Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials
Abstract
:1. Introduction
2. Metal Oxides
3. Other Metal-Containing Nanoparticles
4. Multi-Walled Carbon Nanotubes (MWCNTs)
5. Fullerenes
6. Silica Nanomaterials
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Source | Dataset | Endpoint of Cytotoxicity Measurement | n | R2 1 | Software 2 | Statistical Method | Descriptors |
---|---|---|---|---|---|---|---|
Escherichia coli | |||||||
[25] | [25] | LD50 | 7 | 0.979 | - | Multiple linear regression (MLR) | Metal cation charge |
[20] | [20] | LD50 | 17 | 0.862 | MATLAB | MLR | Enthalpy of formation of a gaseous cation |
[26] | [20] | LD50 | 17 | 0.741–0.838 | CORAL | Monte Carlo | SMILES-based optimal descriptor |
[27] | [20] | LD50 | 17 | 0.933 | Minitab 16 | MLR | Energy gap, hardness, softness, electronegativity, and electrophilicity index |
[28] | [20] | LD50 | 17 | 0.81–0.90 | - | MLR | Electronegativity, charge of the metal cation corresponding to a given oxide |
[29] | [20] | LD50 | 17 | 0.93 | RandomForest package | Random forest (RF) | S1—unbonded two-atomic fragments [Me] … [Me], which were encoded based on Simplex representation of molecular structures (SiRMS)-derived descriptors [34,35], describing distance where potential reaches minimum at van der Waals interactions; rw—Wigner–Seitz radius; ρ—mass density; (CPP)—cation polarizing power; S2—SiRMS-derived electronegativity aligned descriptor of oxides molecules—in a sense of the acid-base property of oxides (this parameter increases with a number of oxygens in molecule); S3—tri-atomic fragments [Me]-[O]-[Me], which were encoded by SiRMS-derived descriptors, encoding electronegativity; and (SV)—proportion of surface molecules to molecules in volume |
[30] | [20] | LD50 | 17 | 0.955 | Ensemble learning | Oxygen percent, molar refractivity, and polar surface area | |
[31] | [20] | LD50 | 17 | - | MATLAB | Read-across | Ionization enthalpy of the detached metal atoms |
[18] | [20] | LD50 | 17 | 0.889–0.982 | CORAL | MLR | SMILES-based optimal descriptor |
[36] | [20] | LD50 | 16 | 0.91 | - | MLR | Enthalpy of formation of a gaseous cation (ΔHMe+), charge of the metal cation (χox), and pEC50 of HaCaT |
[33] | [20] | LD50 | 16 | 0.879 | SYBYL X1.1 and SPSS statistics v.17 | MLR | Enthalpy of formation of a gaseous cation (ΔHme+) and polarization force (Z/r) |
[37] | [20] | LD50 | 16 | 0.79 | CORAL | Monte Carlo | Quasi-SMILES |
[38] | [20] | LD50 | 17 | 0.92 | - | Counter propagation artificial neural network | Metal electronegativity by Pauling scale, number of metal atoms in oxide, number of oxygen atoms in oxide, and charge of metal cation |
[39] | [20] | LD50 | 17 | 0.968 | - | RF | Oxygen in weight percentage and enthalpy of formation of a gaseous cation |
[40] | [20] | LD50 | 17 | 0.877 and 0.903 | - | MLR and support vector machines (SVM) | HOMO energy, α-LUMO and β-LUMO energy, the average of α-LUMO and β-LUMO, the energy gap between the frontier molecular orbitals ∆E, and molar heat capacity |
[8] | [20] | LD50 | 17 | 0.93 | - | Partial least squares (PLS) | Charge of metal ion, metal ion charge-based SiRMS, number of oxygen atoms in brutto formula weighted by ionic potential, covalent index weighted by charge of metal ion, molecular weight of metal oxide weighed by size of nanoparticle, squared thickness of interfacial layer, van der Waals repulsion weighted by size of nanoparticle, and Wigner-Seitz radius weighted by size of nanoparticle |
[32] | [32] | LD50 | 17 | 0.87 | Self-written program | MLR | Electronegativity of metal and electronegativity of metal oxide |
[41] | [41] | IC50 | 24 | - | R | SVM | Conduction band energy and hydration enthalpy (ΔHhyd) |
Human keratinocyte cell line (HaCaT) | |||||||
[29] | [42] | LD50 | 18 | 0.96 | RandomForest package | RF | S1, rw, ρ, (CI)—covalent index of the metal ion, S2, and (AP)—aggregation parameter |
[31] | [42] | LD50 | 18 | - | MATLAB | Read-across | Mulliken’s electronegativity |
[42] | [42] | LD50 | 18 | 0.93 | - | MLR | Enthalpy of formation of metal oxide, Mulliken’s electronegativity |
[18] | [42] | LD50 | 18 | 0.961–0.999 | CORAL | MLR | SMILES-based optimal descriptor |
[36] | [42] | LD50 | 16 | 0.88 | - | MLR | Enthalpy of formation of metal oxide (ΔHf) nano-cluster, electronic chemical potential of the cluster, and pEC50 of E. coli |
[37] | [42] | LD50 | 16 | 0.79 | CORAL | Monte Carlo | Quasi-SMILES |
[39] | [42] | LD50 | 18 | 0.918 | - | RF | 10-based logarithm of solubility measured in mol/L (LogS), topological polar surface area (TPSA), Mulliken’s electronegativity |
[8] | [42] | LD50 | 18 | 0.83 | - | PLS | Atom charge-based SiRMS descriptor, charge of the atom weighted by the bond ionicity, charge of metal ion weighted by ionicity of bond, squared ionic potential, ion change-based SiRMS descriptor, number of oxygen atoms in brutto formula per interfacial layer, mass density weighted by ionicity of bond, Wigner-Seitz radius weighted by ionicity of bond, and ionicity of bond based SiRMS |
[43] | [42,44,45] | Cell viability (%) | 21 | - | CORAL | Hierarchical cluster analysis (HCA) and min–max normalization | Quasi-SMILES |
Transformed bronchial epithelial cells (BEAS-2B) | |||||||
[46] | [46] | % of membrane-damaged cells | 9 | - | Weka | RF | Atomization energy of the metal oxide, period of the nanoparticle metal, nanoparticle primary size, and nanoparticle volume fraction |
[6] | [6] | Cell viability (%) | 24 | - | - | Regression tree | Metal solubility and energy of conduction |
[47] | [6] | Cell viability (%) | 24 | - | RandomForest package | RF | Mass density, covalent index, cation polarizing power, Wigner–Seitz radius, surface area-to-volume ratio, aggregation parameter, and tri-atomic descriptor of atomic charges |
[48] | [48] | LD50 | 24 | - | RapidMiner | SVM | Conduction band energy and ionic index of metal cation |
[49] | [50] | % of membrane-damaged cells | 24 | 0.68 | CORAL | Monte Carlo | SMILES-based optimal descriptor, dose, and exposure time |
[43] | [6,51,52] | Cell viability (%) | 21 | 0.713–0.733 | CORAL | HCA and min-max normalization | Quasi-SMILES |
Murine myeloid cells (RAW 264.7) | |||||||
[6] | [6] | Cell viability (%) | 24 | - | - | Regression tree | Metal solubility and energy of conduction |
[47] | [6] | Cell viability (%) | 24 | - | RandomForest package | RF | Mass density, molecular weight, aligned electronegativity, covalent index, surface area, surface area-to-volume ratio, two-atomic descriptor of van der Waals interactions, tetra-atomic descriptor of atomic charges, and size in DMEM |
[48] | [48] | LD50 | 24 | - | RapidMiner | SVM | Conduction band energy and ionic index of metal cation |
[53] | [53] | Lactate dehydrogenase (LDH) release | 25 | - | R | PLS | Metal cation charge, hydration rate, radius of the metallic cation, and Pauling electronegativity |
Rat L2 lung epithelial cells and rat lung alveolar macrophages | |||||||
[54] | [54] | Membrane damage (units L−1) | 42 | - | - | Multivariate linear regression and linear discriminant analysis (LDA) | Size, concentration, size in phosphate buffered saline, size in water, and zeta potential |
[55] | [54] | Membrane damage (units L−1) | 42 | - | - | MLR and simple classification | Size, concentration, size in phosphate buffered saline, and size in water |
Source | Dataset | Cell Type | Endpoint of Cytotoxicity Measurement | n | R2 | Software | Statistical Method | Descriptors |
---|---|---|---|---|---|---|---|---|
[59] | [60] | Monocytes, hepatocytes, endothelial, and smooth muscle cells | Cellular viability | 51 | 0.72 | WinSVM, ISIDA | SVM classification and k Nearest Neighbors (kNN) regression | Size, zeta potential, R1 and R2 relaxivities |
[59] | [61] | PaCa2 human pancreatic cancer cells, U937 macrophage cell lines, primary human macrophages, HUVEC human umbilical vein endothelial cells | Cellular uptake | 109 | 0.65–0.80 | WinSVM, ISIDA | SVM classification and k Nearest Neighbors (kNN) regression | Lipophilicity, number of double bonds |
[62] | [60] | Smooth muscle cells | Cell apoptosis | 31 | 0.81 | - | MLR and Bayesian regularized artificial neural network | IFe2O3, Idextran, and Isurf.chg |
[63] | [60] | Monocytes, hepatocytes, endothelial, and smooth muscle cells | Cellular viability | 44 | - | - | Naive Bayesian classifier | Primary size, spin-lattice and spin-spin relaxivities, zeta potential |
[64] | [64] | Zebrafish embryo | 24 h post-fertilization mortality | 82 | - | ABMiner | Numerical prediction | Concentration, shell composition, surface functional groups, purity, core structure, and surface charge |
[65] | [65] | Mammalian cell lines | TC50 | 1681 | - | STATISTICA v.6 | LDA | Molar volume, polarizability, and size of the particles |
[66] | [66] | Algae, bacteria, cell lines, crustaceans, plants, fish, and others | CC50, EC50, IC50, TC50, LC50 | 36488 | - | STATISTICA | LDA | Molar volume, polarizability, size of NPs, electronegativity, hydrophobicity, and polar surface area of surface coating |
[67] | [67] | Bacteria, algae, crustaceans, fish, and others | EC50, IC50, TC50, LC50 | 5520 | - | STATISTICA | LDA | Molar volume, electronegativity, polarizability, and nanoparticle size |
[68] | [68] | Algae, bacteria, fungi, mammal cell lines, crustaceans, plants, fishes, and others | CC50, EC50, IC50, TC50, LC50 | 54371 | - | STATISTICA | Artificial neural network | Polar surface area, hydrophobicity, atomic weight, atomic van der Waals radius, electronegativity, and polarizability |
[69] | [70] | Danio rerio, Daphnia magna, Pseudokirchneriella subcapitata, and Staphylococcus aureus | LC50, EC50, MIC (minimum inhibitory concentration) | 400 | - | Weka | Functional tree, C4.5 decision tree, random tree, and CART | Molecular polarizability, accessible surface area, and solubility |
[71] | [71] | E. coli and Chinese hamster ovary (CHO-K1) cells | EC50, MIC | 17 | 0.94 | R | Nonlinear least-squaress | Size and specific surface area (Brunauer-Emmett-Teller surface) |
Source | Dataset | Cell Type | Endpoint of Cytotoxicity Measurement | n | R2 | Software | Statistical Method | Descriptors |
---|---|---|---|---|---|---|---|---|
[74] | [75] | Salmonella typhimurium TA100 | Reverse mutation test TA100 | 24 | 0.65–0.81 | CORAL | Monte Carlo | Quasi-SMILES |
[76] | [77] | Salmonella typhimurium TA100 | Reverse mutation test TA100 | 30 | 0.53–0.64 | CORAL | Monte Carlo | Quasi-SMILES |
[78] | [77,79] | Salmonella typhimurium TA100 | Reverse mutation test TA100 | 44 | 0.60–0.78 | CORAL | Monte Carlo | Quasi-SMILES |
[80] | [80] | Four types of normal human lung cells (BEAS-2B, 16HBE14o-, WI-38, and HBE) | Cell viability (%) | 276 | 0.60–0.80 | CORAL | Monte Carlo | Quasi-SMILES |
Source | Dataset | Cell Type | Endpoint of Cytotoxicity Measurement | n | R2 | Software | Statistical Method | Descriptors |
---|---|---|---|---|---|---|---|---|
[78] | [77,79] | Salmonella typhimurium TA100 | Reverse mutation test TA100 | 44 | 0.60–0.78 | CORAL | Monte Carlo | Quasi-SMILES |
[81] | [79] | S. typhimurium TA100 | Reverse mutation test TA100 | 20 | 0.76 | CORAL | Monte Carlo | Quasi-SMILES |
[82] | [79] | S. typhimurium TA100 | Reverse mutation test TA100 | 20 | 0.63–0.76 | CORAL | Monte Carlo | Quasi-SMILES |
[82] | [79] | E. coli WP2 uvrA/pKM101 | Reverse mutation test WP2 uvrA/pKM101 | 20 | 0.68–0.82 | CORAL | Monte Carlo | Quasi-SMILES |
Source | Dataset | Cell Type | Endpoint of Cytotoxicity Measurement | n | R2 | Software | Statistical Method | Descriptors |
---|---|---|---|---|---|---|---|---|
[83] | [84] | Human embryonic kidney cells HEK293 | Cell viability (%) | 40 | 0.80–0.93 | CORAL | Monte Carlo | Quasi-SMILES |
[24] | [85] | Human kidney cells HK-2 | Cell viability (%) | 42 | 0.83–0.89 | CORAL | Monte Carlo | Quasi-SMILES |
[86] | [86] | 16HBE, A549, HaCaT, NRK-52E, and THP-1 | EC25 | 19 | 0.83 | CORAL | Monte Carlo | Quasi-SMILES |
[86] | [86] | 16HBE, A549, HaCaT, NRK-52E, and THP-1 | EC25 | 19 | 0.87 | R | RF | Aspect ratio and zeta potential |
[87] | [84] | Human embryonic kidney cell line (HEK293) | Cell viability (%) | 40 | 0.80–0.95 | CORAL | Monte Carlo | Quasi-SMILES |
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Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019, 24, 4537. https://doi.org/10.3390/molecules24244537
Buglak AA, Zherdev AV, Dzantiev BB. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules. 2019; 24(24):4537. https://doi.org/10.3390/molecules24244537
Chicago/Turabian StyleBuglak, Andrey A., Anatoly V. Zherdev, and Boris B. Dzantiev. 2019. "Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials" Molecules 24, no. 24: 4537. https://doi.org/10.3390/molecules24244537