A quantitative genomic expression analysis platform for multiplexed in vitro prediction of drug action

Abstract

Genomic expression signatures provide high-content biomarkers of cellular physiology, including the diverse responses to therapeutic drugs. To recognize these signatures, we devised a method of biomarker evaluation called 'sampling over gene space' (SOGS) that imparts superior predictive performance to existing supervised classification algorithms. Applied to microarray data from drug-treated human cortical neuron 1A cell cultures, this method predicts whether individual compounds possess anticonvulsant, antihypertensive, cyclooxygenase inhibitor, or opioid action. Thus, stable cell lines can be suitable for expression signature-based screening of a diverse range of activities. A SOGS-based system also discriminates physiologically active from inactive compounds, identifies drugs with off-target side effects, and incorporates a quantitative method for assigning confidence to individual predictions that, at its most stringent, approaches 100% accuracy. The capacity to resolve multiple distinct drug activities while simultaneously discriminating inactive and potential false-positive compounds in a cell line presents a unified framework for streamlined chemical genomic drug discovery.'

A multistep validation process of biomarkers for preclinical drug development

“…[8][9][10] Molecular biomarkers offer the potential to conduct initial preclinical drug development more quickly and efficiently. Traditionally, disease-specific physiological or behavioral tests are used to measure therapeutic efficacy but these assays are often difficult to perform and have limited gain, which impedes identification of treatments with partial efficacy.…”

“…However, only a few reports have described the validation process for preclinical drug development biomarkers, and these have not focused on in vivo disease models. [8][9][10] The purpose of preclinical biomarkers is to provide quantitative data for rational decision-making in drug development, so biomarkers must be fully validated before use. We propose a linear process for the development of preclinical biomarkers that may serve as an example for other efforts and aid preclinical drug development.…”

A Data Mining Approach to In Vivo Classification of Psychopharmacological Drugs

“…This type of strategy has recently been used in a human neuronal precursor cell line as a means to discover gene-expression patterns predictive of a drug's psychoactive class. In these studies (Gunther et al, 2003(Gunther et al, , 2005 various classification algorithms were used to discover geneexpression profiles that were predictive of antidepressant, antipsychotic, and opioid drug action. Although this is a significant step forward, an in vitro system is necessarily restricted in its ability to mimic a true system pharmacology response to psychiatric drugs, and would ideally be complemented with an in vivo assay for more advanced preclinical testing.…”

“…In this instance specific mechanisms do not need to be invoked, the only criterion is that the gene(s) are highly predictive of the outcome. This type of data mining approach was recently utilized in vitro to discover geneexpression patterns that could be used for the classification of psychoactive drugs (Gunther et al, 2003(Gunther et al, , 2005. It has been proposed that the same approach would also be advantageous for in vivo analysis of behavioral data (Brunner et al, 2002;Tecott and Nestler, 2004), but no such results have been published to date.…”

High-Throughput Screening by RNA Interference: Control of Two Distinct Types of Variance

“…Therefore, in many cases the derived function is no better than a guess. Although true functional genomics studies have been done using complex experimental readouts from known physiological states or positive controls and pattern matching the resulting readouts, [1][2][3][4][5][6] RNAi allows for the first time the direct measurement of gene function in pathways of interest on a genome-wide scale. This approach has been quickly embraced in both academic and industrial research.…”