Advertisement

Environmental Toxicity (Q)SARs for Polymers as an Emerging Class of Materials in Regulatory Frameworks, with a Focus on Challenges and Possibilities Regarding Cationic Polymers

  • Hans SandersonEmail author
  • Kabiruddin Khan
  • Anna M. Brun Hansen
  • Kristin Connors
  • Monica W. Lam
  • Kunal Roy
  • Scott Belanger
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

Polymers are highly diverse and understudied materials from an environmental toxicity point of view. For the past decades, polymers have largely been out of scope regarding detailed safety assessment in most regulatory programs as they are assumed to not possess relevant toxicological properties due to their size. This regulatory exclusion is currently being reconsidered. This chapter discusses the available information about selected cationic polymers and outlines (Q)SAR ((Quantitative) Structure-Activity Relationship) approaches that could be used to develop new models to demonstrate potential aquatic toxicity of polymers. The amount of publicly available, high-quality environmental toxicity data on industrial polymers such as cationic polyquaterniums is extremely limited. Given the large size (dimension and molecular weight) of the materials, typical hydrophobicity-driven toxicity is not expected. Relevant descriptors for cationic polymers need to be identified. Molecular weight and charge density are well-known physical-chemical attributes that are suspected to be correlated with aquatic toxicity, but there might be other relevant descriptors as well.

We suggest models that predict polymer properties may be useful for estimating relevant properties regarding toxicity. Moreover, novel fragment-based 2D and 3D hologram (Q)SAR (H(Q)SAR) may prove relevant in determining these properties that can be used to derive hypotheses about toxic mechanisms and guide experimental test designs. In a regulatory context, (Q)SARs have to be transparent and scientifically robust which extends to fragment-based models that may be useful in categorizing polymers. The toxicity of category members can then be experimentally explored, and read-across strategies developed within the category.

The authors of this chapter are pursuing polymer (Q)SAR strategies in the coming years via generation of novel experimental and computational data on polyquaterniums. We will also evaluate the potential for fragment-based (Q)SARs for polymers in REACH.

Key words

Polymers Chemometric tools Descriptors Environmental toxicity Cationic Polyquaterniums 

Notes

Acknowledgments

This work was supported by Cefic in the LRI project ECO46: iTAP (http://cefic-lri.org/projects/eco-46-improved-aquatic-testing-and-assessment-of-cationic-polymers-itap/). KK thanks Indian Council of Medical Research, New Delhi, for financial support in the form of a senior research fellowship.

References

  1. 1.
    ECHA, Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Article 138(2)Google Scholar
  2. 2.
    Mayo-Bean K, Moran K, Meylan B, Ranslow P (2012) Methodology document for the ECOlogical Structure-Activity Relationship Model (ECOSAR) class program. US-EPA, Washington D.C.Google Scholar
  3. 3.
    Boethling RS, Nabholz JV (1996) Environmental assessment of polymers under the US Toxic Substances Control Act. United States Environmental Protection AgencyGoogle Scholar
  4. 4.
  5. 5.
    Sanderson H, Thomsen M (2009) Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q) SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action. Toxicol Lett 187:84–93PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R (2014) (Q)SAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Connors KA, Dyer SD, Belanger SE (2017) Advancing the quality of environmental microplastic research. Environ Toxicol Chem 36(7):1697–1703PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Biesinger KE, Stokes GN (1986) Effects of synthetic polyelectrolytes on selected aquatic organisms. J Water Pollut Control Fed 58:207–213Google Scholar
  9. 9.
    OECD (2019) Guidance document on aqueous-phase aquatic toxicity testing of difficult test chemicals. Series on testing and assessment. No. 23 (second edition). Paris, 81pGoogle Scholar
  10. 10.
    USEPA (1996) Ecological effects test guidelines OPPTS 850.1085 fish acute toxicity mitigated by humic acid. EPA712–C–96–117. Washington D.C., p 10Google Scholar
  11. 11.
    de Rosemond SJ, Liber K (2004) Wastewater treatment polymers identified as the toxic component of a diamond mine effluent. Environ Toxicol Chem 23:2234–2242PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Liber K, Weber L, Levesque C (2005) Sublethal toxicity of two wastewater treatment polymers to lake trout fry (Salvelinus namaycush). Chemosphere 61:1123–1133PubMedCrossRefPubMedCentralGoogle Scholar
  13. 13.
    Cumming J, Hawker D, Matthews C, Chapman H, Nugent K (2010) Analysis of polymeric quaternary ammonium salts as found in cosmetics by metachromatic polyelectrolyte titration. Toxicol Environ Chem 92:1595–1608CrossRefGoogle Scholar
  14. 14.
    Siebert J, Luyt A, Ackermann C (1990) A new transmission electron microscopic (TEM) method to determine differences between cationic polymers in solution. Int J Pharmaceut 61:157–160CrossRefGoogle Scholar
  15. 15.
    Cumming JL, Hawker DW, Nugent KW, Chapman HF (2008) Ecotoxicities of polyquaterniums and their associated polyelectrolyte-surfactant aggregates (PSA) to Gambusia holbrooki. J Environ Sci Heal A 43:113–117CrossRefGoogle Scholar
  16. 16.
    Cumming J, Hawker D, Chapman H, Nugent K (2011) The fate of polymeric quaternary ammonium salts from cosmetics in wastewater treatment plants. Water Air Soil Pollut 216:441–450CrossRefGoogle Scholar
  17. 17.
    Cumming JL (2008) Environmental fate, aquatic toxicology and risk assessment of polymeric quaternary ammonium salts from cosmetic uses. Griffith University, Mount GravattGoogle Scholar
  18. 18.
    Pereira JL, Vidal R, Goncalves FJM, Gabriel RG, Costa R, Rasteiro MG (2018) Is the aquatic toxicity of cationic polyelectrolytes predictable from selected physical properties? Chemosphere 202:145–153PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Nolte TM, Peijnenburg WJ, Hendriks AJ, van de Meent D (2017) Quantitative structure-activity relationships for green algae growth inhibition by polymer particles. Chemosphere 179:49–56PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Khan K, Baderna D, Cappelli C, Toma C, Lombardo A, Roy K, Benfenati E (2019) Ecotoxicological (Q)SAR modeling of organic compounds against fish: application of fragment based descriptors in feature analysis. Aquat Toxicol 212:162–174CrossRefGoogle Scholar
  21. 21.
    Khan K, Khan PM, Lavado G, Valsecchi C, Pasqualini J, Baderna D, Marzo M, Lombardo A, Roy K, Benfenati E (2019) (Q)SAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors. Chemosphere 229:8–17PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Khan K, Roy K, Benfenati E (2019) Ecotoxicological (Q)SAR modeling of endocrine disruptor chemicals. J Haz Mat 369:707–718CrossRefGoogle Scholar
  23. 23.
    Khan K, Kar S, Sanderson H, Roy K, Leszczynski J (2018) Ecotoxicological assessment of pharmaceuticals using computational toxicology approaches: QSTR and interspecies QTTR modeling. In: Proceedings of MOL2NET 2017, international conference on multidisciplinary sciences, 3rd edn. MDPI AG, Switzerland, Basel, p 1Google Scholar
  24. 24.
    Khan K, Kar S, Sanderson H, Roy K, Leszczynski J (2019) Ecotoxicological modeling, ranking and prioritization of pharmaceuticals using QSTR and i-QSTTR approaches: application of 2D and fragment based descriptors. Mol Inform, 38, article 1800078, http://dx.doi.org/10.1002/minf.201800078CrossRefGoogle Scholar
  25. 25.
    Khan K, Benfenati E, Roy K (2019) Consensus (Q)SAR modeling of toxicity of pharmaceuticals to different aquatic organisms: ranking and prioritization of the DrugBank database compounds. Ecotox Environ Safe 168:287–297CrossRefGoogle Scholar
  26. 26.
    Khan K, Roy K (2017) Ecotoxicological modelling of cosmetics for aquatic organisms: a QSTR approach. SAR (Q)SAR Environ Res 28:567–594CrossRefGoogle Scholar
  27. 27.
    De P, Kar S, Roy K, Leszczynski J (2018) Second generation periodic table-based descriptors to encode toxicity of metal oxide nanoparticles to multiple species: QSTR modeling for exploration of toxicity mechanisms. Environ Sci Nano 5:2742–2760CrossRefGoogle Scholar
  28. 28.
    Braakhuis HM, Kloet SK, Kezic S, Kuper F, Park MV, Bellmann S, van der Zande M, Le Gac S, Krystek P, Peters RJ (2015) Progress and future of in vitro models to study translocation of nanoparticles. Arch Toxicol 89:1469–1495PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    ECHA European Chemicals Agency (2012) Guidance on registration. Version 2.0. Guidance for the implementation of REACHGoogle Scholar
  30. 30.
    Netzeva T, Pavan M, Worth A (2007) Review of data sources, (Q)SARs and integrated testing strategies for aquatic toxicity. European Communities, LuxembourgGoogle Scholar
  31. 31.
    Roy K, Ambure P, Kar S, Ojha PK (2018) Is it possible to improve the quality of predictions from an “intelligent” use of multiple (Q)SAR/QSPR/QSTR models? J Chemom 32:e2992CrossRefGoogle Scholar
  32. 32.
    Roy K, Ambure P, Kar S (2018) How precise are our quantitative structure-activity relationship derived predictions for new query chemicals? ACS Omega 3:11392–11406PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Enslein K, Gombar VK (1997) TOPKAT 5.0 and modulation of toxicity. Mutat Res-Fund Mol M 379:S14–S14CrossRefGoogle Scholar
  34. 34.
    Plošnik A, Zupan J, Vračko M (2015) Evaluation of toxic endpoints for a set of cosmetic ingredients with CAESAR models. Chemosphere 120:492–499CrossRefGoogle Scholar
  35. 35.
    De Vaugelade S, Nicol E, Vujovic S, Bourcier S, Pirnay S, Bouchonnet S (2018) Ultraviolet-visible phototransformation of dehydroacetic acid – structural characterization of photoproducts and global ecotoxicity. Rapid Commun Mass Spectrom 32:862–870CrossRefGoogle Scholar
  36. 36.
    Fendinger NJ, McAvoy DC, Eckhoff WS, Price BB (1997) Environmental occurrence of polydimethylsiloxane. Env Sci Technol 31:1555–1563CrossRefGoogle Scholar
  37. 37.
    Khan PM, Roy K (2018) QSPR modelling for prediction of glass transition temperature of diverse polymers. SAR (Q)SAR Environ Res 29:935–956CrossRefGoogle Scholar
  38. 38.
    Roy K, Kar S, Das RN (2015) A primer on (Q)SAR/QSPR modeling: fundamental concepts. Springer, UK. https://www.rsc.org/journals-books-databases/about-journals/environmental-science-nano/
  39. 39.
    Roy K, Kar S, Das RN (2015) Statistical methods in (Q)SAR/QSPR. In: A primer on (Q)SAR/QSPR modeling. Springer, NY, USA, pp 37–59Google Scholar
  40. 40.
    Mauri A, Consonni V, Pavan M, Todeschini R, software D (2006) An easy approach to molecular descriptor calculations. Match 56:237–248Google Scholar
  41. 41.
    Kuz’min VE, Artemenko AG, Polischuk PG, Muratov EN, Hromov AI, Liahovskiy AV, Andronati SA, Makan SY (2005) Hierarchic system of (Q)SAR models (1D–4D) on the base of simplex representation of molecular structure. J Mol Model 11:457–467PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
  43. 43.
    Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148CrossRefGoogle Scholar
  45. 45.
    Golmohammadi H, Dashtbozorgi Z, Acree WE Jr (2012) Quantitative structure-activity relationship prediction of blood-to-brain partitioning behavior using support vector machine. Eur J Pharm Sci 47:421–429PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Zhang Q, Couloigner I (2005) A new and efficient k-medoid algorithm for spatial clustering. In: International conference on computational science and its applications. Springer, NY, USA, pp 181–189Google Scholar
  47. 47.
    Roy K, Ambure P (2016) The “double cross-validation” software tool for MLR (Q)SAR model development. Chemom Intell Lab Syst 159:108–126CrossRefGoogle Scholar
  48. 48.
    De P, Aher RB, Roy K (2018) Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vector Aedes aegypti: application of ETA indices. RSC Adv 8:4662–4670CrossRefGoogle Scholar
  49. 49.
    Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive (Q)SAR models. Chemom Intell Lab Syst 152:18–33CrossRefGoogle Scholar
  50. 50.
    Langham AA, Khandelia H, Schuster B, Waring AJ, Lehrer RI, Kaznessis YN (2008) Correlation between simulated physicochemical properties and hemolycity of protegrin-like antimicrobial peptides: predicting experimental toxicity. Peptides 29:1085–1093PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Khan PM, Roy K (2019) Consensus QSPR modelling for the prediction of cellular response and fibrinogen adsorption to the surface of polymeric biomaterials. SAR (Q)SAR Environ Res 30:363–382CrossRefGoogle Scholar
  52. 52.
    Kholodovych V, Smith JR, Knight D, Abramson S, Kohn J, Welsh WJ (2004) Accurate predictions of cellular response using QSPR: a feasibility test of rational design of polymeric biomaterials. Polymer 45:7367–7379CrossRefGoogle Scholar
  53. 53.
    J.R. Smith, D. Knight, J. Kohn, K. Rasheed, N. Weber, S. Abramson (2003) Using non-linear regression to predict bioresponse in a combinatorial library of biodegradable polymers, MRS Online Proc Libr, vol 804, Cambridge, UKGoogle Scholar
  54. 54.
    Smith JR, Knight D, Kohn J, Rasheed K, Weber N, Kholodovych V, Welsh WJ (2004) Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces. J Chem Inform Comput Sci 44:1088–1097CrossRefGoogle Scholar
  55. 55.
    Khan PM, Rasulev B, Roy K (2018) QSPR modeling of the refractive index for diverse polymers using 2D descriptors. ACS Omega 3:13374–13386PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    R Duchowicz P, C Comelli N, V Ortiz E, A Castro E (2012) (Q)SAR study for carcinogenicity in a large set of organic compounds. Current Drug Saf 7:282–288CrossRefGoogle Scholar
  57. 57.
    Talevi A, L Bellera C, Di Ianni M, R Duchowicz P, E Bruno-Blanch L, A Castro E (2012) An integrated drug development approach applying topological descriptors. Curr Comput Aided Drug Des 8:172–181PubMedCrossRefPubMedCentralGoogle Scholar
  58. 58.
    Gajewicz A, Jagiello K, Cronin MTD, Leszczynski J, Puzyn T (2017) Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available. Environ Sci Nano 4:346–358CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Hans Sanderson
    • 1
    Email author
  • Kabiruddin Khan
    • 2
  • Anna M. Brun Hansen
    • 1
  • Kristin Connors
    • 3
  • Monica W. Lam
    • 4
  • Kunal Roy
    • 2
  • Scott Belanger
    • 3
  1. 1.Department of Environmental ScienceAarhus UniversityRoskildeDenmark
  2. 2.Drug Theoretics and Cheminformatics Lab, Department of Pharmaceutical TechnologyJadavpur UniversityKolkataIndia
  3. 3.The Procter and Gamble CompanyMasonUSA
  4. 4.The Procter and Gamble CompanyCincinnatiUSA

Personalised recommendations