Virtual chemical analysis and machine learning-based prediction of polyethylene terephthalate nanoplastics toxicity on aquatic organisms as influenced by particle size and properties

Volume 6, Issue 03, Pages 36-53, Sep 2023 *** Field: Computational Analytical method

  • Enyoh Christian Ebere Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama 8570-338, Japan.
  • Chidi Edbert Duru Department of Chemistry, Faculty of Physical Sciences, Imo State University, PMB2000 Owerri, Nigeria
  • Qingyue Wang Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338-8570, Japan
  • Senlin Lu School of environmental and chemical engineering, Shanghai University, Shanghai 200444, China.
Keywords: Analytical methods, Artificial neural networks, Fish, Health risks, Plastic pollution, Simulation, Toxicity


This study focuses on the chemical analysis and prediction of Polyethylene Terephthalate (PET)  toxicity, considering the influence of particle size and properties. The effect PET of different sizes (1, 4, 9, 16 and 25 nm coded NP1 to NP5) on aquatic organisms such as Terpedo californica (electric ray fish) and Danio rerio (zebrafish) as model species was evaluated by virtual chemical techniques and machine learning methodology based on Multilayer Perceptrons Artificial Neural Networks (MLP ANN) and Support Vector Machine. The PET NPs was built and characterized in silico and then docked on the acetylcholinesterase (TcAChE) and cytochrome P450 (Zf CYP450) of the organisms, respectively. The results showed that the binding affinities of the NPs increased steadily from – 7.1 kcal mol-1 to – 9.9 kcal mol-1 for NP1 to NP4 and experienced a drop at NP5 (– 8.9 kcal mol-1) for TcAChE. The Zf CYP450 also had a similar pattern ranging from -5.2 kcal mol-1 to -8.1 kcal mol-1. The MLP ANN showed an accuracy of 85.9 % and 77.3 %. In comparison, SVM showed a better PET NPs toxicity prediction with an accuracy of 99.5 % and 99.4% based on the inherent properties of TcAChE and Zf CYP450, respectively.


A.W. Verla, C.E. Enyoh, E.N. Verla, K. Nwanorh. Microplastic–toxic chemical interaction: a review study on quantified levels, mechanism and implication, SN Appl. Sci. 1 (2019a) 1400.

Y. Chen, A.K. Awasthi, F. Wei, Q. Tan, J. Li, Single-use plastics: Production, usage, disposal, and adverse impacts, Sci. Total Environ., 752 (2021) 141772.

S.A.L. Patrício, J.C. Prata, T.R. Walker, A.C. Duarte, W. Ouyang, D. Barcelò, Increased plastic pollution due to COVID-19 pandemic: Challenges and recommendations, Chem. Eng. J., 405 (2021) 126683.

C.E. Enyoh, Q. Wang, T. Chowdhury, W. Wang, S. Lu, K. Xiao, M.A.H Chowdhury, New analytical approaches for effective quantification and identification of nanoplastics in environmental samples, Processes, 9 (2021) 2086.

C. E. Enyoh, L. Shafea, A. W. Verla, E. N. Verla, W. Qingyue, T. Chowdhury, M. Paredes, Microplastics exposure routes and toxicity studies to ecosystems: An overview, Environ. Anal. Health Toxicol., 35(1) (2020) 1–10.

C.E. Enyoh, A.W. Verla, E.N. Verla, Airborne microplastics: a review study on method for analysis, occurrence, movement and risks, Environ. Monit. Assess., 191 (2019) 668.

C.E. Enyoh, W. Qingyue, V.A. Wirnkor, T. Chowdhury, Index models for ecological and health risks assessment of environmental micro-and nano-sized plastics, AIMS Environ. Sci., 9 (2022) 51-65.

P. Schwabl, S. Köppel, P. Königshofer, T. Bucsics, M. Trauner, T. Reiberger, B. Liebmann, Detection of various microplastics in human stool: A prospective case series, Ann. Inter. Med., 171 (2019) 453–457.

A. Ragusa, A. Svelato, C. Santacroce, P. Catalano, V. Notarstefano, O. Carnevali, F. Papa, M. C. A. Rongioletti, F. Baiocco, S. Draghi, E. D'Amore, D. Rinaldo, M. Matta, E. Giorgini, Plasticenta: First evidence of microplastics in human placenta, Environ. Int., 146 (2021) 106274.

A.P. Araújo, T. Marinho, T. Lopes, Toxicity evaluation of the combination of emerging pollutants with polyethylene microplastics in zebrafish: Perspective study of genotoxicity, mutagenicity, and redox unbalance, J. Hazard. Mater., (2022) 432.

J. Bhagat, L. Zang, N. Nishimura, Y. Shimada, Zebrafish: An emerging model to study microplastic and nanoplastic toxicity. The Science of the total environment, 728, (2020) 138707.

R.L., Bailone, H.C.S. Fukushima, V. Fernandes, Zebrafish as an alternative animal model in human and animal vaccination research, Lab. Anim. Res., 36 (2020) 13.

M. Ignacio, K. Le Menach, M. Devier, M.P. Cajaraville, H. Budzinski, A. Orbea, Screening of the toxicity of polystyrene nano- and microplastics alone and in combination with benzo(a)pyrene in brine Shrimp Larvae and Zebrafish embryos, Nanomater., 12 (2022) 941.

P.S. Pallan, L.D. Nagy, L. Lei, E. Gonzalez, Structural and kinetic basis of steroid 17α,20-lyase activity in teleost fish cytochrome P450 17A1 and its absence in cytochrome P450 17A2, J. Biol. Chem., 290 (2015) 3248-268.

J.V. Goldstone, A.G. McArthur, A. Kubota, Identification and developmental expression of the full complement of Cytochrome P450 genes in Zebrafish, BMC Genom., 11 (2010) 643.

P.R. Last, White W.T., de Carvalho M.R., B. Séret, M.F.W. Stehmann, G.J.P. Naylor, Rays of the world. CSIRO Publishing, Comstock Publishing Associates, i-ix, pp. 1-790, 2016.

S.W. Michael, Reef sharks and rays of the world. A guide to their identification, behavior, and ecology, Sea Challengers Monterey California publisher, 107 pages, 1993. ISBN:9780930118181, 0930118189

M. R. Aidan, ReefQuest Centre for Shark Research, Electric Rays publisher, 2008.

C. Sommer, Schneider W., Poutiers J. M., FAO species identification field guide for fishery purposes, the living marine resources of Somalia, FAO publisher, Rome, 376 pages, 1996.

M.B. Colović, D.Z. Krstić, T.D. Lazarević-Pašti, A.M. Bondžić, V.M. Vasić, Acetylcholinesterase inhibitors: pharmacology and toxicology, Curr. Neuropharmacol., 11 (2013) 315-335.

A. D. Gray, J. E. Weinstein, Size- and shape-dependent effects of microplastic particles on adult daggerblade grass shrimp (Palaemonetes pugio), Environ. Toxicol. Chem., 36 (2017) 3074–3080.

A. Banerjee, L.O. Billey, W.L. Shelver, Uptake and toxicity of polystyrene micro/nanoplastics in gastric cells: Effects of particle size and surface functionalization, PLOS ONE 16 (12) (2021) e0260803.

C.E. Duru, I.A. Duru, C.E. Enyoh, In silico binding affinity analysis of microplastic compounds on PET hydrolase enzyme target of Ideonella sakaiensis, Bull. Natl. Res. Cent., 45 (2021) 104.

V. Zhou, Machine learning for beginners: An introduction to neural networks, Medium, 2019.

C. Duru, C. Enyoh, I.A. Duru, M.C. Enedoh, Degradation of PET nanoplastic oligomers at the novel PHL7 target: Insights from molecular docking and machine learning, J. Niger. Soc. Phys. Sci., 5 (2023) 1154–1154.

F. Yu, X. Hu, Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects, J. Hazard. Mater., 432 (2022) 128730.

X. Wu, Z. Qixing, M. Li, H. Xiangang, Machine learning in the identification, prediction and exploration of environmental toxicology: challenges and perspectives, J. Hazard. Mater., 438 (2022) 129487.

C. E. Enyoh, Q. Wang, P. E. Ovuoraye, T. O. Maduka, Toxicity evaluation of microplastics to aquatic organisms through molecular simulations and fractional factorial designs, Chemosphere, 308(Pt 2) (2022) 136342.

H.M. Greenblatt, C. Guillou, D. Guénard, A. Argaman, S. Botti, B. Badet, The complex of a bivalent derivative of galanthamine with torpedo acetylcholinesterase displays drastic deformation of the active-site gorge: implications for structure-based drug design, J. Am. Chem. Soc., 126 (2004) 15405-15411.

R.T. Peterson, C.A. Macrae, Systematic approaches to toxicology in the zebrafish. Annu Rev Pharmacol Toxicol., 52 (2012) 433-453.

C.E. Duru, Duru I.A., A. Bilar, Computational investigation of sugar fermentation inhibition by bergenin at the pyruvate decarboxylate isoenzyme 1 target of Scharomyces cervisiae, J. Med. Plants Stud., 8(6) (2020) 21-25.

C.E. Duru, Duru I.A., A.E. Adegboyega, In Silico identification of compounds from Nigella sativa seed oil as potential inhibitors of SARS-CoV-2 targets, Bull. Natl. Res. Cent., 45 (2021) 57.

C.E. Enyoh, O.M. Tochukwu, C. E. Duru, S.C. Osigwe, C.B.C. Ikpa, Q. Wang, In silico binding affinity studies of microbial enzymatic degradation of plastics, Journal of Hazardous Materials Advances, 6 (2022)100076.

BIOVIA, Dassault Systemes, San Diego, Discovery studio modeling environment, 2020.

C.E. Enyoh, C.E. Duru, E. Prosper, Q. Wang, Evaluation of nanoplastics toxicity to the human placenta in systems, J. Hazard. Mater., 446 (2023) 130600.

H. Tang, K. C. Tan, Z. Yi, Neural networks: Computational models and applications. Heidelberg, Germany: Springer, 2007.

C.E. Enyoh, Q. Wang, L. Senlin, Optimizing the efficient removal of ciprofloxacin from aqueous solutions by polyethylene terephthalate microplastics using multivariate statistical approach, Chem. Eng. Sci., 278 (2023) 118917.

C.E. Enyoh, P. Ovuoraye, O. Isiuku, C. Igwegbe, Artificial neural network and response surface design for modeling the competitive biosorption of pentachlorophenol and 2,4,6-trichlorophenol to canna indica L. in Aquaponia, Anal. Methods in Environ. Chem. J., 6 (2023) 79-99.

A. Yettou, M. Laidi, A. El Bey, S. Hanini, M. Hentabli, O. Khaldi, and M. Abderrahim Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study, Kem. Ind., 70 (2021) 509−518. KUI-36/2021

C.E. Enyoh, Q. Wang, W. Weiqian, C. Tanzin, H.R. Mominul, I. Md. Rezwanul, Sorption of per- and polyfluoroalkyl substances (PFAS) using Polyethylene (PE) microplastics as adsorbent: Grand canonical Monte Carlo and molecular dynamics (GCMC-MD) studies, Int. J. Environ. Anal. Chem., (2022) 1-19.

R] M. L. Connolly, Analytical molecular surface calculation, J. Appl. Crystallogra., 16 (1983) 548–558.

R] M.A.H. Alzuhairi, B.I. Khalil, R.S. Hadi, Nano ZnO catalyst for chemical recycling of polyethylene terephthalate (PET), Eng. Technol. J., 35, 8 (2017) 831-837.

R] C.E. Enyoh, Q. Wang, P.E. Ovuoraye, Response surface methodology for modeling the adsorptive uptake of phenol from aqueous solution using adsorbent polyethylene terephthalate microplastics, Chem. Eng. J. Adv., 12 (2022) 100370.

R] D. Cortés-Arriagada, Elucidating the co-transport of bisphenol A with polyethylene terephthalate (PET) nanoplastics: A theoretical study of the adsorption mechanism, Environ. Pollut., 270 (2021) 116192.

R] M. Miloloža, K. Bule, V. Prevaric, M. Cvetnic, Assessment of the influence of size and concentration on the ecotoxicity of microplastics to microalgae scenedesmus sp., bacterium pseudomonas putida and Yeast Saccharomyces cerevisiae, Polymers, 14 (2022) 1246.

R] G.D. Christian, Analytical Chemistry, 6th edn., Wiley, NewYork, p. 128, 2003.

R] C.E. Enyoh, B.O. Isiuku, 2, 4, 6-trichlorophenol (TCP) removal from aqueous solution using canna indica l: Kinetic isotherm and thermodynamic studies, Chem. Ecol., 37 (2020) 64-82.

R] Eurachem, The fitness for purpose of analytical method: a laboratory guide tomethod validation and related topics, 1998.

R] C. Xie, Y. Ma, P. Zhang, J. Zhang, Elucidating the origin of the toxicity of nano-CeO2 to Chlorella pyrenoidosa: the role of specific surface area and chemical composition, Environ. Sci.: Nano, 8 (2021) 1701–1712.

R] D. Lison, C. Lardot, F. Huaux, G. Zanetti, B. Fubini, Influence of particle surface area on the toxicity of insoluble manganese dioxide dusts, Arch. Toxicol., 71 (1997) 725–729.

R] O. Schmid, T. Stoeger, Surface area is the biologically most effective dose metric for acute nanoparticle toxicity in the lung, J. Aerosol Sci, 99 (2016) 133–143.

R] T. Chai, R. R. Draxler Root mean square error (RMSE) or mean absolute error (MAE)? arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7 (2014) 1247–1250.

R] H. Timothy, Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Develop., 15 (2022) 5481–5487.

R] C. Sammut, G.I. Webb, Mean Absolute Error. In: Encyclopedia of Machine Learning. Springer, Boston, MA, 2011.

R] R.A. Naqvi, M. Arsalan, G. Batchuluun, H.S. Yoon, K.R. Park, Deep learning-based gaze detection system for automobile drivers using a NIR camera sensor, Sensors,18 (2018) 456.

(References from 41R-55R showed in SM)

How to Cite
Ebere, E., Duru, C., Wang, Q., & Lu, S. (2023). Virtual chemical analysis and machine learning-based prediction of polyethylene terephthalate nanoplastics toxicity on aquatic organisms as influenced by particle size and properties. Analytical Methods in Environmental Chemistry Journal, 6(03), 36-53.
Original Article