as 
 many as 10,000 commercial substances in the environment lack 
 data on their potential androgen receptor ( ar ) bioactivity with hundreds 
 of new chemicals being added to this total each year . environmental protection agency ( epa ) and organization for economic 
 cooperation and development ( oecd ) methods could cost millions of 
 dollars and take decades to complete . toxcast and tox21 
 programs , use high - throughput in vitro screening ( hts ) assays and computational 
 toxicology methods to rapidly and cost - effectively test chemicals 
 for biological activity across a broad range of toxicologically relevant 
 molecular targets and pathways . epa for determining estrogen receptor ( er ) bioactivity and could also be used to identify potential ar - active chemicals . 
 however , application of alternative testing strategies for regulatory 
 decision - making requires performance - based validation against a set 
 of reference chemicals with reproducible responses over a range of 
 potencies . here , we describe an integrated experimental and 
 computational 
 approach combining data from 11 toxcast and tox21 in vitro hts assays 
 measuring activity at multiple points along the androgen receptor 
 ( ar ) pathway including receptor - binding , coregulator recruitment , 
 chromatin - binding of the mature transcription factor , and gene transcription . 
 a certain number of chemicals could be expected to act as true ar 
 agonists or antagonists , but there are also chemicals that are known 
 to interfere with these various assay technologies through false signals 
 such as autofluorescence or cytostatic mechanisms . a well - accepted method of dealing with this issue 
 is to leverage orthogonal assays that help distinguish nonspecific 
 activity from interaction with the intended target . the approach is similar to that demonstrated for the er pathway . here , the data from 11 ar pathway assays were 
 supplemented with an additional antagonist confirmation assay using 
 a higher concentration of the activating ligand to characterize competitive 
 binding . this battery of in vitro ar assays was used to screen a library 
 of 1855 chemicals . observed patterns of assay activity included no 
 assays activated , all agonist assays activated , all antagonist assays 
 activated , specific subsets of assays across technologies activated , 
 and technology - specific assay activation . to navigate this complexity 
 in the results , we developed a computational network model to infer 
 whether chemicals that activate specific patterns of in vitro assays 
 were more likely to be ar agonists , ar antagonists , false positives 
 due to specific types of assay interference , or true negatives . evaluating and validating the ar pathway model requires high - quality 
 reference data for ar agonist and antagonist activity . unlike the 
 er pathway , which has a well - characterized set of in vitro and in 
 vivo reference chemicals , the reference chemical set for the ar pathway is much less developed . previous work focused on identifying chemicals that were positive 
 or negative for ( anti)androgenicity , without a specific emphasis on 
 potency , and often included compounds that were  presumed  
 active or inactive . using a comprehensive 
 list of putative ar - active or -inactive chemicals from past and present 
 international validation studies , we performed a literature search 
 to compile high - quality published in vitro ar binding and transactivation 
 ( ta ) assay data . to facilitate external validation of the ar pathway 
 model results , no toxcast or tox21 we identified a set of chemicals with reliable 
 and reproducible in vitro results from the literature and binned the 
 chemicals into defined potency categories . the list of proposed reference 
 chemicals and the supporting data are provided and were used to evaluate 
 the current computational model of ar pathway activity based on the 
 tox21 and toxcast assays . briefly , 
 high - throughput screening data were generated on 1855 chemicals in 
 11 tox21/toxcast assays that map to key biological events along the 
 ar pathway , and a computational model was used to integrate those 
 data for each chemical to provide overall ar pathway activity predictions . 
 in parallel , a systematic literature review was performed to characterize 
 a list of reference chemicals using previously published work . these 
 reference chemicals , classified based on the existing scientific literature , 
 were used to evaluate the performance of the tox21/toxcast ar pathway 
 model . the internal process consisted of generating hts data on 11 
 ar assays and building a computational model of ar pathway agonist / antagonist 
 activity . the external process involved systematic literature review 
 for reference data and curation of the reference chemical list based 
 on published data . the model performance was evaluated against the 
 reference chemicals , and screening results from the validated model 
 on a large set of environmental chemicals are presented . data on 1855 chemicals 
 were generated during toxcast phases i and ii and tox21 screening 
 using 11 ar - related in vitro assays ( table 1 ) . these include three biochemical radioligand 
 ar binding assays ( novascreen ) , a coactivator recruitment assay 
 measuring protein  protein interaction between ar and src1 at 
 two different time points ( odyssey thera ) , one transactivation assay 
 measuring reporter rna transcript levels ( attagene ) , three transactivation assays measuring reporter protein 
 level readouts ( odyssey thera and tox21 ) , and two transactivation antagonist assays ( tox21 ) . one of the transactivation antagonist assays , the tox21 antagonist 
 luciferase assay in the mdakb2 cell line ( a11 ) , was run as a confirmation 
 assay with a higher concentration of the synthetic ligand r1881 to 
 verify chemical activity specific to the ar pathway . higher concentration 
 of the ligand should shift the potency of true competitive antagonists 
 to higher concentrations . the 
 latter assays were initially tested at a single concentration ( 25 
 m ) , and if significant activity was seen , the chemical was 
 then tested in concentration  response mode . response 
 assay data were analyzed using the toxcast 
 data analysis pipeline , which automates the processes of baseline 
 correction , normalization , curve - fitting , hit - calling , and ac50 ( half - maximal activity ) determination . all in vitro assays except the rna transcript reporter 
 assays ( attagene ) were normalized to the range 0100% using 
 the positive control response . rna transcript reporter data were normalized 
 as a fold - change over the solvent control ( 0.51% dmso , which 
 has been determined to have no effect on assay performance ) and then 
 multiplied by a factor of 25 to yield a range of approximately 0100 . the data from each chemical assay pair was fit to three models : a 
 constant model , a hill model , and a gain - loss model , and the model 
 with the lowest akaike information criterion was selected . the pipeline also detects a variety of potential confounders , 
 which are annotated as  caution flags  . for computational 
 synthesis to be facilitated across different in vitro assays with 
 different numbers of tested concentrations , a set of synthetic concentration response 
 activities was generated through interpolation for each chemical assay 
 pair at standardized concentrations using a hill equation based on 
 the experimentally derived ac50 , hill slope , and top parameters . all ac50 values were in m , 
 and the synthetic concentrations were a 1.5-fold dilutions series 
 of 45 concentrations from 1 pm to 100 m . the pipeline and all 
 raw and processed data and annotations are publicly available ( http://epa.gov/ncct/toxcast/data.html ) , and the data processing is described in detail elsewhere . ar = androgen receptor ; 
 are = androgen 
 response element ; ncgc = nih chemical genomics center , now part of 
 national center for advancing translational sciences ( ncats ) ; src 
 = c - src tyrosine kinase . activity in these assays / nodes could 
 be associated with one or more of the following pathways : ar agonist 
 ( r1 ) , ar antagonist ( r2 ) , or interference ( r3r7 ) . activity 
 in individual assays could also be associated with assay - specific 
 interference ( a1a11 ) . confirmation assay data ( overly 
 high concentration of r1881 ) not used in ar pathway model scores . a computational network model for 
 ar pathway activity ( figure 2 ) was built using 11 toxcast and tox21 in vitro assays ( table 1 ) that map to key 
 events in the biological pathway . figure 2 depicts the network model used to evaluate 
 the integrated in vitro assay responses that mirrors previously published 
 work on the er pathway and is based on 
 the series of molecular events that typically occur in a nuclear receptor - mediated 
 response . an ar agonist will bind to 
 the receptor monomer ( node n1 ) , cause the receptors to dimerize , and 
 translocate to the nucleus and recruit coregulators ( node n2 ) to form 
 the complete , active transcription factor complex . the transcription 
 factor complex binds to the chromatin dna at specific response element 
 sequences and initiates transcription of mrna ( node n3 ) and subsequent 
 translation to protein ( node n4 ) . an ar antagonist acting through 
 the receptor will bind to the receptor monomer ( node n1 ) , cause the 
 receptors to dimerize , and translocate to the nucleus and recruit 
 coregulators ( node n2 ) , forming a transcription factor complex that 
 binds to the chromatin dna at specific response element sequences , 
 but is transcriptionally inactive and results in a lack of downstream 
 protein production ( node n5 ) . each of these key event nodes was assessed 
 by one or more of the 11 in vitro assays listed in table 1 ( represented in figure 2 as white stars ) . figure 2 shows the two modes 
 of the ar pathway : agonist ( nodes associated with r1 ) and antagonist 
 ( nodes associated with r2 ) . the model assumes that a chemical that 
 interacts with the ar will bind and result in either or both of the 
 agonist or antagonist conformations , triggering activity in the appropriate 
 pathway . each of the in vitro assays ( a1a11 ) is subject to 
 processes that can lead to nonspecific activity independent of the 
 ar pathway event that it is supposed to measure . these may be due 
 to technological interference , artifacts , or other sources of experimental 
 noise . further , each group of assays that map to a key event node 
 could be affected by non - ar - mediated activity specific to that key 
 biological event ( such as blocking transcription ) . two examples of 
 interference pathways , one that is assay - specific ( a6 ) and one that 
 is node - specific ( r7 ) , are shown in figure 2 as light gray arrow heads . graphical representation 
 of the ar pathway model based on tox21/toxcast 
 assays : circular nodes ( n1n5 ) represent key biological events 
 along the pathway , where dark gray coloring indicates key events common 
 to agonism and antagonism , and blue and red coloring indicates key 
 events specific to agonism or antagonism , respectively . white stars 
 ( a1a11 ) represent the in vitro assays that measure activity 
 at the biological nodes . colored arrow heads ( r1/r2 ) represent true 
 ar agonism / antagonism , respectively , and are comprised of the nodes 
 listed in the diagram and their associated assays . light gray arrow 
 heads demonstrate examples of technology - specific interference or 
 biological interference pathways , where individual assays or specific 
 groups of assays are positive due to non - ar - mediated activity . each 
 in vitro assay and each key event node has an assay- or biology - specific 
 interference pathway ( defined in table 1 ) . interference pathways r3r7 correspond to 
 nodes n1n5 , respectively , and interference pathways a1a11 
 correspond to the respective assays . two examples of interference 
 pathways , one that is assay - specific ( a6 ) and one that is node - specific 
 ( r7 ) , are shown as light gray arrow heads . following 
 the er pathway example presented in ref ( 16 ) , a simple linear additive model is used to predict 
 the relative ar agonist or antagonist activity of a test chemical 
 based on data from the in vitro assays that map to the ar pathway 
 in figure 2 . in the 
 mathematical representation , the term receptor  can 
 refer to ar - mediated agonism , ar - mediated antagonism , or an interference 
 pathway ( mediated via biological activity or nontarget activity associated 
 with a specific technology ) . r1r7 
 associated with each assay or key event node are listed in table 1 . the model assumes 
 that the value ( the efficacy , a ) returned by an assay 
 at a given concentration is the sum of the contributions from the 
  receptors  that it measures , given as1where the index i ranges 
 over the number of assays and index j over the number 
 of  receptors  ( where j = 1 for agonism , j = 2 for antagonism , and j > 2 for 
 interference ) . the elements of the f matrix are 1 if there is a 
 connection between a the model seeks a set 
 of rj values that minimizes 
 the difference between the predicted assay values ( ai ) and the measured ones 
 ( ai ) for 
 each chemical  concentration pair . a constrained least - squares 
 minimization approach is used , where the function being minimized 
 is2the term penalty(r ) penalizes 
 solutions that predict that many  receptors  are being 
 simultaneously activated by the chemical , such that34the penalty term helps stabilize the solutions 
 and is based on the assumption that it is unlikely that most chemicals 
 will strongly and specifically interact with many dissimilar molecular 
 targets . the model produces a response 
 value ( between 0 and 1 ) for each  receptor  at each 
 concentration . these results are summarized as the integral across 
 the concentration range expressed as area under the curve ( auc ) , such 
 that5the biological response of greatest 
 environmental 
 concern is via antagonism of the ar pathway , which is also where most 
 chemical activity is observed . therefore , the auc values were normalized 
 to yield a value of 1 for the antagonist positive control . the calibration curve plotting 
 the relationship between auc and activity concentration is given in supplemental figure s1 . an auc value of 0.1 corresponds 
 to activity at 100 m ; because this was the top tested 
 concentration of most assays ( except the attagene assays ) , we considered 
 an auc of 0.1 to be positive . auc values between 0.001 and 
 0.1 indicate very weak potential activity and were considered inconclusive . auc values were rounded to 3 significant digits , and values below 
 0.001 were truncated and set to zero . each antagonist assay that measured 
 suppression of protein production ( tox21_ar_bla_antagonist_ratio and 
 tox21_ar_luc_mdakb2_antagonist ) also produced viability readouts measuring 
 cell death . these cytotoxicity assays were analyzed using the toxcast 
 data analysis pipeline , as described above , and the cytotoxicity ac50 was used as a threshold filter for antagonist activity in 
 a pairwise fashion . any antagonist response with an ac50 greater than the cytotoxicity ac50 for that chemical 
 assay combination was discarded . additional filtering approaches that 
 were both more permissive ( no exclusion ) and more restrictive ( exclusion 
 of ac50s within 20% of the cytotoxicity ac50 ) were investigated , and the corresponding results for the ar pathway 
 model ( as well as the paired cytotoxicity data ) are included in supplemental file 4 . for ensuring removal of 
 overtly cytotoxic compounds while still permitting analysis of chemicals 
 that may show antagonist behavior at test concentrations immediately 
 preceding cytotoxicity and for maintaining consistency with the criteria 
 for the reference chemical data extracted from the literature , the 
 threshold approach was chosen for this analysis . in a global analysis of the toxcast 
 data set , it was observed that many different types of assays , both 
 cell - based and cell - free , showed a rapid increase in the frequency 
 of responses at concentrations corresponding to regions of cell stress / cytotoxicity . we flagged potential nonselective assay hits 
 attributed to cell stress using the distance between the logac50 ( assay ) and the median logac50 ( cytotox ) with 
 respect to the global cytotoxicity median of the median absolute deviation 
 ( mad ) of the logac50 ( cytotox ) distributions across all 
 chemicals . briefly , for chemicals with two or more positive 
 responses in assays measuring cytotoxicity or inhibition of proliferation , 
 a  z - score  was calculated for each 
 ar pathway assay hit as6a large z - score indicates 
 an in vitro assay logac50 at concentrations significantly 
 below those causing cytotoxicity or inhibiting proliferation . thus , 
 a hit associated with this z - score is unlikely to 
 be caused by either cell stress or cytotoxicity - related processes 
 and is more likely to be associated with a target - selective mechanism , 
 e.g. , interaction with the ar pathway . one of the transactivation antagonist 
 in vitro assays , the tox21 antagonist luciferase assay in the mdakb2 
 cell line ( figure 2 , a11 ) , was run twice with two different concentrations of the stimulatory 
 ligand r1881 . these data were used to help confirm whether chemical 
 activity was specific to the ar pathway . the first time the assay 
 was run , the concentration of ligand r1881 was 10 nm ( 20 the 
 ec50 of r1881 ) , which resulted in saturation of the assay 
 and a lack of activity for most chemicals , including known weak antagonists , 
 based on the inability to displace the ligand , except for potent steroid 
 antagonists ( e.g. , flutamide - like compounds ) . the second time the 
 assay was run with 0.5 nm r1881 and was sensitive to a wider range 
 of chemicals . this second run , with the appropriate r1881 concentration , 
 was included in the ar pathway model , and the data from the first 
 run , with the high r1881 concentration , were used in a paired fashion 
 to examine compound specificity . a system of flags was applied to 
 identify chemicals that may be activating the pathway through a nonreceptor - mediated 
 mechanism . for true positives , it was expected that they would either 
 be a hit in both runs , with a shift in the ac50 ( from less 
 to more potent ) , or they would be negative in the first run ( when 
 the assay was saturated with r1881 ) and a hit in the second run ( weak 
 antagonists ) . the data were flagged if a chemical was active in both 
 runs at similar concentrations or if a potency shift was observed 
 in the opposite direction than would be expected . significance of 
 the shift between ac50 values was determined using a bootstrapping 
 approach across chemical replicates to define 95% confidence intervals 
 as outlined below and in ( watt et al . 2016 , manuscript in preparation ) , 
 where overlapping confidence intervals were deemed a nonsignificant 
 shift . response 
 curves used in the ar pathway model were analyzed using the r package 
 toxboot v.0.1.0 ( https://cran.r-project.org/web/packages/toxboot/index.html ) . one thousand bootstrap replicates were generated for each curve using 
 smooth nonparametric bootstrap resampling to obtain a distribution 
 of fit parameters , model selections , and activity calls . each bootstrap 
 sample was grouped by chemical and analyzed using the ar pathway model 
 with the same workflow as used to generate the point estimates , resulting 
 in a distribution of 1000 auc values per chemical . the inner 95% confidence 
 interval for each chemical auc value was calculated on this distribution 
 using the quantile function from the r stats package with probabilities 0.025 and 0.975 for the lower and upper 
 thresholds of the confidence interval , respectively . we performed a targeted 
 literature search for quantitative data to refine previously published 
 reference chemical lists and provide potency characterization for 
 ar agonism / antagonism . we identified 158 potential reference chemicals 
 with ar agonist or antagonist activity ( or lack of activity ) from 
 the following international assay validation efforts run by  interagency coordinating committee on the validation of 
 alternative methods  organization 
 for economic cooperation and development  u.s . european union reference laboratory 
 for alternatives to 
 animal testing ( eurl ecvam , ongoing ) korean center 
 for validation of alternative methods ( kocvam , 
 ongoing ) we conducted semiautomated literature searches for 
 in vitro androgen 
 activity data on the superset of chemicals ( n = 158 ) 
 using pubmatrix ( http://pubmatrix.grc.nia.nih.gov/ ) and scopus ( http://www.scopus.com/ ) . data from in vitro ar binding and ta assays were extracted from 
 identified references and compiled into a single database ( supplemental file 1 ) . search keywords are listed 
 in supplemental file 1 . using a standardized 
 ontology , the following information was recorded for each chemical  study 
 combination :  pubmed identifier , author , year chemical tested , chemical abstracts service registry number 
 ( casrn )  table or figure where results were reported  hit , response , response notes  half - maximal 
 activity concentration ( ac50 or 
 ic50 ) , standard error measurement , units assay type ( tissue or cell culture ) , tissue of origin 
 ( for cell culture ) , species of origin  receptor information , 
 species source  reference androgen or antiandrogen  number of concentrations tested , highest concentration 
 tested , units , incubation time binding assays only : 
 binding affinity , dissociation constant , 
 relative binding affinity ( rba )  ta assays only : agonist 
 or antagonist mode , whether cytotoxicity 
 was evaluated , extent of cytotoxicity observed ( i.e. , at ic50 )  ta assays only : reporter type , reporter construct , 
 whether 
 construct was native , transient , or stable to establish reference 
 chemical lists , we examined high - quality ar binding and transactivation 
 ( ta ) data from the literature search , filtered by conditions such 
 as use of the full length receptor and concurrent measurement of cytotoxicity 
 for antagonist - mode data ( detailed in results ) . to determine potency categories , we identified all quantitative 
 ar ta assay data reported as ac50 or ic50 that 
 could be converted to m units and calculated mean , standard 
 deviation , 95% confidence interval , and number of observations for 
 each chemical . binding data were used in a confirmatory fashion , where 
 chemicals had to have positive binding results in the literature to 
 be included as candidate positive agonist and antagonist reference 
 chemicals . on the basis of the distribution of the results , we defined 
 agonist and antagonist reference chemical lists and potency categories 
 according to the following criteria : positives : 
 at least three ta experiments of 
 which at least 70% yielded positive ta results and at least one positive 
 binding result  strong : mean ac50  0.1 m  moderate : 0.1 m < mean ac50 negatives : 
 at least three ta experiments yielding negative results 
 and no ta experiments yielding positive results positives : at least three ta experiments 
 of which at least 70% yielded positive results that were not due to 
 cytotoxicity and at least one positive binding result strong : mean ic50  0.5 m  moderate : 0.5 m < ic50  5 
 m  weak : 5 m < mean ic50  25 
 m  very weak : 25 m < mean ic50 negatives : at least two ta experiments yielding negative results 
 and no ta experiments yielding positive results chemicals with 
 upper 95% confidence intervals that spanned potency 
 categories were given combined category designations such as  strong / moderate  
 or  moderate / weak  . of the 1855 
 chemicals tested in all 11 tox21/toxcast ar assays , 
 1461 ( 78.8% ) were predicted to be inactive in the ar pathway model 
 with both agonist ( r1 ) and antagonist ( r2 ) auc values below 0.001 , 
 whereas 220 chemicals ( 11.9% ) were predicted to be either androgen 
 agonists ( n = 33 ) or antagonists ( n = 192 ) with r1 or r2 auc values > 0.1 . five of the 220 chemicals 
 had significant activity in both agonist ( r1 ) and antagonist ( r2 ) 
 pathways . the remaining 174 chemicals ( 9.4% ) had inconclusive low 
 ar pathway model scores with r1 or r2 auc values of 0.001 to 0.1 . these chemicals were generally weakly active in a small number of 
 assays and were usually also predicted by the model to be acting through 
 interference pathways . of the 1461 chemicals predicted to be inactive 
 against the ar pathway , 1092 chemicals were inactive across all the 
 assays , whereas 369 chemicals demonstrated activity associated with 
 either assay interference or , less likely , weak activity only picked 
 up in one technology type . figure 3 shows the distribution of ar model pathway scores 
 across the toxcast chemical library for 763 chemicals that were active 
 in at least one ar pathway assay . chemicals were either predicted 
 to act via ar agonism ( r1 ) , antagonism ( r2 ) , biology - specific interference 
 ( r3-r7 ) , or assay - specific interference ( a1a11 ) . supplemental figure s1 is a calibration curve 
 to help interpret auc values in terms of pathway activity concentration , 
 and supplemental file 2 contains the results 
 for each assay and the ar pathway model ( auc values and associated 
 confidence intervals for agonism , antagonism , and interference ) for 
 all 1855 chemicals . results of the ar pathway model with uncertainty 
 bounds corresponding to 95% confidence intervals are plotted in supplemental figure s2 . heatmap 
 shows the distribution of model area under the curve ( auc ) values 
 for 763 chemicals that were active in at least one ar pathway assay . the first two columns represent predictions for agonist ( r1 ) and antagonist 
 ( r2 ) activity , and the remaining columns represent predicted assay 
 ( a111)- or biology ( r37)-specific interference corresponding 
 to the pathway diagram in figure 2 and the interference pathways shown in table 1 . the darker red indicates higher 
 auc values corresponding to more potent activity ( scale : 0.0011 ) . the targeted literature search 
 for ar in vitro reference data yielded 4,795 chemical study pairs 
 across 379 publications . experimental protocol details and chemical 
 effects were recorded in a standardized manner in a structured data 
 table ( supplemental file 1 ) . ar binding 
 data were identified for 111 chemicals , and the data were compiled 
 from 1261 experiments reported in 166 publications . commonly used 
 assay platforms included cell culture , tissue preparations , and cell - free 
 systems ( supplemental figure s3a ) . the 
 majority of the binding assays used full - length receptors ( supplemental figure s3b ) . a total of 26 species 
 were represented among all binding assays with most using human ( 39% ) 
 or rat ( 33% ) receptors . the four most commonly used reference androgens 
 were methyltrienolone ( r1881 ; 475 assays , 41% ) , 5-dihydrotestosterone 
 ( dht ; 400 assays , 34% ) , testosterone ( 203 assays , 17% ) , and mibolerone 
 ( 84 assays , 7% ) . data from all assays returned in the literature search 
 can be found in supplemental file 1 . further 
 analyses were conducted on data from binding assays using the full - length 
 receptor and the ligand - binding domain ( 957 experiments on 95 chemicals ) . atrazine , cycloheximide , and 2,4-dinitrophenol had 
 multiple negative binding results and no positive results . there were 
 14 chemicals with only one positive binding result ( and no negatives ) 
 and six chemicals with only one negative binding result ( and no positives ) . the remaining 34 chemicals had both positive and negative binding 
 results reported , although there was usually a clear majority of positive 
 or negative results for each chemical . results for binding affinity 
 were reported in many different formats , the most common being rba 
 or log rba relative to a positive control . the relative binding data 
 included r1881 ( 240 results ) , dht ( 168 results ) , testosterone ( 97 
 results ) , and mibolerone ( 30 results ) . as an example , results for 
 log rba on 61 chemicals relative to the most common positive control 
 compound , r1881 , are shown in figure 4 . chemicals 
 are 
 listed along the x - axis ; y - axis 
 represents the log10 ( rba ) . the size of the dot increases with the 
 number of observations ( range : 115 ) . relative binding affinity 
 decreases moving from top to bottom with a total of 61 chemicals described . 
 ar = androgen receptor ; r1881 = methyltrienolone ; rba = relative binding 
 affinity . ar transactivation data were compiled 
 for 160 chemicals ( 3534 experiments 
 from 287 papers ) . although six different reporter types were used 
 in the experiments , the majority of experiments used assays with a 
 luciferase reporter ( supplemental figure s4a ) . the use of a full - length receptor was also the most common ( supplemental figure s4b ) . many assays used a 
 transiently transfected ar ( 46% ) or stably integrated ar ( 39% ) followed 
 by native receptor expression ( 14% ) . most ta assays used the human 
 ar ( 93% ) , but receptors from a total of 14 species were represented 
 among all assays in the database . the most common reference androgens 
 were dht ( 2262 assays , 64% ) , r1881 ( 703 assays , 20% ) , and testosterone 
 ( 395 assays , 11% ) ; the most common reference antiandrogens were flutamide 
 ( 688 assays , 41% ) , hydroxyflutamide ( 487 assays , 30% ) , bicalutamide 
 ( 220 assays , 13% ) , and cyproterone acetate ( 192 assays , 11% ) . further analyses were conducted on data from the ta assays using 
 the full - length receptor and the ligand - binding domain . positive and 
 negative ta assay results were reported for 2393 experiments on 133 
 chemicals . results were subdivided into modes measuring agonist activity 
 ( 1447 experiments , 60% ) and antagonist activity ( 946 experiments , 
 40% ) . there were 13 chemicals with multiple positive agonist results 
 ( i.e. , increase in ta ) and no antagonist results , all of which also 
 had at least one negative result reported ( i.e. , no agonist or antagonist 
 activity ) . however , for most of these chemicals , the number of positive 
 agonist results far outnumbered the number of negative results , which 
 tended to occur in specific cell or receptor types and/or at low concentrations . there were 32 chemicals with multiple positive antagonist results 
 ( i.e. , decrease in ta ) and no agonist results . all of these chemicals 
 also had at least one negative ta result that tended to occur in specific 
 cell types and/or at low concentrations . there were 17 chemicals with 
 multiple negative ( inactive for ta ) results and no positive ( agonist 
 or antagonist ) results . the remaining 56 chemicals had a mix of positive 
 ( agonist and/or antagonist ) and negative results . however , for most 
 chemicals , there was a clear majority of either agonist or antagonist 
 results . positive results 
 for ta agonist activity were reported in many different formats and 
 with many different units , the most common being lowest effect level 
 ( lel ; 415 results , 49% ) and half - maximal activity concentration ( ac50 ; 406 results , 48% ) . all ta agonist results were converted 
 to log m units where possible , and the respective agonist potencies 
 based on ac50s for each chemical were compared to negative 
 results in terms of highest dose tested ( hdt ) . the distribution of 
 activity for chemicals with both positive ( ac50s , colored 
 dots ) and negative ( hdts , black dots ) results is shown in figure 5 . comparing ar transactivation 
 agonist results . chemicals are listed 
 along the x - axes , and the log transformed doses are 
 listed along the y - axis . the colored dots represent 
 positive results in log10 ( ac50 ) , and the black dots represent 
 negative results in log10 ( hdt ) . the size of the dot increases with 
 the number of observations ( range : 179 ) . agonist potency decreases 
 moving from bottom to top , with a total of 40 chemicals described . 
 ac50 = half - maximal activity concentration ; ar = androgen 
 receptor ; hdt = highest dose tested . we evaluated 
 ar ta antagonist potency using only data from experiments that concurrently 
 measured cytotoxicity ( 520 experiments [ 55% ] representing 105 chemicals ) 
 with clearly stated acceptance criteria ( e.g. , < 20% loss of viability ) . positive results for antagonist activity were reported in many different 
 formats and with many different units , the most common being half - maximal 
 inhibition activity concentration ( ic50 ; 224 results , 64% ) 
 and lel ( 114 results , 33% ) . all ta antagonist results were converted 
 to log m units where possible , and the respective antagonist 
 potencies based on ic50 were compared to the negative results 
 in terms of hdt . the distribution of activity for chemicals with both 
 positive ( ic50s , colored dots ) and negative ( hdts , black 
 dots ) results is shown in figure 6 . comparing ar transactivation antagonist results . chemicals 
 are 
 listed along the x - axes , and the log transformed 
 doses are listed along the y - axis . the colored dots 
 represent positive results in log10 ( ic50 ) , and the black 
 dots represent negative results in log10 ( hdt ) . the size of the dot 
 increases with the number of observations ( range : 121 ) . antagonist 
 potency decreases moving from bottom to top with a total of 54 chemicals 
 described . ar = androgen receptor ; hdt = highest dose tested ; ic50 = half - maximal inhibitory concentration . based on the 
 criteria outlined in the methods for reproducibility and consistency 
 of response , we identified 37 reference chemicals for ar agonism and 
 28 reference chemicals for ar antagonism ( table 2 ) . initial reference chemical categorizations 
 included strong , moderate , weak and very weak agonists and antagonists , 
 and negative categorizations , all of which were based exclusively 
 on the curated results from the published literature and did not include 
 any information from the toxcast or tox21 assays . there were 11 chemicals 
 that fulfilled reference criteria for both agonism and antagonism , 
 usually as a positive reference in one and a negative reference in 
 the other . cyproterone acetate was classified as both a weak agonist 
 and a moderate antagonist based on multiple literature results showing 
 selective androgen receptor modulation with agonist and antagonist 
 effects . of the 54 reference chemicals classified based on data from 
 the literature , 46 were among the 1855 chemicals tested in toxcast / tox21 
 and could be used for performance - based external validation of the 
 ar pathway model results . the 
 predicted activity 
 from the ar pathway model for the 46 reference chemicals was compared 
 with the reference potency categories based on curated published results 
 identified in the literature review . the results of the model predictions 
 are shown in figure 7a ( 29 agonist reference chemicals ) and figure 7b ( 28 antagonist reference chemicals ) . an 
 ar pathway model score greater than 0.1 ( approximate activity at concentrations 
 less than 100 m ) was considered positive with higher model 
 scores corresponding to stronger potency . with respect to the ar agonist 
 reference chemicals , 17a - estradiol was the only false positive , and 
 there were no false negatives . one negative agonist reference chemical , 
 tamoxifen , had an inconclusive agonist auc ( r1 ) score of 0.0335 . if inconclusive scores were 
 considered positive , the ar pathway model had a balanced accuracy 
 of 95.2% ( 100% sensitivity and 90.5% specificity ) against the agonist 
 reference chemicals , and if inconclusive results were excluded , the 
 balanced accuracy was 97.5% ( 100% sensitivity and 95% specificity ) . two of the antagonist reference chemicals , methoxychlor ( weak potency ) 
 and fernarimol ( very weak ) , had antagonist auc ( r2 ) scores in the 
 inconclusive range of 0.0429 and 0.0446 , respectively . zearalenone , 
 categorized in the literature review as a weak antagonist , was a false 
 negative , and there were no false positives for antagonism . the model 
 predicted that zearalenone was causing assay interference through 
 r7 ( corresponding to key event node n5 in figure 2 ) because it hit both tox21 antagonist assays 
 but none of the upstream assays in the antagonist pathway ( binding 
 or coregulator recruitment ) . the ar pathway model had 97.5% balanced 
 accuracy ( 95% sensitivity and 100% specificity ) when predicting the 
 antagonist reference chemicals and counting the inconclusive results 
 as positive or 97.2% balanced accuracy ( 94.4% sensitivity and 100% 
 specificity ) if the inconclusive chemicals were excluded . examples 
 of the concentration  response curves for several reference 
 chemicals are shown in figure 8 . reference chemicals 
 and associated potency categories ( from the literature search ) are 
 listed along the y - axes , and the ar pathway model 
 auc score for ( a ) agonism ( r1 ) or ( b ) antagonism ( r2 ) are listed along 
 the x - axes . green dots represent positive reference 
 chemicals , and red dots represent negative reference chemicals . ar 
 pathway model scores below 0.01 were truncated at 0.01 for plotting 
 purposes . there was one false positive for agonism ( 17a - estradiol ) , 
 and one negative agonist reference chemical with an inconclusive model 
 score ( tamoxifen ) . the initial false negative for antagonism ( zearalenone ) 
 was confirmed as a potential true positive by the antagonist confirmation 
 assay ( tox21_ar_luc_mdakb2_antagonist - confirmation ) . concentration response curves and ar pathway 
 model results for 
 selected reference chemicals . for each chemical , the left - hand panel 
 shows the concentration response data for the 11 in vitro assays , 
 colored by assay group as defined in the legend . the right - hand panel 
 shows the magnitude of the modeled  receptor  responses , 
 where the agonist pathway ( r1 ) is in blue and the antagonist pathway 
 ( r2 ) is in red , and the other interference pathways ( r3r7 ) 
 are colored as defined in the legend . model auc values are displayed 
 below the chemical name , and literature - based reference classifications 
 are displayed in the plot . the median cytotoxic concentration for 
 each chemical is indicated by a vertical red line , and the cytotoxicity 
 region ( representing 3 median absolute deviations ) is indicated by 
 the gray shaded region . the z - score provides a measure 
 of proximity ( how many median 
 absolute deviations ) for a chemical s activity in a particular 
 assay relative to the median concentration for that chemical across 
 33 viability and proliferation inhibition assays in the toxcast library.z - scores for every chemical 
 assay combination in the ar pathway model are reported in supplemental file 2 . a chemical - assay hit with 
 a high z - score ( > 3 ) indicates that ar - related 
 activity 
 occurred at concentrations far below the cytotoxicity threshold and 
 suggests that there was no evidence of cell stress . these hits are 
 more likely to be associated with specific biomolecular interactions 
 with the intended biological target that the assays are designed to 
 measure . examples of chemicals with high auc values for ar antagonism 
 and high average z - scores include hydroxyflutamide , 
 nilutamide , vinclozolin , linuron , spironolactone , and apigenin . hits 
 with low z - scores ( activity concentrations in the 
 cell stress / cytotoxicity region ) are more likely to be associated 
 with an interference process than hits with high z - scores . however , because of the variable concentration spacing , 
 quantitative uncertainties in ac50 values , and differential 
 sensitivity among cell types , the z - score can not 
 be used as a definitive filter and is instead valuable to provide 
 context on the potential specificity of the results . the confirmation 
 assay data from the tox21_mdakb2_luc_antagonist assay with two different 
 concentrations of stimulating ligand ( r1881 ) provided additional insight 
 into chemicals that were potentially acting via a nonreceptor - mediated 
 mechanism ( e.g. , generalized cell stress or cytotoxicity ) relative 
 to chemicals that appeared to be acting via the ar ligand - binding 
 domain . reference chemical , zearalenone , displayed behavior indicative of 
 true weak antagonist potential , where it was active in both screens 
 and exhibited a potency shift in the expected direction , although 
 the shift was flagged as not significant due to overlapping confidence 
 intervals around the ac50 values . it is worth noting that 
 zearalenone is predicted to be a fairly potent er agonist ( auc model 
 score of 0.71 ) . there were 128 chemicals 
 that were only active when the assay was stimulated with the lower 
 r1881 concentration , behavior that is consistent with the potential 
 for weak antagonism . there were 57 chemicals that were active in both 
 runs and exhibited the expected potency shift with nonoverlapping 
 ac50 confidence intervals . most of these were predicted 
 as true antagonists by the model , including positive antagonist reference 
 chemicals triclosan and bisphenols a / b / af . others ( e.g. , endosulfan 
 sulfate , dinoseb , fenoxycarb ) had inconclusive model scores or were 
 predicted to act via interference pathways , such as suppression of 
 protein production ( r7 , node n5 ) because they did not hit the binding 
 or coregulator recruitment assays . there were 128 chemicals that were 
 active in both runs and exhibited the expected potency shift but had 
 overlapping ac50 confidence intervals . there were 65 chemicals 
 that were active in both runs but exhibited a potency shift in the 
 opposite direction ( i.e. , more potent in the assay with a higher r1881 
 concentration ) and 22 chemicals that were only active in the assay 
 with a higher r1881 concentration and inactive in the other run . these 
 included potently cytotoxic compounds ( e.g. , gentian violet ) , cytotostatic 
 compounds ( e.g. , cycloheximide ) , organometallics , and pesticides . there were 1455 chemicals that were inactive in both runs , most of 
 which were also inactive against the ar pathway model . each category 
 of chemical activity is designated by the corresponding  tox21 
 antagonist confirmation assay flag  in supplemental file 2 . the ar pathway 
 model auc scores , cytotoxicity information , and confirmation flags 
 were used to inform a simple summary score for each chemical that 
 translates into confidence that the observed activity is via the ar 
 pathway . the schema for assigning confidence scores chemicals with high antagonist 
 ( r2 ) ar pathway model auc scores were assigned higher confidence scores , 
 as were those chemicals that were active in the concentration region 
 prior to cell stress / cytotoxicity ( high average z - scores across the 11 assays ) . for potential antagonists , those exhibiting 
 the expected potency shift in the confirmation assays were assigned 
 higher confidence scores , whereas those with data indicating that 
 the chemical was not acting via the receptor were assigned negative 
 confidence scores . the confidence scores from each source were then 
 summed to provide an overall confidence score to facilitate chemical 
 prioritization in a manner that incorporates all the contributing 
 data streams . the positive antagonist reference chemicals all had 
 positive activity confidence scores . all 192 chemicals with r2 auc 
 values above 0.1 also had positive activity confidence scores , although 
 there were 36 chemicals with low confidence scores ( 2 ) that 
 were flagged based on the confirmation assay data and may be false 
 positives . out of the 170 chemicals with inconclusive model antagonist 
 auc scores ( between 0.001 and 0.1 ) , 144 chemicals had positive confidence 
 scores and 61 of these had high confidence scores ( 3 ) . there 
 were 294 chemicals with positive confidence scores that were negative 
 in the ar pathway model ( r2 auc values of 0 ) , some of which were predicted 
 agonists , and most of which were predicted to act via interference 
 receptors . of those 294 model negative chemicals , there were 26 chemicals 
 with confidence scores 3 , which may have been missed by the 
 model and should be examined further for potential antagonist activity . there were 1225 chemicals with activity confidence scores 0 , 
 meaning that they were either inactive , caused technology - specific 
 interference , or displayed activity indicative of a non - ar - mediated 
 response ( usually cytotoxicity driven ) . the distribution of ar pathway 
 model antagonist auc values across the different confidence scoring 
 bins is shown in figure 9 . contributions from the three source 
 categories are summed to provide an overall antagonist activity confidence 
 score ranging from 1 to 6 . the current high - throughput 
 ar pathway model , with 11 assays covering 
 five key events , is intended as a potential alternative for the existing 
 low - throughput edsp tier 1 ar binding assay covering one key event . going beyond the binding assay , the model provides functional information , 
 i.e. , agonist versus antagonist activity . in addition , the complementarity 
 of the assays helps overcome assay - specific interferences that can 
 yield false positive and false negative results . there are a total 
 of 101 chemicals with data from the edsp tier 1 ar binding assay and 
 data from the current ar pathway model . tier 1 ar binding data came 
 from two sources : the iccvam assay validation document and results from the first set of test orders 
 issued by the u.s . epa edsp , referred to as  list 1  . the tier 1 assay measured binding rather than 
 agonism or antagonism , so for comparison , we called a chemical model 
 positive if the maximum of the agonist or antagonist auc values was 
 0.1 , negative if the maximum was < 0.001 , and inconclusive 
 if the maximum auc was between 0.001 and 0.1 . for iccvam , rba values 
 were reported ( ic50r1881  100/ic50test  chemical ) , and for the list 1 chemicals , both 
 rba and ic50 values were reported . to facilitate comparison , 
 we developed a calibration curve using the list 1 chemicals based 
 on an observed linear relationship between log(ic50 ) and 
 log(rba ) , which allowed us to estimate ic50 values from 
 rbas for iccvam chemicals . a linear model ( shown in supplemental figure s5 ) between the two yielded a root - mean - square 
 error ( rmse ) of 0.25 and coefficient of determination ( r ) of 0.84 with both slope and of the 101 chemicals , seven had equivocal 
 calls in the tier 1 data and six had inconclusive ar pathway model 
 scores ( 1 chemical overlap ) , yielding 89 chemicals with comparable 
 data . of the 39 list 1 chemicals with both list 1 ar binding 
 assay data and ar model scores , two were positive in both , six were 
 model positive and tier 1 negative , seven were model negative and 
 tier 1 positive , and 24 were negative in both . the list 1 positive 
 and ar model negative chemicals are 2-phenylphenol , carbaryl , diazinon , 
 dichlobenil , metolachlor , myclobutanil , and phosmet . with the exception 
 of phosmet , the ic50 values for these chemicals are well 
 over 100 m , and so would be expected to be negative in the 
 model , as the top tested concentrations in toxcast and tox21 were 
 100 m . the ic50 for phosmet for binding 
 was 10 m in tier 1 , in close agreement with the chimp ar binding 
 assay ( a2 ) ac50 of 18 m in the ar model data ; however , 
 the human and rat binding assays did not yield positive hit calls 
 when tested to 40 m . phosmet was negative in the ar model data 
 transactivation assays in agreement with a previous published report . the model positive / list 1 negative chemicals 
 are abamectin , captan , chlorothalonil , folpet , mgk-264 , and propargite . all of these are classified as antagonists in the model with auc antagonist 
 values ranging from 0.09 to 0.48 . however , all of these chemicals 
 are flagged as potential false positives using the antagonist confirmation 
 assay data based on either a potency shift in the wrong direction 
 ( abamectin , chlorothalonil , folpet , propargite ) or no significant 
 shift ( captan , mgk-264 ) . the two chemicals called positive in both 
 approaches are propiconazole and tebuconazole . both of these were 
 classified in the model as antagonists , and both had significant shifts 
 in the correct direction in the confirmation antagonist assay . in 
 summary , the model positive / list 1 negative chemicals are likely all 
 false positives in the model , but this was detected using the confirmation 
 assay . the model negative , list 1 positive chemicals are all so weak 
 that they would not be detected by the hts assays used in the model 
 because of the upper testing concentration of 100 m with the 
 possible exception of phosmet for which no clear call can be made . 
 the model results , including uncertainty bounds , for all the list 
 1 chemicals are shown in supplemental figure s6 . there were 51 chemicals with data from the model and the 
 iccvam 
 validation set for the tier 1 ar binding assay ( atrazine was also 
 on list 1 ) . of these , 22 were positive in both , 9 were model positive 
 and tier 1 negative , 1 was model negative and tier 1 positive , and 
 19 were negative in both . the single iccvam chemical that was 
 model negative and tier 1 positive was atrazine with an rba of 0.0018 , 
 yielding a modeled ic50 of 53 m , which is near the 
 upper limit of hts testing . atrazine was also evaluated in the list 
 1 process using literature data , which yielded equivocal results but 
 an ultimate list 1 call of inactive . the 10 model positive , tier 1 
 negative chemicals are 17a - estradiol , 4-cumylphenol , apigenin , bisphenol 
 b , clomiphene citrate , cycloheximide , fulvestrant , meso - hexestrol , oxazepam , and reserpine . all of these were classified 
 as antagonists except for 17a - estradiol and oxazepam , although the 
 former had an agonist auc ( r1 ) of 0.67 and antagonist auc ( r2 ) of 
 0.09 . of these chemicals , four had a significant shift in the correct 
 direction in the antagonist confirmation assay ( 17a - estradiol , 4-cumylphenol , 
 apigenin , bisphenol b ) , and three had a shift in the correct direction 
 but with overlapping confidence intervals ( clomiphene citrate , meso - hexestrol , reserpine ) . fulvestrant and oxazepam also had significant 
 activity in interference channels and thus are likely active due to 
 assay interference . in summary , among these model positive , tier 1 
 negative chemicals , the model data support true activity for 17a - estradiol 
 ( mixed agonist / antagonist ) and 4-cumylphenol , apigenin , and bisphenol 
 b ( antagonists ) . additionally , in the iccvam listing , these are noted as  presumed 
 negative  . the remaining six chemicals show evidence for false - positive 
 activity in the model . the model results , including uncertainty bounds , 
 for all the iccvam chemicals are shown in supplemental figure s7 . implementation of hts 
 intoxcast and tox21 has generated high - quality 
 quantitative data on thousands of chemicals and potential environmental 
 pollutants . the inclusion of orthogonal assays that query key events 
 along a biological pathway in multiple ways has produced novel hazard 
 screening capabilities . a similar mechanistic network model to the 
 one presented here is already being used by the u.s . the er pathway model was validated against a 
 well - defined set of reference chemicals , which heretofore 
 was not possible for the ar pathway due to the lack of a well - characterized 
 reference chemical set . in this study , we have reported the results 
 from a comprehensive literature review on potential ar reference chemicals 
 and used the resulting set to evaluate the performance of the ar pathway 
 model based on 11 tox21/toxcast assays . every assay has inherent 
 limitations driven by technological specifications 
 and an applicability domain . a biological pathway - based approach that 
 integrates multiple assays mapping to key upstream and downstream 
 events provides a weight of evidence for the true potential of a chemical 
 to activate or repress signaling , in this case via the ar . this type 
 of additive model compensates for the individual shortcomings of any 
 one assay . for example , there were 105 chemicals that were predicted 
 to act through a receptor interference pathway ( a7 , figure 2 ) because they were only active 
 in the ot_ar_areluc_ag_1440 luciferase reporter gene assay measuring 
 downstream transcriptional activation via protein production . none 
 of these chemicals are known to be ar agonists , so it is likely that 
 their activity was correctly flagged as interference and may have 
 been a result of nonspecific transcriptional effects . alternatively , 
 these specific samples may have had cross - contamination from strong 
 reference chemicals during the experimental protocol . there are also 
 a large number of chemicals that produced hits in one or more of the 
 cell - free receptor binding assays and were therefore predicted as 
 a1a3 or r3 . many of these chemicals are surfactants , indicating 
 that these chemicals may have reacted with the proteins or otherwise 
 caused denaturation , leading to displacement of the radioligand and 
 a binding - like signal . cytotoxicity and response specificity 
 were further considered and 
 flagged based on chemical patterns across viability assays ( i.e. , z - score ) and confirmation assay data . an important point 
 about the z - score is that , in practice , it is more 
 useful as a flag than an absolute cutoff . in the toxcast data analysis pipeline , there are additional types of flags , e.g. , to indicate noisy 
 data or hits due to a single point crossing the statistical threshold 
 for activity . these do not change the hit call but provide the user 
 a set of cautions or warnings when evaluating data for a particular 
 chemical assay pair . similarly , the analysis 
 of the confirmation assay data produces a set of flags that instills 
 more or less confidence in true ar antagonist behavior . the initial 
 tox21_mdakb2_luc_antagonist assay run with a stimulatory r1881 concentration 
 of 10 nm ( 20 ec50 ) identified predominately 
 only the strong antagonists , i.e. , steroid pharmaceuticals , that could 
 compete with the high agonist concentration , and many of the weak 
 environmental antiandrogens were inactive . the assay run with 0.5 
 nm of r1881 ( ec50 ) identified many more of the 
 weak antagonists . the shift in potency between the two conditions 
 was useful for identifying indirect inhibitors of the assay signal . chemicals that had high model scores for antagonism ( r2 auc > 0.1 ) 
 but were flagged for a lack of a potency shift in the confirmation 
 results may not actually be acting through the ar but rather through 
 generalized cell stress or technology interference . examples of chemicals 
 in this group include the dyes basic blue 7 , rhodamine 6 g , and fd&c 
 green no . 3l , the organometallics tributyltin methacrylate and zinc 
 pyrithione , and the pesticides abamectin and propargite . conversely , 
 chemicals that were missed by the binding ( a1a3 ) and coregulator 
 recruitment ( a4 and a5 ) assays , but exhibited a potency shift in the 
 confirmation data , may have been incorrectly predicted by the model 
 as acting through interference pathways ( e.g. , r7 , corresponding to 
 activity in only a10 and a11 ) . it is also possible that some antagonists 
 may bind outside the ligand binding domain , otherwise block dimerization , 
 or act on some later step in the pathway . for example , a group of 
 seven conazoles were classified as antagonists by the ar pathway model , 
 had activity in both runs of the tox21_mdakb2_luc_antagonist assay , 
 and a corresponding significant potency shift . another six had a shift 
 in the correct direction but the confidence intervals for the two 
 ac50s overlapped . a clear shift in the confirmation assay 
 data may be sufficient evidence of ar - mediated activity , regardless 
 of model score . chemicals with this type of response that may have 
 been missed by the model were identified and prioritized by the activity 
 confidence scoring system . having 11 diverse orthogonal assays 
 along the ar pathway protects 
 against spurious results being driven by one particular technology 
 type . this is evident when considering the excellent performance of 
 the ar pathway model ( > 95% for both agonism and antagonism ) against 
 the reference chemicals . an interesting exception is the putative 
 reference chemical 17a - estradiol , which was classified negative for 
 ar agonism based on multiple literature results ; however , the literature 
 hdts were 10 m . all 11 tox21/toxcast ar assays were 
 activated by 17a - estradiol ( ac50/ic50 range 
 0.110 m ) , resulting in a model prediction of both agonist 
 and antagonist activity . these results could be indicative of true 
 selective ar modulation by this chemical or heightened sensitivity 
 of the hts assays to strong steroid pharmaceuticals . with the publication 
 of these analyses , and the availability of the toxcast and tox21 data 
 ( https://www.epa.gov/chemical-research/toxicity-forecasting ) , 
 the reference chemical list can be updated to reflect the contribution 
 of these assays to the body of published literature . we refrained 
 from doing so here to provide an external validation for the current 
 ar pathway model , but future work could incorporate the toxcast , tox21 , 
 and other assays into an expanded reference chemicals list . in that 
 case , the contradictory results between the literature analysis and 
 the toxcast / tox21 data would suggest removal of 17a - estradiol from 
 future negative reference classifications if the source of crosstalk , 
 whether it is biological or technological , can be determined . another 
 potential lesson learned from validating the ar pathway model against 
 the reference chemicals concerns the threshold for positive activity . two of the weak / very weak antagonist reference chemicals had auc values 
 in the inconclusive range , around 0.04 , due to lack of activity in 
 the binding assays . a limitation of the binding assays specifically 
 is that chemicals were only tested in concentration response if they 
 were active in a single high - concentration screen . both of these chemicals 
 ( fenarimol and methoxychlor ) had similar profiles against the remainder 
 of the pathway with activity at 3040 m in one of the 
 coregulator recruitment assays ( a5 ) and both of the tox21 antagonist 
 assays ( a10 and a11 ) . depending on the application and the desire 
 to minimize false negatives in a regulatory setting , the threshold 
 could be adjusted to consider all nonzero model values ; chemicals 
 with both inconclusive and positive ar pathway activity would then 
 be prioritized for further testing . here , we presented a comparison 
 of the ar pathway model integrating 
 11 hts assays and the existing in vitro ar binding assay in the u.s . the overall summary of the comparison between 
 the model and the tier 1 ar binding assay is that the model correctly 
 identifies binders with potency in the tested range ( ic50 under 100 m ) but yields a significant number of false positives , 
 especially as putative antagonists . however , most of these are identified 
 as false positives using a combination of the antagonist confirmation 
 assay and examination of assay interference channels . finally , the 
 model provides evidence in contradiction to the iccvam designations 
 for at least four chemicals ( 17a - estradiol , 4-cumylphenol , apigenin , 
 and bisphenol b ) , which should prompt further investigation . epa edsp tier 1 results , including the in vivo 
 hershberger assay for ar agonists and 
 antagonists , may help in understanding the relative performance of 
 the ar pathway model based on toxcast and tox21 assays . like the er 
 model , it appears the ar pathway model 
 is more sensitive and also more quantitative than the edsp tier 1 
 assays based on the diversity of the 11 hts assays and the computational 
 network that integrates those data . the model and associated assays 
 cover a broader range of biological processes than the tier 1 binding 
 assay and therefore yield a stronger weight of evidence for true ar 
 agonist or antagonist activity . limitations of this model , and 
 most hts - based approaches , include 
 the lack of or limited metabolic capacity of the systems and the restriction 
 to chemicals that are dmso soluble . there is a current challenge for 
 the scientific community to tackle the issue of incorporating metabolism 
 ( http://www.transformtoxtesting.com/ ) , and structure - based models are under development to identify chemicals 
 predicted to undergo transformation to more bioactive metabolites . further , although the hts results and computational model 
 predictions have demonstrated the ability to effectively prioritize 
 environmental compounds for endocrine disrupting potential , they should 
 be integrated with exposure estimates for decision making in a risk 
 assessment framework . for ultimately interpreting ar pathway activity and other 
 mechanistic 
 events in a biological framework that includes potentially adverse 
 in vivo outcomes , efforts are underway to establish reference chemicals 
 for additional end points and map these to adverse outcome pathways . it is important to note that the reference chemicals presented here 
 are for agonist or antagonist behavior mediated through the ar , and 
 some chemicals may have other endocrine relevant effects via pathways 
 such as steroidogenesis . following the example of the uterotrophic 
 database , work is ongoing to compile 
 in vivo androgen and antiandrogen data from the u.s . experimental reverse 
 toxicokinetic measurements are being used to parametrize models for 
 in vitro - to - in vivo extrapolation to facilitate a direct comparison 
 to demonstrated effects in vivo and administered doses . these efforts can be used to validate additional high - throughput 
 in vitro assays , and for some chemical classes , integration of hts 
 assays and computational models may be adequate to predict more apical 
 developmental and reproductive effects . we 
 have compiled a database of literature results that includes 
 a wide array of ar binding and transactivation data and used it to 
 characterize a range of potential ar agonist and antagonist reference 
 chemicals . the proposed reference chemical lists and associated potency 
 categories can be used for current and future test method evaluations 
 and will be submitted to oecd via the validation management group  non - animal 
 to facilitate international harmonization . the ar pathway model based 
 on results from 11 tox21/toxcast hts assays was validated against 
 this independently curated set of reference chemicals and shown to 
 be over 95% accurate for predicting both ar agonism and antagonism . the tox21 confirmation assay data assisted in identifying chemicals 
 that exhibited a shift in potency indicative of a true ar antagonist 
 response and can be combined with cytotoxicity information to contextualize 
 the ar pathway model results . a number of environmental chemicals 
 were identified as potential ar antagonists , with varying degrees 
 of confidence , and should be examined in the context of human and 
 environmental exposures , metabolism , and persistence to characterize 
 the risk of endocrine disruption and adverse outcomes in humans or 
 wildlife .
testing thousands 
 of chemicals to identify potential androgen receptor 
 ( ar ) agonists or antagonists would cost millions of dollars and take 
 decades to complete using current validated methods . 
 high - throughput 
 in vitro screening ( hts ) and computational toxicology approaches can 
 more rapidly and inexpensively identify potential androgen - active 
 chemicals . 
 we integrated 11 hts toxcast / tox21 in vitro assays into 
 a computational network model to distinguish true ar pathway activity 
 from technology - specific assay interference . 
 the in vitro hts assays 
 probed perturbations of the ar pathway at multiple points ( receptor 
 binding , coregulator recruitment , gene transcription , and protein 
 production ) and multiple cell types . 
 confirmatory in vitro antagonist 
 assay data and cytotoxicity information were used as additional flags 
 for potential nonspecific activity . 
 validating such alternative testing 
 strategies requires high - quality reference data . 
 we compiled 158 putative 
 androgen - active and -inactive chemicals from a combination of international 
 test method validation efforts and semiautomated systematic literature 
 reviews . 
 detailed in vitro assay information and results were compiled 
 into a single database using a standardized ontology . 
 reference chemical 
 concentrations that activated or inhibited ar pathway activity were 
 identified to establish a range of potencies with reproducible reference 
 chemical results . 
 comparison with existing tier 1 ar binding data 
 from the u.s . 
 epa endocrine disruptor screening program revealed that 
 the model identified binders at relevant test concentrations ( < 100 
 m ) and was more sensitive to antagonist activity . the ar pathway 
 model based on the toxcast / tox21 assays had balanced accuracies of 
 95.2% for agonist 
 ( n = 29 ) and 97.5% for antagonist 
 ( n = 28 ) reference chemicals . out of 1855 chemicals 
 screened in the ar pathway model , 
 220 chemicals demonstrated ar agonist 
 or antagonist activity and an additional 174 chemicals were predicted 
 to have potential weak ar pathway activity .