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

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

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

Edited by Floyd E. Bloom, The Scripps Research Institute, La Jolla, CA, and approved June 11, 2003 (received for review April 30, 2003) Assays of drug action typically evaluate biochemical activity. How- tion tree (CT) and random forest (RF) supervised classification ever, accurately matching therapeutic efficacy with biochemical schemes can be used to predict the functional category of activity is a challenge. High-content cellular assays seek to bridge members of each of these drug classes with good accuracy, based this gap by capturing broad information about the cellular phys- on analysis of the gene expression profile induced by a drug. iology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs Materials and Methods fall, based on high-content statistical categorization of gene ex- Cell Cultures. Primary human neuronal precursor cells (Clonex- pression profiles induced by these drugs. When we used the press, Gaithersburg, MD) were cultured for 7 days in growth classification tree and random forest supervised classification al- media (GM) (50:50 DMEM͞F12, 5% FBS, 10 ng͞ml basic gorithms to analyze microarray data, we derived general ‘‘efficacy fibroblast growth factor, 10 ng͞ml epidermal growth factor, profiles’’ of biomarker gene expression that correlate with anti- 1:100 Clonexpress neuronal cell supplement, penicillin͞ depressant, and opioid drug action on primary hu- streptomycin), and differentiated for 7 days in six-well plates at man neurons in vitro. These profiles were used as predictive 900,000 cells per well in GM plus 100 ␮M dibutyrtyl cAMP, 20 models to classify naı¨ve in vitro drug treatments with 83.3% ng͞ml nerve growth factor, 1:100 matrigel, with 72-h media (random forest) and 88.9% (classification tree) accuracy. Thus, the changes. Morphologically neuronal cells comprised Ϸ80% of the detailed information contained in genomic expression data is cultures. sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify Drug Treatments. Drugs were dissolved in DMSO and added to therapeutic efficacy on the basis of gene expression signatures in cultures at a final DMSO concentration of 0.04%. Drug con- vitro has potential utility in drug discovery and drug target vali- centrations represented pharmaceutically relevent doses: 2.0 ␮M dation. amoxepine, 2.0 ␮M , 2.0 ␮M , 1.0 ␮M , 2.0 ␮M , 1.0 ␮M , 0.7 ␮M pharmacogenomics ͉ predictive efficacy ͉ drug screening nortryptyline, 0.4 ␮M , 1.5 ␮M , 0.3 ␮M , 0.3 ␮M , 1.4 ␮M , 0.4 ␮M icroarray-based gene expression patterns can be used as tranylcypromine, 0.8 ␮M phenelzine, 0.6 ␮M iproniazid, 2.0 ␮M Mfingerprints of cellular physiology. The variety of cellular trazadone, 1.0 ␮M , 0.5 ␮M , 1.5 ␮M physiologies discernable by gene expression profile fingerprint- , 2.3 ␮M , 1.0 ␮M , 1.0 ing is expanding as an increasing range of cell types and cellular ␮M trifluperazine, 0.8 ␮M , 0.05 ␮M , manipulations are investigated, and statistical methods of ex- 4.0 ␮M , 0.2 ␮M , 0.04 ␮M , 0.5 pression profile classification are refined. In yeast, distinctive ␮M , 0.1 nM BW373U86, 1.0 ␮M Enkephalin, 0.1 nM profiles of genomic expression have been used to characterize U50488, 1.0 ␮M U62066, 1.0 ␮M Endomorphin, 0.1 ␮MTyr- cellular responses to diverse environmental transitions (1), func- D-Arg-Phe-Lys-NH2 (DALDA), 0.1 ␮MTyr-D-Ala-Gly-NMe- tionally classify genetic manipulations, and discover a novel Phe-Gly-ol (DAMGO), 0.1 ␮M Dynorphin A. Three control target for a drug of partially characterized function (2). In cancer cultures were treated with DMSO only; two were treated with 5.0 studies, microarray data has been used to classify solid tumors ␮M (PCP) or 5.0 ␮M . All treat- (3), correlate tumor characteristics to clinical outcome (4), and ments were 24 h in duration and conducted simultaneously. cluster cell lines on the basis of their tissue of origin and response Drugs were purchased from Sigma or Tocris Cookson (Ellis- to drugs (5–9). In the area of toxicogenomics, large-scale gene ville, MO). expression analysis of toxin-treated cells and animals has yielded a highly accurate capacity to recognize the toxic potential of Sample Processing. Cells were lysed in Trizol. Biotin-labeled novel drug candidates (10–14), resulting in an increase in the cDNA was made by using 15 ␮g of total RNA with poly(T) efficiency of drug triage in the pharmaceutical development primers. Gene expression was evaluated by hybridization to the pipeline. proprietary CuraChip microarray (CuraGen, New Haven, CT) Multiple statistical methods have been applied to classification of Ϸ11,000 oligonucleotide probes. Slides were hybridized for and recognition of expression profiles. Supervised classification 15 h at 30°C with constant rotation, washed for 30 min at room analysis methods, which can classify patterns of novel data based temperature (RT), incubated in streptavidin solution (4°C, 30 on prior knowledge of sample classes, include linear discriminant min), washed three times for 15 min at RT, incubated in analysis and genetic algorithm/K-nearest neighbors (11, 15), Cy3-conjugated detection buffer (4°C, 30 min), and washed three Fisher discriminant analysis (16), support vector machines (17), times for 15 min at RT. Slides were scanned (GMS 418 Scanner, neural networks (18), and tree-based analysis (19). Here, we use Genetic Microsystems, Woburn, MA) and analyzed by using human primary neurons treated with multiple members of multiple classes of drugs, antipsychotic drugs, and opioid receptor agonists to generate DNA microarray gene This paper was submitted directly (Track II) to the PNAS office. expression data representative of these classes of treatment. We Abbreviations: CT, classification tree; RF, random forest; PCP, phenylcyclidine; SSRI, selec- investigate whether example gene expression profiles from these tive reuptake inhibitor. drug treatments can be used to construct statistical models *E.C.G. and D.J.S. contributed equally to this work. capable of predicting drug efficacy. We find that the classifica- †To whom correspondence should be addressed. E-mail: [email protected].

9608–9613 ͉ PNAS ͉ August 5, 2003 ͉ vol. 100 ͉ no. 16 www.pnas.org͞cgi͞doi͞10.1073͞pnas.1632587100 Downloaded by guest on September 28, 2021 IMAGENE software (BioDiscovery, Marina Del Rey, CA). Of Table 1. Classification of gene expression profiles induced by 11,000 genes on the microarray, Ϸ4,700 were found to be different drug treatments ϫ expressed at least 3 background. True CT RF Compound class pred pred Subclass Data Analysis. All genes detectable at least 3ϫ background after signal normalization were included in the data sets for analysis. Amoxepine AD AD AD Data were prefiltered by using a generous Kruskal–Wallis filter Clomipramine AD AD AD Tricyclic (P Ͻ 0.001, Ϸ4,700 genes over 36 samples). Both CT and RF Desipramine AD OP AD Tricyclic models were constructed by using the data sets from the 36 Doxepin AD AD AD Tricyclic drug-treated samples, and calculated within a leave-one-out Imipramine AD AD AD Tricyclic cross-validation loop to minimize the influence of marker pre- Maprotiline AD AD AD Tricyclic filtering on model accuracy. The two highest-count markers Nortryptyline AD AD AD Tricyclic selected by the CT decision tree were then removed from the Protriptyline AD AD AD Tricyclic data set and this process was repeated, then repeated again with Trimipramine AD AD AD Tricyclic the two top markers from the second iteration removed as well. Amitriptyline AD AD AD Tricyclic For RF and each iteration of the CT algorithm, the samples were Citalopram AD AD AD SSRI weighted such that an unknown would have an equal probability Paroxetine AD AD AD SSRI of falling within any class, and not default to the over- Sertraline AD AD AD SSRI represented class (). The RF algorithm was also Fluoxetine AD AD AD SSRI calculated with 1,000 trees grown and two random inputs Fluvoxamine AD AD AD SSRI attempted at each split. Two additional RF models were gener- Tranylcypromine AD AD AD MAOI ated with the entire selective serotonin reuptake inhibitor Phenelzine AD AP AP MAOI (SSRI) or tricyclic class removed in a leave-one-subclass-out Iproniazid AD AD AP MAOI cross-validation loop. All statistical algorithms were performed Trazadone AD AD AD Atypical by using the ‘‘R’’ statistical software system (www.cran.r- Buproprion AD AD AD Atypical project.org). Chlorpromazine AP AP AP Classic Three-dimensional graphs were generated with DECISIONSITE Trifluperazine AP AP AP Classic (Spotfire, Somerville, MA). For CT iteration-one results, class Triflupromazine AP AP AP Classic separation provided by the four identified biomarkers was Pimozide AP AP AP Classic graphed for the four possible three-way combinations (leaving Clozapine AP AD AD Atypical each gene out once). For RF results, class separation provided Haloperidol AP AP AP Atypical by the three markers with greatest confidence measures was Risperidone AP AP AP Atypical graphed. The volumes occupied by the respective sample classes Loxapine AP AP AD Atypical were delineated by lines interconnecting all of the correctly BW373U86 OP OP AD ␦ OPR classified members in each group. Enkephalin OP AD AD ␦ OPR U50488 OP OP OP ␬ OPR Results U62066 OP OP OP ␬ OPR Supervised Classification of Drug-Treated Samples. The supervised Dynorphin A OP OP AD ␬/␮ OPR classification methods of RF and CT were used to analyze the DALDA OP OP OP ␮ OPR gene expression profiles of drug-treated neurons. These methods DAMGO OP OP OP ␮ OPR have the advantage that examples of known classes can be used Endomorphin OP OP OP ␮ OPR to build models of salient features that provide categorical Percent ‘‘correct’’ 88.9 83.3 distinction between the data sets used to build the models. These True class, known therapeutic utility; AD, antidepressant; AP, antipsy- PHARMACOLOGY models can then be tested empirically with data sets of known chotic; OP, opioid receptor agonist. CT pred and RF pred, classes predicted by class that were not used in model construction (cross validation). those methods for the expression profiles elicited by the drugs listed on the The ability of the model to correctly classify naı¨ve samples serves left; boldface designations, predicted therapeutic classes different from the as a measure of model quality. With both CT and RF methods, ‘‘true’’ class. Subclass, the common pharmacological sub-classification; MAOI, a ‘‘leave-one-out’’ training and testing series was conducted for monoamine oxidase inhibitor; ␦, ␬, ␮OPR, ␦, ␬, ␮ opioid receptor agonist, all 36 drug-treated samples. Thus, 36 individual models were respectively. constructed, each trained with 35 example gene expression profiles, with one profile withheld from training for evaluation. After construction of each model, the profile excluded from the resolution between classes, as indicated by the high marker count training set was tested by the model for assignment to one of the for these genes (Table 2). Three-dimensional graphical repre- three drug treatment categories. Overall effectiveness of each sentation of all possible three-way combinations of these four method was calculated as the percent correct classifications out biomarkers illustrates the robust class separation provided by of the total 36 training and testing events conducted by the expression level comparison (Fig. 1). method. Random Forest. The RF method classified 30 of 36 expression Classification Tree. The CT method classified 32 of 36 expression profiles in the category corresponding to the therapeutic appli- profiles in the category corresponding to the therapeutic appli- cation of the drug used to treat the cells (83.3% correct cation of the drug used to treat the cells (88.9% correct classification) (Table 1). Of the 20 antidepressants, 18 were classification) (Table 1). Of the 20 antidepressants, 18 were correctly classified. Of the eight , seven were correctly classified. Of the eight antipsychotics, seven were correctly classified. Of the eight opioid receptor agonists, five correctly classified. Of the eight opioid receptor agonists, seven were correctly classified. The RF analysis identified 326 markers were correctly classified. Interestingly, four gene markers were (not shown) used to construct the predictive models. The sufficient to provide this level of resolution accuracy among markers assumed an importance measure between zero and one. these expression profiles, with pentaxin 3 (PTX3) and integrin- A large importance measure indicates that random permutation linked kinase (ILK) being sufficient to provide the majority of of that gene causes samples to be misclassified more often (hence

Gunther et al. PNAS ͉ August 5, 2003 ͉ vol. 100 ͉ no. 16 ͉ 9609 Downloaded by guest on September 28, 2021 Table 2. Marker sets resulting from three sequential iterations of pable of identifying novel outgroups as separate from estab- the CT analysis method lished drug classes, we withheld vehicle-treated and nontarget Marker class drug-treated control culture microarray data from the CT Marker identity count and RF training sets, to serve as unfamiliar outgroups. After the genes that distinguish the drug classes were identified, the top CT iteration 1: 88.9% accuracy biomarkers from the CT and RF methods were depicted graph- PTX3, pentaxin 3 35 ically, incorporating all of the data from each drug class as well ILK, integrin linked kinase 34 as control samples (Figs. 1 and 2). A minimum volume encom- ENTPD6, ectonucleoside triphosphate 1 passing the correctly classified samples was depicted by linearly diphosphohydrolase 6 connecting all members of each drug class. Vehicle-treated GPCR CG50207 1 samples within these views were clearly excluded from the CT iteration 2: 72.2% accuracy minimum volumes of each of the three drug classes, thereby SFRS7, splicing factor, arginine/serine-rich 7 34 illustrating a means by which outgroups may be effectively ENTPD6, ectonucleoside triphosphate 24 distinguished from therapeutic treatments, even when those diphosphohydrolase 6 outgroups are not included in the training data sets. PCP- and CBRC7TM࿝424 GPCR 1 amphetamine-treated samples were effectively separated from APAF-1, apoptotic protease activating factor 1 1 the three main drug classes by RF, and fell very close to but ERMAP, erythroblast membrane-associated protein 8 outside the opioid category in the CT visualization. CGFLC࿝31120 1 GPCR CG50207 1 Multiple Iterations of Classification Tree. Because relatively few LDHA, lactate dehydrogenase A 1 genes were identified as drug class markers by the CT, we CT iteration 3: 80.6% accuracy investigated the robustness of the approach in the absence of LYPLA1, lysophospholipase 1 34 these markers by successively performing a second and third GPCR CG50207 26 iteration of the analysis, excluding from the data set the pre- CBRC7TM࿝424, GPCR 1 dominant gene markers identified by the first and second APAF-1, apoptotic protease activating factor 1 8 iterations, respectively. The second iteration resulted in the CGFLC࿝31120 1 identification of eight gene markers sufficient to provide 72.2% LDHA, lactate dehydrogenase A 1 resolution accuracy. Misclassified as antidepressants in this For each iteration, the two most frequent markers from the previous second iteration were trifluperazine, clozapine, enkephalin, iteration were deleted from the expression data training set. Percent accuracy, DALDA, and dynorphin. Misclassified as antipsychotics were the proportion of drug treatments correctly predicted by the respective clomipramine, phenelzine, iproniazid, and buproprion. Parox- marker set. G protein-coupled receptor (GPCR) CG50207 and CGFLC࿝31120 are etine was misclassified as opioid. Interestingly, the third iteration novel sequences of GPCR and unknown function, respectively. Marker count, resulted in the identification of fewer gene markers (six) than the number of times out of the 36 model building episodes a particular gene was second iteration (Table 2), sufficient to provide greater resolu- selected as a marker. tion accuracy (80.6%) between treatment classes, even though the strongest markers from the second iteration were removed from the data set. Misclassfications made by the third iteration that gene is important). Thirty-two markers had an importance were identical to the second iteration, except paroxetine, bu- measure Ͼ0.35. Three markers had an importance measure Ͼ ͞ proprion and DALDA were correctly classified. Because CT and 0.75: SFRS7 (splicing factor, arginine serine-rich 7), SCG3 RF are based on similar recursive partitioning algorithms, the (secretogranin III), and hypothetical protein CG187232-01. two approaches would be expected to identify many of the same Class separation provided by only these three top biomarkers markers. Indeed, both methodologies identified SFRS7 as a top (Fig. 2) is less distinct than the separation yielded by the marker. Overall, 55% of the markers from CT iterations 1–3, and biomarkers identified by the CT. This is probably because of the six of the eight CT markers with counts Ͼ1, are among the 326 relative proficiency of the CT and RF algorithms with data sets RF markers. containing a few strong markers, or a large number of weak markers, respectively. Discussion The goal of the present study was to determine whether in vitro Novel Subclass Prediction. The ability to accurately predict the gene expression profiles of drug efficacy could be generated and functional class of an unrepresented drug type was tested by used to predict the therapeutic classes of compounds adminis- constructing models with an entire subclass omitted from the tered to cell cultures. Recognition of toxin-induced expression training data set. Two independent iterations of RF were profiles has proven possible because of the characteristic tran- conducted, with all expression data from SSRI-treated samples scriptional responses to various types of cellular insult. Whether or tricyclic-treated samples withheld from the training data, different classes of drugs applied at nontoxic doses elicit dis- respectively. Each model was then used to classify members of cernable signature expression profiles has not been investigated. the antidepressant drug subclass withheld from training. The In particular, we were interested in whether drugs distinguished SSRI-naı¨ve model correctly identified five of five (100%) SSRI by therapeutic indication at the patient level induce distinct treatments as antidepressants, and the tricyclic-naı¨ve model expression profiles at the cellular level that correlate with clinical correctly predicted 10 of 10 (100%) as antidepressants, efficacy. To test this hypothesis, we selected three general classes even though neither model was constructed with explicit exam- of psychotropic compounds that might be expected to elicit ples of these respective subclasses. transcriptional responses in human neurons: two that are used clinically (antidepressants and antipsychotics) and one that acts Outgroup Identification by Graphical Depiction. A measure of model on a therapeutically relevant target class (opioid receptor ago- strength is the ability to distinguish inactive treatments from nists). Within each of these three categories, multiple subclasses active drugs of various classes. In the most stringent case, a were represented in order to capture general transcriptional model should be able to exclude outgroup treatments from the features of each drug class. target categories, even with outgroups not used to build the CT and RF supervised classification schemes proved effective model. To determine whether the constructed models are ca- at predicting the physiological consequence of drug treatments

9610 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.1632587100 Gunther et al. Downloaded by guest on September 28, 2021 PHARMACOLOGY

Fig. 1. Three-dimensional representation of class discrimination on the basis of biomarker expression: classification tree. All possible three-way combinations of the four-gene marker set from CT iteration-one are displayed: (CG50207 vs. ENTPD6 vs. PTX3) (A), (CG50207 vs. ILK vs. PTX3) (B), (ILK vs. ENTPD6 vs. PTX3) (C), and (CG50207 vs. ENTPD6 vs. ILK) (D). Axes represent relative expression levels of marker genes, with means set to 1.0. Each graph is shown perpendicular to the XY plane (Left), and from 45° rotation around the y axis (Right). Red, antidepressant; dark blue, antipsychotic; green, opioid; brown, PCP; black, amphetamine; light blue, vehicle control; squares, correctly predicted treatments; triangles, misclassified treatments. Lines connecting correctly predicted treatments delineate the volume occupied by each accurately defined sample class. Note distinct class separation and placement of untreated control samples outside the treatment classes.

Gunther et al. PNAS ͉ August 5, 2003 ͉ vol. 100 ͉ no. 16 ͉ 9611 Downloaded by guest on September 28, 2021 Fig. 2. Three-dimensional representation of class discrimination on the basis of biomarker expression: random forest. The three biomarkers with importance measures Ͼ0.75 are depicted: (SFRS7 vs. SCG3 vs. CG187232–01). Axes represent relative expression levels of marker genes, with means set to 1.0. The graph is shown perpendicular to the XY plane (Left), and from 45° rotation around the y axis (Right). Red, antidepressant; dark blue, antipsychotic; green, opioid; brown, PCP; black, amphetamine; light blue, vehicle control; squares, correctly predicted treatments; triangles, misclassified treatments.

on the basis of induced gene expression profile. The progressive depression]. This suggests that these drugs may share a common leave-one-out strategy of model building and testing allowed us mechanism of action downstream from their proximal biochem- to comprehensively assess the ability of these schemes to accu- ical activities. Detected by our microarray were many genes rately classify profiles induced by ‘‘novel’’ drugs (drugs not used involved in the transmitter systems targeted by the three drug to build the statistical predictive model). Because each of the classes, including MAO-a, MAO-b, multiple serotonin recep- three drug categories were weighted equally in our analyses, the tors, D2, and other receptors. Several expected tar- accuracy of categorization based simply on random chance gets, such as the serotonin transporter, trans- would be expected to be 33.3%. The 83.3% and 88.9% success porter, and the ␦, ␬, and ␮ opioid receptors were not detected rates of RF and CT, respectively, indicate that salient features of by the microarray, indicating they may be expressed below the the distinct cellular physiologies induced by these drug classes level of detection, or that other, unconventional targets are the are being identified by these methods, and that they can be used primary locus of action in this culture system. The presence of to correctly predict the clinical activity of unfamiliar compounds. gene expression biomarkers that robustly predict drug class is In principle, this approach can be expanded beyond the three consistent with the presence of convergent mechanisms of action classes resolved here to encompass a large number of drug among the respective subclasses of the three broad drug classes classes and cellular physiologies. Such high-content output is studied here. principally limited only by the number of pharmaceutical classes Although the practical emphasis in expression profile finger- to which a given cell type is responsive. printing to date has been to discern drug-toxicity profiles, The difference in accuracy between the CT and RF is likely drug-efficacy profiles may have utility in several important areas. caused by relative emphases on individual marker strength. Both One apparent application is to prioritize lead compounds early methods partition feature-space to classify samples (20). CT in drug development. The absence of a clear understanding of performs this split once, to identify a few genes that each explain the mechanisms of and depression has prevented the a large portion of the differences among classes. RF calculates development of a general cellular assay for drugs treating these many trees to identify many genes that each account for a small disorders. For example, there is no one cellular assay for amount of the differences among classes. Although the CT ‘‘antidepression.’’ Apart from direct binding of specific drugs to method has several advantages over more traditional learning different molecular targets, antidepressant activity can only be algorithms, the hierarchical nature of the data partition is tested in vivo at the present time, presenting throughput limi- especially sensitive to outliers, manifesting in high model vari- tations. Pharmaceutical development has thus been largely ance and misclassification rates (20, 21). By virtue of its iterative confined to ‘‘me-too’’ molecules of specific activity similar to character, RF improves model variance, generally yielding clas- existing compounds. Our discovery of efficacy profiles common sification accuracies equal to or better than CT augmented by to drugs of disparate activity but within a common therapeutic variance-reduction techniques such as boosting or bagging (20, class provides a simple in vitro tool for finding functionally 22, 23). Our finding that the first iteration of CT has a lower valuable drugs that act via a range of targets. Moreover, this misclassification rate than RF (Table 1) suggests that these CT method works whether the mechanism of action is known or markers are especially important in distinguishing among the novel. In our study, even when entire drug subclasses were left drug groups. This is supported by the subsequent two iterations out of the RF training protocol, their medical utility was of CT which each result in classification accuracies lower than correctly identified upon testing of the models. The accurate the RF (Table 2). classification of SSRI and tricyclic antidepressants by models How the specific biomarkers identified here relate systemat- naı¨ve to these important pharmaceutical classes illustrates the ically to the medical processes with which they correlate, and prospect of discovering therapeutically valuable drugs that func- whether these biomarkers are also functional intervention points tion by unknown mechanisms. Had SSRIs not been discovered for further pharmaceutical development, are questions under before this study, for example, they could still have been investigation. Subclasses of drugs within each broad class are accurately identified in our assay as having antidepressant medicinally similar, but mechanistically heterogeneous [e.g., activity. SSRIs, monoamine oxidase (MAO) inhibitors, and norepineph- In future refinements, it will be necessary to recognize false rine-uptake inhibitors act on distinct drug targets within multiple positives by including outgroups into the modeling protocol or neurotransmitter systems, yet can exert similar effects on clinical establishing an alternative classification of ‘‘insufficiently close

9612 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.1632587100 Gunther et al. Downloaded by guest on September 28, 2021 to any efficacy profile.’’ Three-dimensional graphical depiction functionally assaying, in vivo, novel compounds that progres- of vehicle-treated control sample biomarker values placed them sively define revised hypervolume borders. outside the minimum volume encompassing the respective drug Potentially of interest in analyses of this kind are trends in classes (Figs. 1 and 2). Significantly, these control samples were misclassification. For example, ␬ and ␮ opioid receptor agonist- omitted from the data sets used to construct the efficacy profiles. induced expression profiles were predominantly correctly clas- Thus, their effective partition supports the ‘‘insufficiently close sified by both the RF and the first iteration of CT. However, ␦ to any efficacy profile’’ approach to novel outgroup identifica- opioid receptor agonist-induced profiles were predominantly tion. Control samples treated with compounds of other neuro- classified as antidepressant profiles (Fig. 2). A question raised by active classes (PCP and amphetamine) also fell distinctly outside this result is whether targeting the ␦ opioid receptor system the three drug class volumes in the RF depiction. In the CT would have utility in the treatment of depression. Studies of ␦ depictions, although these neuroactive drugs fell technically opioid agonist action in mouse models of depression, in fact, outside the three classes, they were very close to the opioid support the idea that this receptor system mediates depressive treatments, perhaps because of shared aspects of cellular phys- behavior (24). Thus, in vitro analyses of this kind may have utility iology. Any compound tested will necessarily have a position in in discovering novel therapeutic applications of drugs. n-dimensional biological descriptor space that falls closest to one Another potential application of efficacy profiling is the of the centroids created by the different classes of drug treat- validation of genes as therapeutic drug targets, an early step in ment. In our present cheminformatic scheme, this closest cen- the drug development process. Manipulation of a specific gene troid determines the class of the drug being tested. An optimized in culture, either by overexpression or gene silencing, may version of this assay may establish a minimum n-dimensional identify it as a drug target when a therapeutically relevant hypervolume surrounding all known members of a drug class, expression profile is induced. The derivation of biomarker outside of which a given drug under evaluation may be classified profiles as signatures of specific pharmacological functions as not belonging to any known therapeutic class. In practice, as provides a foundation for in vitro assessment of the therapeutic new bioactive compounds are detected by the assay, the bound- relevance of presumptive disease-mediating biological mole- aries of each hypervolume may be empirically optimized by cules, and the efficacy of therapies devised to target them.

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