Improved prediction of fish bioconcentration factor of Hydrophobic Chemicals

Full Terms & Conditions of access and use can be found at /action/journalInformation?journalCode=gsar20Download by: [South China University of Technology]Date:15 June 2016, At: 07:01SAR and QSAR in Environmental ResearchISSN: 1062-936X (Print) 1029-046X (Online) Journal homepage: /loi/gsar20Improved prediction of fish bioconcentration factor of Hydrophobic ChemicalsJ.C. Dearden & N.M. ShinnaweiTo cite this article: J.C. Dearden & N.M. Shinnawei (2004) Improved prediction of fish bioconcentration factor of Hydrophobic Chemicals, SAR and QSAR in Environmental Research,15:5-6, 449-455, DOI: 10.1080/10629360412331297489To link to this article:/10.1080/10629360412331297489Published online: 29 Oct 2010.Submit your article to this journalArticle views: 49View related articles Citing articles: 22 View citing articlesIMPROVED PREDICTION OF FISH BIOCONCENTRATION FACTOR OF HYDROPHOBIC CHEMICALS*J.C.DEARDEN †and N.M.SHINNAWEI School of Pharmacy and Chemistry,Liverpool John Moores University,Byrom Street,Liverpool L33AF ,England,UK (Received 13May 2004;In final form 9July 2004)Using a large heterogeneous data-set of 640organic chemicals,we have developed predictive Quantitative Structure–Activity Relationship models for fish bioconcentration factor (BCF).For 539chemicals with a log K ow (octanol–water partition coefficient)range of 22.3to 6.0,we developed a model with r 2¼0:664and a standard error of 0.661;the primary descriptor was log K ow ,and others were polarisability,number of amino groups,hydrogen bond acceptor ability and a molecular shape factor.For 101chemicals with a log K ow range of 6.0–12.7,we developed a model with r 2¼0:710and a standard error of 0.777;the descriptors were aqueous solubility (reflecting the importance of this property in governing uptake from aqueous solution),polarity,polarisability,hydrogen bond donor ability and molecular size.Bearing in mind the very great range of BCF values of highly hydrophobic chemicals,our model offers good predictivity of this important environmental property.Keywords :Bioconcentration factor;Prediction;Partition coefficient;Aqueous solubility;Hydrogen bonding;Molecular size INTRODUCTION The risk that environmental pollutants pose is a function of a number of factors,one of which is the ability of a chemical to be taken up by an organism from the surrounding milieu.Oneimportant uptake route is by fish and other aquatic species from aqueous solution;it is assumed that such uptake eventually reaches steady state,owing to metabolism and excretion of the chemical by the organism.The ratio of concentrations in the organism and in the aqueous phase at steady state is termed the bioconcentration factor (BCF),and has been measured for many hundreds of chemicals:BCF ¼Concentration of Chemical in Organism at Steady StateConcentration of Chemical in Water ð1ÞISSN 1062-936X print/ISSN 1029-046X online q 2004Taylor &Francis LtdDOI:10.1080/10629360412331297489*Presented at the 11th International Workshop on Quantitative Structure–Activity Relationships in the Human Health and Environmental Sciences (QSAR2004),9–13May 2004,Liverpool,England.†Corresponding author.E-mail:j.c.dearden@SAR and QSAR in Environmental Research ,V ol.15(5–6),October–December 2004,pp.449–455D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016Since over 100,000chemicals are released into the environment,it is clearly advantageous if BCF values can be predicted rather than measured,for reasons of cost,speed and animal use.There have in fact been numerous attempts to predict BCF values using quantitative structure–activity relationship (QSAR)approaches,and these have recently been reviewed by Dearden [1].In Eq.(1),BCF equates approximately to a distribution constant,and this suggests that BCF can be modelled by a partition coefficient such as the octanol–water partition coefficient K ow .Numerous studies have in fact shown that to a first approximation this is so.For example,Veith and Kosian [2]found,for a large set of diverse chemicals,the following QSAR:log BCF ¼0:79log K ow 20:40n ¼122r 2¼0:865s ¼not given ð2Þwhere n is the number of chemicals in training set,r is the correlation coefficient,and s is the standard error of the estimate.Devillers et al.[3]found that several QSAR models based on log K ow gave similar predictions,for chemicals with log K ow values ,6.However,it is important to note that for very hydrophobic chemicals ðlog K ow .6Þthe rectilinear correlation between log BCF and log K ow often fails,with many chemicals having measured log BCF values much lower than would be predicted by QSARs such as Eq.(2).Nendza [4]has given several reasons for these low BCF values:the test period may be too short for steady state to be achieved,metabolism and/or chemical degradation may occur,large molecules may not penetrate well through membranes into the organism,there may be considerable inaccuracy in the measurement of BCF and K ow values for very hydrophobic chemicals,the octanol–water solvent pair may not be a good surrogate for lipid–water for very hydrophobic chemicals,there may be specific sub-structural effects such as were reported for 2,4-dinitrophenols [5]and aqueous solubility may be too low to allow adequate uptake by the organism.Several workers have attempted to correct for these,for example by the use of correlations that are biphasic in log K ow [6,7]by the use of separate QSARs for different log K ow ranges,together with structural correction factors [8]and by the inclusion of a molecular diameter descriptor [9].Dearden [1]has suggested that hydrophobic chemicals capable of hydrogen bonding have lower BCF values than predicted by correlations such as Eq.(2).The BCFWIN software (freely available from /oppt/exposure/docs/episuitedl.htm)for theprediction of log BCF incorporates the approach of Meylan et al.[8].Other approaches have utilised molecular connectivities [10,11],linear solvation energy relationships [12,13],aqueous solubility [14,15],theoretical molecular descriptors [16]and fragment constants and structural correction factors [17].It thus appears that factors such as aqueous solubility,molecular size,hydrogen bonding ability (and,by extension,polarity and polarisability)and ability to metabolise may play a part in lowering BCF values of very hydrophobic chemicals.We have therefore examined whether or not we could improve the QSAR prediction of BCF values by incorporating descriptors accounting for the above factors.So far as we are aware,hydrogen bonding descriptors have not previously been used in the QSAR prediction of BCF values.METHODSA data-base containing almost 700organic chemicals with measured fish log BCF values and mostly measured log K ow values was very kindly supplied by Syracuse Research Corporation.J.C.DEARDEN AND N.M.SHINNAWEI450D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016A number of the chemicals were organometallics,and others were charged species.These were deleted,save for salts of simple acids and bases which were converted to their free acid or base forms,because some of the software that we used could not handle such structures.Our final data-base comprised 647chemicals.Descriptors were calculated using QsarIS ver.1.1(now MDL QSAR:),TSAR ver.3.3()and HYBOT (www.ibmh.msk.su/molpro.hybot.html);aqueous solubilities were calculated using WSKOWWIN (/oppt/exposure/docs/episuitedl.htm).A total of 203descriptors was calculated,including a number of molecular connectivities,since these are information-rich.The step-wise linear regression routine in MINITAB ver.13()was used to select the descriptors that best modelled log BCF.However,as noted above,deviations from the rectilinear log BCF–log K ow model become much more pronounced for chemicals with log K ow values .6(see Fig.1).We therefore treated chemicals with log K ow .6separately from those with log K ow ,6:RESULTS AND DISCUSSIONChemicals with log K ow >6The correlation of log BCF with log K ow is extremely poor for this group of chemicals,as has been noted by other workers [4]:log BCF ¼0:412log K ow þ5:74n ¼107r 2¼0:217Q 2¼0:195s ¼1:269F ¼29:4ð3Þwhere Q is the cross-validated correlation coefficient (leave-one-out procedure)and F is the Fisher statistic.Step-wise regression yielded a much-improved five-descriptor correlation with r 2¼0:598and s ¼0:930:There were,however,six outliers with predicted values more than two standard deviations from the measured values (acid red-114,a -chlordane,1,2-dibromodecane,2-fluorobutanamide-5-[2-(2,4-di-t -amylphenoxy)butanamide]phenol,2,20,4,40,5,50-hexa-chlorobiphenyl and p -terphenyl),and no consistent chemical explanations could befound FIGURE 1Correlation of log BCF with log K ow for 647organic chemicals.PREDICTION OF FISH BCF 451D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016to account for these.Nevertheless,because of the considerable error known [4]to be involved in the measurement of BCF,we felt justified in removing them on the basis of putative experimental error;we believe this to be acceptable for a small proportion of the data-set,especially when the data are taken from numerous sources.It may be noted that,for example,other chlorinated and brominated hydrocarbons (including polychlorinated biphenyls)and other polyaromatic hydrocarbons in the data-set did not appear as outliers.The same five descriptors were used,which yielded the following correlation:log BCF ¼0:245log S aq þ0:512HDC max 20:184DPM Y 20:0156Pol WMZZ 20:0380nAtoms þ7:23n ¼101r 2¼0:710Q 2¼0:675s ¼0:777F ¼46:5ð4Þwhere S aq ¼aqueous solubility (M),HDC max ¼maximum hydrogen bond donor free energy factor,DPM Y ¼dipole moment Y vector,Pol WMZZ ¼whole-molecule polarisability in ZZ plane,and nAtoms ¼number of atoms (including hydrogen).All terms were significant at better than the 0.01level.The log K ow range of the chemicals was 6.0–12.7.The correlation is shown graphically in Fig.2.It is interesting to note that aqueous solubility replaces partition coefficient in Eq.(4).This confirms that poor aqueous solubility of highly hydrophobic chemicals is a significant factor in the low BCF values frequently observed.Aqueous solubility is not simply acting as a surrogate for partition coefficient here;firstly,the two properties are inversely correlated [18],whereas in Eq.(4),log S aq has a positive coefficient,as does log K ow in many published QSARs for bioconcentration prediction [1]and secondly log S aq and log K ow are not a syzygy in our data-set,being only poorly correlated ðr 2¼0:661Þ:Factors that increase intermolecular interaction (hydrogen bonding and polarity)lower the bioconcentration factor (HDC max values are all negative);this suggests either that such factors encourage molecules to remain in the aqueous phase,or that these factors cause binding to membranes and thereby hinder penetration into the organism.It should be noted that polarity terms other than the Y-vector of dipole moment (including the whole-molecule dipole moment)also gave good correlations;we chose to use the Y-vector because it gave the best results,albeit by only a smallincrement.FIGURE 2Measured versus predicted log BCF values for 101chemicals in log K ow range 6.0–12.7.J.C.DEARDEN AND N.M.SHINNAWEI452D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016Polarisability,reflecting dispersive interactions,would be expected to increase uptake into the pinguid phase.Since polarisability is inversely related,albeit weakly,to aqueous solubility,the negative coefficient of Pol WMZZ may be a consequence of the importance of aqueous solubility in controlling BCF values of highly hydrophobic chemicals.As with the polarity term,polarisability descriptors other than Pol WMZZ (including whole-molecule polarisability)yielded correlations that were almost as good.Molecular size,represented by the total number of atoms in a molecule,lowers BCF,as was noted previously [9].The second-order valence molecular connectivity term used by Sabljic ´[10]to model BCF is also a size descriptor.We validated Eq.(4)by removing 25%of the chemicals from the training set,re-developing the QSAR and using it to predict the log BCF values of the 25chemicals removed;we found that observed and predicted values were well correlated ðn ¼25,r 2¼0:650;s ¼0:881Þ:We conclude that Eq.(4)is a mechanistically valid QSAR with good predictive ability for BCF values of highly hydrophobic chemicals in the log K ow range 6.0–12.7(bearing in mind the errors in their measurement).Chemicals with log K ow <6It is clear from Fig.1and Eq.(5)that even for chemicals with log K ow ,6;the correlation of log BCF with log K ow is far from perfect:log BCF ¼0:475log K ow þ0:237n ¼540r 2¼0:550Q 2¼0:546s ¼0:765F ¼658:7ð5ÞTo some extent this must be due to the fact that experimental error on measured BCF values can range over several orders of magnitude [4].However,some of the aforementioned factors that could affect BCF values of very hydrophobic chemicals could also affect chemicals with lower hydrophobicity,albeit probably to a lesser extent.That this is so seems to be borne out by the QSAR correlation that we have found,with the omission of one outlier (ethylenediamintetraacetic acid):log BCF ¼0:397log K ow þ0:00925Pol WM þ0:398nNH 220:435HAE maxþ0:147d 4x v p þ0:740n ¼539r 2¼0:664Q 2¼0:655s ¼0:661F ¼210:4ð6Þwhere Pol WM is mean whole-molecule polarisability,nNH 2is the number of amino groups,d 4x v p is the difference from 4th order valence path molecular connectivity of a chemical to that of a chemical with the same atoms in a straight chain,and HAE max is the maximum hydrogen bond acceptor enthalpy factor.All terms were significant at better than the 0.001level.The log K ow range of the chemicals was 22.3to 6.0.The correlation is shown graphically in Fig.3.From Eq.(6),polarisability increases BCF,as would be expected (vide supra ).The number of amino groups may reflect hydrogen bond donating ability,although it does not correlate with any of the other hydrogen bond donor descriptors that we used;we suggest that it may represent a specific amino group interaction which increases BCF.Amino groups are present in 68(12.6%)of the 539chemicals modelled by Eq.(6).Hydrogen bond acceptor ability lowers BCF.This is to be expected,since one would expect such interactions to encourage a molecule to remain in the aqueous phase.PREDICTION OF FISH BCF 453D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016The d 4x v p term is interesting.As stated earlier,it is the difference of 4th order valence path molecular connectivity of a chemical and that of its straight-chain (linear)equivalent containing identical atoms.It thus models molecular shape.The descriptor bears a positive coefficient,but since d 4x v p values are negative,deviations from molecular linearity give rise to lower BCF values.This suggests that straight-chain molecules are better able to penetrate membranes,which intuitively seems reasonable,although Dimitrov et al.[9]found that effective cross-sectional molecular diameter did not correlate so well with BCF values as did maximal cross-sectional molecular diameter.We validated Eq.(6)by removing 25%of the chemicals from the training set,re-developing the QSAR and using it to predict the log BCF values of the 135chemicals removed;we found that observed and predicted values were well correlated ðn ¼135,r 2¼0:637;s ¼0:617Þ:We conclude that Eq.(6)is a mechanistically valid QSAR with good predictive ability for BCF values of chemicals in the log K ow range 22.3to 6.0(bearing in mind the errors in their measurement and the extremely diverse nature of the 539chemicals used to derive Eq.(6)).CONCLUSIONSUsing a large data-base of 640organic chemicals,we have developed two mechanistically-based QSARs for the prediction of fish log BCF values,covering log K ow ranges 22.3to 6.0and 6.0–12.7,with standard errors of 0.661and 0.777,respectively.Bearing in mind the errors inherent in the measurement of BCF values,these correlations permit the reasonably accurate estimation of log BCF values even for highly hydrophobic chemicals,and throw light on the processes involved in bioconcentration.Only two previous studies of BCF prediction have utilized very large data-sets.The model of Meylan et al.[8]used 694chemicals and yielded r 2¼0:73and mean error ¼0.48,using log K ow and unspecified indicator variables.However,their data had to be divided into 12separate sub-sets in order for this level of prediction to be achieved,and their method offers no mechanistic insight.The model of Dimitrov et al.[7]utilized 443narcoticchemicalsFIGURE 3Measured versus predicted log BCF values for 539chemicals in log K ow range 22.3to 6.0.J.C.DEARDEN AND N.M.SHINNAWEI454D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016only,and yielded,from a Gaussian-type correlation with log K ow ,r 2¼0:73and s ¼0:65;which is comparable to our results.Again,this model offers no mechanistic insight.AcknowledgementsWe are grateful to Drs P.H.Howard and W.M.Meylan of Syracuse Research Corporation for supplying us with their fish BCF data-base.References [1]Dearden,J.C.(2004)“QSAR modeling of bioaccumulation”,In:Cronin,M.T.D.and Livingstone,D.J.,eds,Predicting Chemical Toxicity and Fate (CRC Press,Boca Raton,FL),pp 333–355.[2]Veith,G.D.and Kosian,P.(1983)“Estimating bioconcentration potential from octanol/water partition coefficients”,In:Mackay,D.,Paterson,S.,Eisenreich,S.J.and Simmons,M.S.,eds,Physical Behavior of PCBs in the Great Lakes (Ann Arbor Science,Ann Arbor,MI),pp 269–282.[3]Devillers,J.,Bintein,S.and Domine,D.(1996)“Comparison of BCF models based on log P”,Chemosphere 33,1047–1065.[4]Nendza,M.(1998)Structure-Activity Relationships in Environmental Sciences (Chapman &Hall,London,UK),p.135.[5]Deneer,J.W.,Sinnige,T.L.,Seinen,W.and Hermens,J.L.M.(1987)“Quantitative structure-activity relation-ships for the toxicity and bioconcentration factor of nitrobenzenes towards the guppy (Poecilia reticulata )”,Aquat.Toxicol.10,115–129.[6]Bintein,S.,Devillers,J.and Karcher,W.(1993)“Nonlinear dependence of fish bioconcentration on n -octanol/water partition coefficient”,SAR QSAR Environ.Res.1,29–39.[7]Dimitrov,S.D.,Mekenyan,O.G.and Walker,J.D.(2002)“Non-linear modelling of bioconcentration using partition coefficients for narcotic chemicals”,SAR QSAR Environ.Res.13,177–184.[8]Meylan,W.M.,Howard,P.H.,Boethling,R.S.,Aronson,D.,Printup,H.and Gouchie,S.(1999)“Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient”,Environ.Toxicol.Chem.18,664–672.[9]Dimitrov,S.D.,Dimitrova,N.C.,Walker,J.D.,Veith,G.D.and Mekenyan,O.G.(2002)“Predicting bioconcen-tration factors of highly hydrophobic chemicals.Effects of molecular size”,Pure Appl.Chem.74,1823–1830.[10]Sabljic ´,A.(2001)“QSAR models for estimating properties of persistent organic pollutants required in evaluation of their environmental fate and risk”,Chemosphere 43,363–375.[11]Lu,X.,Tao,S.,Hu,H.and Dawson,R.W.(2000)“Estimation of bioconcentration factors of non-ionic organic compounds in fish by molecular connectivity indices and polarity correction factors”,Chemosphere 41,1675–1688.[12]Park,J.H.and Lee,H.J.(1993)“Estimation of bioconcentration factor in fish,adsorption coefficient for soils and sediments and interfacial tension with water for organic nonelectrolytes based on the linear solvation energy relationships”,Chemosphere 26,1905–1916.[13]Ivanciuc,O.(1998)“Artificial neural networks applications.Part 7.Estimation of bioconcentration factors infish using solvatochromic parameters”,Rev.Roum.Chim.43,347–354.[14]Isnard,P.and Lambert,S.(1988)“Estimating bioconcentration factors from n -octanol/water partitioncoefficient and aqueous solubility”,Chemosphere 17,21–34.[15]Jørgensen,S.E.,Halling-Sørensen, B.and Mahler,H.(1998)Handbook of Estimation Methods inEcotoxicology and Environmental Chemistry (Lewis Publishers,Boca Raton,FL),p.65.[16]Gramatica,P.and Papa,E.(2003)“QSAR modelling of bioconcentration factor by theoretical moleculardescriptors”,QSAR Comb.Sci.22,374–385.[17]Tao,S.,Hu,H.,Xu,F.,Dawson,R.,Li,B.and Cao,J.(2001)“QSAR modeling of bioconcentration factors infish based on fragmental constants and structural correction factors”,J.Environ.Sci.Health B 36,631–649.[18]Yalkowsky,S.H.and Valvani,S.C.(1980)“Solubility and partitioning I:solubility of nonelectrolytes in water”,J.Pharm.Sci.69,912–922.PREDICTION OF FISH BCF 455D o w n l o a d e d b y [S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ] a t 07:01 15 J u n e 2016。