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PNAS 100 (16): 9608-9613

Copyright © 2003 by the National Academy of Sciences.


BIOLOGICAL SCIENCES / PHARMACOLOGY

Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro

Erik C. Gunther*,{dagger}, David J. Stone*, Robert W. Gerwien, Patricia Bento, and Melvyn P. Heyes

CuraGen Corporation, 322 East Main Street, Branford, CT 06405

Accepted for publication June 11, 2003.

Received for publication April 30, 2003.

Abstract: Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general "efficacy profiles" of biomarker gene expression that correlate with anti-depressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naïve in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.

Key Words: pharmacogenomics • predictive efficacy • drug screening


{dagger} To whom correspondence should be addressed. E-mail: egunther{at}curagen.com.

* E.C.G. and D.J.S. contributed equally to this work.

Edited by Floyd E. Bloom, The Scripps Research Institute, La Jolla, CA

This paper was submitted directly (Track II) to the PNAS office.

Abbreviations: CT, classification tree; RF, random forest; PCP, phenylcyclidine; SSRI, selective serotonin reuptake inhibitor.


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