PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-6 (6)
 

Clipboard (0)
None
Journals
Authors
more »
Year of Publication
1.  Pharmacogenomic Responses of Rat Liver to Methylprednisolone: An Approach to Mining a Rich Microarray Time Series 
The AAPS journal  2005;7(1):E156-E194.
A data set was generated to examine global changes in gene expression in rat liver over time in response to a single bolus dose of methylprednisolone. Four control animals and 43 drug-treated animals were humanely killed at 16 different time points following drug administration. Total RNA preparations from the livers of these animals were hybridized to 47 individual Affymetrix RU34A gene chips, generating data for 8799 different probe sets for each chip. Data mining techniques that are applicable to gene array time series data sets in order to identify drug-regulated changes in gene expression were applied to this data set. A series of 4 sequentially applied filters were developed that were designed to eliminate probe sets that were not expressed in the tissue, were not regulated by the drug treatment, or did not meet defined quality control standards. These filters eliminated 7287 probe sets of the 8799 total (82%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series data sets. The remaining data can then be further analyzed by clustering and mathematical modeling techniques.
doi:10.1208/aapsj070117
PMCID: PMC2607485  PMID: 16146338
Data mining; gene arrays; glucocorticoids; mathematical modeling; pharmacogenomics
2.  Pharmacogenomic responses of rat liver to methylprednisolone: An approach to mining a rich microarray time series 
The AAPS Journal  2005;7(1):E156-E194.
A data set was generated to examine global changes in gene expression in rat liver over time in response to a single bolus dose of methylprednisolone. Four control animals and 43 drug-treated animals were humanely killed at 16 different time points following drug administration. Total RNA preparation from the livers of these animals were hybridized to 47 individual Affymetrix RU34A gene chips, generating data for 8799 different probe sets for each chip. Data mining techniques that are applicable to gene array time series data sets in order to identify drug-regulated changes in gene expression were applied to this data set. A series of 4 sequentially applied filters were developed that were designed to eliminate probe sets that were not expressed in the tissue, were not regulated by the drug treatment, or did not meet defined quality control standards. These filters eliminated 7287 probe sets of the 8799 total (82%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series data sets. The remaining data can then be further analyzed by clustering and mathematical modeling techniques.
doi:10.1208/aapsj070117
PMCID: PMC2607485  PMID: 16146338
Data mining; gene arrays; glucocorticoids; mathematical modeling; pharmacogenomics
3.  Interactions of Everolimus and Sorafenib in Pancreatic Cancer Cells 
The AAPS Journal  2012;15(1):78-84.
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.
doi:10.1208/s12248-012-9417-7
PMCID: PMC3535103  PMID: 23054975
everolimus; MiaPaCa-2; modeling interactions; Panc-1; sorafenib
4.  Study Reanalysis Using a Mechanism-Based Pharmacokinetic/Pharmacodynamic Model of Pramlintide in Subjects with Type 1 Diabetes 
The AAPS Journal  2012;15(1):15-29.
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.
doi:10.1208/s12248-012-9409-7
PMCID: PMC3535104  PMID: 23054970
diabetes; glucose; pharmacodynamics; pharmacokinetics; pramlintide
6.  Translational Biomarkers: from Preclinical to Clinical a Report of 2009 AAPS/ACCP Biomarker Workshop 
The AAPS Journal  2011;13(2):274-283.
There have been some successes in qualifying biomarkers and applying them to drug development and clinical treatment of various diseases. A recent success is illustrated by a collaborative effort among the US Food and Drug Administration, the European Medicines Agency, and the pharmaceutical industry to provide a set of seven preclinical kidney toxicity biomarkers for drug development. Other successes include, but are not limited to, clinical biomarkers for cancer treatment and clinical management of heart transplant patients. The value of fully qualified surrogate endpoints in facilitating successful drug development is undisputed, especially for diseases in which the traditional clinical outcome can only be assessed in large, multi-year trials. Emerging biomarkers, including chemical genomic or imaging biomarkers, and measurement of circulating tumor cells hold great promise for early diagnosis of disease and as prognostic tests for managing treatment of chronic diseases such as osteoarthritis, Alzheimer disease, cardiovascular disease, and cancer. To advance the success of treating and managing these diseases, efforts are needed to establish the temporal relationship between changes in inflammatory or imaging biomarkers with the progression of the chronic disease, and in the case of cancer, between the extent of circulating cancer cells and tumor progression or remission.
doi:10.1208/s12248-011-9265-x
PMCID: PMC3085704  PMID: 21448748
biomarkers; diagnostic; diseases; gene expression; imaging

Results 1-6 (6)