response variable are assumed to be additive, independent, and normal with zero mean and variance proportional to some power of the mean response. Optimal design theory was used extensively to assess the role of both dose and sampling times. Our designs were generated in Mathematica (ADAPT 5 typically produces identical results). G-optimality was used to verify that the generated designs were indeed D-optimal. Such designs are efficient and robust when good prior knowledge of the estimated parameters is available. The efficiency of unconstrained D-optimal designs (4 dose, sampling time pairs) does not improve much when the drug doses are allowed to differ, compared with constrained single dose designs (4 sampling times) with one maximal feasible dose. Also, explored were efficiencies of alternative study designs and results from parameter misspecification. This analysis substantiates the importance of larger doses yielding greater certainty in parameter estimation in pharmacodynamics.
Pharmacodynamics; D-optimal design; Indirect response models; Parameter estimation
An important feature of mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models is the identification of drug- and system-specific factors that determine the intensity and time-course of pharmacological effects. This provides an opportunity to integrate information obtained from in vitro bioassays and preclinical pharmacological studies in animals to anticipate the clinical and adverse responses to drugs in humans. The fact that contemporary PK/PD modeling continues to evolve and seeks to emulate systems level properties should provide enhanced capabilities to scale-up pharmacodynamic data. Critical steps in drug discovery and development, such as lead compound and first in human dose selection, may become more efficient with the implementation and further refinement of translational PK/PD modeling. In this review, we highlight fundamental principles in pharmacodynamics and the basic expectations for in vitro bioassays and traditional allometric scaling in PK/PD modeling. Discussion of PK/PD modeling efforts for recombinant human erythropoietin is also included as a case study showing the potential for advanced systems analysis to facilitate extrapolations and improve understanding of inter-species differences in drug responses.
allometric scaling; cell life span models; mechanism-based modeling; pharmacodynamics, PD; pharmacokinetics, PK; receptor occupancy; recombinant human erythropoietin, rHuEpo; target-mediated drug disposition, TMDD
This comparison employs mathematical disease progression models to identify a rat model of arthritis with the least inter-animal variability and features lending to better study designs.
Arthritis was induced with either collagen (CIA) or mycobacterium (AIA) in either Lewis or Dark Agouti (DA) rats. Disease progression was monitored by paw edema and body weight. Models with production, loss, and feedback components were constructed and population analysis using NONMEM software was employed to identify inter-animal variability in the various disease progression parameters.
Onset time was the only parameter different within all four groups (DA–AIA 11.5 days, DA–CIA 16.5 days, Lewis–AIA 11.9 days, Lewis–CIA 13.9 days). The loss-of-edema rate constant was 20% slower in DA (0.362 h−1) than Lewis (0.466 h−1) rats. Most models exhibited peak paw edema 20 days post-induction. Edema in CIA returned to 150% of the initial value after the disease peaked. DA rats displayed more severe overall responses.
No statistical differences between groups were observed for inter-animal variation in disease onset, progression and severity parameters. Onset time varies and should be noted in the design of future studies. DA rats may offer a more dynamic range of edema response than Lewis rats.
arthritis; disease; model; progression; rat
Dexamethasone (DEX) is often given for the treatment of rheumatoid arthritis and clinical dosing regimens of DEX have often been based empirically. This study tests whether the inflammation processes in a rat model of rheumatoid arthritis alters the clearance and volume of distribution of DEX when compared with healthy controls. Groups of healthy and arthritic male Lewis rats received either a low (0.225 mg/kg) or high (2.25 mg/kg) intramuscular dose of DEX. Arthritis was induced by intradermal injection of type II porcine collagen in incomplete Freund's adjuvant emulsion at the base of the tail. DEX was dosed in the arthritic animals 22 days post arthritis induction. Plasma DEX concentrations were determined by HPLC. Plasma concentration versus time data were analysed by non-compartmental analysis and pharmacokinetic model fitting using the population pharmacokinetic software NONMEM V. A linear bi-exponential pharmacokinetic model with extravascular input described the data for both healthy and arthritic animals. Clearance was the only parameter determined statistically different between both groups (healthy=1.05 l/h/kg, arthritic=1.19 l/h/kg). The steady-state volume of distribution for both groups was 4.85 l/kg. The slight difference in clearance was visibly undetectable and unlikely to produce meaningful changes in DEX disposition in arthritic rats.
dexamethasone; pharmacokinetics; arthritis; collagen
Mechanisms related to the adverse effects of corticosteroids on glucose homeostasis were studied. Five groups of adrenalectomized (ADX) rats were given methylprednisolone (MPL) intravenously at 10 and 50 mg/kg, or a continuous 7 day infusion at rates of 0, 0.1, 0.3 mg/kg/h via subcutaneously implanted Alzet mini-pumps. Plasma concentrations of MPL, glucose and insulin were determined at various time points up to 72 h after injection or 336 h after infusion. The pharmacokinetics of MPL was captured with a two-compartment model. The Adapt II software was used in modeling. Injection of MPL caused a temporary glucose increase over 6 h by stimulating gluconeogenesis. The glucose changes stimulated pancreatic β-cell secretion yielding a later insulin peak at around 10 h. In turn, insulin can stimulate glucose disposition. However, long-term MPL treatment caused continuous hyperglycemia during and after infusion. Insulin was increased during infusion, and immediately returned to baseline after the infusion was terminated, despite the almost doubled glucose concentration. A disease progression model incorporating the reduced endogenous glucose disposition was included to capture glucose homeostasis under different treatments. The results exemplify the importance of the steroid dosing regimen in mediating pharmacological and adverse metabolic effects. This mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model quantitatively describes the induction of hyperglycemia and provides additional insights into metabolic disorders such as diabetes.
corticosteroids; methylprednisolone; pharmacodynamics; pharmacokinetics; glucose; insulin
A physiologic pharmacodynamic model was developed to jointly describe the effects of methylprednisolone (MPL) on adrenal suppression and glycemic control in normal rats. Six groups of animals were given MPL intravenously at 0, 10 and 50 mg/kg, or by subcutaneous 7 day infusion at rates of 0, 0.1 and 0.3 mg/kg/h. Plasma concentrations of MPL, corticosterone (CST), glucose and insulin were determined at various times up to 72 h after injection and 336 h after infusion. The pharmacokinetics of MPL was described by a two-compartment model. A circadian rhythm for CST was found in untreated rats with a stress-altered baseline caused by handling, which was captured by a circadian harmonic secretion rate with an increasing mesor. All drug treatments caused CST suppression. Injection of MPL caused temporary increases in glucose over 4 h. Insulin secretion was thereby stimulated yielding a later peak around 6 h. In turn, insulin can normalize glucose. However, long-term dosing caused continuous hyperglycemia during and after infusion. Hyperinsulinemia was achieved during infusion, but diminished immediately after dosing despite the high glucose concentration. The effects of CST and MPL on glucose production were described with a competitive stimulation function. A disease progression model incorporating reduced endogenous glucose uptake/utilization was used to describe glucose metabolism under different treatments. The results exemplify the roles of endogenous and exogenous hormones in mediating glucose dynamics. The pharmacokinetic/pharmacodynamic model is valuable for quantitating diabetogenic effects of corticosteroid treatments and provides mechanistic insights into the hormonal control of the metabolic system.
corticosterone; methylprednisolone; pharmacodynamics; pharmacokinetics; glucose; insulin
This study derives and assesses modified equations for Indirect Response Models (IDR) for normalizing data for baseline values (R0) and evaluates different methods of utilizing baseline information. Pharmacodynamic response equations for the four basic IDR models were adjusted to reflect a ratio to, a change from (e.g., subtraction), or percent change relative to baseline. The original and modified IDR equations were fitted individually to simulated data sets and compared for recovery of true parameter values. Handling of baseline values was investigated using: estimation (E), fixing at the starting value (F1), and fixing at an average of starting and returning values of response profiles (F2). The performance of each method was evaluated using simulated data with variability under various scenarios of different doses, numbers of data points, type of IDR model, and degree of residual errors. The median error and inter-quartile range relative to true values were used as indicators of bias and precision for each method. Applying IDR models to normalized data required modifications in writing differential equations and initial conditions. Use of an observed/baseline ratio led to parameter estimates of kin = kout and inability to detect differences in kin values for groups with different R0, whereas the modified equations recovered the true values. An increase in variability increased the %Bias and %Imprecision for each R0 fitting method and was more pronounced for ‘F1’. The overall performance of ‘F2’ was as good as that of ‘E’ and better than ‘F1’. The %Bias in estimation of parameters SC50 (IC50) and kout followed the same trend, whereas use of ‘F1’ or ‘F2’ resulted in the least bias for Smax (Imax). The IDR equations need modifications to directly assess baseline-normalized data. In general, Method ‘E’ resulted in lesser bias and better precision compared to ‘F1’. With rich datasets including sufficient information on the return to baseline, Method ‘F2’ is reasonable. Method ‘E’ offers no significant advantage over ‘F1’ with datasets lacking information on the return to baseline phase. Handling baseline responses properly is an essential aspect of applying pharmacodynamic models.
Indirect response models; Turnover models; Baseline responses; Pharmacodynamics; Modeling and simulation
Maintenance of energy metabolism and glucose homeostasis is achieved by the regulatory effects of many hormones and their interactions. Glucocorticoids produced from adrenal cortex and adiponectin produced by adipose tissue play important roles in the production, distribution, storage, and utilization of energy substrates. Glucocorticoids are involved in the activation of a number of catabolic processes by affecting the expression of a plethora of genes, while adiponectin acts primarily as an insulin sensitizer. Both are regulated by a number of physiological and pharmacological factors. Although the effects of glucocorticoids on adiponectin expression have been extensively studied in different in vitro, animal and clinical study settings, no consensus has been reached. This report reviews the primary literature concerning the effects of glucocorticoids on adiponectin expression and identifies potential reasons for the contradictory results between different studies. In addition, methods to gain better insights pertaining to the regulation of adiponectin expression are discussed.
Everolimus is an immunosuppressant that blocks growth factor-mediated proliferation of hematopoietic cells by targeting the mammalian target of rapamycin (mTOR). Sorafenib is a multikinase inhibitor that inhibits cell proliferation by arresting cells in the G0–G1 phase of the cell cycle. These agents are under investigation as combination therapy for various cancers. Because the two drugs individually inhibit lymphocyte proliferation, this study examined the effects of everolimus and sorafenib on lymphocyte proliferation in order to anticipate possible immunosuppression.
Inhibition of lymphocyte proliferation was evaluated ex vivo over a range of concentrations of these drugs, alone and in combination. Data analysis, using a population approach to characterize interactions, employed the Ariens noncompetitive interaction model, which was modified to accommodate interactions of the two drugs.
Everolimus alone caused partial inhibition of lymphocyte proliferation, with a mean IC50 of 4.5 nM for females and 10.5 nM in males. Sorafenib alone caused complete inhibition, with a mean IC50 of 11.4 µM and no difference between genders.
The population estimate for the interaction term was greater than 1, suggesting that the two drugs exert slight antagonism in terms of inhibition of lymphocyte proliferation.
everolimus; lymphocyte proliferation; modeling interactions; population analysis; sorafenib
To provide a mechanism-based model to quantitatively describe GLP-1 pharmacokinetics (PK) and pharmacodynamics (PD) in rats.
Intravenous (IV), infusion (IF), subcutaneous (SC), and intraperitoneal (IP) doses of GLP-1 were administered after glucose challenge in healthy Sprague–Dawley rats. Blood was analyzed for GLP-1, glucose, and insulin. The PK-PD modeling was performed with ADAPT 5. The concentration-response curve was generated and analyzed in comparison with other incretin-related therapeutics.
The PK of GLP-1 was described using a two-compartment model with a zero-order input accounting for endogenous GLP-1 synthesis. For SC and IP dosing, sequential zero-order and first-order absorption models reasonably described the rapid absorption process and flip-flop kinetics. In dynamics, GLP-1 showed insulinotropic effects (3-fold increase) after IV glucose challenge in a dose-dependent manner. The concentration-response curve was bell-shaped, which was captured using a biphasic two-binding site Adair model. Receptor binding of GLP-1 exhibited high capacity and low affinity kinetics for both binding sites (KD=09.94×103 pM, K2=1.56×10−4 pM−1).
The PK of GLP-1 was linear and bi-exponential and its PD showed glucose-dependent insulinotropic effects. All profiles were captured by the present mechanistic model and the dynamic analysis yields several implications for incretin-related therapies.
glucagon-like peptide-1; glucose; incretin; insulin; pharmacokinetics; pharmacodynamics
GLP-1 is an insulinotropic hormone that synergistically with glucose gives rise to an increased insulin response. Its secretion is increased following a meal and it is thus of interest to describe the secretion of this hormone following an oral glucose tolerance test (OGTT). The aim of this study was to build a mechanism-based population model that describes the time course of total GLP-1 and provides indices for capability of secretion in each subject. The goal was thus to model the secretion of GLP-1, and not its effect on insulin production. Single 75 g doses of glucose were administered orally to a mixed group of subjects ranging from healthy volunteers to patients with type 2 diabetes (T2D). Glucose, insulin, and total GLP-1 concentrations were measured. Prior population data analysis on measurements of glucose and insulin were performed in order to estimate the glucose absorption rate. The individual estimates of absorption rate constants were used in the model for GLP-1 secretion. Estimation of parameters was performed using the FOCE method with interaction implemented in NONMEM VI. The final transit/indirect-response model obtained for GLP-1 production following an OGTT included two stimulation components (fast, slow) for the zero-order production rate. The fast stimulation was estimated to be faster than the glucose absorption rate, supporting the presence of a proximal–distal loop for fast secretion from L-cells. The fast component (st3 = 8.64·10−5 [mg−1]) was estimated to peak around 25 min after glucose ingestion, whereas the slower component (st4 = 26.2·10−5 [mg−1]) was estimated to peak around 100 min. Elimination of total GLP-1 was characterised by a first-order loss. The individual values of the early phase GLP-1 secretion parameter (st3) were correlated (r = 0.52) with the AUC(0–60 min.) for GLP-1. A mechanistic population model was successfully developed to describe total GLP-1 concentrations over time observed after an OGTT. The model provides indices related to different mechanisms of subject abilities to secrete GLP-1. The model provides a good basis to study influence of different demographic factors on these components, presented mainly by indices of the fast- and slow phases of GLP-1 response.
GLP-1; L-cells; Oral glucose tolerance test (OGTT); Indirect response model; NONMEM
Effects of high fat diet (HFD) on obesity and, subsequently, on diabetes are highly variable and modulated by genetics in both humans and rodents. In this report, we characterized the response of Goto-Kakizaki (GK) rats, a spontaneous polygenic model for lean diabetes and healthy Wistar-Kyoto (WKY) controls, to high fat feeding from weaning to 20 weeks of age. Animals fed either normal diet or HFD were sacrificed at 4, 8, 12, 16 and 20 weeks of age and a wide array of physiological measurements were made along with gene expression profiling using Affymetrix gene array chips. Mining of the microarray data identified differentially regulated genes (involved in inflammation, metabolism, transcription regulation, and signaling) in diabetic animals, as well as the response of both strains to HFD. Functional annotation suggested that HFD increased inflammatory differences between the two strains. Chronic inflammation driven by heightened innate immune response was identified to be present in GK animals regardless of diet. In addition, compensatory mechanisms by which WKY animals on HFD resisted the development of diabetes were identified, thus illustrating the complexity of diabetes disease progression.
diabetes; high fat diet; gene expression; microarray
The Goto-Kakizaki (GK) rat, a polygenic non-obese model of type 2 diabetes, is a useful surrogate for study of diabetes-related changes independent of obesity. GK rats and appropriate controls were killed at 4, 8, 12, 16 and 20 weeks post-weaning and differential muscle gene expression along with body and muscle weights, plasma hormones and lipids, and blood cell measurements were carried out. Gene expression analysis identified 204 genes showing 2-fold or greater differences between GK and controls in at least 3 ages. Array results suggested increased oxidative capacity in GK muscles, as well as differential gene expression related to insulin resistance, which was also indicated by HOMA-IR measurements. In addition, potential new biomarkers in muscle gene expression were identified that could be either a cause or consequence of T2DM. Furthermore, we demonstrate here the presence of chronic inflammation evident both systemically and in the musculature, despite the absence of obesity.
type 2 diabetes; skeletal muscle; inflammation; microarrays; gene expression