Abstract
Purpose
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.
Results
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.
Conclusions
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.
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Abbreviations
- ADX:
-
adrenalectomized
- BS:
-
biosignal
- CS:
-
corticosteroids
- CV:
-
coefficient of variation
- DR(N):
-
nucleus drug-receptor complex
- GR:
-
glucocorticoid receptor
- GRE:
-
glucocorticoid response element
- MPL:
-
methylprednisolone
- PD:
-
pharmacodynamics
- PG:
-
pharmacogenomics
- PK:
-
pharmacokinetics
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Acknowledgments
We would like to acknowledge Ms Nancy Pyszczynski for the conduct of animal studies. This work was supported by grants GM24211 and GM67650 from the National Institute of General Medical Sciences, National Institutes of Health, in part by grant R24HD050846 from NICHD National Center for Medical Rehabilitation Research and a research grant from NASA.
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Yao, Z., Zhao, B., Hoffman, E.P. et al. Application of Scaling Factors in Simultaneous Modeling of Microarray Data from Diverse Chips. Pharm Res 24, 643–649 (2007). https://doi.org/10.1007/s11095-006-9215-y
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DOI: https://doi.org/10.1007/s11095-006-9215-y