Advertisement

Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool “PaDEL-Descriptor”

  • 471 Accesses

  • 7 Citations

Abstract

Predictive regression-based models for bioconcentration factor (BCF) have been developed using mechanistically interpretable descriptors computed from open source tool PaDEL-Descriptor (http://padel.nus.edu.sg/software/padeldescriptor/). A data set of 522 diverse chemicals has been used for this modeling study, and extended topochemical atom (ETA) indices developed by the present authors’ group were chosen as the descriptors. Due to the importance of lipohilicity in modeling BCF, XLogP (computed partition coefficient) was also tried as an additional descriptor. Genetic function approximation followed by multiple linear regression algorithm was applied to select descriptors, and subsequent partial least squares analyses were performed to establish mathematical equations for BCF prediction. The model generated from only ETA indices shows importance of seven descriptors in model development, while the model generated from ETA descriptors along with XlogP shows importance of four descriptors in model development. In general, BCF depends on lipophilicity, presence of heteroatoms, presence of halogens, fused ring system, hydrogen bonding groups, etc. The developed models show excellent statistical qualities and predictive ability. The developed models were used also for prediction of an external data set available from the literature, and good quality of predictions (R 2 pred = 0.812 and 0.826) was demonstrated. Thus, BCF can be predicted using ETA and XlogP descriptors calculated from open source PaDEL-Descriptor software in the context of aquatic chemical toxicity management.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Ahrens A, Traas TP (2007) Environmental exposure scenarios: development, challenges and possible solutions. J Expo Sci Environ Epidemiol 17:S7–S15. doi:10.1038/sj.jes.7500602

  2. Arnot JA, Gobas FAPC (2003) A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR Comb Sci 22(3):337–345. doi:10.1002/qsar.200390023

  3. Arnot JA, Gobas FAPC (2006) A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ Rev 14(4):257–297. doi:10.1139/a06-005

  4. Arnot JA, Mackay D, Parkerton TF, Bonnell M (2008) A database of fish biotransformation rates for organic chemicals. Environ Toxicol Chem 27(11):2263–2270. doi:10.1897/08-058.1

  5. CAESAR. http://www.caesar-project.eu. Accessed 23 Oct 2013

  6. Cerius2 version 4.10. Cerius 2 Version 4.10 is a product of Accelrys Inc., San Diego, CA

  7. CHEMPROP. http://www.ufz.de/index.php?en=6738. Accessed 23 Oct 2013

  8. Dearden JC, Hewitt M (2010a) QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors. SAR QSAR Environ Res 21(7–8):671–680. doi:10.1080/1062936X.2010.528235

  9. Dearden JC, Hewitt M (2010b) QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors. SAR QSAR Environ Res 21(7–8):671–680. doi:10.1080/1062936X.2010.528235

  10. Devier MH, Augagneur S, Budzinski H, Mora P, Narbonne JF, Garrigues P (2003) Microcosm tributyltin bioaccumulation and multibiomarker assessment in the blue mussel Mytilus edulis. Environ Toxicol Chem 22(11):2679–2687. doi:10.1897/02-413

  11. Dimitrov S, Breton R, MacDonald D, Walker JD, Mekenyan O (2002) Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites. SAR QSAR Environ Res 13(3–4):445–455. doi:10.1080/10629360290014313

  12. Dimitrov S, Dimitrova N, Parkerton T, Comber M, Bonnell M, Mekenyan O (2005) Base-line model for identifying the bioaccumulation potential of chemicals. SAR QSAR Environ Res 16(6):531–554. doi:10.1080/10659360500474623

  13. Dougherty ER, Barrera J, Brun M, Kim S, Cesar RM, Chen Y, Bittner M, Trent JM (2002) Inference from clustering with application to gene-expression microarrays. J Comput Biol 9(1):105–126. doi:10.1089/10665270252833217

  14. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi-and megavariate data analysis: principles and applications, 2nd edn. Umetrics Academy, Umetrics, Umea, Sweden

  15. Everitt B, Landau S, Leese M (2001) Cluster Analysis. Arnold, London

  16. Fernandez A, Lombardo A, Rallo R, Roncaglioni A, Giralt F, Benfenati E (2012) Quantitative consensus of bioaccumulation models for integrated testing strategies. Environ Int 45:51–58. doi:10.1016/j.envint.2012.03.004

  17. Garrigues P (2005) Environmental chemistry: the ultimate challenge in analytical chemistry. Anal Bioanal Chem 381(1):3–4. doi:10.1007/s00216-004-2931-4

  18. Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graphics Modell 20(4):269–276. doi:10.1016/S1093-3263(01)00123-1

  19. Haranczyk M, Urbaszek P, Ng GE, Puzyn P (2012) Combinatorial × computational × cheminformatics (C3) approach to characterization of congeneric libraries of organic pollutants. J Chem Inf Model 52(11):2902–2909. doi:10.1021/ci300289b

  20. Hartung T (2009) Toxicology for the twenty-first century. Nature 460:208–212. doi:10.1038/460208a

  21. Hewitt M, Cronin MT, Enoch SJ, Madden JC, Roberts DW, Dearden JC (2009) In silico prediction of aqueous solubility: the solubility challenge. J Chem Inf Model 49(11):2572–2587. doi:10.1021/ci900286s

  22. Hu C, Liu X, Li X, Zhao Y (2013) Evaluation of growth and biochemical indicators of Salvinia natans exposed to zinc oxide nanoparticles and zinc accumulation in plants. Environ Sci Pollut Res. doi:10.1007/s11356-013-1970-9

  23. Johnson AR, Wichern WD (2005) Applied multivariate statistical analysis. Pearson, Delhi

  24. Jorgensen WL (2010) Drug discovery: pulled from a protein’s embrace. Nature 466:42–43. doi:10.1038/466042a

  25. Katritzky AR, Radzvilovits M, Slavov S, Kasemets K, Tamm K, Karelson M (2010a) Quantitative structure-activity relationship modeling of bioconcentration factors of polychlorinated biphenyls. Toxicol Environ Chem 92(7):1233–1247. doi:10.1080/02772240903306417

  26. Katritzky AR, Radzvilovits M, Slavov S, Kasemets K, Tamm K, Karelson M (2010b) Quantitative structure–activity relationship modeling of bioconcentration factors of polychlorinated biphenyls. Toxicol Environ Chem 92(7):1233–1247. doi:10.1080/02772240903306417

  27. Kubinyi H, Hamprecht FA, Mietzner T (1998a) Three-dimensional quantitative similarity–activity relationships (3D QSiAR) from SEAL similarity matrices. J Med Chem 41(14):2553–2564. doi:10.1021/jm970732a

  28. Liu HX, Yao XJ, Zhang RS, Liu MC, Hu ZD, Fan BT (2005) Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines. J Comput Aided Mol Des 19(7):499–508. doi:10.1007/s10822-005-9003-5

  29. Liu H, Yao X, Zhang R, Liu M, Hu Z, Fan B (2006) The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine. Chemosphere 63(5):722–733. doi:10.1016/j.chemosphere.2005.08.031

  30. Lombardo A, Roncaglioni A, Boriani E, Milan C, Benfenati E (2010) Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish. Cent Eur J Chem 4(Suppl 1):S1. doi:10.1186/1752-153X-4-S1-S1

  31. Lu X, Tao S, Hu H, Dawson RW (2000a) Estimation of bioconcentration factors of nonionic organic compounds in fish by molecular connectivity indices and polarity correction factors. Chemosphere 41(10):1675–88. doi:10.1016/S0045-6535(00)00050-3

  32. Lu X, Tao S, Hu H, Dawson RW (2000b) Estimation of bioconcentration factors of nonionic organic compounds in fish by molecular connectivity indices and polarity correction factors. Chemosphere 41(10):1675–1688. doi:10.1016/j.bbr.2011.03.031

  33. Meylan WM, Howard PH, Boethling RS, Aronson D, Printup H, Gouchie S (1999) Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient. Environ Toxicol Chem 18(4):664–672. doi:10.1002/etc.5620180412

  34. Mitra I, Saha A, Roy K (2010) Chemometric modeling of free radical scavenging activity of flavone derivatives. Eur J Med Chem 45(11):5071–5079. doi:10.1016/j.ejmech.2010.08.016

  35. OECD Document (2007) Guidance Document on the Validation of (Quantitative) 1226. http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=env/jm/mono(2007)2&doclanguage=en. Accessed 23 Oct 2013

  36. Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure activity relationships and quantitative structure property relationships. J Chem Inf Comput Sci 34(4):854–866. doi:10.1021/ci00020a020

  37. Roy K, Das RN (2011) On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modeling of fundamental physicochemical properties. SAR QSAR Environ Res 22(5–6):451–472. doi:10.1080/1062936X.2011.569900

  38. Roy K, Ghosh G (2003) Introduction of Extended topochemical atom (ETA) indices in the valence electron mobile (VEM) environment as tools for QSAR/QSPR studies. Internet Electron J Mol Des 2(9): 599–620. http://biochempress.com/Files/iejmd_2003_2_0599.pdf

  39. Roy K, Ghosh G (2004) QSTR with extended topochemical atom indices. 2. Fish toxicity of substituted benzenes. J Chem Inf Comput Sci 44(2):559–567. doi:10.1021/ci0342066

  40. Roy K, Sanyal I, Roy PP (2006) QSPR of the bioconcentration factors of non-ionic organic compounds in fish using extended topochemical atom (ETA) indices. SAR QSAR Environ Res 17(6):563–582. doi:10.1080/10629360601033499

  41. Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52(2):396–408. doi:10.1021/ci200520g

  42. Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN (2013) Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data. J Comput Chem 34(12):1071–1082. doi:10.1002/jcc.23231

  43. Scherb H, Voigt K (2011) Adverse genetic effects induced by chemical or physical environmental pollution. Environ Sci Pollut Res 18(5):695–696. doi:10.1007/s11356-010-0332-0

  44. Schuurmann G, Ebert RU, Chen J, Wang B, Kuhne R (2008) External validation and prediction employing the predictive squared correlation coefficients test set activity mean vs training set activity mean. J Chem Inf Model 48(11):2140–2145. doi:10.1021/ci800253u

  45. Tasmin R, Shimasaki Y, Tsuyama M, Qiu X, Khalil F, Okino N, Yamada N, Fukuda S, Kang IJ, Oshima Y (2013) Elevated water temperature reduces the acute toxicity of the widely used herbicide diuron to a green alga, Pseudokirchneriella subcapitata. Environ Sci Pollut Res Int. doi:10.1007/s11356-013-1989-y

  46. Toropova AP, Toropov AA, Martyanov SE, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) CORAL: Monte Carlo method as a tool for the prediction of the bioconcentration factor of industrial pollutants. Mol Inf 32(2):145–154. doi:10.1002/minf.201200069

  47. Wang R, Fu Y, Lai L (1997) A new atom-additive method for calculating partition coefficients. J Chem Inf Comput Sci 37(3):615–621. doi:10.1021/ci960169p

  48. Wang R, Gao Y, Lai L (2000) Calculating partition coefficient by atom-additive method. Perspect Drug Discovery Des 19(1):47–66. doi:10.1023/A:1008763405023

  49. Williams ES, Panko J, Paustenbach DJ (2009) The European Union’s REACH regulation: a review of its history and requirements. Crit Rev Toxicol 39(7):553–675. doi:10.1080/10408440903036056

  50. Wold S (1995) PLS for multivariate linear modelling. In: van de Waterbeemd H (ed) Chemometric methods in molecular design. VCH, Weinheim, pp 195–218

  51. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466–1474. doi:10.1002/jcc.21707

  52. Zhao C, Boriani E, Chana A, Roncaglioni A, Benfenati E (2008) A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere 73(11):1701–1707. doi:10.1016/j.chemosphere.2008.09.033

Download references

Acknowledgments

The authors thank Council of Scientific and Industrial Research (CSIR), New Delhi for awarding a major research project (no. 01 (2546)/11/EMR-II) to KR and a senior research fellowship to SP.

Declaration of interest

None declared

Author information

Correspondence to Kunal Roy.

Additional information

Responsible editor: Michael Matthies

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOC 56 kb)

Table S1

(DOC 19037 kb)

Table S2

(XLS 1545 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pramanik, S., Roy, K. Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool “PaDEL-Descriptor”. Environ Sci Pollut Res 21, 2955–2965 (2014). https://doi.org/10.1007/s11356-013-2247-z

Download citation

Keywords

  • QSAR
  • BCF
  • PaDEL-Descriptor
  • Mathematical modeling
  • ETA
  • XlogP