Pest Management Science Pest Manag Sci 56:821±824 (2000)

Correlation of physico-chemical parameterswith toxicity of metal ions to plants †

Monica Enache,1 Piali Palit,1 John C Dearden1* and Nicholas W Lepp2

1School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK2School of Biological and Earth Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

(Rec

* Co3AF,† Baof thWare

# 2

Abstract: The in vivo toxicities of 12 metal ions to cabbage plants have been correlated with ion-speci®c

physico-chemical parameters. Several regression models were statistically signi®cant but two

different electronegativities in combination with Kaiser's electrochemical potential appeared to be

the best determinants of metal ion toxicity, giving a good correlation (r2adjusted=0.81 for 11 metal ions

excluding copper, and 0.66 for all 12 metal ions).

# 2000 Society of Chemical Industry

Keywords: QSAR; metals; ions; toxicity; plants

1 INTRODUCTIONThe study of the relationships between the physical or

chemical structure of metal ions and their physio-

logical actions began more than a century and a half

ago. Since then many theories have been formulated

for metal ion toxicity to vertebrate and invertebrate

species and these continue to be developed. However,

literature on similar relationships using plant material

is still very limited, although there is an increasing

need of structure-activity data in the expanding ®elds

of ecotoxicology and phytotoxicology. Previous quan-

titative structure±activity relationship (QSAR) studies

on metal ion toxicities have indicated that a variety of

ion characteristics play a role in predicting the toxicity

of metal ions. Biesinger and Christensen,1 studying the

toxic effects of metals on Daphnia magna Straus found

correlations between the chronic toxicity of metals and

the solubility of metal sul®des, electronegativity and

the equilibrium constant of the metal±ATP complex.

Various groups applied the theory of hard and soft

acids and bases (HSAB theory) when investigating

information on metal ion toxicity. Jones and Vaughn2

obtained signi®cant correlations when plotting mouse

and rat LD50 data as a function of two softness

parameters sp and sk,3 and the results were con®rmed

for mouse and Drosophila by Williams et al. 4 In

addition, LD50 values for acute toxicity of metal ions

in mice were shown by Turner et al5 to correlate better

with the softness parameter, sp of Pearson and

Mawby6 than with many other physico-chemical

characteristics of metal ions that were investigated.

Kaiser,7,8 using parameters derived from ionization

potential and redox potential, developed a two-

eived 27 May 1997; revised version received 29 March 2000; accepte

rrespondence to: John C Dearden, School of Pharmacy and ChemisUK

sed upon a Poster presented at the postgraduate meeting ‘Bioactivee Physicochemical and Biophysical Panel of the Pesticides Group of, Hertfordshire

000 Society of Chemical Industry. Pest Manag Sci 1526±498X/2

variable equation which was used successfully in

predicting metal toxicity to both aquatic and terrestrial

biota for three groups of ions with similar electronic

structure. Recently Newman and McCloskey9 found

the ®rst hydrolysis constant (jlogKOHj) to be most

effective predictor of toxicity to bacteria. They also

obtained signi®cant regression models with various

other ion characteristics.

One of the major dif®culties in developing QSARs

for toxicity to plants lies in obtaining from the

literature uniform and consistent biological data sets

on large numbers of metal ions. The varying complex-

ity of plant systems, coupled with differing cellular

composition, energy sources and routes of metal

access to living cells, compared to vertebrates and

invertebrates, will almost certainly produce a different

picture from those described above. The aim of the

present work was to identify correlations between

toxicity values in plants, taken from Hara and

Sonoda,10 and a number of relevant physico-chemical

parameters for metal ions.

2 METHODHara and Sonoda10 studied the toxicity of 12 metal

ions to cabbage plants (Brassica oleracea L var capitatacv Soshu). From the graphical presentation of the

results, the heavy metal content in the outer leaves

which produced 50% reduction of relative growth

(Rrg50) was calculated and the values were trans-

formed into molar concentrations. Since no informa-

tion was given in the paper about the chemical form in

which copper was supplied to the nutrient solution, the

d 11 April 2000)

try, Liverpool John Moore’s University, Byrom Street, Liverpool L3

molecules: research approaches’, organised by M Smith on behalfthe SCI and held on 22 April 1997 at Glaxo Wellcome Laboratories,

000/$30.00 821

Table 1. Regression models with a coefficient ofdetermination greater than 0.50 of metal ion toxicityto cabbage plants (Hara and Sonoda10) withphysicochemical ion characteristicsa

ÿ(log Rrg50)=f(x) r2 adj s p F n

12.2ÿ8.54 XARÿ1.59 DE0ÿ3.04 X 0.81 0.49 0.002 15.06 11b

ÿ1.02�0.0187 AW ÿ1.10 DE0�0.264 DIP 0.73 0.59 0.006 10.04 11b

ÿ1.33�0.0498 AN ÿ1.07 DE0�0.268 DIP 0.72 0.60 0.007 9.65 11b

8.57ÿ7.26 XARÿ2.51 DE0�4.45 X 0.66 0.78 0.009 7.98 12

ÿ1.55�0.0442 AN�0.246 DIP 0.65 0.67 0.006 10.35 11b

ÿ1.28�0.0165 AW�0.242 DIP 0.65 0.66 0.006 10.47 11b

ÿ1.36ÿ1.53 DE0�1.20 X2ri�0.239 DIP 0.55 0.90 0.024 5.51 12

ÿ0.74�0.0195 AW ÿ1.94 DE0�0.295 DIP 0.55 0.90 0.025 5.42 12

ÿ1.07�0.0519 AN ÿ1.91 DE0�0.299 DIP 0.54 0.90 0.026 5.36 12

ÿ0.39�0.713 Z ÿ2.47 DE0�1.43 X2ri 0.54 0.90 0.025 5.39 12

0.58�0.250 Z/riÿ2.41 DE0�1.35 X2ri 0.51 0.93 0.033 4.84 12

a XAR=electronegativity (Allred±Rochow formula),15 DE0=absolute value of the electrochemical

potential between the ion and its ®rst stable reduced state,16 X =Pauling's electronegativity,14

AW =atomic weight, DIP =the difference in ionisation potentials between ion oxidation number (OX)

and (OX - 1),16 AN =atomic number, ri=crystal ionic radius,19 Z =charge on the ion.b Cu2� omitted from the analysis.

M Enache et al

oxidation state for copper was assumed to II.11

Toxicity data were as follows [ÿlogRrg50 (molkgÿ1

dry weight)]: Mn2�=0.96, Zn2�=1.88, Fe3�=1.92,

Co2�=2.07, Ni2�=2.22, Cd2�=3.06, Cr6�=3.79,

V3�=3.79, Cr3�=3.81, Hg�=4.10, Hg2�=4.33 and

Cu2�=5.64. Properties considered to be relevant in

metal ion±macromolecule interactions and a number

of ion characteristics that have appeared in previous

QSAR correlations were included as par-

ameters.1,5,7,9,12,13 In total, 40 parameters were

generated, but values for all 12 metal ions of Hara

and Sonoda were found in the literature for only 20

parameters. Step-wise regression was carried out for

these 20 parameters and linear regression for the

incomplete data sets. In the case of the softness

parameter sp, the regression analysis included only

ions of the same oxidation state.3 A genetic algorithm

written in-house in Basic language using MatLab was

used to generate sets of two and three independent

variables for regression models. The regression analy-

sis was carried out using the Minitab statistical

package.

3 RESULTS AND DISCUSSIONThe statistical analysis on the 12 metal ions of the

Table 2. Correlation matrix (r ) of parametersa usedin the regression model

DE0 XAR

XAR 0.348

X2ri 0.186 ÿ0.2

DIP 0.005 0.0

AN 0.286 ÿ0.6

AW 0.295 ÿ0.6

X 0.212 0.1

Z 0.321 ÿ0.0

Z/ri 0.335 0.0

a XAR=electronegativity

radius, DIP =ionisation p

ion, DE0=absolute value

state.

822

training set produced a number of signi®cant equa-

tions, but improved results were obtained when the

copper ion was omitted from the analysis (Table 1).

The outlier position of Cu2� is due to its exceptionally

high toxicity determined experimentally by Hara and

Sonoda,10 which was found to be comparable to that

of Cr6�, Cd2� and Hg2�, although copper is an

element essential to plants. Table 2 shows the

correlation matrix of parameters used in the regression

models presented.

The best model was obtained with a combination of

two sets of electronegativity and the standard potential

difference. Electronegativity determines the nature of

the chemical bond between metal ions and ligands,

measuring the ability of an atom to gain electrons in a

chemical bond. This is dif®cult to quantify because it

relies on indirect determination. It is not a uniquely

de®ned characteristic of an atom or ion, so that the

value for a metal may vary, according to how it was

assigned or to the ligand with which the metal

combines. There are different sets of electronegativity

values in the literature, sometimes based on entirely

different approaches. On one hand, the Pauling±

HaõÈssinsky scale14 relates to the square root of the

average heat of rupture of metal-halide bonds to

ground-state atoms. On the other hand, electronega-

X2ri DIP AN AW X Z

40

14 ÿ0.294

03 0.870 ÿ0.444

17 0.868 ÿ0.431 1.000

85 0.592 0.375 0.241 0.239

34 ÿ0.409 0.784 ÿ0.460 ÿ0.451 0.364

02 ÿ0.359 0.786 ÿ0.437 ÿ0.428 0.418 0.989

(Allred±Rochow formula), X =Pauling's electronegativity, ri=crystal ionic

otential differential, AN =atomic number, AW =atomic weight, Z =charge on

of the electrochemical potential between the ion and its ®rst stable reduced

Pest Manag Sci 56:821±824 (2000)

Correlation of physico-chemical parameters with metal in toxicity to plants

tivity according to the Allred±Rochow scale15 is related

to the electrostatic force of attraction between the

nucleus and an electron at a distance of the covalent

radius. These different methods of calculation account

for the lack of correlation (r =0.185, Table 2) between

the two electronegativity parameters X and XAR in

Table 1. Electronegativity is a basic property essential

to the consideration of chemical reactivity, and toxicity

is often directly dependent on it. The standard poten-

tial difference DE0 was described by Kaiser7 and

re¯ects the ability of the ion to change its electronic

state in aqueous solution.16

Other parameters used in the various regression

models were: atomic weight, ionisation potential

differential, atomic number (AN), covalent index,

ionic charge and ionic potential. Ionisation potential

differential (DIP) is a parameter which was also

described by Kaiser7 and re¯ects the effects of atomic

ionisation potentials. Ionisation potential is related to

the biochemical signi®cance of the metal because very

low ionisation potentials render elements unreactive.

The terms DIP and DE0 are related to outer orbital

electronic properties of atoms; Kaiser used the par-

ameters log AN/DIP and DE0 together in bivariate

toxicity models.7,8

The atomic number of the element re¯ects the

molecular size of an ion in a manner analogous to the

periodic classi®cation. Within each vertical group of

the periodic table, increased electropositivity and

inherent toxicity are generally associated with in-

creased atomic number or weight. Exceptions to this

are lithium and beryllium, which are light and less

electropositive but more toxic than the other elements

in their group, due to both their small ionic radius and

higher charge-to-radius ratio. These factors are re-

sponsible for penetration and diffusion into tissues.

The heavier metals in each group are able to form

irreversible, stable complexes with biomolecules,

frequently resulting in a toxic response. Lighter metals

form reversible complexes with macromolecules,

enabling essential functions to proceed. Some authors

have associated atomic number with the inertia of the

ion.

The oxidation state or the charge which the metal

ion has in an ionic complex re¯ects a particular elec-

tronic con®guration. The stability of a given oxidation

state of a metal is a result of the electronic structure of

the valence orbitals. Toxicity can be related to stability

of electronic con®guration; it is sometimes associated

with the oxidation state and the speed at which the

metal ion undergoes oxidation and reduction.

The covalent index X2r was ®rst introduced by

Nieboer and McBryde12 and was developed further by

Nieboer and Richardson.13 The latter authors pre-

sented two variables useful as indices of metal±ligand

complex stability in aqueous solution. These indices

represent also a quanti®cation of the tripartite classi-

®cations of metal ions derived from the original

division of acceptors by Ahrland et al. 17 The covalent

index re¯ects class B (softness) properties quantifying

Pest Manag Sci 56:821±824 (2000)

the importance of covalent interactions in the metal±

ligand complexation relative to ionic interactions,

while class A properties (hardness) are described by

another parameter, Z2/ri (the ionic index (the ion

charge (Z) squared divided by the ion radius (ri)). This

re¯ects the energy of an ion when interacting electro-

statically with a ligand. Also related to the ionic index

is the charge-to-radius ratio of a metal (Z/ri) (the ionic

potential). This is also considered an index of the

tendency to form ionic bonds because it indicates the

distance between an ion and another charge and is

proportional to the size of the electrostatic attraction.

Strong binding af®nities to charged groups in macro-

molecules would characterise acceptors with a large

value of the ionic potential. Charge-to-radius ratio has

found limited application as an index for class A

acceptors.12

Signi®cant linear regression models were obtained

between Rrg50 and the formation constants of metal

ion complexes with glycine (K1)18 (r2 adjusted=0.75,

s =0.76, p =0.007, F =19.49, n =7) and between

Rrg50 and the formation constants of metal complexes

with glutamic acid (K1)18 (r2 adj=0.81, s =0.70,

p =0.009, F =22.86, n =6). The formation constant

represents a measure of the tendency toward forma-

tion of metal complexes in aqueous solution and gives

a quantitative measure of the relative stabilities of

various metal complexes. The activity/toxicity of a

metal ion may depend on the stability of this metal±

ligand bond and the kinetics of formation and

degradation.

The present investigation has shown that it is

possible to model metal ion toxicities in whole plants.

In addition, the results obtained indicate that a variety

of ion characteristics seem to play a role in predicting

the toxicity of metal ions, and this is in accordance

with a literature survey in this ®eld of study. However,

no completely satisfactory theory of cation toxicity has

yet been formulated, and the correlation of metal ion

toxicity with some physical, chemical or other type of

property of the metals still presents a challenging

problem.

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Pest Manag Sci 56:821±824 (2000)