evaluating 
 the safety and hazard of chemicals for potential human 
 health and environmental effects is undergoing a major transformation . this 21st century toxicology paradigm has emerged 
 from the limitations of the current paradigm in regard to cost , time , 
 and throughput , as well as the development of modern biological tools . these tools can probe chemical  biological interactions at fundamental 
 levels , focusing on the molecular and cellular pathways that are targets 
 of chemical disruption . in this manner , 
 we can begin to understand mechanisms of chemical toxicity that may 
 invoke disease or health effect end points . a more mechanistic understanding 
 will help elucidate common pathways of toxicity and susceptibilities 
 underlying human - relevant outcomes . toxicity information is 
 limited or absent for tens of thousands 
 of compounds potentially entering the environment . even for pharmaceuticals designed with a 
 particular biological activity in mind , there is little public information 
 about unexpected toxicities or adverse responses that may be initiated 
 by off - target binding to nuclear receptors , g - protein - coupled receptors , 
 and receptor tyrosine kinases , or by a myriad of events upstream or 
 downstream to receptor engagement . evaluating 
 the untested chemicals through the current safety assessment paradigm 
 is limited in throughput , cost , time , and mechanistic revelation . 
 as such , high - throughput screening ( hts ) of in vitro chemical target interactions across chemicals , including 
 pharmaceuticals and chemicals of known and unknown toxicities through 
 a broad range of biochemical assays will help describe the chemical our broader 
 hypothesis is that biochemical hts , when combined with the diverse 
 assays within the toxcast portfolio , provides an anchor for predictive 
 signatures and mechanistic pathways leading to toxicity . with further 
 analysis , these types of screens may help identify novel initial molecular 
 events potentially associated with pathways of toxicity and inform systems modeling efforts aimed at 
 characterizing adverse outcome pathways . epa s toxcast project and the federal tox21 collaboration 
 are generating hts data and building modeling approaches to identify 
 and characterize biological pathways of toxicity . this approach employs a large , structurally 
 diverse chemical library to probe a wide spectrum of biological targets 
 and cell - based activities , which enables grouping and prioritizing 
 of chemicals based on their in vitro activity profiles , 
 as well as deeper exploration of system biology relationships linking 
 biological activities to in vivo toxicology . further 
 applications of this approach have the potential to enhance and refine 
 structure , metabolism , or presumed mode of action - based read - across 
 methods , as well as to identify potential 
 targets for molecular initiating events in adverse outcome pathways 
 for diverse toxicities . these approaches can be applied to testing 
 prioritization , hazard and safety assessment workflows , design of 
 green alternative chemicals , or screening for adverse effects for 
 drug development processes . toxcast phase i screened 310 unique 
 compounds , mainly food - use 
 pesticides with rich in vivo data profiles in 467 
 biochemical or cell - based assays from 9 assay technologies . despite the somewhat limited chemical diversity of this initial 
 test library , pesticidal compounds were found to have sufficiently 
 rich bioactivity profiles , in vitro and in 
 vivo , to yield promising new insights and initial predictive 
 models . to expand on these efforts , toxcast phase ii has significantly 
 broadened the scope and diversity of the chemical library with 667 
 newly added chemicals , selected based on factors such as production 
 volume , potential for human exposure , possible hazards to human and 
 ecological populations , and availability of in vivo toxicity data . some of these chemicals have known biological activities , 
 whereas most have limited toxicity data as compared to phase i or 
 have not been previously characterized . specifically , mammalian biological 
 data , including toxicity , are publically unavailable for the majority 
 of these chemicals . the phase ii chemical set includes phthalates , 
 alternative plasticizers , antimicrobials , pesticides , food additives , 
 toxicity reference compounds , and 111 failed drugs donated by six 
 major pharmaceutical companies . previously , we reported an analysis 
 of the original toxcast 310 
 phase i chemical set ( phasei_v1 ) in a biochemical screen consisting 
 of 292 protein targets ( note : toxcast phase i originally reported 
 results for 309 unique chemicals . upon further review , 2 compounds 
 previously labeled as separately sourced replicates were found to 
 be distinct stereoisomers , yielding a revised total of 310 unique 
 chemicals ) . the assays were selected from 
 a commercial novascreen panel ( nvs ) for preclinical drug development . these assays measure chemical binding to nuclear receptors , g - protein - coupled 
 receptors ( gpcr ) , transporters , and ion channels , and enzymatic inhibition 
 or activation for a range of proteins including kinases , phosphatases , 
 cyp450s , proteases , and histone deacetylases . these results are augmented 
 in the present study with newly reported nvs results for the toxcast 
 phase ii chemical set bringing the chemical inventory to 976 unique 
 chemicals , spanning considerably greater chemical structure diversity 
 and use categories , and with the potential to probe a greater number 
 of biological pathways than phase i compounds . this report provides 
 the first critical analysis of findings for 
 976 chemicals from toxcast phase i and ii profiled across 331 assays 
 within the nvs panel . the 331 assays evaluated here are currently 
 all of the cell - free assays in toxcast phase ii . these comprise most 
 of the cell - free assays run in phase i , with the addition of one new 
 assay ( hcar_agonist ) . the rationale for 
 assay selection was addressed in the phase i article , and the reduced 
 number of assays in the present study reflect the elimination of assays 
 shown to have lesser value based on redundancy ( i.e. , a subset of 
 cytochrome p450s , cyps ) and first - generation predictive models . we 
 focused on relative specificity toward different assay types and their 
 sensitivity to different chemical classes , extending the phase i study 
 of 310 chemicals . in addition , assays and chemicals were analyzed 
 for similarity based on the chemical  assay activities . target combinations , 
 as well as off - site targets for some well - characterized compounds . phases i and ii 
 of the toxcast chemical library considered in this study contain 1020 
 diverse compound samples , consisting of 976 unique structures , and 
 44 replicate samples for quality control purposes . these chemicals 
 more specifically correspond to the toxcast phasei_v1 and phase iia , b 
 chemical libraries . the rationale for chemical selection was based 
 on several criteria , including chemical nominations within the epa 
 and other federal agencies ( e.g. , national toxicology program / national 
 institutes for environmental health sciences ; national center for 
 advancing translational sciences / national institutes of health ; u.s . food and drug administration ) ; international organizations such as 
 the organisation for economic co - operation and development ; and other 
 stakeholder groups . the chemical use groups for the nominated chemicals 
 include ( but are not limited to ) marketed drugs with known target 
 activities and over 100 failed ( terminated ) pharmaceuticals donated 
 by several pharmaceutical companies ; phthalates and alternative plasticizers ; 
 antimicrobials ; pesticides ; and food additives . a tabular listing and structure data format ( sdf ) file 
 of the complete toxcast chemical library , of which the 976 compound 
 library is a subset , is available for download at http://www.epa.gov/ncct/dsstox/sdf_toxcst.html . chemical 
 information associated with the toxcast library ( i.e. , chemical names , 
 cas rn , and substance description ) was quality reviewed and structure - annotated 
 within the u.s . chemical samples were commercially procured , diluted in dimethyl 
 sulfoxide ( dmso ) to a stock concentration of 20 mm , and plated by 
 evotec ( formerly biofocus dpi , south san francisco , ca ) . analytical 
 qc for the phase i chemical inventory consisted of high - throughput 
 liquid and gas chromatography mass spectrometry determination of sample 
 purity , parent mass , and sample stability in dmso over time ( http://www.epa.gov/ncct/toxcast/chemicals.html ) . similar analytical 
 qc methods are being applied to analyzing the phase ii portion of 
 the current toxcast library in association with the tox21 project 
 and will be made publically available upon completion . additional 
 information about these chemicals can be found by querying the dsstox_gsid , 
 from supporting information , table s2 , 
 in the publically available dsstox structure browser ( http://epa.gov/dsstox_structurebrowser/ ) , which provides additional structure - based link - outs to information 
 in epa s actor database ( http://actor.epa.gov ) and 
 in pubchem ( http://pubchem.ncbi.nlm.nih.gov/ ) . the nvs assays , 
 developed and run by caliper , a perkinelmer company ( hanover , md ) , 
 consisted of 331 assays that detect whether a test chemical alters 
 the binding of ligands to receptors ( 131 ) or inhibits enzymatic activity 
 ( 100 ) . the 100 inhibitory enzymatic assays were also assessed for 
 enzymatic activation , resulting in an additional 100 assays . details 
 on individual assays , catalog numbers , quality assurance methods , 
 and literature references can be obtained at www.caliperls.com / products / contract - research/. supporting information , table s1 contains 
 a list of all assays , their technical specifications , and active links 
 to specific assay protocols . the assay distribution by protein family 
 consists of the binding format : 77 g - protein coupled receptors ( gpcr ) , 
 including 32 aminergic gpcrs ; 19 nuclear receptors , 10 from subfamily 
 1 and 9 from subfamily 3 ( steroid ) ; 7 ion channels ; 13 ligand - gated 
 ion channels ( lgic ) , including 9 cys - loop and 4 ionotropic glutamate 
 receptors ; 11 transporters ; and 3 other binding assays ( other ) ; and 
 enzymatic inhibition ( and activation ) format , 37 kinases ; 19 phosphatases ; 
 15 proteases ; 3 cholinesterases ; 10 cyps ; and 17 other enzymes including 
 histone deacetylases , phosphodiesterases , monoamine oxidases , and 
 cyclooxygenases . most assays use human ( 240 ) or rat ( 60 ) targets ; 
 the remaining assays use bovine ( 10 ) , guinea pig ( 10 ) , sheep ( 4 ) , 
 mouse ( 3 ) , rabbit ( 2 ) , mixed rodent ( 1 rat / mouse ) , and boar ( 1 ) targets . actives based on the number 
 of ac50 
 values recorded within an assay category ; active fraction ( active 
 % ) indicates the percentage of theoretical maximum . the screening 
 strategy for phase ii follows the same protocol as that used with 
 the phase i chemicals . for phase ii , all 
 chemicals were initially screened at a single concentration in duplicate 
 ( 161,700 chemical  assay pairs in duplicate ) . a single concentration 
 of 10 m was used for cyp assays and 25 m for all other 
 assays . assay chemical combinations were defined as active 
 in the primary screening if they met a predefined threshold of the 
 mean assay signal differing by at least 30% from the vehicle ( dmso ) 
 control signal or the mean assay signal varying by a minimum of 2.0 
 median absolute deviations from the median ( mad2 ) . from these criteria , 
 14,961 active chemical assay combinations were conducted in 
 concentration  response along with 25 inactive chemical  assay 
 combinations ( 14,986 total chemical  assay pairs ordered , 9% 
 of original single concentration pairs ) . each chemical was run in 
 8 serial dilutions using half - log spacing descending from a top concentration 
 of 500.023 m ( or 20 m0.009 m 
 for cyp assays ) . consistent with phase i , lower chemical concentrations 
 were used for cyp assays due to increased sensitivity to inhibition , 
 as expected for enzymes active against xenobiotic chemicals . the raw data ( percent 
 activity versus positive control ) were received by the epa and processed 
 within a common custom workflow for all assays within the toxcast 
 project . all phase i and ii data were 
 subjected to custom curve - fitting algorithms for processing and computing 
 ac50 values ( chemical concentration at which 50% of maximum activity 
 is achieved ) . these algorithms were developed in the open source r 
 language , similar to the analysis used for the phase 
 i chemicals . the r scripts utilized 
 a four parameter hill function to fit the data and included the following 
 assumptions : all concentration responses were assumed to be monotonic ; 
 outliers were discounted when there was a monotonic curve ; a variable 
 slope was allowed ; and negative inhibition ( activation ) was allowed 
 for enzymatic assays . the asymptotes of the curve were constrained 
 to be between 10 and 10% activity ( lower ) and 100 and 110% 
 activity ( upper ) , to allow for consistent extrapolation of the ac50 
 across assay  chemical combinations . ac50 extrapolations were 
 allowed at concentrations 3-fold lower than the lowest concentration 
 tested and 3-fold higher than the highest concentration tested to 
 capture potential activity outside of the tested concentrations . additional 
 criteria for generating ac50 values included hill curves r coefficient of determination values 0.6 , p value 0.01 ( from a test of significant difference 
 between the top and bottom of the curve fit ) , and emax ( maximum activity )  30% baseline activity . 
 to capture activity when a dose  response curve was nonmonotonic 
 or could not be calculated from these criteria , a special case ac50 
 was assigned to the first concentration when a chemical inhibited 
 an assay by at least 50% at any two consecutive concentrations . assay pair special cases , which can 
 be seen from the curve fits provided in the supporting 
 information . when chemical  assay data did not meet these 
 requirements , a dose  response curve was not generated , and 
 the chemical was deemed inactive in the assay . in cases where a chemical 
 was tested in both phases i and ii , data were not combined to calculate 
 ac50s due to potential variation during temporally separate experimentation ; 
 thus , the lowest ac50 value was taken . assay 
 pairs were set at 1000000 m ( 1 m ) before transformation to ensure 
 a potency of zero . transformed 
 data ( i.e. , 
 log(ac50/1000000 ) were clustered with partek discovery suite 
 6.6 software ( partek , inc . , st . assay 
 unsupervised hierarchical clustering was performed using euclidean 
 distance as the similarity metric and ward s method for assembling 
 clusters . assay and chemical  chemical similarity 
 matrices were built using pearson s correlation as the similarity 
 metric and ward s method for assembling clusters . in order to identify 
 the assays affected by chemicals clustered together in the chemical chemical 
 similarity matrix , we developed an assay category enrichment score 
 ( es ) for each chemical . the es scores were calculated for each chemical 
 over the 21 assay categories , showing which categories the chemical 
 is preferentially affecting . initial esc ,  ac = avg(ac50)c ,  ac / avg  ( ac50)c ,  ac_rest , where c is a given chemical , ac is a 
 given assay category , and ac_rest is all other 
 assay categories not associated with the given ac . for example , if the ac was gpcr_aminergic , then ac_rest would be all assays except gpcr_aminergic and gpcr_other . each esc , ac within an ac is normalized 
 by the maximum es , i.e. , final esc ,  ac = initial esc ,  ac /maximum(es)c univariate 
 analyses between chemical  assay categories and chemical structure 
 fragments were used to determine chemical structure fragment assay 
 category associations . assay category es scores were 
 calculated as in 2.7 ( above ) , but using only ac50s 10 m 
 ( i.e. , calculated ac50s > 10 m were assigned an ac50 of zero ) 
 to reduce potential nonspecific interactions . a fingerprint is a bit string indicating 
 whether a chemical structure contains a particular chemical fragment . the fragment libraries used are the fp3 , fp4 , and maccs fingerprint 
 sets available with open babel , padel , and pubchem ( http://pubchem.ncbi.nlm.nih.gov ) fingerprint sets . fingerprinting was carried out using open babel and comparing the 
 fragment smarts strings to the chemical smiles strings . in total , 
 there were 6655 fragments used , but only 2950 fragments were found 
 in at least one chemical structure , and 1907 fragments were found 
 in 5 or more structures . for each fragment assay pair , a 2 
  2 table was built ( have fragment , yes or no vs active in assay , 
 yes or no ) , and standard statistical metrics were calculated . all 
 univariate associations were used where there were at least 5 true 
 positives ( cases where a chemical had a fragment and was active in 
 an assay ) , and the positive predictive value ( ppv ) was greater than 
 0.5 . these associations were plotted in cytoscape : an open source 
 platform for complex network analysis and visualization ( www.cytoscape.org ) . chemical structures were visualized 
 through marvinsketch viewer ( chemaxon ltd . ) using the u.s . since data analyzed 
 in the present study are a compilation of two temporally distinct 
 phases of testing , phase i and phase ii , assay performance was evaluated 
 using chemical replicates between phases and within phases , on ac50s 
 up to the highest dose tested . for the 11 chemicals replicated across 
 phases i and ii , there was a 95% concordance over 3045 chemical assay 
 pairs ( 2907 concordant , including 100 positives and 2807 negatives ) . nine chemicals replicated within phase ii showed 93% concordance over 
 2079 chemical  assay pairs ( 1941 concordant , including 27 positives 
 and 1914 negatives ) . all subsequent results described 
 herein aggregate results for the 44 total replicates of 11 phase i 
 compounds and thus consider the set of 976 unique chemicals among 
 the 1020 chemical sample inventory . results are summarized in 
 table 1 for 976 unique chemical actives tested 
 against the 331 biochemical assay portfolio . active chemical assay 
 pairs refer to a recorded ac50 value listed in supporting information , table s2 . overall , 729 of 976 chemicals 
 ( 75% ) were active in at least one assay , and 256 of 331 assays ( 77% ) 
 had at least one active chemical . the assay categories most 
 affected include gpcr ( aminergic ) , gpcr ( other ) , and cyp categories , 
 with 1579 , 1175 , and 843 actives , respectively . limiting the ac50s 
 to a threshold of 10 m or 1 m gave 2329 and 624 chemical the gpcr ( aminergic ) category had the greatest 
 number of actives at 1 m ( 148 ) . a chemical affected 
 10 assays on average , ranging from 0 ( 274 chemicals ) to 90 ( 1 chemical ) . an assay was affected by 28 chemicals on average , ranging from 0 ( 75 
 assays ) to 264 ( 1 assay ) . there were 75 assays not affected by any 
 chemical tested , of which 70 are activator assays measuring increased 
 assay activity . chemicals and assays were assessed for their 
 promiscuity and potency 
 ( micromolar ac50 value ) . they represented a broad range 
 of chemical categories , including heavy metal chemicals ( phenylmercuric 
 acetate , mercuric chloride , tributyltin methacrylate , and tributyltin 
 chloride ) , surfactants ( sodium dodecylbenzenesulfonate , perfluorooctane 
 sulfonic acid , dodecylbenzene sulfonate triethanolamine ( 1:1 ) ) , dithiocarbamate 
 fungicides ( mancozeb , maneb , and metiram zinc ) , and a variety of pharmaceuticals . they represent a broad range of assay categories , including 
 transporters ( translocator protein ( pbr / tspo ) , dopamine , norepinephrine ) , 
 cyps ( cyp1a2 , cyp2b6 , cyp2c19 , cyp2c9 ) , gpcr ( aminergic , 5ht7 and 
 drd1 ) , gpcr ( other , opiate  and opiate  ) , nuclear receptor 
 ( subfamily 3 , ar and gr ) , nuclear receptor ( subfamily 1 , pregnane 
 x receptor ( pxr ) ) , ion channel ( nach ) , other enzyme ( maoac ) , protease 
 ( bace ) , and other ( sigma receptor ) . columns indicate the top 20 most 
 promiscuous chemicals , the total number of calculated ac50s , and the 
 number of ac50s at 10 m and 1 m , respectively . the number of ac50s corresponds to the number of assays affected by 
 a given chemical at the ac50 value constraint . columns indicate 
 the topmost promiscuous 
 assay targets ( corresponding to one assay ) , the respective assay category , 
 the total number of calculated ac50s , and the number of ac50s affecting 
 the assay at 10 m and 1 m . the number 
 of ac50s corresponds to the number of chemicals affecting a given 
 assay at the ac50 value constraint . fifty - four chemicals had ac50s at the lowest concentration 
 tested 
 for one or more assays ( lct 0.009 m for cyps and 0.023 
 m for non - cyps ) , indicating high potency for these chemical for many of these chemicals , high potency was observed for 
 two or more assays of the same or related category ( e.g. , cp-471358 
 and cp-544439 inhibition of matrix metalloproteinases mmp2 , mmp9 , 
 and mmp13 ; chlorpromazine inhibition of gpcr ligand binding activity 
 for drd1 , drd2s , 5ht2c , adra1a , adr1b , and h1 and inhibition of net 
 transporter binding activity ) ( table 4 ) . more 
 than half of the compounds listed in table 4 have literature references to substantiate these observed chemical results from the data overview indicate that both broad 
 and specific biochemical activities are detected across 323,056 possible 
 chemical  assay pairs in toxcast phases i and ii . columns indicate chemicals with 
 ac50s at the lowest dose tested , the total number of calculated ac50s 
 for the given chemical , the number of ac50s at the lowest dose ( i.e. , 
 number of assays affected at the lowest dose ) , the assay target(s ) 
 with ac50 values at the lowest dose tested , and the literature evidence 
 of the chemical  target interaction , if applicable . target 
 interactions included ( 1 ) chemicals that missed one assay within the 
 same category ( haloperidol affected all 5ht receptors , except h5ht2a ; 
 zamifenacin affected all muscarinic receptors , except hm1 ) ; ( 2 ) assays 
 with species orthologues which were not affected ( cp-105696 inhibited 
 the guinea pig but missed the human leukotriene assay , hltb4_blt1 ; 
 pha-00554613 inhibited the rat but missed the human neonicotinoid 
 receptor , hnnr_nbungsens assay ; and uk-416244 inhibited the rat but 
 missed the human serotonergic transporter , hsert ) ; ( 3 ) specific chemical 
 inhibitors which missed the nonspecific assays ( cp-457920 and 2,6-difluoro-5-[3-(2-hydroxypropan-2-yl)imidazo[1,2-b]triazin-7-yl]biphenyl-2-carbonitrile inhibited 
 bgabara5 but not bgabar_agonist or rgabar_nonselective ) ; ( 4 ) chemicals 
 that inhibited one or no assays in a category ( elzasonan a 5ht1b antagonist 
 did not inhibit any 5ht assays ; emamectin benzoate is known to bind 
 to mammalian gaba receptors , albeit at a lower affinity than in insects , 
 but it only inhibited 1/5 gaba assays ; 5,5-diphenylhydantoin is a 
 substrate and inhibitor for many cyps , although it did not inhibit 
 any cyps in this screen ) . evaluating this list for false negatives 
 ( missing chemical  target 
 interactions ) is limited by publically available information . to evaluate 
 false negatives and true positives ( where ac50s were calculated for 
 known chemical target interactions ) in a systematic way , we 
 chose well - known chemical the epa s endocrine disruptor screening program 
 ( edsp ) has an estrogen receptor reference chemical list of known estrogenic 
 compounds of varying potency and nonestrogenic compounds with categories 
 of potency defined as follows : strong actives exhibit an effective 
 concentration at 50% maximal activity ( ec50 ) of < 10 m ; moderate 10 to 10 m ; 
 weak 10 to 10 m , and very 
 weak > 10 m ( http://www.regulations.gov/#!documentdetail;d=epa-hq-opp-2012-0818-0017 ) . twenty - two chemicals in common were evaluated in the ber , her , 
 and mera assays . we found that all inactives ( 6 ) , strong actives ( 3 ) , 
 and antagonists ( 1 ) were correctly identified . none of the very weak 
 ( 3 ) chemicals were correctly identified , but a majority of the weak 
 positives ( 7 of 9 ) were in at least two of the assays . together , these 
 findings indicate that these data are robust with respect to correctly 
 identifying activity within an assay category for weak to strong inhibitors 
 ( 77% , 17 of 22 ) correct using two assays ) ; however , limitations are 
 also realized with respect to potentially identifying very weak inhibitors . to determine 
 how the chemical  assay space was structured , we performed hierarchical 
 clustering based on chemical  assay potency ( figure 1 ) . assay 
 space into sectors ranging from no activity to high activity . for 
 some clusters , it was possible to annotate by assay category or chemical 
 class ( figure 1a ) . figure 1b shows the clustering for 10 of the most promiscuous chemicals 
 including heavy metal chemicals , surfactants , and dithiocarbamate 
 fungicides . distinct effects can be seen among these chemical classes , 
 indicating some degree of specificity . four functional categories 
 listed across the top of the hierarchical cluster ( pesticides , pharmaceuticals , 
 consumer use chemicals , and phthalates and alternative plasticizers ) 
 give a diverse representation of the chemicals evaluated in this data 
 set ( figure 1a ) . these data show varying degrees 
 of sensitivity and specificity as will be discussed in section 3.3 . hierarchical clustering was based on chemical  assay potency 
 as measured by the inverse log of ac50s ( in micromolar ) divided by 
 1,000,000 ( log(ac50/1000000 ) ) . gray indicates inactive 
 and white not tested in phase i ( hcar_agonist assay ) . ( a ) left ribbon 
 indicates clusters of 21 assay categories ( see assay ribbon key ) . left colored tree structure indicates relatively homogeneous assay 
 category clustering including gpcr ( other ) , red ; nuclear receptor 
 ( subfamily 3 ) , yellow ; kinases and phosphatases , blue ; gpcr ( aminergic ) , 
 green . top colored 
 tree structure indicates highly promiscuous , mostly pharmaceuticals 
 ( green ) and promiscuous , mostly pesticides ( blue ) . ( b ) details for 
 10 of the most promiscuous chemicals listed in table 2 . heavy metals ( 1-phenylmercuric acetate , 2-mercuric chloride , 
 3-tributyltin methacrylate , 4-tributyltin chloride ) ; surfactants ( 5-sodium 
 dodecylbenzenesulfonate , 6-perfluorooctane sulfonic acid , and 7-dodecylbenzene 
 sulfonate triethanolamine ( 1:1 ) ) ; and dithiocarbamate fungicides ( 8-mancozeb , 
 9-maneb , and 10-metiram ) . prominent assay clusters containing relatively homogeneous 
 assay 
 categories included gpcr ( other ) , nuclear receptor ( subfamily 3 ) , 
 kinase and phosphatase , and gpcr ( aminergic ) ( figure 1a ) . the clustering was clearly following at least some of 
 the biology of the constituent chemical  assay pairs . for example , 
 the gpcr aminergic cluster ( green ) included several adrenergic gpcrs 
 ( radra1a / b , radra1/2_nonselective , hadrb1/2 , hadra2a / c , and rmadra2b ) 
 and corresponding transporters ( hnet ) and also included several dopaminergic 
 gpcrs ( hdrd1 , hdrd2s , hdrd4.4 , and bdr_nonselective ) and their corresponding 
 transporters ( hdat and gdat ) . this implies a degree of consistency 
 and specificity to the chemical biology of the perturbation . the kinase 
 and phosphatase cluster ( blue ) was highly enriched with these assays 
 and also included a phosphodiesterase ( pde4a1 ) and class - iii hdac 
 ( sirt2 ) . the largest nuclear receptor cluster ( yellow ) contained 8 
 assays ( 386 chemical  assay actives ) all within subfamily 3 , 
 the steroid receptors ( gr , ar , pr , and er ) . many of these assays were 
 also among the most promiscuous ( see table 3 ) . pesticide active compounds , 
 a main component of the toxcast phase i testing due to the availability 
 of in vivo data , are designed to be biologically 
 active in target species ( i.e. , insects , weeds , and fungi ) , while 
 producing minimal effects on human biology . a diverse set of 470 pesticides 
 in the combined phase i and phase ii library considered here affected 
 a large number of assays ( 236 , 71% ) . roughly 20% ( 105 ) affected no 
 assays , whereas 41% ( 195 ) were included among the most promiscuous 
 clusters ( indicated by the top green and blue colored tree structures 
 ( figure 1a ) containing a total of 315 chemicals ) . the top active assays affected by pesticides included human cyp2c19 , 
 cyp2c9 , cyp1a2 , and cyp2b6 enzymatic inhibition assays ( 165 , 79 , 74 , 
 and 55 chemicals , respectively ) , human pxr binding assay ( 116 ) , and 
 rat and human tspo binding assays ( 104 and 74 ) . examples of chemical carbofuran , an insecticide known to inhibit cholinesterase , inhibited 
 only 3 assays : butyrylcholinesterase and , at submicromolar ac50 concentrations , 
 the human and rat acetylcholinesterase enzymatic assays . picoxystrobin , 
 a strobilurin fungicide that inhibits fungal respiration , affected 
 only 4 assays , including the human cyp2c9 and cyp2c19 enzyme assays , 
 as well as the human tspo and , at submicromolar ac50 concentrations , 
 the rat tspo enzymatic assay . two other members of the strobilurin 
 class of fungicides , described in the previous phase i analysis , also affected a small number of assays ( 39 ) , 
 including tspo . hence , for a number of examples , the activity displayed 
 in this biochemical platform for selected pesticides reflected bioactivities 
 associated with their known pesticidal modes of action and/or potentially 
 related targets . pharmaceuticals 
 ( 245 ) , including the 111 donated compounds , are intended for biologic 
 activity like pesticides but toward mammalian biology . there were 36 pharmaceuticals 
 that did not produce an ac50 for any of the target assays tested here ; 
 however , 37 pharmaceuticals clustered tightly by inhibition of gpcr 
 binding activities ( figure 1a ) . volinanserin , 
 a serotonin receptor antagonist , inhibited 
 41 different assays , the majority of which were gpcrs and transporters 
 ( 78% ) , including the serotonergic system ( affecting 9 out of 11 total 
 serotonergic assays ) ( see supporting information , table s2 ) . for example , 
 anthralin is a topical antiproliferative , anti - inflammatory agent 
 used to treat psoriasis ; however , the 
 mechanisms of action are unknown . anthralin inhibited 22 assays , almost 
 exclusively enzymes , including caspases 15 and 8 ( where the 
 inflammatory caspase 5 activity was inhibited at submicromolar ac50 
 concentrations ) , mmps 1/2/3/7/13 , and cyclooxygenases ptgs1/cox1 and 
 ptgs2/cox2 ( see supporting information , 
 table s2 ) . these data indicate that the biochemical activity profiles 
 of pharmaceuticals provide information on potential novel effects 
 in addition to their known activities , giving possible leads for further 
 targeted studies . consumer 
 use chemicals tested here ( 171 ) , exclusive of pesticides and pharmaceuticals 
 that were described above , showed spotted activity based on the number 
 of targets affected across the assay space ( 42% of this chemical class ) 
 or , in some cases , were highly promiscuous ( 9% of this chemical class ) 
 ( figure 1a ) . example chemicals in this class 
 include food additives and ingredients in sunscreens , cosmetics , soaps , 
 and shampoos . of the chemicals showing activity ( 100 ) , the median 
 number of assays affected per chemical was 2 , with a range of 1 to 
 72 assays . caffeine and the structurally related methylxanthine , 
 theophylline , inhibited all three adenosine receptor binding assays 
 ( hado2a , hado1 , and nonselective bado receptor ) . interestingly , adenosine 
 receptor binding activities were also inhibited by the 2-deoxyadenosine 
 analogue , cladribine , an effect recently reported . the 13 perfluorinated compounds , which are industrial and 
 consumer use products ( e.g. , surfactants and nonstick applications ) , 
 showed a wide range of chemical - specific activity ( median 6 assays ) . for example , 1,1,2,2-tetrahydroperfluoro-1-decanol inhibited none 
 of the assays , whereas perfluorooctane sulfonic acid ( pfos ) inhibited 
 72 assays . across all perfluorinated compounds , the most commonly 
 affected assay ( inhibited by 10 of 13 chemicals ) was the leukotriene 
 b4 ( gpcr_gltb4 ) . other leukotriene assays ( gpcr_hltb4_blt1 , gltd4 ) 
 were inhibited by 30% ( 4 ) of the perfluorinated compounds . although 
 these data indicate that most chemicals labeled for consumer use in 
 the toxcast library , excluding pesticides , were generally inactive 
 in the biochemical hts platform , there are case examples where some 
 broad use categories are active against particular assay categories . alternatives to phthalate plasticizers have 
 been proposed to eliminate this undesired effect ; however , many alternatives have not undergone rigorous 
 testing . thirty - four plasticizers , including 
 7 phthalates and 27 potential alternatives , were tested here . overall , 
 12 plasticizers were inactive across all 331 assays ( see figure 1a ) . of the remaining 22 compounds , 15 were active in 1 to 
 2 assays ( 3 phthalates and 12 nonphthalates ) , and 7 were active in 
 3 to 6 assays ( 3 phthalates and 4 nonphthalates ) . the most frequent 
 assay targets for these compounds were cyps ( cyp2c19 and cyp1a2 ) and 
 tspo ( human and rat ) . only two plasticizers inhibited any nuclear 
 receptor binding and only at higher concentrations ( dehp inhibited 
 hgr at an ac50 = 95 m , and diethyl succinate inhibited rmr 
 at an ac50 = 49 m ; see supporting information , table s2 ) . limited phthalate and estrogen receptor binding is not 
 surprising , given that half - maximal inhibitory concentrations for 
 binding have been reported to be 1000 m . no statistical difference ( p > 0.05 
 from t test ) between the responses of the phthalate 
 and nonphthalate 
 plasticizers to these particular assays could be concluded . to evaluate the similarities among assays ( figure 2 ) and chemicals ( figure 3 ) , we performed 
 pearson s correlation based on chemical  assay activities . assays or chemicals that are most associated together cluster as blocks 
 along the diagonal . nonoverlapping clusters of assays ( figure 2 ) or chemicals ( figure 3 ) 
 with positive pearson s correlations the left - hand ribbon indicates the number of chemicals active in an 
 assay ( figure 2 ) or assays active for a given 
 chemical ( figure 3 ) . a caveat of this correlation 
 model is that the size of a cluster may be driven by one to two assays 
 in the chemical similarity matrix or one or two chemicals in the assay 
 similarity matrix . pearson correlations ( 0.3 to 0.5 ) indicated the 
 strength of associations , which were visualized in the heatmap from 
 blue to red , respectively . . selected clusters ( i , ii , and iii ) are shown 
 as examples and listed in table 5 . the left 
 color bar indicates the number of chemicals affecting a given assay . the chemicals are listed along with 
 their relative potency for the estrogen receptor ( as noted within 
 the epa s endocrine disruptor screening program ) and calculated 
 ac50 values for the three estrogen assays . pearson correlations ( 0.3 to 0.5 ) indicated the 
 strength of associations , which were visualized in the heatmap from 
 blue to red , respectively . similar chemicals clustered along the diagonal 
 have high associations . selected clusters ( i , ii , iii , and iv ) are 
 shown as examples and listed in table 6 . the 
 left color bar indicates the number of assays affected by a given 
 chemical . the right panel indicates the assay enrichment scores ( defined 
 in the experimental procedures section ) for 
 each chemical and can be used to describe the chemicals in the clusters . assay similarity was performed over the 256 active 
 assays 
 ( figure 2 ) . the basis for highly similar clusters 
 ( pearson s correlation 0.65 ) in some cases was associated 
 with a specific response to relatively few chemicals , such as the 
 case with the human caspases 13 ( active for 59 chemicals ) 
 and histone deacetylases 3 and 6 ( active for 58 chemicals ) . 
 in other cases , high - similarity clustering was driven by a large number 
 of chemicals causing activity in related assays , such as that seen 
 with tspos ( inhibited by 117147 chemicals ) , acetylcholinesterases 
 ( 4146 chemicals ) , and estrogen receptors ( 1830 chemicals ) . three other clusters are highlighted in figure 2 and table 6 to demonstrate the composition 
 of a cluster that aggregated due to chemical two subclusters ( nonoverlapping 
 clusters with pearson s correlation 0.65 ) within cluster 
 i each contain peptide gpcrs , including glutamate , vasoactive intestinal 
 peptide , endothelin , and angiotensin ii gpcrs , as well as the neuropeptide 
 y gpcrs . the assays were mainly within the gpcr_aminergic ( 32 ) , gpcr_other 
 ( 7 ) , and transporter ( 6 ) categories . one subcluster contained rhodopsin - like 
 biogenic amine gpcrs , including histamine , muscarinic acetylcholine , 
 adrenergic , serotonergic , and dopaminergic gpcrs , another containing 
 three opiate gpcrs , and the third containing two ion channels . the assays represented 
 were mainly within the kinase ( 21 ) and phosphatase ( 14 ) categories . both subclusters included relatively equal numbers of kinases and 
 class i cysteine - based protein tyrosine phosphatases ; however , the 
 kinases were distinct for each subcluster with tyrosine and cam kinases 
 in one and mainly tyrosine kinases in the other . the assay similarities 
 revealed clusters of targets known to be similar ( e.g. , gpcrs ) , as 
 well as varied or dissimilar targets . these findings demonstrate a 
 biological basis to clustering of chemicals in the assay similarity 
 matrix . there are 
 55 such clusters ( > 65% similar ) based on a small number of assays 
 ( < 4 ) that range in size from 2 to 17 chemicals . some examples include 
 a cluster of 17 chemicals active in only the cyp2c19 assay and a cluster 
 of 14 chemicals active in only the human pxr assay . other clusters 
 are driven by a few promiscuous chemicals , such as the cluster containing 
 maneb , mancozeb , and metiram , and another cluster containing surfactants 
 such as sodium dodecyl sulfate and pfos . smaller clusters with chemicals 
 active in a limited number of assays revealed specificity within the 
 data set : -cyclodextrin , 17-estradiol , and genistein 
 ( active in 59 assays ) inhibited the three estrogen assays ; 
 two compounds cp-544439 and cp-471358 ( active in 67 assays ) 
 inhibited mmps 1/2/3/9/13 , as well as the leukotriene b4 gpcr ; rifampicin 
 and tert - butylhydroquinone ( active in 67 
 assays ) inhibited ptgs1/cox1 and ptgs2/cox2 enzymatic activities , 
 as well as cyp2c9 and cypc19 ; and eight chemicals ( caffeine , theophylline , 
 cladribine , cyanazine , triflusulfuron - methyl , and prometryn , active 
 in 39 assays ) inhibited three human adenosine a receptors 
 a1 . four other clusters are highlighted in figure 3 and table 7 to demonstrate the substructure 
 of a cluster that aggregated due to chemical  assay response 
 similarity . to describe the clustered chemicals in terms of activity 
 in the 21 assay categories , we used an enrichment score for each chemical 
 ( figure 3 , right panel ) . this chemical assay 
 category score indicates specificity of a chemical to affect assays 
 within a given assay category over other categories . cluster i has 
 29 chemicals active in 1 to 19 assays , enriched for other enzyme . 
 the subclusters include1-[cis-1-(3-ethoxyphenyl)-4-methylcyclohexyl]-4-phenylpiperazine 
 methanesulfonate ( 1:1 ) ( pharma ) , coumarin , dtbp , pioglitazone hydrochlorine , 
 a number of nitrotoluene and phenol compounds , michler s ketone , 
 dichlobenil , and 3-({(3r,4r)-6-[(5-fluoro-1,3-benzothiazol-2-yl)methoxy]-4-hydroxy-3,4-dihydro-2h - chromen-3-yl}methyl)benzoic acid ( pharma ) . cluster ii 
 includes 36 chemicals active in 7 to 65 assays , mostly enriched with 
 gpcr ( other ) and ion channel assays , and includes five other assay 
 enrichment categories . subcluster1 includes 4-dodecylbenzenesulfonic acid and docusate 
 sodium and is mostly enriched for the gpcr ( other ) assay category , 
 whereas the other three subclusters are enriched for the nuclear receptor 
 ( subfamily3 ) category and includes subcluster2 , with 4,4-sulfonylbis[2-(prop-2-en-1-yl)phenol ] 
 and clorophene ; subcluster3 , with bisphenol a and b ; and subcluster4 , 
 with progesterone and androstenedione . cluster iii involves 49 chemicals 
 active in 12 to 90 assays , mostly enriched for gpcrs ( both categories ) 
 and ion channels , as well as five others . five subclusters were identified , 
 including two subclusters with the most promiscuous chemicals , tributyltin 
 chloride and tributyltin methacrylate , and phenylmercuric acetate 
 and mercuric chloride . the other three clusters include at least half 
 of the pharmaceuticals , most likely in the gpcr assay cluster from 
 figure 2 . cluster iv involves 149 chemicals 
 active in 2 to 48 assays , enriched for cyps , transporters , and nuclear 
 receptor ( subfamily 1 ) . the other subfamilies not 
 listed were almost exclusively pesticides from phase i of testing 
 and discussed previously . the chemical 
 similarities revealed known assay clusters ( e.g. , for bisphenols or 
 heavy metal compounds ) and previously unreported clusters ( e.g. , for 
 pharmaceuticals ) . these findings demonstrate a structural basis to 
 clustering of assays in the chemical similarity matrix . the chemicals within 
 each cluster were enriched for a number of assay categories , with 
 the most associated in bold . numbers in parentheses indicate the number 
 or chemicals within the cluster and associated with the assay category . similarity cluster , enriched assay categories can be identified . in 
 turn , evaluating similar chemical structure fragments enriched in 
 the assay categories can help describe the types of chemicals that 
 are clustering together ( figure 4 ) . univariate 
 analyses linking chemical structure fragments to assay categories 
 ( based on ac50s 10 m ) revealed 107 associations for 
 84 unique chemical fragments and 9 assay categories . cyps had the 
 most associated chemical fragments , with a majority being subfragments 
 involving phosphoric acid , phosphinic acid , and phosphonous derivatives ; 
 however , 1,2,4-triazole had the highest ppv . a benzene ring with two 
 constituents and 4-phenylpiperidine were structures shared by the 
 cyps , transporters , gpcrs , and ion channel categories . three assay categories that did not share chemical structures with 
 other categories included nr_subfamily3_steroid associated with cyclopentanol 
 and 2,2,4-trimethylpentane ; the other enzyme category associated with 
 isopropylamine ; and cholinesterase associated with n - methylcarbamate . overall , the chemical fragments from chemicals 
 in this data set that are enriched for affecting an assay category 
 are generally unique , but overlap is observed . univariate 
 associations between chemical - structure fragments and enrichment scores 
 for chemical  assay categories were performed to identify chemical 
 fragments in chemicals specifically enriched for affecting an assay 
 category . chemical fragment  assay categories with true positives 
 5 and positive predictive value ( ppv ) > 0.5 are visualized . purple nodes are the chemical fragments , and green diamonds are the 
 assay categories . example chemical fragment structures are shown for 
 the associations with the highest ppv ( indicated by increasing edge 
 thickness ) . numbers of true positives are indicated by edge color 
 from gray ( 59 ) to black ( 1026 ) . the data set analyzed here 
 profiles 976 structurally and categorically 
 diverse chemicals in the toxcast library across 331 biological assays . these data were derived from two temporally separated testing phases : 
 ( 1 ) 310 unique chemicals analyzed across 292 assays from the original 
 phasei_v1 of toxcast , reanalyzed using an improved algorithm for automated 
 curve - fitting across 230 assays retained in phase ii ; and ( 2 ) new 
 data generated for 676 unique structures ( including 9 phase i compounds 
 in replicate ) in phase ii analyzed across 231 assays . in total , the 
 combined set provides bioactivity data for 976 chemicals ( phase i , 
 ii ) profiled across 331 assays ( 231 assays plus 100 analyzed for activation ) 
 for ligand  receptor binding and enzymatic activities . patterns 
 derived from these profiles can only be interpreted within the context 
 of the specific assays covered here and do not imply in vivo toxicity or lack thereof since the biochemical screen does not consider 
 kinetics and metabolism ( adme ) and intra / inter - cellular signaling . results from this study extend the previous analysis to a broader chemical landscape , revealing both major and 
 minor patterns for groups of chemicals and biochemical assays . roughly a quarter of the 976 compounds tested showed no demonstrable 
 activity ( ac50 ) in any of the assays used herein , including many consumer 
 use chemicals , phthalates , and green alternatives . the remaining compounds 
 tested here showed specific or promiscuous activities . clustering 
 revealed coarse relationships by chemical use category , chemical structure , 
 and/or biological target . for example , most pesticidal actives and 
 pharmaceuticals could be resolved by target signatures , ache and nuclear 
 receptors on the one hand versus gpcrs and transporters on the other . 
 a similar degree of promiscuity ascribed to the phase this promiscuity , directed across the gpcr assays , 
 partly reflected the large contingent of gpcr assays included in this 
 screen but also suggests a potential for off - target effects . inherent 
 chemical factors such as surfactant or detergent properties and metal - based 
 or metal - chelating properties could also account for promiscuity . 
 in particular , the polymeric dithiocarbamate pesticides affected multiple 
 kinase and phosphatase assays that require divalent cations ( magnesium 
 or manganese ) and/or have active site cysteine residues sensitive 
 to oxidation . this specific effect was noted previously for maneb , 
 mancozeb , and metiram , consistent with 
 a mechanism of action associated with the inhibition of metal - dependent 
 and sulfhydryl enzymes , and seems to be specific 
 to these compounds across the broader chemical landscape tested here . lastly , compounds containing tin ( e.g. , tributyltin ) and mercury ( e.g. , 
 phenylmercuric acetate ) broadly disrupted gpcr ligand - binding activities 
 but not kinase or phosphatase activities . these examples suggest a 
 systematic basis to the promiscuity and a degree of specificity to 
 the biological target or assay conditions . for example , the promiscuous gpcr activity of the mercury - containing 
 compounds is consistent with the neuropsychiatric effects seen in 
 mercury poisoning , such as minamata disease or mad hatter syndrome , 
 whose symptoms include delirium and hallucinations , ataxia , numbness 
 in hands and feet , general muscle weakness , narrowing of the field 
 of vision , and damage to hearing and speech . target 
 interactions that may suggest potential modes of action or off - target 
 effects . anthralin , for example , is a topical drug used for treatment 
 of psoriasis , but its mechanism of action is unknown . consistent with 
 the biology of psoriasis as an autoimmune skin disease , anthralin specifically inhibited the activity 
 of several protein targets that may play a role in inflammatory pathways 
 ( caspases , mmps , and cox1 and 2 ) . tannic acid , which is known for 
 inhibiting inflammatory processes , also 
 inhibited these protein targets . uk-416244 , a selective serotonin 
 reuptake inhibitor ( ssri ) ( http://www.chemspider.com/chemical-structure.8123181.html ) , inhibited the rat serotonin transporter ( ac50 of 0.023 m ) 
 as expected but also showed moderate activity on gpcrs including the 
 human and rat adrenergic receptors ( ac50s of 0.2912 m ) , 
 which are known off - targets for ssris . although this is not a known interaction , the interaction 
 is plausible since leukotrienes are fatty acid signaling molecules , 
 and perfluorinated compounds are hydrophobic and lipophilic . in addition , 
 both chemicals and proteins are suggested players in immune function 
 and may give clues to off - target effects . pharmaceuticals , of which 245 are included in this study , 
 serve 
 not only as an anchor for known interactions and off - target effects 
 but also as a kernel for clustering environmental chemicals by potency , 
 efficacy , and specificity . a similarity matrix clustering chemical 
 compounds by their assay targets classified two triazine herbicides 
 ( cyanazine and prometryn ) with caffeine , theophylline , and cladribine 
 based on a relatively selective effect on human adenosine receptors . the latter three compounds are known ( or suspected ) to disrupt pathways 
 in adenosine signaling or metabolism and may serve to anchor the potential 
 activity of cyanazine or prometryn to adenosine signaling . co - clustering 
 of biologically related assays is also useful in 
 discovering potentially unknown interactions . consider , for example , 
 assay clusters for biogenic amine gpcrs and peptide gpcrs from the 
 assay by assay similarity matrix . a number of chemical compounds preferentially 
 affected these two gpcr subfamilies . in addition , the biogenic amine 
 gpcrs had a closer relationship with the disruption of opiate gpcrs , 
 transporters for norepinephrine , serotonin , and dopamine , and sodium 
 or calcium channels . consideration to the broader range of biochemical 
 targets may be informative data to train structure models for predicting 
 chemical  target interactions . the major and minor patterns 
 of associations from the biochemical profiling can be used in conjunction 
 with ( or to inform ) computational approaches , such as the structure additional analyses for 
 discovering unknown interactions may come 
 from the enrichment of common chemical fragments in association with 
 specific or multiple assay targets . these chemical fragment associations 
 suggest that if a new chemical contains this chemical fragment , it 
 has a greater likelihood of affecting a given assay category and could 
 provide clues governing chemical interactions for a specific or multiple 
 assay targets . for example , phenol - containing compounds are well known 
 to interact with the estrogen receptor ( within the steroid subfamily 
 3 of nuclear receptors ) , but chemicals 
 containing the phenol fragment were also associated with promiscuous 
 inhibition of cyps , thereby not specifically associating with the 
 steroid subfamily 3 category . similarly , organophosphates are known 
 cholinesterase inhibitors for some insecticides , and chemicals containing 
 this fragment were also associated with the inhibition of various 
 cyps and not specifically with cholinesterase inhibition . the cyclopentanol 
 and 2,2,4-trimethylpentane fragments are inherent features of but 
 not robust drivers of chemicals predicted to inhibit steroid receptor 
 binding . the gross association of fragments 
 with assay enrichment in some cases and not others may provide a departure 
 point for building more specific structure activity relationship 
 models , i.e. , determining chemical property or feature modifiers that 
 could potentially distinguish more specific activity subclasses within 
 the fragment - containing clusters . however , examples of fragment  assay 
 associations consistent with a priori knowledge include the association 
 of an n - methylcarbamate fragment of some insecticides 
 to anticholinesterase activity and the 
 isopropylamine fragment to some inhibitors of monoamine oxidases . beyond feature enrichment for certain chemical 
 clusters ( chemotyping ) , this biochemical hts , together with nonbiochemical 
 toxcast assays and structure similarity , can serve as a broader resource 
 for mapping structure  activity relationships to biological 
 profiles and in vivo toxicities . the full range of 
 the biochemical hts could be used , for example , to test the hypothesis 
 that structural analogues and/or chemicals sharing a common mechanistic 
 category show sufficiently similar signals and whether or not such 
 response signatures are specific enough to delineate a unique read - across 
 grouping . assay 
 effects and adverse outcomes , where a considerable amount of in vivo data is available . a challenge will be to address the clustering of 
 phase i and phase ii chemicals by biological response given the lack 
 of in vivo data for most of the phase ii chemicals . biochemical inhibition of cyp activity may indicate a potential 
 for the chemical to disrupt intermediary metabolism in vivo or alternatively that the xenobiotic may be preferentially metabolized in vivo . other specific interactions , either unrelated to 
 cellular mechanisms or without obvious biological significance may 
 be detected in the biochemical screen . for example , the soluble dietary 
 fiber -cyclodextrin inhibited all three estrogen assays ( her , 
 mera , and ber ) and clustered with the known er ligands estradiol and 
 genistein . this interaction may be similar to that known for -cyclodextrin 
 through clathrate formation to sequester estrogen in a lattice . cyclodextrins are relatively nontoxic , however , 
 due to their inability to penetrate lipophilic membranes . although these cell - free data may not always 
 point to a biologically relevant process , the specificity of activity 
 within a complex chemical  assay matrix may , nevertheless , provide 
 useful information about the chemical compounds in their interaction 
 with specific protein targets . since these chemicals were tested 
 at or below 50 m , it may 
 be difficult to extrapolate the current ac50 concentrations to in vivo activity since the concentrations are high relative 
 to what might be achieved during in vivo exposure . however , a number of chemical  assay pairs 
 showed an ac50 at or below 1 m , which would seem attainable 
 at least in high - dose testing animal studies . along these lines , negative 
 compounds that exhibit adverse outcomes in vivo could 
 have been missed in the cell - free biochemical screen due to ( a ) bioavailability 
 resulting in a xenobiotic metabolite not in the toxcast chemical inventory ; 
 ( b ) the biological target not being represented , e.g. , the 331 assays 
 do not cover all possible targets ; ( c ) the 50 m concentration 
 not being high enough ; or ( d ) the fact that the chemical  assay 
 pair activity was not observed in the single screen due to assay variability , 
 compound instability , or assay interference and hence not followed 
 up by concentration  response . furthermore , the concentration  response 
 data is being made public with this article for user interpretation 
 ( http://actor.epa.gov ) . these points indicate areas for 
 future research , specifically performing more extensive adme analyses , 
 consulting other cell - based assays for target validation and further 
 expansion of the assay set , and identifying bioactivity signatures 
 of broad chemical classes for which we could then query the toxicological 
 space . moving forward , this large data set contributes significantly 
 toward 
 advancing new a regulatory paradigm through predictive toxicity and 
 a better understanding of mechanisms . the 
 biochemical hts screen offers preliminary evidence for chemical targets 
 in a cell or tissue that , when combined with information from the 
 literature or targeted studies , indicates potential pathways of toxicity . predictive signatures developed from combinations of assays may be 
 linked to an adverse outcome pathway . efforts are underway to incorporate chemical features into these 
 types of analyses to identify potential structural alerts that in 
 combination with the hts assays may be more predictive . these types 
 of data and alerts can potentially provide biological predictions 
 about chemicals with little to no biological information . in addition , in silico models are incorporating this information with 
 cell  cell and tissue level behavior to predict outcomes of 
 changing dose and time within a developing organ , without the need 
 to pick up a pipet . overall , these 
 results expand the toxcast database to include a 
 large and more structurally diverse set of chemicals . they also contribute 
 to a novel framework for describing the interaction of environmental 
 chemicals with important biochemical targets . by broadly surveying 
 both the chemical landscape and biological target space , patterns 
 of biochemical activity have been identified which can help focus 
 future research toward potential chemical  biological interactions 
 that may result in adverse outcomes in vivo . the 
 use of a quantitative approach in determining the potency of chemicals 
 against each biological target , utilizing a diverse chemical library 
 containing many reference chemicals with known activities as chemical 
 probes , provides the context to better understand the potential for 
 hazard of those chemicals with limited toxicity information . as these 
 interactions become better defined through methods such as incorporation 
 into adverse outcome pathways , these in vitro screening methods may ultimately serve as an efficient means of 
 avoiding unwanted biological activity early in the chemical design 
 and development process .
understanding potential health risks 
 is a significant challenge 
 due to the large numbers of diverse chemicals with poorly characterized 
 exposures and mechanisms of toxicities . 
 the present study analyzes 
 976 chemicals ( including failed pharmaceuticals , alternative plasticizers , 
 food additives , and pesticides ) in phases i and ii of the u.s . 
 epa s 
 toxcast project across 331 cell - free enzymatic and ligand - binding 
 high - throughput screening ( hts ) assays . 
 half - maximal activity concentrations 
 ( ac50 ) were identified for 729 chemicals in 256 assays ( 7,135 chemical 
 assay 
 pairs ) . some of the most commonly affected assays were cyps ( cyp2c9 
 and cyp2c19 ) , transporters ( mitochondrial tspo , norepinephrine , and 
 dopaminergic ) , and gpcrs ( aminergic ) . 
 heavy metals , surfactants , and 
 dithiocarbamate fungicides showed promiscuous but distinctly different 
 patterns of activity , whereas many of the pharmaceutical compounds 
 showed promiscuous activity across gpcrs . 
 literature analysis confirmed 
 > 50% of the activities for the most potent chemical 
 assay 
 pairs 
 ( 54 ) but also revealed 10 missed interactions . 
 twenty - two chemicals 
 with known estrogenic activity were correctly identified for the majority 
 ( 77% ) , missing only the weaker interactions . in many cases , 
 novel 
 findings for previously unreported chemical  target combinations 
 clustered with known chemical  target interactions 
 . results 
 from this large inventory of chemical  biological interactions 
 can inform read - across methods as well as link potential targets to 
 molecular initiating events in adverse outcome pathways for diverse 
 toxicities .