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1、GivingMoleculesanIdentity.OntheInterplayBetweenQSARsandPartialOrderRankingMolecules2004,9,1010-1018moleculesISSN1420-3049parisonwithexperimentallywe11-characterized,structurallysimilarcompounds.ItisdisclosedthatexperimentallyWeIl-CharaCteriZedcompoundsmayserveassubstitutesforhighlytoxiccompoundsinex
2、perimentalstudieswithoutexhibitingthesameextremetoxicity,whilefromanoverallviewpointtheyexhibitanalogousenvironmentalcharacteristics.Keywords:Noise-deficientQSARs：PartialOrderRanking;HasseDiagrams;Organo-phosphates;Nerveagents.IntroductionThelackofdataforthevastmajorityofexistingchemicalsiswel1known
3、andconstitutesobviouslyasignificantprobleminrelationtoe.g.,riskassessment.Thus,accordingtotheEuropeanCommissiononlyinthecaseofapproximately14%oftheHPV(HighProductionVolume)chemicalsontheEINECSlist,comprising100,116entries,theminimumrequireddataforevaluatingthechemicalswereavai!able.Forapproximately2
4、1%ofthecompoundsnodataatallconcerningtheirpotentialimpactontheenvironmentandhumanhealthwerefound1.InastudybytheDanishEPA2itwasconcludedthateveninmajorsourcesoftestdata,informationonselectedecotoxicologicaleffectscouldonlybefoundforverylimitednumberofthecompoundsontheEINECSlist(acutetoxiceffect:10.5%
5、,reproductivedamage:2.2%,geneticdamage:3.2%,carcinogeniceffect:1.6%,effectontheaquaticenvironment:3.5%).Sinceintensiveandexperimentalevaluationsofchemicalsarcrathercostly3,andreferencestherein,QSRderiveddataforphysico-chemicalaswel1astoxicologicalMolecules2004,91011endpointsappearasanattractivealter
6、native.However,althoughthelackofdatacanberemediedtoacertainextentthroughQSARmodeling,thiswillleaveuswiththepossibilityofcharacterizingthesinglemoleculesbasedonsingleparameters,suchassolubility,octanol-waterpartitioning,vaporpressure,biodegradation-andbioaccumulationpotential.However,toestablishanide
7、ntityforagivenmolecule,e.g.,asapotentialPBTsubstancerequirestakingseveralparametersintoaccountsimultaneously,i.e.,Persistence,BioaccumulationandToxicity.Inthepresentstudytheadvantageoususeofso-callednoise-deficientQSARs,developedusingdatafromexperimentallywe11-characterizedcompoundsasthetrainingset,
8、asapreprocessingtooltoderivethedesiredendpointsforsubstanceswhereexperimentaldataarenotavailable.Subsequently,theseendpointswillbeappliedasdescriptorsinestablishingapartialorderingofcombinedsetsofcompounds,herebygivingtheexperimentallynotinvestigatedcompoundsanidentitybycomparingtostructurallyrelate
9、d,experimentallywel!-characterizedcompounds4,53.MethodsQSARInthepresentstudytheend-pointsaregeneratedthroughQSARmodeling,theEPISuitebeingtheprimarytool6.Togeneratenew1inearnoise-deficientQSARmodels,EPIgeneratedvaluesfor,e.g.,logSol,logK0W,logVPandlogH1.Carefurthertreatedbyestimatingtherelationshipsb
10、etweentheEPIgenerateddataandavai!ableexperimentaldata7fortheaseriesofexperimentallywell-characterizedcompoundsinthetrainingset,thegeneralformulafortheend-points,Di,tobeusedbeingDi=aiDEPI+bi(1)DEPIistheEPIgeneratedend-pointvalueandaiandbibeingconstants.ThelogKOWvaluesgeneratedinthiswayaresubsequently
11、usedtogeneratelogBCFvaluesaccordingtotheConnellformula8logBCF=6.910-3(logKow)1.8510-1(log4K)3+1.55(logKow)2ow4.181ogKow+4.72(2)Themodelwassomewhatmodified.Thus,a1ineardecreaseoflogBCFwithlogKOWwasassumedintherange1logK0W2.33,thelogBCF=0.5forlogKOW1,thelattervaluebeinginaccordancewithBCFWin6.Subseque
12、ntlydatafornotcharacterizedcompoundsarecalculatedbasedontheseformulaeandtheappropriateEPIgenerateddata.Inthepresentstudyatrainingsetconsistingofupto65organophosphorus(OP)insecticidesareapplied.Duetothelackofexperimentaldataforthetrainingsetcompoundswithregardstotheirbiodegradation,theaboveprocedurew
13、asnotapplicabletothebiodegradationpotential,BDP3.Thus,dataonBDP3areusedasestimatedbytheappropriatemodulesintheEPlSuite.Molecules2004,91012PartialOrderRankingThetheoryofpartialorderrankingispresentedelsewhere9anditsapplicationinrelationtoQSRispresentedinpreviouspapers1013.Inbrief,PartialOrderRankingi
14、sasimpleprinciple,whichaprioriincludesastheonlymathematicalrelation.Ifasystemisconsidered,whichcanbedescribedbyaseriesofdescriptorspi,agivencompound,characterizedbythedescriptorspi（八）canbecomparedtoanothercompoundB,characterizedbythedescriptorspi(B),throughcomparisonofthesingledescriptors,respective
15、ly.Thus,compoundwillberankedhigherthancompoundB,i.e.,BA,ifatleastonedescriptorforAishigherthanthecorrespondingdescriptorforBandnodescriptorforislowerthanthecorrespondingdescriptorforB.If,ontheotherhand,pi()pi(B)fordescriptoriandpj()pj(B)fordescriptorj,AandBwillbedenotedincomparable.Inmathematicalter
16、msthiscanbeexpressedasBApi(B)pi（八）foralli(3)Obviously,ifalldescriptorsforAareequaltothecorrespondingdescriptorsforB,i.e.,pi(B)=pi（八）foralli,thetwocompoundswillhaveidenticalrankandwillbeconsideredasequivalent.ItfurtherfollowsthatifABandBCthenAC.IfnorankcanbeestablishedbetweenandBthesecompoundsaredeno
17、tedasincomparable,i.e.theycannotbeassignedamutualorder.Inpartialorderrankingincontrasttostandardmultidimensionalstatisticalanalysis-neitherassumptionsaboutlinearitynoranyassumptionsaboutdistributionpropertiesaremade.Inthiswaythepartialorderrankingcanbeconsideredasanon-parametricmethod.Thus,thereisno
18、preferenceamongthedescriptors.However,duetothesimplemathematicsoutlinedabove,itisobviousthatthemethodaprioriisrathersensitivetonoise,sinceevenminorfluctuationsinthedescriptorvaluesmayleadtonon-comparabi1ityorreversedordering.Thegraphicalrepresentationofthepartialorderingisoftengiveninaso-calledHasse
19、diagram14-17.InpracticethepartialorderrankingsaredoneusingtheWHassesoftware17.1.inearextensionsThenumberofincomparableelementsinthepartialorderingmayobviouslyconstitutea1imitationintheattempttoranke.g.aseriesofchemicalsubstancesbasedontheirpotentialenvironmentalorhumanhealthhazard.Toacertainextentth
20、isproblemcanberemediedthroughtheapplicationoftheso-called1inearextensionsofthepartialorderranking18,19.Ainearextensionisatotalorder,whereallcomparabilitiesofthepartialorderarereproduced9,16.Duetotheincomparisonsinthepartialorderranking,anumberofpossiblelinearextensionscorrespondstoonepartialorder.If
21、allpossiblelinearextensionsarefound,arankingprobabilitycanbecalculated,i.e.,basedonthe1inearextensionstheprohabi1itythatacertaincompoundhaveacertainabsoluterankcanbederived.Ifallpossible1inearextensionsarefounditispossibletocalculatetheaverageranksofthesingleelementsinapartiallyorderedset20,21.Theav
22、eragerankissimplyIhcaverageoftheranksinallthe1inearextensions.Onthisbasisthemostprobablyrankforeachelementcanbeobtainedleadingtothemostprobablylinearrankofthesubstancesstudied.Molecules2004,91013ThegenerationoftheaveragerankofthesinglecompoundsintheHassediagramisobtainedapplyingthesimpleempiricalrel
23、ationrecentlyreportedbyBrggemannetal22.Theaveragerankofaspecificcompound,ci,canbeobtainedbythesimplerelationRkav(ci)=(N+l)-(S(ci)+1)(N+l)/(N+l-U(ci)(4)whereNisthenumberofelementsinthediagram,S(ci)thenumberofsuccessorstociandU(ci)thenumberofelementsbeingincomparabletoci22.ResultsandDiscussionThebasic
24、ideaofusingpartialorderrankingforgivingmoleculesanidentityisillustratedinFigure1.Thus,letusassumethatasuiteof10compoundshastobeevaluatedandthattheevaluationshou1dbebasedonthreepre-selectedcriteria,e.g.,persistence,bioaccumulationandtoxicity.1.ettheresultingHaSSediagrambetheonedepictedinFigure1.Ifwea
25、pplythethreedescriptorsrepresentingrespectively,sothemorepersistent,themorebioaccumulatingandthemoretoxicasubstancewouldbethehigherinthediagramitWOUldbefound,FigureIAdisclosesthatthecompoundsinthetoplevel,i.e.,compounds1,3,4,7and8onacumulativebasiscanbeclassifiedastheenvironmentallymoreproblematicof
26、the10compoundsstudiedwithrespecttotheirPBTcharacteristics,whereascompound10thatafoundinthebottomofthediagramisthelesshazardous.Figure1.IllustrativeHassediagramof:10compoundsusingthreedescriptorsandB:thesame10compoundsplusonenewcompoundX.A12354679810B135467982X10Molecules2004,91014SubsequentlyWecanin
27、troducecompoundssolelycharacterizedbyQSRderiveddatainordertogivethisnewcompound,X,anidentity,e.g.,inanattempttoelucidatetheenvironmentalimpactofX.Adoptingtheabovediscussed10compoundsandthecorrespondingHassediagram(FigureIA)wcthenintroducedthecompoundX.TherevisedHassediagram,nowincluding11compoundsis
28、visualizedinFigureIB.ItisimmediatelydisclosedthatcompoundXhasnowobtainedanidentityincomparisontotheoriginallywe11-characterizedcompounds,asitisevaluatedaslessbiodegradation,bioaccumulationandtoxicity,environmcntalIyharmfulthancompounds4and7,butmoreharmfulthancompound10.Thus,throughthepartialorderran
29、kingthecompound,X,hasobtainedanidentityinthescenariowithregardtoitspotentialenvironmcntalimpact.Toi1lustratetheaboveanexamplefromourcurrentstudyonthephysico-chemicalcharacteristicsofOPcompoundswithspecialemphasisonchemicalwarfarenerveagentsastheG-agents,likeTabun,SarinandSoman,andV-agcnts,likeVX,sha
30、l1beused4,5.InthepresentstudyweshallfocusontheaqueouspersistenceofOPinsecticidesandknowandpotentialnerveagentsasexpressedthroughthesolubility(Sol),thebiodegradationpotential(BDP)andtheHenrys1.awConstants(H1.C),thelatterbeingderivedbasedontheEPIva1uesasgivenbyHenryWin6.AsmentionedtheEPISuite6hasbeent
31、heprimarytoolforQSARmodeling,thesingleEPIgeneratedvaluesforlogSol,logK0W,logVPandlogH1.Cbeingfurthertreatedtogeneratenew1inearnoise-deficientQSARmodels,cf.eqn.14.Asanexamplethenewnoise-deficientQSRmodelforlogH1.CisdepictedinFigure2,thecorrespondingmodelbeingexpressedthrougheqn.54.logH1.C=0.946logH1.
32、CEPI1.168;r2=0.636(5)Figure2.VisualizationoftheEPIbasedmodifiedQSARmodelingoflogH1.Cbasedon49OPinsecticides0-12.000-10.000-8.000-6.000-4.000-2.0000.000-2-4-6-8-10-12logH1.CEPIThenoise-deficientQSARforthesolubi1itywasderivedanalogously,theresultingmodelbeingdescribedthrougheqn.64.logSol=0.983logSol(E
33、PI)+0.625;n=64,r2=0.830(6)Molecules2004,91015Thegeneratedend-pointaresubsequentlyusedtogeneratepartialorderrankingsofthethe65OPinsecticidestogetherwiththe16knownpotentialnerveagentstakingtwoormoredescriptorssimultaneouslyintoaccount.Thus,asintotal81compoundsareincludedinthesubsequentrankingprocedure
34、,theresultingHassediagramsmayseemsomewhatconfusing.Figure3depictstheHassediagramdisclosingthemutualrankingofthecompoundsduetotheiraqueouspersistence,i.e.,bringingsimultaneouslythesolubi1ity(logSol),thebiodegradationpotentialforultimatebiodegradation(BDP3)andHenrys1.awConstant(1ogHI.C)intoplay.Figure
35、3.Hassediagramdisplayingtheaqueouspersistenceofthe65OPinsecticides(whitered)and16nerveagent(yellow/blue),ThenumberscorrespondstothenumberingoftheOPinsecticidesintheFADNAPdatabase7FromtheabovefigureitcanbeseenthatthenerveagentVXislocatedatthesamelevelasthecompounds61(AniIofos),71(Azinphosmethyl),194(
36、Chlorfenvinphos),217(Chlorpyriphosmethyl),296(Dialifos),319(Dicrotophos),372(Ditaiimfos),705(Monocrotophos),795(Phosalone),798(Phosmet),799(Phosphamidon)and869(Pyraclofos),inadditiontotheRussianversionofVX(RVX)andthepotentialnerveagentmMe(Amitonmethyl).Apriorithelocationofthecompoundsonthesameleveli
37、ntheHassediagramsuggeststhesecompoundstobecloseintheiroverallcharacteristicsbasedonthesetofdescriptorsused,i.e.solubi1ity,biodegradationpotentialandHenrys1.awConstant.However,afurtheranalysisappearstobenecessaryinordereventuallytodisclosehowclosethesecompoundsactuallyare.Forthisanalysistheconceptofa
38、veragerank4,5,22,23wasadopted.Thus,itisassumedthatiftheaverageranks,Rkav,oftwocompoundsareclose,thetwocompoundswi11onanaveragebasisdisplaysimilarcharacteristicsasbeingdeterminedbythesetofdescriptorsapplied.InTable1theaverageranksfortheabove-mentionedOPsaregiventogetherwithminimumacuteoraltoxicityand
39、acutepercutaneoustoxicity,respectively,inbothcasesforrats7.Molecules2004,9Table1.Averageranksfortheaqueouspersistenceasdeterminedbythe1016solubility,thebiodegradationpotentialandtheHenrys1.awConstantsforaseriesofOPinsecticidesandVX(thecompoundIDreferstotheFADTNAPdatabase,cf.theabovetext：na:notavai!a
40、ble)AverageRankAcuteOralToxicity(mgkg)472424163051756602013516017.92370.088AcutePercutaneousToxicity(mgkg)2000220313700na11020001121500na37420000.1CompoundRkav20.525.69.618.2419.119.310.335.121.96.218.95.3AnilofosAzinphosmethylChlorfenvinphosChlorpyriphosmethylDiaIifOSDicrotophosDitalimfosMonocrotop
41、hosPhosalonePhosmetPhosphamidonPyraclofosVXItisimmediatelyseenthatalthoughlhecompoundswereplacedonthesamelevelintheHassediagram,onlythroughtheanalysisofaverage1inearrankthetrueidentityofthesinglecompoundsaredisclosed.Thus,inthepresentcaseitisobviousthatVX(Rkav=5.3)thatinthepresentcontextistheunknown
42、compoundachievesanidentitythatcanbecomparedtoPhosphamidon(Rkav=6.2)astheclosestcounterpart.Thus,withregardtoaqueouspersistence,theabovecombinedQSARandpartialorderrankinganalysisindicatesthatVXandPhosphamidonwilldisplayclosetoidenticalbehavior.ThisfurthermeansthatPhosphamidon,withinthepresentsetofcom
43、poundsincludedintheinvestigation,appearsastheoptimalsubstituteforVXinexperimentalstudieswhereaqueouspersistenceisacrucialparameter.11isnotedthattheacuteoraltoxicityassociatedwithPhosphamidonisapproximately200timeslowerthanthatofVXandinthecaseofacutepercutaneoustoxicity,Phosphamidonappearstobenearly4
44、000timeslesstoxicthanVX.ConclusionsThepresentstudyhasdemonstratedhowunknowncompoundsmayobtainanidentitybycomparingtostructurallyrelated,experimentallywe11-characterizedstructurallysimilarcompounds.Theidentitycanbeestablishedbyacloseinterplaybetweenso-callednoise-deficientQSARs,inthepresentstudygener
45、atedusingtheEPISuiteasthemodelingonset.Subsequently,thegeneratedphysico-chemicalend-pointsareusedasdescriptorsinapartialorderbasedrankingandthesubsequentanalysisoftheaverage1inearrank.Itissuggestedthatexperimentallywell-characterizedcompoundsmayserveassubstitutesforhighlytoxiccompounds,suchasthenerv
46、eagentinexperimentalstudieswithoutexhibitingthesameextremetoxicity,howeverfromanoverallviewpointexhibitanalogousenvironmentalcharacteristics.Molecules2004,91017ReferencesandNotes1.EINECS(EuropeanInventoryofExistingComUiercialChemicalSubstances).cf.EuropeanCommission1967:Directive67548EEContheapplica
47、tionoflaws,regulationsandadministrativeprovisionsrelatingtotheclassification,packagingandlabelingofdangeroussubstancesandthe6amendment:Directive79/831/EEC;art.13thNimcla,J.Workingdocumentontheavailabilityofdataforclassificationandlabellingof2.chemicalsubstancesattheEuropeanmarket,19943.Walker,J.D.;C
48、arlsen,1.;Hulzebos,E.;Siinon-Hettich,B.GovernmentpplicationsofAnalogues,SRsandQSRstoPredictAquaticToxicity,ChemicalorPhysicalProperties,EnvironmentalFateParametersandHealthEffectsofOrganicChemicals,SRQSREnviron.Res.2002,13,607-6194.Carlsen,1.QSARApproachtoPhysico-ChemicalDataforOrganophosphateswithS
49、pecialFocusonKnownandPotentialNerveAgents.Submittedforpublication5.Carlsen,1.PartialOrderRankingofOrganophosphateswithSpecialEmphasisonNerveAgents.Commun.Math.Comp.Chern.-MATCH,inpress6.PollutionPrevention(P2)Framework,EP-758-B-00-001：maybeobtainedthroughthelinkiP2Manual6-00.pdffoundatparisonofpartialordertechniquewiththreemethodsofmulti-criteriaanalysisforrankingofchemicalsubstances,J.Chem
