Cation toxicity, predicting with QSARs

This chapter discusses quantitative structure-activity relationships (QSARs) for predicting cation toxicity, bioconcentration, biosorption, and binding strength. Several approaches were used to identify these QSARs. First, the test systems, test substances, QSARs, and statistical analyses of each QSAR were extracted from the references cited by Walker et al. (2003). These efforts produced 21 references associated with 97 QSARs for predicting cation toxicities (Table 5.1). These QSARs are discussed in more detail in chapter Sections 5.2,5.3, and 5.5. 

Tatara et al. (1997,1998) QSARs for predicting cation toxicity using standard reduction-oxidation potential alone or in combinations with standard reduction-oxidation potential, atomic number, ionization potential differential, covalent index, logarithm of the first hydrolysis constant, or Pearson and Mawby softness parameter alone or in combination with logarithm of the first hydrolysis constant... 

Table 5.19 lists the 21 QSARs that only used the Pearson and Mawby softness parameter (Op) to predict cation toxicity. Two of these QSARs are duplicates. The Turner et al. (1983) QSAR with r =0.360 is a duplicate of the Williams et al. (1982) QSAR with the same r value. The Turner et al. (1985) QSAR with r =0.879 is a duplicate of the Turner et al. (1983) QSAR with the same r value. The Jones and Vaughn (1978) QSAR for Ag+, Am, Cd " and Hg had the highest perhaps because of their proximity in the periodic table. The Turner et al. (1983) QSAR for Mn +, cm+, Ni +, Cu +, zm+, Cd +, Hg +and Pb " had the second highest r, perhaps because Mn +, Co +, NE+, and Zn " were in row 4 of the periodic table and Cd +, Hg + and Pm+ were in close proximity. For the Jones and Vaughn (1978) and Turner et al. (1983) references with 4 and 3 QSARs, respectively, the r decreased as the number of divalent cations increased (Table 5.18). The Babich et al. (1986) and Magwood and George (1996) QSARs both had high r and almost identical cations. The Babich et al. (1986) QSAR had almost identical cations to those used for the Turner et al. (1983) QSAR with r =0.879 (Table 5.18). The Enache et al. (1999) QSAR also had a high r. However, the Mendes et al. (2010) QSAR with a high r was the best for the highest number (18) of cations (Table 5.19).

Table 5.20 lists the QSARs with less common physicochemical properties used to predict cation toxicity. Only a few of the test systems for the references listed in Table 5.20 have not been previously described. These test systems are described here. 

Enache et al. (2003) developed 18 QSARs to predict cation toxicity (Table 5.1). Enache et al. (2003) determined the concentration at which there is a 50% decrease in the rate of callus development in the sunflower (Helianthus annuus F.l. Sunspot) and found a large number of correlations with relevant physical and chanical descriptors for 16 of their QSARs (Table 5.20). 

As discussed in Section 5.2.5, Van Kolck et al. (2008) developed 4 QSARs to predict the 96-hour LC50 values of 5 cations to the mussel Mytilis edulis and 4 QSARs to predict the 96-hour LC50 values of 6 cations to the mussel Perna viridis (Table 5.17). Six of these QSARs included 3 of the less numerous physical properties used to predict cation toxicity, viz., covalent index (x r), absolute value of the logarithm of the first hydrolysis constant (Hog XqhI), and ionic index (Z /r). The QSARs developed with the covalent index (x r) produced the highest value (Table 5.20). 

Roy et al. 2009 Sacan et al. 2009 Mendes et al. 2010 Su et al. 2010). Analysis of these studies produced 14 references associated with 183 QSARs for predicting cation bioconcentration potential, biosorption capacity, binding strength, and toxicity (Table 5.4). These QSARs are discussed in more detail in Sections 5.2, 5.3, 5.5, 5.6, and 5.7. The acronyms used in Chapter 5 and associated tables are defined in the Chapter 5 appendix. 

Walker et al. (2007) developed 6 QSARs to predict the toxicity of 17 cations to sunflower seeds (F.l. Helianthus annuus Sunspot) from the metal s physical properties and natural occurrence levels (Table 5.4). The QSARs predicted EC50 values based on the cation concentration producing a 50% inhibition of radicle growth one day after emergence. The QSAR developed with density of the elements (p), enthalpy of formation of metal sulfides (AHJ, and the stability constants of metal ions with sulfate (log Ki [sulphate]) produced the highest adjusted (Table 5.20). For natural occurrence levels, the QSAR developed with metal concentrations in soil (log Msoi,), the median elemental composition of soils (mg X/kg soil), and the calculated mean of the elemental content in land plants (Land Plants) produced the highest adjusted... 

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