Predicting mammalian metabolism and toxicity of pesticides in silico

12 Pages·2017·1.29 MB·English

Mini-review
Received:21December2017 Revised:2April2018 Acceptedarticlepublished:15May2018 PublishedonlineinWileyOnlineLibrary:
(wileyonlinelibrary.com)DOI10.1002/ps.4935
Predicting mammalian metabolism
and toxicity of pesticides in silico
Robert D Clark*
Abstract
Pesticidesmustbeeffectivetobecommerciallyviablebuttheymustalsobereasonablysafeforthosewhomanufacturethem,
applythem,orconsumethefoodtheyareusedtoproduce.Animaltestingiskeytoensuringsafety,butitcomeslateinthe
agrochemicaldevelopmentprocess,isexpensive,andrequiresrelativelylargeamountsofmaterial.Surrogateassaysusedasin
vitromodelsrequirelessmaterialandshiftidentificationofpotentialmammaliantoxicitybacktoearlierstagesindevelopment.
Moderninsilicomethodsarecost-effectivecomplementstosuchinvitromodelsthatmakeitpossibletopredictmammalian
metabolism,toxicityandexposureforapesticide,cropresidueorothermetabolitebeforeithasbeensynthesized.Theirbroader
usecouldsubstantiallyreducetheamountoftimeandeffortwastedinpesticidedevelopment.Thiscontributionreviewsthe
kindofinsilicomodelsthatarecurrentlyavailableforvettingideasaboutwhattosynthesizeandhowtofocusdevelopment
efforts;thelimitationsofthosemodels;andthepracticalconsiderationsthathavesloweddevelopmentinthearea.Detailed
discussions are provided of how bacterial mutagenicity, human cytochrome P450 (CYP) metabolism, and bioavailability in
humansandratscanbepredicted.
©2018TheAuthors.PestManagementSciencepublishedbyJohnWiley&SonsLtdonbehalfofSocietyofChemicalIndustry.
Supportinginformationmaybefoundintheonlineversionofthisarticle.
Keywords:absorption;absorption,distribution,metabolismandexcretion(ADME);bioavailability;cytochromeP450(CYP);mammalian
toxicity;metabolism;modeling;quantitativestructure–activityrelationship(QSAR)
1 INTRODUCTION development,makingitalmostasimportanttobeabletopredict
thelikelyresultsofanimaltesting.Doingsorequiresreliablepre-
Anticipating mammalian toxicity of agrochemical pesticides or
dictionofwhethertheactiveingredientitselfislikelytobetoxic,
their metabolites is a key part of agrochemical development.
howitislikelytobemetabolized,andwhetherthemetabolites
Environmental problems can be identified fairly readily by tests
producedarelikelytobetoxicorinnocuous.Infact,insilicomodel
oninvertebratesandfish.Testingnewcandidatecompoundsin
predictionsprovideanimportantwaytoreducetheneedforani-
humansandlargemammalsisgenerallyunethicalandrarelyprac-
maltestingandtomakesureitisdoneefficientlywhenitisneeded.
ticalearlyindevelopment,however,sorodentsareusedinstead.
Theycanalsocomplementbothinvitroandinvivotestsbyidenti-
Humanitarianandcostconcernshavecombinedtodrivetesting
fyinginstanceswherethoseresultsaremisleadingorincorrect.
awayfromanimaltestingandtowardsgreaterrelianceoninvitro
Predictiveinsilicomodelscouldbebuilttoaddressmanydiffer-
assays.Theseserveasusefulmodelsoftherelevantinvivoend-
entaspectsofxenobioticmetabolismandtoxicity,buttechnical
points, but they can be quite far removed from what they are
andpracticalissueslimitthosethatarecurrentlyavailable.Here
intended to predict. Moreover, the more definitive invitro tests
wewillfocusprimarilyonthosethatarebestpositionedatpresent
oftenrequireasubstantialamountofpurematerial,whichcanbe
tofacilitateagrochemicalpesticidediscoveryanddevelopment:
especiallychallengingwhenitcomestometabolites.Analterna-
those addressing mutagenicity, rodent toxicity, metabolism by
tiveapproachistouseknowledgeofmolecularstructure–activity
cytochromeP450s(CYPs),andbioavailability.Factorsholdingback
relationships (SARs) gleaned from past assay results to build in
developmentofquantitativemodelsforpotentiallyimportantpro-
silicomodelsthatcanpredictmetabolismandtoxicityofacom-
cesseslikeglucuronidationandglutathioneconjugationwillalso
poundevenbeforeit,itsmetabolitesoritscropresidueshavebeen
bedescribed.
synthesized.
Mostinsilicometabolismandtoxicitymodelsdependimplicitly
Such quantitative SAR (QSAR) models can be used to guide
or explicitly on predicted physicochemical properties relevant
on-target pharmaceutical optimization and to minimize unde-
to absorption, distribution, metabolism, excretion and toxicity
sirable off-target activities.1 In the case of pesticide develop-
ment,mammaliantoxicity–orlackthereofasaconsequenceof
metabolicinactivation–isakeyoff-targetactivitythatneedsto
∗ Correspondenceto:RDClark,SimulationsPlus,Inc.,4250510thStreetWest,
beconsidered.Thepossibilityofhumantoxicityiscertainlyacon-
Lancaster,CA93534,USA.E-mail:[email protected]
cern.Asapracticalmatter,however,evidenceoftoxicityinother
species–e.g.,rodentsorlivestock–isjustascapableofderailing SimulationsPlus,Inc.,Lancaster,CA,93534,USA
©2018TheAuthors.PestManagementSciencepublishedbyJohnWiley&SonsLtdonbehalfofSocietyofChemicalIndustry.
ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttribution-NonCommercial-NoDerivsLicense,whichpermitsuseand
distributioninanymedium,providedtheoriginalworkisproperlycited,theuseisnon-commercialandnomodificationsoradaptationsaremade.
www.soci.org RDClark
(ADMET).Lipophilicity(commonlyexpressedaslogP,thecommon Broadly speaking, computer models can be characterized in
logarithmoftheoctanol:waterpartitioncoefficientfortheneutral termsofhowtheendpointbeingpredictedisdescribed,thekind
species),logPataspecifiedpH(logD),thenegativelogarithmof ofdescriptorsusedtocharacterize themolecularstructure,and
thedissociationconstant(pK ),andsolubilityunderphysiological themathematicalformusedtorelatethedescriptorstothemodel
a
conditionsarekeyexamplesofsuchphysicochemicalproperties. output. Endpoints may be quantitative (e.g., what is the rate of
ManymodelsforindividualADMETpropertieshavebeenpub- metabolisminlivermicrosomes?),multiclass(e.g.,isitmostlikely
lished,andinmostcasestheirusefulnesshasnotbeenexplored tobeasubstrateforCYP1A2,-2C9,-2D6or-3A4?)orbinary(e.g.,is
outside the group that developed them. This is often because itmutagenicornot?).
eithertheyarenotreadilyavailabletootherresearchersorthey QSARdescriptorscanbethoughtofasatom-centered(e.g.,isthis
havenotbeenintegratedeffectivelywithothermodels.Herewe substructurefoundinthemolecule?)ormolecule-centered[e.g.,
willhighlightsoftwarepackagesthatprovideaccesstomultiple is the molecule as a whole polar and hydrophilic (water-loving)
models, especially those that combine their outputs somehow. or is it nonpolar and lipophilic (lipid-loving) or is it some-
Most that go beyond simple tabular outputs are commercial where in between?]. Either kind of descriptor can require full
products. The information presented in Supporting Information three-dimensional(3D)molecularstructuresasinputormayonly
TableS1 illustrates the range of mammalian metabolism, toxic- require the connectivity of the molecule–i.e., which atoms are
ity and bioavailability models offered as of February 2018, with bondedtowhichandthenatureofthebondtypes.Thelatterare
linksprovidedtothewebsiteassociatedwitheachsoftwarepack-
referredtoas‘2D’descriptors.Onedifferencebetweenthetwois
age.Thoseincludedwerechosenbecausetheyaremoreorless
that 3D descriptors can take chirality into account, which often
directly applicable to mammalian metabolic and toxicological affectstheactivityofdrugsandagrochemicalsalike7 butwhich
aspects of pesticide discovery and early development; models hasrelativelylittleeffectonmostADMEproperties.8Thepotential
thataddressenvironmentaleffectssuchasbioconcentrationand
drawbackof3Ddescriptorsisthattheirvaluesaredependenton
aquatictoxicitywerenottakenintoconsideration.Integratedsys-
molecular conformation. Hence the active site plasticity under-
temswerefavoredoverlessstructuredcompendiaofmodelssuch
lying the promiscuity of many enzymes involved in xenobiotic
as ChemProp (http://www.ufz.de). More general and exhaustive
absorptionandmetabolismlimitstheirusefulnessformodeling
tabulationsofADMET-relatedsoftwarepackageshavebeenpub-
ADMEspecificityandkinetics.9
lishedrecentlyelsewhere.2–4
Descriptorscanalsobecharacterizedassubstructuralormolec-
The agrochemicals chosen to illustrate how the models dis-
ular.Theformercharacterizemoleculesintermsoftheindividual
cussed herein can be applied reflect the author’s experience
atomsorsubstructuresthatmakethemup,withseparatesetsof
in the area. They include: acetochlor and alachlor (chloroac-
descriptors for different substructures. Molecular descriptors, in
etamides); glyphosate [an 5-enolpyruvylshikimate-3-phosphate
contrast,reflectpropertiesofthemoleculeasawhole,withthe
synthase(EPSPS)inhibitor];theauxin2,4-dichlorophenoxyacetic
result that different molecules may have very similar descriptor
acid(2,4-D);imazapyrandsulfometuronmethyl[acetolactatesyn-
values(e.g.,ofmolecularvolumeandoverallcharge)eventhough
thase (ALS) inhibitors]; and oxyfluorfen [a protoporphyrinogen
theirsubstructuralcompositionshavelittleincommon.
IX oxidase (PPO) inhibitor]. Twenty-five herbicides drawn from
Finally, the mathematical form of the relationship between
a recent compilation of herbicides introduced since the turn of
the endpoint and the descriptors can vary considerably, with
thecentury5werealsoincluded,alongwiththreefungicidalsuc-
themostcommonbroaddistinctionsbeingbetweenlinearand
cinate dehydrogenase (SDH) inhibitors (isopyrazan, benzovindi-
nonlinear [e.g., artificial neural network (ANN)10] models. One
flupyrandsedaxane).
shouldbeaware,however,thatlinearmodelscanaccountindi-
rectlyfornonlineareffectsifthedescriptorsusedarecorrelated
2 MODELINGAPPROACHES in some nonlinear way. Unfortunately, the effect of such inter-
relatedness on the interpretability of (nominally) linear models
Metabolism and toxicity are complex processes at the molec-
is generally underappreciated. This paper will focus on general
ular level and the inherent ‘noisiness’ of biological endpoints
considerations to be kept in mind when selecting and apply-
onlycomplicatesmattersfurther.Asaresult,allmodelsintended
ing a QSAR model. The kinds of models offered in commercial
to make predictions about such a process–whether insilico or
invitro–riskbeingoverlyreductionist.6 ‘Top-down’approaches software and in academic programs available in open source
generate a single over-arching model for each overall outcome environmentsusesimilarcombinationsofendpoints,descriptors
(e.g.,livermicrosomalclearancebyCYPs).‘Bottomup’approaches and mathematical forms. Top-down nonlinear models derived
buildmodelsforthedifferentmechanismscontributingtoapar- from substructural descriptors currently dominate both groups.
ticularendpointandthencombinethosepredictions. ADMET Predictor™ (Simulations Plus, Inc. Lancaster, CA, USA;
Atop-downmodelforoverallCYP-mediatedclearanceinliver http://www.simulations-plus.com) is atypical in that it relies
microsomescanbebuiltfromfewerdatathanasetofbottom-up on artificial neural net ensemble models built from molecu-
modelsforindividualCYPisoforms,butitcanonlyprovideappar- lar descriptors rather than substructural ones. It also takes a
entaffinity(K )estimatesandcannotaccountforgeneticvariants bottom-upapproachtomostendpoints–e.g.,byprovidingquan-
m
inindividualisoforms.Asetofbottom-upmodelscanbeusedto titativekineticestimatesforfiveindividualhumanCYPsaswellas
predictkineticparametersandeffectsofgeneticvariation,butwill site-of-metabolismpredictionsforeachofnineisoforms.Itisused
missclearancecontributionsfromisoformsforwhichnospecific heretoillustrategeneralpointsthatshouldbeborneinmindwhen
modelhasbeenbuilt.Thecompositebottom-upmodelmaymiss workingwithmetabolismandtoxicitypredictionsoftware,inpart
informationfromimplicitintermediates(e.g.,conjugatesformed becauseitoffersawiderangeofendpointpredictions(TableS1)
afteraninitialoxidationorreductionstep)thatcancontributeto that makes it easy to illustrate how the various model outputs
atop-downmodel,whereasthelatterriskserroneouslyassuming can be combined and in part because of its availability to the
thattherequisitereductionwilloccurinsomeothermolecule. author.
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3 TOXICITYPREDICTION quantitative indication of how reliable a particular prediction is
3.1 Amestestinginsilico goingtobe,i.e.,ofhowconfidentonecanbeinit.14
Despitethequestionsthathavebeenraisedabouttheirrelevance The ‘bottom-up’ approach taken in ADMET Predictor creates
topesticideregistration,11,12 Amesmutagenicitytests13 remaina a separate model for each of the ten constituent assays, then
combinestheresultsintoanoverallmutagenicityrisk.Thedefault
majorregulatoryhurdlethatmustbepassedearlyinagrochemical
behavior is to flag a compound as probably mutagenic in the
pesticidedevelopment.Althoughhundredsofthousandsofthese
aggregated output16 if the compound is predicted to give a
assayshavebeenperformed,themajorityoftheresultsobtained
positiveresultformorethanonestrain.Doingsoallowsforoneof
arenotpubliclyavailable.Compoundsthattestedpositiveearly
thepredictionstobeaninsilicofalsepositive,whichisconsistent
indevelopmentandwerenotfollowedupareunderrepresented
withthereproducibilityoftheAmesassayitself–about85%even
because there is no legal requirement to publish such results.
allowingreplicateteststoresolveborderlinecases.17Italsoallows
Hence the available training data are not representative of the
forthepresenceofspurious‘positive’resultsinthetrainingdata
compoundsforwhichapredictionisdesired.
forcompounds(e.g.,ALSinhibitors)thatmaystressthebacteria
MostliteraturecompilationsgeneratedforbuildingQSARmod-
usedinthetest.Onecanadjustthatthresholdifamoreorless
elsareskewedintheotherdirection,oftenbeingunrepresenta-
conservativecriterionisdesired.Itmakessense,forexample,to
tively balanced because the model-building tools used tend to
applyalessstringentfilterearlyindiscoverywhenlargenumbers
workpoorlyonunbalanceddata.Thisisunfortunate,asweighting
ofcompoundsarebeingconsidered.
thecontributionstotheoutput(‘objective’)function14,15byclass
In a ‘real’ Ames test, assays are replicated and borderline or
sizecangenerallyaccommodateclassimbalanceswithout‘down
ambiguous results prompt a retest. The equivalent for in silico
sampling’thelargerclasstoartificiallybalancethetrainingdata.
‘assays’istoattachaconfidenceoruncertaintytoeachprediction.
MultipleassayoutcomescontributetoanAmestestresult:five
Having some such a measure attached to each prediction is
bacterialstrainsbearingdifferentinactivatingmutationsinthetar-
especiallyimportantforcaseswhereonlyoneofthetenmodels
get gene are checked for reversion when treated with the test
returnsa‘positive’prediction.
compound alone or with reduced nicotinamide adenine dinu-
Arecentpublication18 providesamorethoroughdiscussionof
cleotidephosphate(NADPH)andtheS9microsomalfractionfrom
howthevariousinsilicomodelsforbacterialmutagenicitywork
ratliver.13A‘positive’inanysingleassaymeansthatthecompound
andanevaluationoftheirperformance.
inquestiongetscategorizedas‘mutagenic.’Compilationsofthe
overalloutcome,however,almostalwayscategorizeoutcomesas
3.2 Predictiveuncertainty
simply‘positive’or‘negative.’HencemostofthemanyQSARmod-
Anyinsilicopropertypredictionforanewcompoundshouldnot
elsthathavebeenbuilttopredictthemarebinaryclassification
belimitedtoabsoluteterms.Rather,itshouldcomewithtwoqual-
modelsbuiltonbinarycategorizationdata.Thisunnecessarilydis-
ifications:anestimateofhowreliablethatpredictionislikelytobe
cardspotentiallyusefulinformationaboutmutationeventsspe-
andanindicationofwhetherornotthecompoundisreasonably
cifictooneparticulargenesequence.
similartothecompoundsusedtobuildthemodel–i.e.,whether
InsilicoAmestestsillustratetherangeofapproachesthatcan
itis‘outofscope’becauseitfallsoutsidethemodel’s‘applicability
be used to model toxicity. The Computer Automated Structure
domain’.Thetwokindsofqualificationareconnected,especially
Evaluation(CASE)program(MultiCASEInc.Beachwood,OH,USA;
whentheonlyreliabilityestimateisameasureoftheaveragequal-
http://www.multicase.com),DerekNexus(LhasaLtd;Leeds,United
ityofprediction.
Kingdom;http://www.lhasalimited.org),andtheGenetoxExpert
Forclassificationmodels,performancestatisticslikesensitivity
Alerts in Leadscope’s Non-human Genetic Toxicity Suite Colum-
(the fraction of positive examples that are correctly classified),
bus, OH, USA; (http://www.leadscope.com) are expert systems
specificity(thefractionofnegativesthatarecorrectlyclassified),
that look for particular substructures (‘alerts’, ‘toxicophores’ or
or the positive and negative predictive values (the fraction of
‘biophores’)thathavebeenfoundincompoundsthathavetested
positive and negative predictions that are correct) are typically
positive invitro. In practice, any such ‘guilt by association’ must
reported.Forregressionmodels,themostrelevantstatisticisthe
betemperedbyallowingforthepossibilitythatothersubstruc-
rootmeansquareerrororthemeanabsoluteerror.Thecoefficient
tureselsewhereinthemoleculemayattenuateoreliminatesuch of determination (R2) is often reported as well, but it is very
toxicity. Such programs are broadly applicable but intrinsically
sensitive to the range of the dependent variable and discounts
qualitativeinnature. linearsystematicbiases.Whateverthestatistic,thevalueobtained
Inmanycases,suchsystemsalsoprovideanindicationofthe forthetestset–compoundssetasideandnotusedinanyway
likelymodeofactionforacompoundpredictedtobetoxic,e.g.,by to train the model–is the best indicator of what to expect for
returningstrain-specificAmestestresultsforsimilarcompounds. predictionsmadefornewcompounds.
Theyare‘topdown’inthesensethatthebinarytestresult(positive Thetestsetsensitivitiesforthetencomponentmodelsprovided
ornegative)isthemodeledendpoint,ratherthantheindividual inADMETPredictor8.5fortheinsilicoAmestestarefairlyrepresen-
bacterial strain(s) responsible for that overall result. This can be tativeofusefulclassificationmodels,rangingfrom0.80to0.89.The
unfortunate,astheinformationdiscardedcanprovidecluesabout correspondingspecificitiesrangefrom0.81to0.92.Underlyingdif-
thelikelymechanismofDNAdamageunderlyingthepredicted ferencesinassaysensitivityandinthekindofcompoundstested
mutagenicity. leadtodifferingdegreesofimbalancebetweenpositiveandnega-
‘Statistical’modelstakeadifferentapproach.SarahNexus(Lhasa tiveexamples,withratiosrangingfrom2.2:1to7.3:1.Acuterattox-
Ltd),Leadscope’sGenetoxQSARModels,andADMETPredictor’s icityisafairlytypicalregressionmodel.Therootmeansquareerror
ToxicityModuleallfallintothiscategory.Thefirsttworeturnbinary andmeanabsoluteerrorstatisticsforitare0.59and0.45,respec-
classificationsandmakeuseofsubstructuredescriptors.ADMET tively,onthelogarithmicscaleuponwhichthatmodelwasbuilt.
Predictordiffersinthatitreliesmostlyonmoleculardescriptors. Theformertranslatestoageometricmeanfolderrorof3.9-fold,
Statistical models are typically better able to provide a more whichisfairlytypicalforaninvivotoxicityendpoint.
PestManagSci(2018) ©2018TheAuthors. wileyonlinelibrary.com/journal/ps
PestManagementSciencepublishedbyJohnWiley&SonsLtdonbehalfofSocietyofChemicalIndustry.
www.soci.org RDClark
Figure1.Displayingsometoxicitypredictionsforfourherbicides.Thetwoentriesforpyrasulfatolecorrespondtoitsmajor(hydroxyl)andminor(keto)
tautomers.The‘Target’columnindicatesthemodeofherbicidalaction:4-hydroxyphenylpyruvatedioxygenase(HPPD)inhibition,protoporphyrinogenIX
oxidase(PPO)oracetolactatesynthase(ALS)inhibition.The‘Tox’starplotssummarizerelativetoxicitypredictions.Movingclockwisefrom12o’clock:skin
andrespiratorysensitization(greenandbluewedges);acuterattoxicity(magenta);chronicratandmousetoxicity(yellowandred);hepatotoxicitymarkers
serumaspartatetransaminase,alanineaminotransferaseandlactatedehydrogenase(cyan,purpleandgray,respectively);andmutagenicityrisk(blue).
Cross-hatchedwedgesrepresentpredictionsforout-of-scopecompounds.Thetoxicityriskscoreshowninthe‘TOXRisk’columnrangesfrom0to6;itis
≥2.0for17%ofthecompoundsinareferencesetofpharmaceuticalsfromtheWorldDrugIndex.Mnemonicsshowninthe‘TOXCode’columnindicate
thepotentialliabilityidentified:mutagenicity(‘MUT’),hepatotoxicity(‘HEPX’),chronicmousetoxicity(‘Xm’);andacuterattoxicity(‘rat’).Thetenwedges
shownintheinsilicoAmesmutagenicity(‘AmesMut’)columncorrespondtopairsofpredictions(e.g.,MUT_97+1537andMUT_m97+1537;thelatteris
activatedwithS9microsomalfraction)forthefivestandardmutantgenesequencesassayed.Resultsforfourofassaysarepresentedinthe‘MUT-XXX’
columns.The‘Rat_Acute’columngivesthepredicted50%lethaldoseforacuterattoxicityinunitsofmg/kgbodyweight.Theimagewasgeneratedusing
ADMETPredictor8.5.
Regardlessofwhichperformancestatisticisreported,itcanonly of presenting that much information all at once lies in finding
bereliedontoreflectthetypicaluncertaintyforcompoundsthat effectiveandefficientwaystocommunicatetheresults.Fulldetails
lieclosetoorwithinthemodel’sapplicabilitydomain;allbetsare (e.g., training set sizes, data sources, plots of predicted versus
offforcompoundsthatfallveryfaroutsideofit. observed values) are available in the documentation provided
Applicabilitydomainscanbedefinedbyamolecule’sstructure with most programs, but the interface spreadsheet should pro-
or by its properties. The former approach is based on some videthestructureofeachmoleculeofinterest;abriefdescription
measureofsimilarity,typicallyonebasedonthenumberofshared ofaggregatestatisticsforeachmodel;and–whereavailable–a
substructures. The latter approach is based on how similar the classification confidence estimate. Predictions for out-of-scope
propertiesofthemoleculeofinterestaretothoseofcompounds compounds should be highlighted in such a way that cells can
used in model building. Similarity in this case is determined by becolor-codedwithoutlosinginformation.Confidenceestimates
distanceinthedescriptorspaceorbywhethersomeproperties arenotnecessarilywelldefinedforthosecompounds,soitisbest
of the new compound (usually in the form of descriptors) fall nottoprovideindividualconfidenceestimatesforthem.Thereare
outside the range seen for compounds used to construct the manydifferentwaystoaddresstheneedtointegratedatavisually.
model.Differentmodelsareoftenbuiltfromdifferentdatasetsand
Mostprogramsoptforaspreadsheet,tiledordatapageformat.
canusedifferentdescriptors,soacompoundthatisoutofscope
Somesupportmultipledatapresentationmodes.
foronemodelmaybeinscopeforanother.
Spreadsheetsaremostusefulforconsideringmanypropertiesof
Animplicitassumptionisoftenmadewhenreportingaggregate
manycompoundsatonce,whereastiledviewsarewellsuitedfor
statisticsandanapplicabilitydomainthatallpredictionswithinthe
visualizingstructuraldiversityacrossoneorafewproperties.Data
applicabilitydomainareequallyreliable,whichisdemonstrably
pagesareforanalyzingmanypredictionsforoneorafewcom-
notalwaysthecase.19Itwouldbebettertomakeuseofconfidence
pounds.TheconventionsusedinADMETPredictor’sspreadsheet
modelsthatrelatepredictiveabsoluteorsquarederrorstosome
areillustratedinFig.1.Asistypical,abbreviationsareusedexten-
setofmoleculardescriptors19–21 ortothespreadinpredictions
sivelytomaximizethedensityofinformation.
fromanensemblemodel.14,22Robustmethodsforestimatinghow
Onewaytorelatedisparatepredictionstoeachotherandtovisu-
muchconfidenceoneshouldhaveinanindividualpredictionhave
alizetrendsacrosscompoundsistocolor-codecellsinaspread-
onlyrecentlybecomeanactiveareaofresearch,however,sothey
sheet based on their value, i.e., from red to blue to generate a
arenotyetavailableinsomesoftwarepackages.
‘heat map’. Another is to provide radar or star plots. Using star
plotshasthevirtueofmakingiteasiertoconveypredictedval-
3.3 Puttingpredictionsincontext ues while highlighting any that are out of scope, e.g., by using
Mostprogramsthatmodelmetabolicfateortoxicityreturnpre- hashedfillpatternsforthecorrespondingwedgesratherthansolid
dictions for multiple endpoints (TableS1). ADMET Predictor, for fills(Fig.1).Amorecompletesetofphysicochemicalandtoxico-
example, generates predictions for many distinct toxicity end- logicalpropertypredictionsforthe40agrochemicalsevaluatedis
points.Twenty-threethatareparticularlyrelevanttomammalian providedinTableS2.TheresultsshowninFig.1illustratesomeof
toxicityareshowninTableS2.Oneofthemostchallengingaspects thesubtletiesthatinterpretinginsilicopredictionscaninvolve.The
wileyonlinelibrary.com/journal/ps ©2018TheAuthors. PestManagSci(2018)
PestManagementSciencepublishedbyJohnWiley&SonsLtdonbehalfofSocietyofChemicalIndustry.
Predictingpesticidemetabolitesandtoxicity www.soci.org
4-hydroxyphenylpyruvatedioxygenase(HPPD)inhibitormesotri- conversiontoinnocuousmetabolitescanbeadvantageousifthe
oneisshowninthefirstrowofthespreadsheet.Ithasamutagenic- parentpesticideistoxic.
ityriskof2andsoisflaggedaspotentiallybeingmutagenicinthe Occupational exposure of human beings to intact pesticide
Amesassay,whichitisnot.ThepositivepredictionsforstrainTA97 molecules and their metabolites is dominated by three routes:
tested without S9 activation (‘S97’) and TA102 (‘S102’) are both throughtheskin(dermal),byinhalationandbyingestion.Mod-
weakpositives,however,withconfidencesofonly42and46%. elingthefirsttworoutesaccuratelyrequiresthesortofdetailed
Positivepredictionswithconfidences<50%areallowedinsuch pharmacokinetic simulations carried out late in development.
models because the immediate cost of a false alarm is much Exposure via those routes also typically depends on the physi-
less thanthedownstreamcostofmischaracterizing amutagen. calforminwhichthepesticideisdeliveredtothesubjectrather
Whether it actually merits being resolved by running an Ames thanonthepesticide’smolecularstructure,sotheyaremuchless
assayratherthanbeingtakenatfacevaluedependsonthequality well suited to QSAR modeling. The focus here is on ‘first pass’
andquantityofalternativeleadsavailable. metabolism after ingestion, which includes all enzymatic trans-
Oxyfluorfen (second row) is correctly predicted to be hepato- formations that take place as pesticides and other xenobiotics
toxic(asindicatedbytheentryinthetoxicitycodecolumn)but movethroughtheenterocytesliningthegutand–viatheportal
notmutagenic.23Mutagenicitypredictionsareshowninthe‘Ames vein–throughthelivertothegeneralcirculation.Thesetransfor-
Mut’column.SalmonellastrainsTA98(yellowwedge)andstrain mations fall into three broad categories: hydrolytic, conjugative
TA100(redandcyanwedges)tendtobothbepositiveorbothbe andoxidative.Eachkindoftransformationiscarriedoutbysystems
negative,soeachaddsonly0.5tothepredictedmutagenicityrisk thataregenerallysimilarinplants,pestsandanimalsbutthatcan
scorewheneitherofitstworesults(withorwithoutS9activation) differsharplyintheparticulars.25
ispositive.Thetotalmutagenicityriskscoreis1inthiscaseandno
mutagenicityflagissetinthemutagenicityriskcodecolumn. 4.1 Hydrolyticandconjugativemetabolism
Florasulam(theALSinhibitorshowninthethirdrow)ispredicted Hydrolyticenzymesinvolvedinpesticidemetabolismcanbevery
to elevate serum levels for two liver enzymes: aspartate amino- specificbuttheyusuallyarenot.Inmostcases,theyattackacom-
transferaseandlactatedehydrogenase,representedbythecyan poundbecauseitisdesignedtobehydrolyzednonspecifically,typ-
and gray wedges, respectively. Alanine aminotransferase is pre- icallytounmaskastructuralelement–e.g.,aphenolichydroxyl
dictedtobenormal,however,sonohepatotoxicityflagissetfor grouporcarboxylate–thatisrequiredforadesiredeffectwithin
it;requiringbothaminotransferasestobeelevatedparallelsthe thetargetorganismbutwouldinterferewithabsorption.Estersof
wayclinicallaboratoryresultsareinterpreted.Themutagenicity carboxylicacidsaremorehydrophobicthantheacidsthemselves,
riskforflorasulamisalso0.Twoofthenegativepredictionsareout sotheypenetrateplantcuticlesmorereadily.Thismakesesterifi-
ofscope,however,whichputstheoverallclassificationindoubt; cationapopularwaytocreatepro-pesticides,26,27inpartbecause
therecommendationforitwouldbetoruntheinvitrotest.Besides theenzymesthatcleavethemareubiquitous.Henceestersthat
resolving this particular ambiguity, the data obtained for it and arereadilycleavedinplants,fungiorinsectsarealsolikelytobe
similarout-of-scopecompoundscouldeventuallybeusedtobuild cleavedinthehumangastrointestinaltractorliver.28Thereisrarely
alocalmodelortobuildanewglobalmodelwithanexpanded muchpointintryingtobuildaQSARmodelforsuchareaction.
applicabilitydomain. Thebetterapproachtopro-pesticidesistoidentifyethylandother
TwotautomericformsoftheHPPDinhibitortopramazoneare short-chain,unbranchedesters29and‘cleave’theminsilicobefore
showninthelastrowsofFig.1.Thehydroxylformpredominates applyingQSARmodels.Failuretodosoislikelytogeneratefalse
inaqueoussolution,butasignificantfractionisintheminorform negativesduringdevelopmentprojectsandwhencomparingpre-
at equilibrium. In such a situation it is prudent to consider the dictionstoinvivometaboliteprofilesduringmodelvalidation.Fail-
predictedpropertiesofboth.Bothtautomersarepredictedtoele- uretoaccountforesterasespresentinmicrosomesbyincludinga
vateserumlevelsofaspartateaminotransferase(cyanwedge),but controllackingtheNADPHrequiredbyCYPs,ontheotherhand,is
onlytheminortautomerispredictedtoelevatealaninetransami- likelytoproducefalsepositivesforoxidativemetabolism.
naselevels.Lactatedehydrogenasemustalsobeelevatedforthe Sulfotransferases and UDP-glucuronosyltransferases (UGTs)
hepatotoxicity flag to be set, but its serum levels are predicted are the dominant mammalian metabolic conjugative enzymes
to be normal. Its herbicidal activity is attributable to inhibition for electron-rich groups like alcohols, phenols, carboxylic acids,
of plant HPPD, but that enzyme is also found in mammals. The aminesandaromaticnitrogens.Glutathione-S-transferases(GSTs),
elevatedserumtyrosinelevelsthatHPPDinhibitionproducesin in contrast, act at electron-deficient sites (e.g., in halogenated
rats causes a range of transient toxic effects, albeit non-lethal moleculesandquinones).Theconjugatesproducedbybothkinds
ones.24 This is consistent with the acute rat toxicity flag being of enzyme are more water soluble than the parent compounds
set, as is shown in Fig.1, which is also set for five of 13 other and ‘targeted’ for excretion. Susceptible sites are less common
HPPDinhibitorsexamined(TableS2).Thetoxicityriskscoreof0.76 in pharmaceuticals than in their primary (‘Phase I’) metabolites,
reflectsthefactthatboththepredictedacuterattoxicity(LD50 which has led to the sulfotransferases, UGTs and GSTs being
270mgkg−1; right-most column shown in Fig.1) and the pre- classifiedassecondary(‘PhaseII’)metabolicenzymes.
dictedmousechronictoxicity(LD5030mgkg−1day−1;notshown) This generalization is less applicable to natural products and
areborderlinewhencomparedwithapanelof2270commercial pesticides.Chloroacetamideherbicidesandsafeners,inparticular,
drugs.16 aregoodsubstratesformammalianGSTs.Asithappens,theyand
theirconjugatedmetabolitesarealsosubjecttoCYPoxidationto
quinoniminesthatthenundergoasecondroundofglutathione
4 METABOLICPREDICTIONS conjugationinrats.30,31
Pharmaceuticalsideeffectsareoftenattributabletometabolites Verylittledetailedinformationisavailableregardingthespeci-
ofactiveingredientsratherthanthedrugsthemselves.Thesame ficityofsulfotransferasesandtheirlevelsofexpressiontendtobe
canbetrueforpesticidesandtheircropresidues,butintheircase quitevariable.EnoughinformationisavailabletoconstructQSAR
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modelsforpredictingthoseUGTisoformsforwhichacompound metabolism–CYPs1A2,2C9,2C19,2D6,and3A4.SeeTableS1for
islikelytobeasubstrate,especiallywhentheglucuronideformed thescopeofothersoftwarepackages.Forpredictingbioavailabil-
isstable.Identifyingthesiteofglucuronidationisdifficultwhen ity,itisalsoimportanttopredicttotalCYPactivityinhumanand
themetaboliteispronetorearrangementorspontaneoushydrol- ratlivermicrosomes.Eventhatlevelofdetailfailstofullyaccount
ysis back to the parent. This is a practical as well as a technical for the existence of multiple alleles for some CYPs or variations
challenge:ifaparticularproductiscorrectlypredictedtoformbut inexpressionlevelsacrossethnicgroups.Italsofailstoaddress
spontaneouslyrearrangesorbreaksdownunderassayconditions metabolism in many other species (e.g., cattle37 and other live-
toregeneratetheparentcompound,isthepredictioncorrector stock)thatarerelevanttoagrochemicalpesticidedevelopment.
not? Only data from substrates can contribute to a kinetic QSAR
Unfortunately, other analytical difficulties limit the amount of model, so it is a good idea to create a complementary sub-
usefulkineticinformationavailableforUGTsandmakesthemdiffi- strate/nonsubstrate classification model if possible. It is difficult
culttomodel.28Inparticular,theactivesitesofUGTsareinternally or impossible for a kinetic regression model to ‘learn’ about
oriented(facingthelumen)inlivermicrosomes.29 Henceinvivo molecules that are too big to enter the binding cavity of an
activity often depends on transporters that are inactive in the enzyme,forexample,iftheynever‘see’them.Mostnonsubstrate
microsomalpreparationsusedinvitro,necessitatingtheaddition examplesforCYPsarenecessarilypresumptive,inthattheyhave
ofdetergenttomaketheenzymesaccessibletothesubstrate.This notbeenreportedasasubstratefortheisoformbeingmodeled
distortskineticsandmeansthatmanycompoundsthattest‘pos- buttheyhavebeenreportedtobesubstratesforsomeotherCYP.
itive’ as substrates invitro turn out not to be glucuronidated in Similarly,negativeexamplesforsitesofmetabolismaresitesthat
vivo.Asaresult,mostavailableUGTsite-of-metabolismmodelsare havenotbeenreportedinmoleculesforwhichsomesitehasbeen
basedmostlyonmetabolitesidentifiedthroughinvivoanalysis. reported.Suchabsenceofevidenceisonlycircumstantialevidence
ofabsence,however.Asaresult,itisnotuncommonforseemingly
4.2 Oxidativemetabolism erroneouspredictionsmadebygoodCYPmodelstobeproven
correctafterthefact.34
Mammals have several oxidative enzyme systems for degrad-
The constrained promiscuity of CYPs makes this a particularly
ingxenobiotics,includingaldehydeoxidaseandmonoamineoxi-
importantconsideration.Theconstraintcomesfromanunusual
dase. CYPs are the most broadly active and best studied group
form of evolutionary selection pressure due to what they must
of oxidative enzymes, however, and one which is very relevant
to pesticides.32,33 CYP kinetic behavior can be complex, but is notmetabolize.AmutationthatcausesaCYPtooxidizeglucose,
for example, will make the liver unable to carry out glycolysis
straightforwardenoughthatagreatdealofdataareavailablefrom
andwilldepletetheNAD(P)Hreductantpoolincellswhereitis
which to build models. Many of those data sets are in need of
expressed.Thatwilllikelybefataleveniftheoxidationproduct
extensivecuration,however.TheCYPliteratureisrifewitherrorsas
is innocuous, because it short-circuits gluconeogenesis. Having
aconsequenceofthecomplexnomenclatureofmetabolites,typo-
robustsubstrateclassificationmodelsmakesitpossibletoidentify
graphicalerrorsinactivityunits,anddatafrommutantenzymes
beingascribedtowild-typeenzymes.34Auniquesourceofincon- xenobioticsthatarenotCYPsubstratesbecausethey‘resemble’
privilegedendogenousmolecules,anddosoinawaythatmodels
sistencyistheolderdataobtainedfrombespokerecombinantand
builtonlyupondataforsubstratescannot.
reconstitutedassaysystemsusedbeforerobustcommercialprepa-
rationscontainingasingleCYPisoformandNADPH:cytochrome
P450reductase35becameavailable. 4.3 IdentifyingsitesofCYPmetabolism
Even with those caveats, CYP metabolic transformations are CYP oxidation typically involves direct attack on the lone pair
muchmoreamenabletoQSARmodelingthanarethosecarried of a heteroatom or hydrogen atom abstraction from a carbon.
out by UGTs. Indeed, reasonably ‘good’ predictions are easy in Stability of the free radical produced is necessary for oxidation
someuncomplicatedscenarios.Supposeoneknows,forexample, butitisnotasufficientcondition:thesubstratemustbebound
that a compound is a substrate for CYP1A2, -2C9, -2D6 or -3A4 insuchawaythattheatomtobeoxidizedliescloseenoughto
andthatitisasubstrateforonlyoneofthefour.Onecanassign the activated oxygen bound to the CYP’s heme to react.33 The
the correct isoform most of the time by applying a few simple easeofoxidationperseisafairlylocalizedatomicpropertythatis
rules of thumb in sequence: acids are CYP2C9 substrates; large essentiallythesameforallCYPisoforms.Mostdifferencesbetween
molecules are CYP3A4 substrates; flat (aromatic) molecules are CYPs therefore reflect differences in binding. Nonetheless, the
CYP1A2substrates;andanythingelseisaCYP2D6substrate.For sizeandplasticityofCYPbindingsitesingeneralandofCYP3A
compoundsthatsatisfytheassumptions,thecompoundwillbe isoforms in particular mean that many compounds can bind in
assignedcorrectlymostofthetime.36Suchsimplifiedmodelshave morethanonewaytoeachisoform.Alternativebindingmodes
very narrow ranges of applicability, however. Many compounds subjectsubstratestoattackatmultiplesitesasaresult,whichleads
arenotsubstratesforanyofthosefourCYPsbutaresubstrates tomultiplemetabolitesbeingproduced.
foranotherisoform,andmanymorearesubstratesforatleasttwo Alloftheseconsiderationsmakepredictingsitesofmetabolism
ofthefourisoforms.Moregenerallyapplicablemodelsaremuch by docking alone difficult.9,38 QSAR approaches have been
hardertoconstruct. somewhat more successful.39–41 The result of applying site of
Programs need to make a lot of predictions to cover the rel- metabolism models to the dominant tautomer of pyrasulfotole
evant enzymatic properties for enough metabolic enzymes to (an HPPD inhibitor), for example, is shown in Fig.2. The methyl
be useful. ADMET Predictor, for example, generates over 58 dif- groupsatN1andC3arehighlightedasbeingpotentiallysuscep-
ferent metabolic predictions (TableS3). Of those, 36 come from tibletoattack,asistheunsubstitutedN2.Thisisindicatedbythe
substrate/nonsubstrateclassificationorsite-of-metabolismmod- cross-hatched circles around the atoms in the output and the
els for nine recombinant human CYPs as well as kinetic models arrowsaddedforemphasisinthefigure.
forK ,maximalrateofreaction(V )andintrinsicmicrosomal ThecorrespondingCYPsubstrateclassificationmodels,however,
m max
clearanceforthefiveisoformsthatdominatehumanxenobiotic predictthattheherbicideisnotlikelytobeasubstrateforCYP1A2,
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Figure2.PredictedsitesofmetabolismforpyrasulfotoleforthemajorhumanCYPisoforms.Grayarrowsandcrosshatchingindicatepotentialsitesof
metabolismbyCYPspredictednottoattacktheherbicide,whereasredarrowsandcrosshatchingindicatelikelysitesofmetabolismforisoformsforwhich
pyrasulfotoleispredictedtobeasubstrate.
-2C19 or -2D6. Here, the highlighting is gray for those isoforms Asnotedabove,oneCYPcanattackmultiplesitesonthesame
toindicatethesitespredictedif–contrarytoexpectations–they molecule and different CYPs can attack the same site. In some
aresubstrates,whichcanbehelpfulifdirectassayindicatesthatit cases, attack at different sites can yield the same product. This
hasbeenmisclassifiedbythesubstrate/nonsubstratemodel.The makes it important to integrate the various predictions so as
highlightingisredforCYP2C9andCYP3A4toindicatethattheyare to provide metabolic maps similar to that shown in Fig.3 for
predictedtooxidizetheherbicide.Theoverallpredictionisthatthe thedominanttautomerofpyrasulfotole(themapfortheminor
twomethylgroupsonthepyrazoleringwillbeoxidizedbutthat keto tautomer, which is qualitatively similar, is not shown). It
theunsubstitutednitrogenwillnot. might be possible to recover the metastable N-hydroxymethyl
The C3-hydroxy metabolite M1 is predicted to be nontoxic intermediate, but in most circumstances it will spontaneously
andsubjecttoglucuronidation(notshown).TheN-demethylated eliminateformaldehydetoformM2.Theketotautomershownis
metaboliteM2,incontrast,ispredictedtobehepatotoxicandto predictedtobethemorestableoneforthedesmethylcompound.
haveanacuterat50%lethaldose(LD50)of23mgkg−1.Thatwould Havinganestimatefortheoverallintrinsicclearanceprovides
makeitmorethan20timesmorepotentthatpyrasulfatoleitself, an indication of how rapidly a compound will be metabolized,
which is not predicted to be hepatotoxic (Fig.1). That probably andhavingpredictionsforeachCYPisoformateachsitemakes
reflectstheexposureoftheunsubstitutedhydrazide[C(=O)NHNH] itpossibletoestimatetherelativeproportionsofeachthatwill
groupbydemethylation.Thepredictionmayreflectinhibitionof beproduced.Forpyrasulfotole,theratioofC-hydroxymethylM1
ratHPPD,butitisimpossibletotell;theacuterattoxicitymodelis toN-desmethylproductM2ispredictedtobeabout2:1(Fig.3).
‘top-down’innature,somechanisticdetailsarenotpredicted. Themetabolismschemeshownprovidesasuccinctsummaryof
Otherprogramsusecolor-codedcirclesand/orhalostoconvey datafrom23separateCYPmodels:nineforsubstrateclassification,
nineforsiteofmetabolismidentification,andfiveforclearance
thepredictedsusceptibilitytoattack.Doingsodoesagoodjobof
kinetics.SuchmetabolicmapsforhumanCYPsdonotnecessarily
communicatingthe‘fuzziness’ofsite-of-metabolismpredictions.
translate directly to rats, which have a different complement of
CYPisoforms.Thatsaid,themajorCYPsintherespectivespecies
(CYP3A4andCYP3A1)belongtothesamefamily(3A)andhave
4.4 QuantitativeCYPmetaboliteprediction
somewhatsimilarsubstratespecificities.
Identifying sites of metabolism does not itself predict observ-
This prediction turns out to be consistent with the available
ablemetabolites,becausetheimmediateoxidationproductsare
experimentaldatafromtheTOXNETrecordforpyrasulfotole(AE
oftenunstable.Moreover,itsaysnothingaboutthedistribution
0317309; http://www.TOXNET.nlm.nih.gov, accessed November
ofmetabolites;doingthatrequireskineticpropertypredictions.
2017),whichstatesthat:
Thesecandifferfordifferentsites,althoughtheCYPaffinities(K
m
values)areoftenfairlysimilar.MostK valuesare10𝜇Morabove,
m Following both oral and intravenous administration [in rat],
as is common for promiscuous enzymes. The main exception is
most of the dose was excreted unchanged as AE 0317309.
CYP2D6, which often exhibits sub-micromolar K values for its
m
substrates.
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forintrinsicclearancebyotherenzymeclassesandinotherspecies
Hydroxymethyl AE 0317309, desmethyl AE 0317309, and AE
than human and rat are out of reach unless experimental data
B197555 were observed as minor metabolites in the urine
becomeavailable.Thesameistrueforactivetransportprocesses,
and feces. Following oral dosing, approximately 70% of the
e.g.,inthekidney.
radioactivitywasexcretedintheurineand30%inthefecesby
48or52hr.
Thereportedfractionabsorbed(70%)islessthanthe100%pre- 5 BIOAVAILABILITYANDEXPOSURE
dicted (TableS3). That may reflect errors in one or more of the
Theintrinsicmammaliantoxicityofacompoundanditsmetabo-
propertypredictionsinvolvedoritmayreflectmicrobialdegrada-
litesareimportantconsiderationsinpesticidedevelopment,but
tioninthegut.42,43
theyneedtobeconsideredinlightofthelikelysystemicexpo-
QSAR models are not available for individual CYP isoforms
suretotheparentcompound.Insomecases,thepesticideitself
from rat, but models of overall CYP metabolism by rat liver
isinnocuousbutitsmetabolitesaretoxic.Thissectiondescribes
microsomes are. The small extent of metabolism reported is
howQSARmodelsforADMEpropertiesingeneralandCYPactivity
consistent with the relatively low predicted intrinsic clearance
(16𝜇Lmin−1mg−1 microsomal protein) for rat liver microsomes. inparticularcanbeusedtoestimatehowmuchofanorallydeliv-
eredbolusofpesticideislikelytoreachthegeneralcirculationin
Bindingtoplasmaproteinswillfurtherattenuatemetabolismby
humansandrats.
reducingtheamountoffreeherbicideavailabletodiffuseintothe
Manythingsmustbetakenintoaccountwhenestimatingexpo-
hepatocyteswhereCYPscanactuponit.Thelowpredictedfrac-
sure, including: how pesticide molecules get into an organism
tionunboundinrat(6%)isalsoconsistentwiththemetabolicpro-
(absorption);howtheymovethroughitsbody(distribution);how
fileobtainedexperimentally.
theyarebiochemicallytransformedwithinthebody(metabolism);
AE B197555 is the benzoic acid formed by complete removal
andhowtheyareclearedfromthebody(excretion).EachADME
of the pyrazole ring in pyrasulfotole, possibly via subsequent
oxidation of M2. That possibility can be explored manually propertyisimportant,butitishowtheyinteractthatdetermines
metabolitebymetabolite,butitismoreconvenienttogenerate toxicologicalexposure.Whetheraxenobioticismetabolizedinthe
metabolitesacrossmultiplestepsenmasse;thatmakesiteasyto liverornot,forexample,islargelyirrelevantifitisnotabsorbed
checkeachmetaboliteforpredictedtoxicity.Indoingso,however, fromthegutordistributedthroughthebody.
itisimportanttotakenoteoftheexpectedoverallyieldaswellas Such interactions can be complex but in most cases they
thelikelytotalclearancerate.Metabolitesthatareclearedfaster can be well simulated once the relevant ADME properties
thantheyaregeneratedareunlikelytobeaproblem.Aromatic are known (or can be accurately predicted) by applying
aldehydesgeneratedbyoxidationofhydroxymethylgroupslike physiologically based pharmacokinetic modeling.44,45 Doing
theoneinM1aboveareacaseinpoint.Weretheytoaccumulate, socanalsomakeitpossibletounderstanddifferencesbetween
theywouldlikelyhavesignificanttoxiceffects.Inmostcasesthey toxicity in humans and other mammals37 that result from
do not, because they are rapidly oxidized to the corresponding differences in exposure. GastroPlus™ (Simulations Plus,
(usually innocuous) carboxylic acid by a CYP or by aldehyde Inc.; Lancaster, CA, USA; http://www.simulations-plus.com),
oxidase. SimCyp®(Certara;https://www.certara.com/software/pbpk-mod
The availability of individual quantitative clearance models eling-and-simulation Princeton, NJ, USA) and PK-Sim® (Bayer;
makesitpossibletoevaluatethispossibilityinhumans.Estimates Leverkusen, Germany; http://www.systems-biology.com/) are
Figure3.CYPmetabolitesandyieldspredictedforthe5-hydroxytautomerofpyrasulfotole.ThetotalCYPintrinsicclearanceestimatesforthefivemajor
CYPisoformsinhumanlivermicrosomesandforratlivermicrosomesareindicatedbelowtheparentherbicide.Theparentheticalvaluesalongthereaction
arrowsindicatetheintrinsicclearancesfortheassociatedCYPisoformsforthatpathway.ReactionarrowswithoutCYPannotationsrepresentspontaneous
orenzymaticreactions.ThebenzoicacidmetaboliteshownisexpectedtoresultfromseveralfurtherroundsofoxidationofM2;itwasaddedmanuallyto
thefigure.
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amongtheprogramsavailableforcarryingoutdetailedanalyses predictedacutelytoxicdosesfora250-grat.Itis,however,well
ofsuchprocesses.46,47 abovethesolubilitiesofoxyfluorfen,pinoxaden,isoxaflutoleand
Thecompound-dependentparametersneededforpharmacoki- pyrazolynate, which accounts for their relatively low predicted
neticsimulationscanbeobtainedfromphysicochemicalmeasure- bioavailability.
mentsandbiochemicalassayslateindevelopment,buttheyare Formulationeffects(e.g.,gavagedosingasdispersionsinveg-
unlikelytobeavailableearlieronandinsilicoQSARmodelsmust etableoil)canbecomplicatedandarehardtoaccountformech-
beused.Thebiochemistryisdifferentindifferentspecies,somulti- anistically.Thatsaid,theeffectsarequalitativelysimilartothatof
plemodelsarerequired.Becausetheamountofdatainthepublic bilesalts,whichhavebeentakenintoaccounthere.Moresophisti-
domainislimited,allofthepurelyinsilicomodelsneededareonly catedprogramscanbeusedifmoreexactsimulationsareneeded,
availableforhumanandratatpresent. but commercial formulations are unlikely to be known anyway
Absorption of ingested compounds involves generic physico- earlyindevelopment.
chemical properties like solubility and lipophilicity (logP). Only Limiting solubility probably also underlies the fact that the
passive uptake is considered in the pharmacokinetic simula- pinoxadenislesswellabsorbedthanitsdes-acylhydrolysisprod-
tions provided in ADMET Predictor, which use a streamlined, uct: the latter is more polar but also much more soluble. The
‘high-throughput’ version of the advanced compartmental and relationshipbetweenthiencarbazone,sulfometuronandmesosul-
transitmodelfromGastroPlustopredictthefractionabsorbed(F ) furonandtheirmethylestersismorestraightforward:themore
a
alongthefulllengthofthegastrointestinaltract.Activetransport lipophilicesterisbetterabsorbedwhenitssolubilityisnotlimiting.
canaffectabsorption,buttheonlygeneralmodelsavailableare ThekindofdifferencesseeninpanelsCandDinFig.4illustrate
qualitativeonesthatclassifyacompoundasaninhibitororsub- those to be expected between rat and human absorption and
strate(TableS1).Thedatarequiredforconstructingusefulquan- bioavailability.Theyaresubstantialinmanycases.Suchdiscrep-
titativemodelsarelacking.Fortunately,ignoringthecasewhere anciesshowthelimitstowhichrodentstudiescanbeexpectedto
activetransportismostlikelytooccur–P-glycoproteineffluxin anticipatewhatwillbeseeninhumans,buttheyalsodemonstrate
thegut–yieldsaconservativeestimateofexposure. howmechanisticsimulationscanbeusedtoaccountforthediffer-
Distributionisusuallydominatedbybindingtoproteinsinthe encesbetweenspecies.Moreover,experimentalconfirmationof
plasma,bloodcells,andlipidsinthevariousorgansandperipheral ratsimulationpredictionsforadvancedcandidatemoleculeswill
tissues.Forpesticides,mostsuchbindingisrelativelynon-specific increaseconfidenceinthecorrespondingpredictionsofhuman
andlikelytobenon-saturableatrelevantplasmaconcentrations.It exposure.
isgovernedmostlybylipophilicityandionization–e.g.,whether
amoleculeisneutral,acidic,basicorzwitterionicatphysiological
6 CAVEATS
pH.Itsrelevanceinthekindofsimplescenariosconsideredearly
inagrochemicaldevelopmentmostlyinvolveshowfreelyacom- Models of the kind described here can be very useful, but they
poundcandiffusefromthebloodintotheliver,whereitcanbe need to be applied with some care.48 Their predictive accuracy
metabolized. varies with the chemistry involved, so the best way to assess
Metabolism in such simulations can usually be approximated theirusefulnessforaparticularareaofchemistryistocompare
reasonablywellbyassumingitalltakesplaceintheliver.Inthe theirpredictionstotheobservedvaluesforcompoundsonehas
absence of experimental data, the hepatic clearance is calcu- recently encountered whose properties have been measured.
lated from the total CYP liver microsomal clearance predicted Applicabilitydomaininformationalonemaynotbeadequatefor
for the relevant species. The fraction that reaches the gen- herbicides,whicharenotwellrepresentedinmanyADMETdata
eral circulation after first-pass hepatic extraction is the fraction sets.49 Moreover, training sets are likely to be biased towards
bioavailable(F ). either very well-behaved commercial compounds or notorious
b
PredictedF andF valuesfor31selectedpesticidesareshown oneslikemethylviologenanddioxins,forexample,insteadofthe
a b
inTableS3andFig.4alongwiththoseforsignificantalternative kindofleadcompoundslikelytobeencounteredinagrochemical
tautomeric forms and ester hydrolysis products. Note that the discoveryanddevelopment.
fractionthatsurvivespassagethroughthelivercannotbehigher One should, in principle, be able to assess how well different
thanthefractionabsorbed(F ),soallpointsfallbelowthediagonal modelsperformbycomparingthepredictivestatisticsobtained
a
1:1lineinpanelsAandB. fortestsetsheldbackwhenbuildingthemodels.TableS4gives
SomeofthepredictionsdisplayedinFig.4illustratethereasonit training pool and test set statistics for the site of metabolism
isimportanttoconsiderhowphysicochemicalpropertiesinteract modelsinADMETPredictor.Inpractice,thisdoesnotworkaswell
with dose when evaluating exposure. In particular, oxyfluorfen asonewouldlike,inpartbecausedifferenttestsetsarechosen
absorptionandmetabolism(thedegreetowhichF fallsbelow foreachmodel.Siteofmetabolismmodelssufferfromafurther
b
thediagonal)arequitelowinbothspeciesdespiteitspredicted complication:someauthorsincludeatomswhichcannotbesites
hydrophobicity (logP and logD at pH 7.4), which is shown in of metabolism (e.g., quaternary carbons, carbonyl oxygens or
Table1 along with some other relevant property predictions. A sulfonylgroups)whencalculatingstatistics.Then,too,theymay
morethoroughlistisprovidedinTablesS2andS3.Asexpected, reportunusualperformancemeasuressuchas‘top2’(or‘top3’)
the passive intestinal permeability is predicted to increase with for sites of metabolism: a ‘hit’ is scored if metabolism has been
increasinghydrophobicity,butthisisoffsetinmanycasesbythe reportedatanysiterankedamongthetoprankedtwo(orthree)
concomitantdecreaseinpredictedaqueoussolubility. foramolecule.TableS4includes‘top1’,‘top2’and‘top3’statistics
The rate of absorption is proportional to the product of the toillustratetheeffectthedifferentcriteriahaveonthestatistics
concentration of compound free in solution and its effective obtained.
permeability.ThesimulationresultsshowninFig.4arebasedon Comparisonsbetweenprogramsarebestdonebythirdparties
dosesof0.1mgin2.5mLforratsand10mgin250mLforhumans, whohaveaccesstomostoralloftheprogramsthatareavailable.
i.e.,anominalconcentrationof40𝜇gmL−1.Thisiswellbelowthe Ideallythevalidationdatausedrepresentmeasurementscarried
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Figure4.High-throughputpharmacokineticsimulationresultsforselectedagrochemicalsandtheirhydrolysisproductsdosedat100𝜇gin2.5mLofwater
(rat)orat10mgin250mLofwater(human).Gastrointestinalabsorptionwasmodeledusingtheadvancedcompartmentalabsorptionsystemdeveloped
forGastroPluswithsubsequentlivermetabolismcalculatedonthebasisoftheratorhumanintrinsicclearancemodelsinADMETPredictor8.5andrenal
clearancecalculatedfromtheglomerularfiltrationrate.(a)Estimatedpercentbioavailable(F )asafunctionoftheexpectedpercentabsorbed(F )ina
b a
fasted250-grat.(b)EstimatedF asafunctionofF ina70-kgfastedhumanmale.(c)EstimatedhumanF asafunctionofestimatedratF .(d)Estimated
b a a a
humanF asafunctionofestimatedratF .
b b
outwiththesameassayandunderidenticalprotocols.Moreover, molecular structure of each ingredient and its metabolites to
thedataneedtonotalreadybeinthepublicdomain;otherwise physicochemical properties and biological activities–or lack
some programs will have already incorporated the values to a thereof–independently.Theytakenoaccountofformulationor
greaterorlesserdegreeduringtraining,i.e.,somewillhavealready interactionsbetweencomponents.Theabilityofsafenerstopro-
‘seen’thecompounds. tectcropplantsfromherbicideswhentheyareappliedtogether
Independentcomparativevalidationsaretechnicallydemand- oftenreflectssharedroutesofmetabolism,e.g.,byformationof
ingbutfeasibleforpropertiesthatarehardenoughtodetermine glutathione conjugates or metabolism by plant CYPs.27,56 Such
accuratelythattheyhavenotbeenmeasuredcarefullyformany interactionscaninmanycasesbesimulatedquitewellinmam-
existingcompounds(e.g.,pK ,50–52solubility,53andlogP54).Unfor- malian pharmacokinetic analyses57 and should be taken into
a
tunately, ‘naïve’ toxicological and metabolic data for mammals accountwhenpossible.
aremuchmoredifficulttoobtain.Dataobtainedduringpesticide
development will be published in a regulatory filing if a com-
poundiscommercializedandwillbekeptconfidentialifitisnot.
7 CONCLUSION
Accessibleunpublisheddataarevirtuallynonexistentasaresult,
which makes doing meaningful toxicity prediction comparisons Acompleteprospectiveaccountingoflikelypesticidemetabolism
extremelydifficult.55 isacomplexundertakingthatrequiresgeneratingawiderange
Finally, existing in silico prediction software handles mixtures ofpredictions.ManyoftheQSARmodelsneededformakingthe
as though their components do not interact, relating the required physicochemical and CYP metabolism predictions are
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