Insecticide Distribution Model in Human tissues

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MATERIAL AND METHODS
The insecticides evaluated in the present work
were selected among those registered for
agriculture use in Brazil and those having the
required information for the fugacity model in the
literature.
The level I fugacity model was applied to estimate
the percent distribution of thirty nine insecticide
s
among human tissues: muscles, viscera, skin, fat,
blood, liver, kidney and gut, assuming a person
body mass of 70 kg. The fugacity of a pesticide is
related to its concentration,
C
(mol m
-3
), by the
fugacity capacity,
Z
(mol m
-3
Pa
-1
), in the
compartment for a specific insecticide. Intuitively
,
the fugacity capacity expresses the pesticide
solubility in a compartment. The concentration is
estimated by
C Zf
=
where
f
(Pa) is the pesticide
fugacity in the compartment. In a steady-state
equilibrium system, pesticide fugacity from the
tissues might be set equal. Thus, due to the
proportionality between
C
and
Z
, tissues with
high fugacity capacities for an insecticide will al
so
show high insecticide concentrations.
 

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The fugacity capacity in every tissue and for each
insecticide was estimated by the sum of fugacity
capacity of water volumetric fraction in that tissu
e
and the fugacity capacity of lipid volumetric
fraction in the same tissue, that is,
i ow
i i
i
w
K
(1 )
Z
H H
λ
− λ ρ
= + ×
ρ
, where
H
(Pa m
3
mol
-1
)
is the Henry’s law constant for the insecticide,
i
λ
is the lipid volumetric content of tissue
i
,
ow
K
is
the octanol-water partition coefficient,
w
ρ
is the
water density (1000 kg m
-3
) and
i
ρ
(kg m
-3
) is the
tissue density (Cahill and Mackay, 2001;
Maruyama et al., 2002).
In the level I fugacity model, all insecticide
fugacity values are assumed to be equal and
constant, that is, (f
i
= f
j
= f) for
every
i, j J,

{
}
J M, V, S, F, B, L, K, G ,
=
where
M = muscles, V = viscera, S = skin, F = fat, B =
blood, L = liver, K = kidney and G = gut. From
this supposition, if each compartment
j J

was a
well-defined volume
i
V
. Thus, the percent
distribution of insecticide m in tissue
i
is
estimated by
m
m
i i
i
m
i i
i J
Z V
P 100
Z V

= ×

, where 1

m

39
are indexes representing insecticides and
m
i
Z
is the
fugacity capacity of tissue i for the insecticide m
.
Some data of this study were obtained from the
literature as tissue volumes (Kissel and Robarge,
1988), lipid volumetric contents and tissue
densities (Maruyama et al., 2002). The molar
mass, water solubility, vapor pressure, Henry’s
law constants and octanol-water partition
coefficients for the thirty nine insecticides were
obtained in SCR (2005), except for the data for
abamectin, acetamiprid and lufenuron, which were
from Tomlin (2000).
Cluster analysis for insecticides as the tissue
distribution pattern similarity was performed using
the cluster mean and Euclidian distance as criteria
for cluster formation. Cluster analysis for tissues
as the insecticide distribution pattern similarity
was performed using the method of Ward (Ward
 

valley ranch

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RESULTS
Tissue distributions of thirty nine insecticides an
d
their classification in groups and subgroups
according to their distribution pattern similarity
are presented in Table 1. Fat and muscles were the
main compartments tending to accumulate
insecticides, since they presented percent fraction
s
higher than 69% for all the evaluated insecticides.
For 90% of the insecticides (groups 1 and 2), the
fraction in fat tissues was estimated to be higher
than 50% (Table 1).
The dendogram resulting from cluster analysis of
tissues according to the similarity of insecticide
pattern distribution is presented in Fig. 1. The
dendogram resulting from the cluster analysis of
insecticides in relation to the similarity of human
tissue distribution pattern showed that insecticide
s
could be classified in three groups and subdivided
in subgroups: the first group consisted of low
water solubility insecticides with
ow
LogK 1.7

(group 1 and subgroups 1.1, 1.2 and 1.3); the
second consisted of intermediate water solubility
insecticides with
ow
0.51 LogK 1.3
≤ ≤
(group 2
and subgroups 2.1 and 2.2); and the third, of high
water solubility insecticides with
ow
LogK 0.2
≤ −
(group 3 and subgroups 3.1 and 3.2). A straight
line at the 2.8 distance allowed separating three
great groups of tissues: the first consisting of li
ver
and kidneys; the second, of gut, muscle, blood,
skin and viscera; and the third, of fat. These resu
lts
pointed out the importance of volumetric lipid
contents of tissues in the insecticide percent
distribution.
 

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Table 1
- Insecticide groups and sub groups of similar per
centage of distribution in human tissues.
% distribution
Insecticides groups subgroups
muscle
viscera
skin
fat blood
liver kidney
gut
abamectin 10.25 1.27 0.41 86.34 0.21 0.89 0.04 0.59
acrinathrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.
59
alfa-cypermethrin 10.24 1.27 0.41 86.35 0.21 0.89 0
.04 0.59
azocyclotin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.
59
bifenthrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.5
9
carbofuran 11.08 1.35 0.50 85.05 0.42 0.93 0.05 0.6
3
carbosulfan 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.
59
chlorfluazuron 10.24 1.27 0.41 86.35 0.21 0.89 0.04
0.59
chlorpyrifos 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0
.59
cyflutrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.59
cypermethrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0
.59
deltamethrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0
.59
diflubenzuron 10.26 1.27 0.41 86.31 0.22 0.89 0.04
0.59
disulfoton 10.26 1.27 0.41 86.32 0.22 0.89 0.04 0.5
9
endosulfan 10.27 1.27 0.42 86.31 0.22 0.89 0.04 0.5
9
ethion
10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.59
fenpropathrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04
0.59
fenthion 10.26 1.27 0.41 86.33 0.21 0.89 0.04 0.59
fipronil 10.26 1.27 0.41 86.32 0.22 0.89 0.04 0.59
lambda-cyhalothrin 10.24 1.27 0.41 86.35 0.21 0.89
0.04 0.59
lufenuron 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.59
parathion-methyl 10.49 1.29 0.44 85.97 0.27 0.90 0.
04 0.60
permethrin 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.5
9
profenofos 10.24 1.27 0.41 86.34 0.21 0.89 0.04 0.5
9
teflubenzuron 10.25 1.27 0.41 86.34 0.21 0.89 0.04
0.59
triazophos 10.32 1.28 0.42 86.22 0.23 0.89 0.04 0.5
9
triflumuron 10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.
59
zeta-cypermethrin
1.1
10.24 1.27 0.41 86.35 0.21 0.89 0.04 0.59
thiodicarb 1.2 13.58 1.58 0.75 81.18 1.02 1.05 0.07
0.77
aldicarb
1
1.3 20.79 2.27 1.47 69.99 2.77 1.41 0.13 1.18
acetamiprid 28.55 3.00 2.25 57.95 4.65 1.79 0.20 1.
62
dimethoate 29.10 3.05 2.31 57.09 4.78 1.82 0.20 1.6
5
methomyl
2.1
34.29 3.54 2.83 49.04 6.04 2.07 0.25 1.94
imidacloprid 35.18 3.62 2.92 47.66 6.26 2.12 0.25 1
.99
trichlorfon
2
2.2
36.97 3.79 3.10 44.88 6.69 2.21 0.27 2.10
monocrotophos 3.1 54.03 5.40 4.81 18.41 10.83 3.05
0.42 3.06
acephate 60.00 5.96 5.41 9.14 12.28 3.34 0.47 3.40
methamidophos 59.77 5.94 5.38 9.51 12.22 3.33 0.46
3.38
methidathion
3
3.2
59.77 5.94 5.38 9.51 12.22 3.33 0.46 3.38
 

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REFERENCES:

Abel, E., Bammler, T., Eaton, D. (2004),
Biotransformation of methyl parathion by glutathion
e
S-transferases.
Toxicological Sciences
,
79
, 224-232.
Andersen, M.E., Thomas, R.S., Gaido, K.W., Conolly,
R.B. (2005), Dose–response modeling in reproductive
toxicology in the systems biology era.
Reproductive
Toxicology
,
19
, 327-337.
Caldas, E., Souza, L. (2000), Avaliação de risco cr
ônico
da ingestão de resíduos de pesticidas na dieta
brasileira.
Revista de Saúde Pública
,
34
, 529-537.
Castro, V., Silveira, M., Perez, M. (1999), Applica
tion
of clinical indicators of exposition in the evaluat
ion of
family agriculture health: the Sumaré case - Brazil
.
International Journal of Sustainable Development
and World Ecology
,
6
, 172-184.
Cahill, T.M., Mackay, D. (2001), Generalized human
physiologically-based pharmacokinetic model for
multiple chemical species based on fugacity.
Abstracts of Papers of the American Chemical
Society
, 221, 186-Part 1.
Czub, G., McLachlan, M. (2004), A food chain model
to predict the levels of lipophilic organic
contaminants in humans.
Environmental Toxicology
and chemistry 23. 2356-2366
 

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Hi, Nothing has to be done with it. Monsanto has arranged a report.



Seminar: “Monsanto: Who Do They Think They Are?”

Date/Time:
Friday, March 6, 2015 - 12:20pm to 1:10pm


101 ASI Building
Event Categories:
University Park
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Pesticides on Food
I am concerned about pesticides in my food. Is this something I should be worried about?



Answer - The issue of pesticide residue in food is quite controversial. Pesticides are used because they have beneficial properties in terms of crop production and yield. With the use of pesticides, farmers can maximize their efforts in the field, thus minimizing the cost of the produce to the consumer. Pesticides are used by farmers to prevent fungal invasion, insect damage, and the growth of unwanted (and often poisonous) plants. This has a positive benefit in terms of public health because fungi, insects, and non-crop plants can contaminate crops with many natural toxins.

Pesticides are probably one of the most regulated chemical products used in the U.S. Several major organizations regulate the use of pesticide. These include the Environmental Protection Agency, the Food & Drug Administration, and the U.S. Department of Agriculture. There are more than 14 separate regulations governing the use of pesticides. All of these regulations are in place to help protect human health.

Despite the many regulations, pesticide residues are found in our food supply. Because residues are an inevitable by-product of pesticide use, many of the current regulations are in place to address the public health implications of pesticide use. Therefore, there are very strict restrictions on the amount of pesticides residues that are allowed in food.

One of the regulations that is currently in place requires that pesticide manufacturers conduct toxicity testing on the pesticide before it can be permitted for use on products either directly or indirectly destined for human consumption (this includes animal feed). This toxicity testing not only determines the health effects of pesticides, but also the level at which there are no toxic effects on the most sensitive population (i.e. children and the elderly). This 'No Toxic Effect Level' (NOEL) becomes the basis for the permitted residue limit. The regulations set the permitted residue level at a level that is from 10 to 100 times lower than the NOEL. Furthermore, if a pesticide is tested and a NOEL can not be determined, then it is unlikely to be permitted for use on food crops. This helps ensure that if a person, child or adult, eats a larger than normal amounts of a particular food, or several different foods with the same or similar pesticide residue, they will still not reach the level of exposure required for a toxic effect to occur, even if they are more sensitive than the general population.

So, while pesticides may be found in many products, the levels at which they are present fall far below the levels known to not cause any health effects. The fact that they are found at all is only due to the significant advances in analytical chemistry. The tests are now so sensitive that the detection level that can be easily reached is equivalent to detecting one teaspoon of salt in one million gallons of water. Levels even lower than that can sometimes be detected. The mere presence of a trace amount of a pesticide does not mean that the product is unhealthy. On the contrary, eating a diet full of a variety of fruits, grains, and vegetables has been shown to significantly decrease your risk of a variety of health problems from high blood pressure to cancer. Variety is the key to good health.

From: The National Institute of Environmental Health Sciences (NIEHS) is one of 27 Institutes and Centers of the National Institutes of Health (NIH),which is a component of the Department of Health and Human Services (DHHS).
 

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Valley, are you able to link to this so we can view it from the website? I'm interested in seeing the results table as formatted.
 
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