Separation of the convulsions and antidepressant-like effects produced by the delta-opioid agonist SNC80 in rats

Delta-opioid agonists produce a number of behavioral effects, including convulsions, antinociception, locomotor stimulation, and antidepressant-like effects. The development of these compounds as treatments for depression is limited by their convulsive effects. Therefore, determining how to separate the convulsive and antidepressant-like characteristics of these compounds is essential for their potential clinical use.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46370/1/213_2005_Article_138.pd

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

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