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.
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.
Authors: Richard R Almon; Josephine Chen; Grayson Snyder; Debra C DuBois; William J Jusko; Eric P Hoffman Journal: Pharmacogenomics Date: 2003-11 Impact factor: 2.533
Authors: Rohini Ramakrishnan; Debra C DuBois; Richard R Almon; Nancy A Pyszczynski; William J Jusko Journal: J Pharmacol Exp Ther Date: 2002-01 Impact factor: 4.030
Authors: Zhenling Yao; Eric P Hoffman; Svetlana Ghimbovschi; Debra C Dubois; Richard R Almon; William J Jusko Journal: Mol Pharm Date: 2008-02-14 Impact factor: 4.939