Application of scaling factors in simultaneous modeling of microarray data from diverse chips.
OBJECTIVE
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.


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.
Yao
Zhenling
Z
Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, 565 Hochstetter Hall, Buffalo, NY 14260, USA.
Zhao
Baiteng
B
Hoffman
Eric P
EP
Ghimbovschi
Svetlana
S
DuBois
Debra C
DC
Almon
Richard R
RR
Jusko
William J
WJ
D000315:Adrenalectomy; D000818:Animals; D000893:Anti-Inflammatory Agents / Q000008:administration & dosage* / Q000493:pharmacokinetics; D003627:Data Interpretation, Statistical; D004334:Drug Administration Schedule; D015870:Gene Expression / Q000187:drug effects*; D020869:Gene Expression Profiling / Q000379:methods*; D047908:Intracellular Signaling Peptides and Proteins / Q000235:genetics / Q000378:metabolism; D008099:Liver / Q000187:drug effects / Q000378:metabolism; D008297:Male; D008565:Membrane Proteins / Q000235:genetics / Q000378:metabolism; D008775:Methylprednisolone / Q000008:administration & dosage* / Q000493:pharmacokinetics; D048053:Mitogen-Activated Protein Kinase 6 / Q000235:genetics / Q000378:metabolism; D008954:Models, Biological; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000187:drug effects / Q000378:metabolism; D000076250:Myristoylated Alanine-Rich C Kinase Substrate; D020411:Oligonucleotide Array Sequence Analysis; D009955:Ornithine Decarboxylase / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000378:metabolism; D051381:Rats; D017208:Rats, Wistar; D012680:Sensitivity and Specificity; D013997:Time Factors; D014335:Tropomyosin / Q000235:genetics / Q000378:metabolism
D000893:Anti-Inflammatory Agents; D047908:Intracellular Signaling Peptides and Proteins; D008565:Membrane Proteins; D012333:RNA, Messenger; C490773:Tpm1 protein, rat; D014335:Tropomyosin; D000076250:Myristoylated Alanine-Rich C Kinase Substrate; D048053:Mitogen-Activated Protein Kinase 6; D009955:Ornithine Decarboxylase; D008775:Methylprednisolone