title: Application of scaling factors in simultaneous modeling of microarray data from diverse chips.
abstract: 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.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, 565 Hochstetter Hall, Buffalo, NY 14260, USA.
authors.forename: Zhenling
authors.initials: Z
authors.lastname: Yao
authors.forename: Baiteng
authors.initials: B
authors.lastname: Zhao
authors.forename: Eric P
authors.initials: EP
authors.lastname: Hoffman
authors.forename: Svetlana
authors.initials: S
authors.lastname: Ghimbovschi
authors.forename: Debra C
authors.initials: DC
authors.lastname: DuBois
authors.forename: Richard R
authors.initials: RR
authors.lastname: Almon
authors.forename: William J
authors.initials: WJ
authors.lastname: Jusko
mesh_terms: 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
chemical_list: 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