Abstract
The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.
Highlights
The necessity of dose information in interpreting drug effects has been recognized since the 16th century, when Paracelsus observed: ‘‘All things are poison, and nothing is without poison: the dose alone makes a thing not poison’’ [1]
We describe analysis of transcription profiling studies of the dose responses to four kinase inhibitors: imatinib, nilotinib, dasatinib and PD0325901
Transcriptional profiling is arguably the most powerful hypothesis-free method for investigating biological effects of drugs—so why do the experiments typically use outmoded single-dose designs? Such single-dose experiments will co-mingle effects that can occur with different potency
Summary
The necessity of dose information in interpreting drug effects has been recognized since the 16th century, when Paracelsus observed: ‘‘All things are poison, and nothing is without poison: the dose alone makes a thing not poison’’ [1]. Doseresponse models are routinely used to evaluate drug effects in biochemical and cell-based assays. Pharmacological parameters such as the widely used EC50 value (half-maximal Effective Concentration) are central to any discussion of drug activities. Single-dose experiments cannot distinguish effects that have different potencies, and they limit the utility of expression data relative to other bioassays. This is regrettable given the many applications of transcriptional profiling in drug discovery [3,4,5,6,7,8]
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