Search tips
Search criteria

Results 1-6 (6)

Clipboard (0)
more »
Year of Publication
Document Types
1.  Modeling of Corticosteroid Effects on Hepatic Low-Density Lipoprotein Receptors and Plasma Lipid Dynamics in Rats 
Pharmaceutical research  2007;25(4):769-780.
This study examines methylprednisolone (MPL) effects on the dynamics of hepatic low-density lipoprotein receptor (LDLR) mRNA and plasma lipids associated with increased risks for atherosclerosis.
Materials and methods
Normal male Wistar rats were given 50 mg/kg MPL intramuscularly (IM) and sacrificed at various times. Measurements included plasma MPL and CST, hepatic glucocorticoid receptor (GR) mRNA, cytosolic GR density and hepatic LDLR mRNA, and plasma total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high density lipoprotein cholesterol (HDLC), and triglycerides (TG).
MPL showed bi-exponential disposition with two first-order absorption components. Hepatic GR and LDLR mRNA exhibited circadian patterns which were disrupted by MPL. Down-regulation in GR mRNA (40–50%) was followed by a delayed rebound phase. LDLR mRNA exhibited transient down-regulation (60–70%). Cytosolic GR density was significantly suppressed but returned to baseline by 72 h. Plasma TC and LDLC showed increases (55 and 142%) at 12 h. A mechanistic receptor/gene pharmacokinetic/pharmacodynamic model was developed to describe CS effects on hepatic LDLR mRNA and plasma cholesterols.
Our PK/PD model was able to satisfactorily capture the MPL effects on hepatic LDLR, its relationship to various plasma cholesterols, and builds the foundation to explore this area in the future.
PMCID: PMC4196440  PMID: 17674160
cholesterol; corticosteroids; glucocorticoid receptors; LDL receptors; lipids; pharmacodynamics
2.  Application of Scaling Factors in Simultaneous Modeling of Microarray Data from Diverse Chips 
Pharmaceutical research  2007;24(4):643-649.
Microarrays have been utilized in many biological, physiological and pharmacological studies as a high-throughput genomic technique. Several generations of Affymetrix GeneChip® microarrays are widely used in gene expression studies. However, differences in intensities of signals for different probe sets that represent the same gene on various types of Affymetrix chips make comparison of datasets complicated.
Materials and Methods
A power coefficient scaling factor was applied in the pharmacokinetic/ pharmacodynamic (PK/PD) modeling to account for differences in probe set sensitivities (i.e., signal intensities). Microarray data from muscle and liver following methylprednisolone 50 mg/kg i.v. bolus and 0.3 mg/kg/h infusion regimens were taken as an exemplar.
The scaling factor applied to the pharmacodynamic output function was used to solve the problem of intensity differences between probe sets. This approach yielded consistent pharmacodynamic parameters for the applied models.
Modeling of pharmacodynamic/pharmacogenomic (PD/PG) data from diverse chips should be performed with caution due to differential probe set intensities. In such circumstances, a power scaling factor can be applied in the modeling.
PMCID: PMC4181592  PMID: 17318415
bioinformatics; computational biology; pharmacodynamics; pharmacogenomics; pharmacokinetics
3.  Modeling Circadian Rhythms of Glucocorticoid Receptor and Glutamine Synthetase Expression in Rat Skeletal Muscle 
Pharmaceutical research  2006;23(4):670-679.
The circadian rhythm of endogenous corticosterone (CS) may produce fluctuations of downstream gene expression in normal rats. This study examined changes in glucocorticoid receptor (GR) and glutamine synthetase (GS) expression in rat skeletal muscle in relation to plasma CS over a 24-h period.
Fifty-four normal male Wistar rats were sacrificed at 18 time points (n = 3) over 24 h. Plasma CS concentrations and gastrocnemius muscle GR and GS mRNA and GS activity were measured.
The circadian rhythm of plasma CS was captured by a two-harmonic function. The expression of GR and GS mRNA and GS activity follow a circadian rhythm in normal rat skeletal muscle. GR mRNA reaches a trough at 4 h after the peak of plasma CS and it fluctuates between 0.55 and 0.9 fmol g tissue−1. GS mRNA and activity reach peaks at 6 and 12 h after the endogenous CS peak. GS mRNA oscillates between 3 and 6 fmol g tissue−1, whereas GS activity fluctuates between 17 and 23 µmol min−1 g protein−1. Mechanistic receptor/gene-mediated pharmacodynamic models were applied to describe the temporal patterns of GR mRNA, GS mRNA, and GS activity within the circadian cycle.
The integrated models were able to capture the circadian expression patterns of plasma CS, and GR and GS in normal rat skeletal muscle showing a dependence of tissue gene expression on plasma CS.
PMCID: PMC4178542  PMID: 16673181
biological rhythm; mathematical model; pharmacodynamics; pharmacokinetics
4.  Quantitative Dynamic Models of Arthritis Progression in the Rat 
Pharmaceutical research  2008;26(1):196-203.
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.
PMCID: PMC3725549  PMID: 18758921
arthritis; disease; model; progression; rat
5.  Pharmacokinetic-Pharmacodynamic Disease Progression Model for Effect of Etanercept in Lewis Rats with Collagen-Induced Arthritis 
Pharmaceutical research  2011;28(7):1622-1630.
To develop a pharmacokinetic-pharmacodynamic disease progression (PK/PD/DIS) model to characterize the effect of etanercept in collagen-induced arthritis (CIA) rats on rheumatoid arthritis (RA) progression.
The CIA rats received either 5 mg/kg intravenous (IV), 1 mg/kg IV, or 5 mg/kg subcutaneous (SC) etanercept at day 21 post-disease induction. Effect on disease progression was measured by paw swelling. Plasma concentrations of etanercept were assayed by enzyme-linked immunosorbent assay (ELISA). PK profiles were fitted first; parameter estimates were applied to fit paw edema data for PD and DIS-related parameter estimation using ADAPT 5 software.
The model contained a two-compartment PK model with Michaelis-Menten elimination. For SC administration, two additional mathematical functions for absorption were added. The disease progression component was an indirect response model with a time-dependent change in paw edema production rate constant (kin) assumed to be inhibited by etanercept.
Etanercept has modest effects on paw swelling in CIA rats. The PK and PD profiles were well described by the developed PK/PD/DIS model, which may be used for other anti-cytokine biologic agents for RA.
PMCID: PMC3726066  PMID: 21360252
arthritis; etanercept; model; pharmacodynamics; pharmacokinetics
6.  Assessment of Pharmacologic Area Under the Curve When Baselines are Variable 
Pharmaceutical research  2011;28(5):1081-1089.
The area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements.
An algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses.
This algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance.
The variable nature of the baseline is an important aspect to consider when calculating the AUC.
PMCID: PMC3152796  PMID: 21234658
AUC; baseline; bioinformatics; microarrays; pharmacogenomics

Results 1-6 (6)