Quantitative Predictive Methods On Octanol-Air Partition Coefficient (K_OA) Of Toxic Organic Pollutants

The octanol-air partition coefficient(K_OA) is a key physicochemical parameter for describing the partition of toxic organic pollutants between air and environmental organic phases.K_OA plays an important role in evaluating the global distribution,transport and biomagnification of toxic organic compounds.Experimental determination of K_OA is costly, time-consuming,and restricted by lack of sufficiently pure chemicals,thus there is a need to develop a simple and accurate method to estimate K_OA.In the current thesis,fragment constant models and 3D- quantitative structure-activity relationship(QSAR) model based on Dragon descriptors were developed and five predictive methods of K_OA were evaluated and compared.Firstlyly,a fragment constant method based on 5 fragment constants and 1 structural correction factor,was developed for predicting logK_OA at temperatures ranging from 10℃to 40℃.As aromatic compounds that contain C,H,O,Cl and Br atoms,were included in the training set for model development,the fragment constant model can be applied to a wide range of chlorinated and brominated aromatic pollutants,such as chlorobenzenes(CBs), polychlorinated naphthalenes(PCNs),polychlorinated biphenyls(PCBs),polychlorinated dibenzo-p-dioxins and dibenzofurans(PCDD/Fs),polycyclic aromatic hydrocarbons(PAHs), and polybrominated diphenyl ethers(PBDEs),all of which are typical persistent toxic substance(PTS).It can be inferred from internal validation(Jackknife test) and external validation that the fragment constant models have good predictive ability and robustness. Compared to QSAR model based quantan chemical descriptors,the present model has the advantage that it is much easier to implement.Secondly,in order to expand the utility of the fragment constant method,272 experimental values of logK_OA at 25℃were collected from literatures.The K_OA data set included a wide range of compound classes,such as PCBs,CBs,PCNs,PCDD/Fs,PBDEs,PAHs, organochlorine pesticides,hydroxy alkyl nitrates,polyfluorinated sulfonamide, sulfonamidoethanols,telomere alcohols,halogenated hydrocarbons,alcohols,ketones, aldehydes,acids,esters and ethers etc.Based on training set of the 272 compounds,the fragment constant model was developed for predicting logK_OA values at the ambient temperature(25℃).The best combination of 23 atom centered fragments was selected by stepwise regression-partial least squares(SR-PLS) variable selection method.For the training set,R² = 0.977 and root mean square error(RMSE) =0.43.Internal and external validation indicated that the fragment constant model was ideal for predicting logK_OA of new compounds within application domain(AD).This fragment constant model could be used to estimate K_OA for a wide set of heterogeneous organic compounds at 25℃.Thirdly,based on the training set including 272 compounds,3D-QSAR model for predicting logK_OA was developed using Dragon descriptors.The best combination of 0～3 dimension(D) Dragon descriptors was selected by SR-PLS variable selection method.The optimal model contained nine descriptors(Xlsol,GATS2p,C006,C025,H050,Mor04p,L3s, C005,N072),leading to R²=0.982 and RMSE=0.38.It was concluded from the optimal QSAR model that the main factors governing K_OA are dispersive interactions in octanol solution,potential of hydrogen bond,3D structural feature of symmetry and shape, distribution of conjugated electric charge.Internal and external validation has shown the 3D-QSAR model with good robustness and good predictive power for compounds within AD.Currently,there are five predictive methods for K_OA:(a) the fast estimation method that employ the octanol-water partition coefficients(Kow) and Henry's law constant(K_H),(b) a direct method by computing the solvation free energy of organic chemical molecules in octanol(â–³Gs) using quantum chemical solvent models,(c) the QSAR model that employ quantum chemical descriptors,(d) the fragment constants model,(e) the QSAR model based on Dragon descriptors.For the five predictive methods for K_OA,models were evaluated and compared by goodness of fit,robustness and prediction power,AD and algorithms.The K_OW-K_H method had a broader AD,but it was limited by the lack of sufficient data of K_OW,K_H and their temperature dependence.Besides,experimental or estimative errors in K_OW and K_H values could be propagated or magnified due to the division.Theoretically the AD of theâ–³Gs method could be extended to every compound;nevertheless the method relied on the accurate calculation ofâ–³Gs.The quantum chemical descriptors of the QSAR method are favorable for mechanistic interpretation,and this method could identify isomers(high resolution).The fragment constant model was a simple and transparent method,but its AD was restricted by the coverage of the training set.The 3D-QSAR model based on Dragon descriptors gave more accurate predictions for the compounds within the AD,and had high resolution for isomers.