Inflammatory Response to TiO₂ and Carbonaceous Particles Scales Best with BET Surface Area

Referencing: In Search of the Most Relevant Parameter for Quantifying Lung Inflammatory Response to Nanoparticle Exposure: Particle Number, Surface Area or What?

In an attempt to identify the proper dose metric for particle toxicity, Wittmaack (2007) reanalyzed our dose–response data (Stoeger et al. 2006) and that of Oberdörster et al. (2005) on acute lung inflammation in rodents after instillation of various particle types. Out of particle BET surface area (S_BET), particle number, joint length, and "geometric" surface area, Wittmaack concluded that particle number tends "to work best" as dose metric. We disagree with his conclusion.

Figure 1. Acute pulmonary inflammatory response (PMNs) to TiO2 [Oberdörster et al. 2005; Figure 4 and Figure S-2 (Supplemental Material available online at http://ehp.niehs.nih.gov /members/2005/7339/supplemental.pdf)] and carbonaceous particles (Stoeger et al. 2006; Figure 1) in rats and mice, with particle number (A) and SBET (B) as the dose metric.

First, we wonder why Wittmaack (2007) used our data but ignored the data of Oberdörster et al. (2005) for the identification of the best dose metric. Figure 1 shows our dose–response data (in mice) for six different types of ultrafine carbonaceous particles (10–50 nm) and the data of Oberdörster et al. (2005) for fine (~ 250 nm) and ultrafine (~ 20 nm) TiO₂ particles; we present the data for rats, which was reanalyzed by Wittmaack, and also the mouse data from Oberdörster et al. (2005). In Figure 1 the inflammatory response after 24 hr is expressed as the ratio of the polymorphonuclear leukocytes (PMNs) to lavaged cells, and the instilled dose is normalized to lung weight, because this facilitates interspecies comparison (Oberdörster et al. 2005). As suggested by Wittmaack (2007), we limit our discussion to the linear response regime [analogous to his Figure 3 (Wittmaack 2007)]. For this data set, the linear correlation coefficient R² is 0.46, 0.51, 0.67, and 0.72 for particle number, joint length, "geometric" surface area, and S_BET, respectively. Particularly, the response to the fine particles, as represented by the red fit line (almost identical to the y-axis in Figure 1A), is not adequately described by particle number (Figure 1A), whereas S_BET works well for all particle sizes (Figure 1B). Although we do not suggest S_BET as a "universal" dose metric (chemistry, charge, etc., are also relevant), we conclude that for the dose metric examined here, S_BET is the most relevant dose parameter. Wittmaack's preference for particle number appears to be the result of an unsubstantiated restriction of his analysis to our data, which is dominated by particles in a relatively narrow size regime between about 10 and 25 nm.

Second, all investigated dose parameters (except S_BET) depend on accurate determination of the mean particle diameter, <d>, requiring tedious and potentially uncertain single particle analysis. Wittmaack (2007) acknowledged potentially large errors in <d> for particles below about 20 nm [i.e., for four out of our six (carbonaceous) particle types]. Being aware of these limitations, we intentionally reported only a range of observed particle diameters (not <d>) in our article (Stoeger et al. 2006). Unfortunately, Wittmaack did not discuss his conclusions in light of these methodologic limitations. Especially for the smallest particle type (here spark-generated carbon particles with <d> = 9.8 nm), preferential particle selection is likely to result in an overestimation of <d>. Assuming a 25% sizing error, this yields a systematic error of + 100% in particle number (~ <d>^–3), which shifts these data points far away from the linear fit line (see error bars in Figure 1A). In contrast, S_BET requires only a single measurement on an aliquot of the administered particles; that is, it is not adversely affected by problems associated with single particle analysis, and it adequately accounts for potentially important particle characteristics such as particle morphology and surface porosity.

In summary, we do not agree with the dose–response interpretation of our data by Wittmaack (2007). We conclude that S_BET (and not particle number) is the best dose parameter, accounting for 72% (R²= 0.72) of the observed inflammatory response for both data sets spanning a size range of 10–250 nm.

The authors declare they have no competing financial interests.

References

Oberdörster G, Oberdörster E, Oberdörster J. 2005. Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environ Health Perspect 113:823–839.

Stoeger T, Reinhard C, Takenaka S, Schroeppel A, Karg E, Ritter B, et al. 2006. Instillation of six different ultrafine carbon particles indicates a surface area threshold dose for acute lung inflammation in mice. Environ Health Perspect 114:328–333.

Wittmaack K. 2007. In search of the most relevant parameter for quantifying lung inflammatory response to nanoparticle exposure: particle number, surface area, or what? Environ Health Perspect 115:187–194; doi: 10.1289/ehp.9254 [Online 3 October 2006].

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Dose and Response Metrics in Nanotoxicology: Wittmaack Responds to Oberdörster et al. and Stoeger et al.

In their letters, Oberdörster et al. and Stoeger et al. present some comments on a few out of many issues that I addressed in my reanalysis of literature data on lung inflammatory response to nanoparticle exposure (Wittmaack 2007). I appreciate the opportunity to strengthen and expand my arguments.

Figure 1. Particle-size dependence of the A/M and the ΣAae. The straight line relates to TiO2 particles, the open and solid circles indicate ambient aerosol particles, and the crosses indicate two BET data. According to Oberdoerster et al.'s letter, the so-called 20-nm TiO2 particles may well have been 30 nm in size.

I argue that results of nanoparticle toxicology studies should not be interpreted on the basis of the reasoning that the number of surface atoms, relative to all atoms in a (spherical) particle, increases as the inverse of the diameter, D (Oberdörster et al. 2005). If the toxicity of an insoluble particle scales with the number of surface atoms, it is the surface area (A) that counts, not its ratio to the mass (M). Figure 1 shows the size dependence of the specific surface area (S = A/M = 6/D) for TiO₂ particles [mass density, (anatase) = 3.9 g/cm³]. Also presented is an example for the cumulative surface area (ΣA_ae) calculated from the mean number concentration of an ambient aerosol (Wittmaack 2002), including extrapolated data for D < 10 nm. ΣA_ae decreases rapidly with decreasing D, notably for D < 100 nm. In contrast, S_ae = ΣA_ae/ΣM_ae = ΣA_ae/ΣV_ae (= 1.5 g/cm³) increases as 1/D, for D < 200 nm, where V is the particle volume. If toxicity is assessed by reference to S_ae rather than to A_ae, the danger of exposure to nanoparticles (e.g., for D = 30 nm), compared to fine particles (D = 1 µm), is overestimated by a factor of 1,130. By taking the ratio A/M, we compare apples (the surface area of insoluble particles) and oranges (the mass of soluble particles).

This type of reasoning in terms of S_ae (Oberdörster et al. 2005) has been used often (Kreyling et al. 2006; Nel et al. 2006 ); Gwinn and Vallyathan (2006) even characterized ultrafine particles (UFPs; i.e., particles with D ≤ 100 nm) as "UFPs with larger surface area."

In their Figure 1, Oberdörster et al. (2005) reproduced some of their own data in two ways: as the number (n_PMN) of lavaged polymorphonuclear leukocytes (PMNs) and as the ratio (r_P,m) of n_PMN to the number (n_ma) of macrophages (r_P,m = n_PMN/n_ma). To demonstrate that the particle number is not an appropriate dose metric in the special case of TiO₂, the data could have been presented in a single graph. I found that particle number is a suitable dose metric for differently prepared carbon nanoparticles (Wittmaack 2007). In their letter, Oberdörster et al. use the comparison between n_PMN and r_P,m to argue that "the choice of the response metric is irrelevant." Data analysis shows that in their study n_ma was essentially constant (10.9 ± 0.5) 10⁶. Hence, if n_PMN is divided by n_ma ≅ constant, on appropriate scales, the ratio r_P,m looks essentially the same as the n_PMN. Clearly, this result is not proof of the cited assertion.

Figure 2. Response of rats to the instillation of 250 nm TiO2 particles shown as the rP,m as reported by Oberdörster et al. in their letter, and the derived fP,mcorresponds to the linear fit through the rP,m data.

To explore this issue further, Figure 2 shows a direct comparison of r_P,m with the corresponding fractions f_P,m= n_PMN /(n_PMN + n_ma) = r_P,m /(1+r_P,m) for the 250-nm TiO₂ data, according to Oberdörster et al.'s letter. The solid line in Figure 2, derived by linear regression analysis of the r_P,m data, agrees well with previous results (Wittmaack 2007). Further evaluation provided the clue to the issue in question. By converting the r_P,m regression data to fractions f_P,m, I obtained the curve (dashed line), which is clearly nonlinear. Hence, using the f_P,m approach, Oberdörster (2000) converted an existing linear dose–response relationship (for n_PMN or r_P,m) artificially to a dependence that feigns the onset of saturation effects. Therefore, the choice of the response metric is not irrelevant.

Preparing Figure 1 of their letter, Stoeger et al. changed from the right (n_PMN) (Stoeger et al. 2006) to the wrong (f_P,m) response metric. For mice exposed to different types of carbon particles except for those with high carbon content (SootH), I derived from their Figure 1B rather high mean lung masses of 0.287 ± 0.047 g, and even higher values (0.469 ± 0.028 g) for the SootH-exposed animals. The ratio of these two masses (0.61) is the same as that of the ratio S_BET(SootH)/S_BET (SootL). This means that their data were erroneously permuted. Also, the f_P,m carbon particle data are poorly correlated with the original n_PMN data (Stoeger et al. 2006) because the numbers of "lavaged cells," presumably macrophages, derived from the n_PMN and f_P,m data, differ vastly (i.e., between about 2 10⁵ and 3 10⁶. Hence, either the f_P,m data in the letter of Stoeger et al. were miscalculated, or n_ma exhibited a biologically unreasonable spread. Furthermore, they include 15 response data for carbon in their letter, but the linear dose–response region contains only 13 (Wittmaack 2007).

In their effort to show that the surface area constitutes a proper all-particle dose metric, Stoeger et al. (2006) discredited their own transmission electron microscopy analysis. Their argument is irrelevant because the spark-generated particles contributed only one data point to a total of 13. Finally, Stoeger et al. do not accept one of the most important points of my article: Carbon particles of different origin exhibit large differences in surface toxicity and, therefore, they cannot be used to identify the best dose metric. Moreover, combining TiO₂ and carbon data in one graph is not an appropriate comparison.

The author declares he has no competing financial interests.

References

Gwinn MR, Vallyathan V. 2006. Nanoparticles: health effects—pros and cons. Environ Health Perspect 114:1818–1825.

Kreyling WG, Semmler-Behnke, Möller W. 2006. Health implications of nanoparticles. J Nanoparticle Res 8:534–562.

Nel A, Xia T, Mädler L, Li N. 2006. Toxic potential of materials at the nanolevel. Science 311: 622–627.

Oberdörster G. 2000. Toxicology of ultrafine particles: in vivo studies. Phil Trans R Soc Lond A358:2719–2740.

Oberdörster G, Oberdörster E, Oberdörster J. 2005. Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environ Health Perspect 113:823–839.

Stoeger T, Reinhard C, Takenaka S, Schroeppel A, Karg E, Ritter B, et al. 2006. Instillation of six different ultrafine carbon particles indicates a surface area threshold dose for acute lung inflammation in mice. Environ Health Perspect 114:328–333.

Wittmaack K. 2002. Advanced evaluation of size-differential distributions of aerosol particles. J Aerosol Sci 33:1009–1025.

Wittmaack K. 2007. In search of the most relevant parameter for quantifying lung inflammatory response to nanoparticle exposure: particle number, surface area, or what? Environ Health Perspect 115:187–194; doi:10.1289/ehp.9254 [Online 3 October 2006].