on
the importance of mixtures
earlier
work by Rajapakse et al.
A
recurring criticism of the idea that endocrine disruptors
might harm people is that the levels of xenoestrogens are so low
compared to the normal levels and potency of natural estrogen in
the body that they could not have an effect. This argument is not
based on empirical evidence, but rather on what would appear to
be common sense. How can the addition of a tiny amount of relatively
weak xenoestrogen disrupt a system in which there are significant
levels of the real stuff?
In
this paper, Rajapakse et al. demonstrate conclusively
that this criticism is wrong. Their experiments show that
xenoestrogens in a mixture can have a very significant impact even
in the presence of estrogen. The additive impact of a collection
of xenoestrogens, each of them at concentrations beneath their individual
"no effect" level, was to more than double the effect
of natural estrogen by itself.
What
is important to bear in mind in interpreting these results, is that
humans never are exposed to just one xenoestrogen.
We all have multiple xenoestrogens in our bodies at the same time,
most likely many more than the 11 used by Rajapakse et al.
Hence the approach they used is highly relevant to life in the real
world.
What
did they do? Rajapakse et al. performed a series
of experiments using the now-classic yeast estrogenicity assay that
quantifies the degree to which a compound can stimulate an estrogenic
response via binding with the estrogen receptor.
They
first ran their experiments one chemical at a time. Using these
results, they established dose-response curves for each compound,
including estrogen. This allowed them to determine the "no-effect"
level, i.e., the concentration at which each compound, by itself,
had no detectable estrogenic response.
They
then created different mixtures of the natural estrogen, 17ß-estradiol
and 11 different xenoestrogens in varying concentrations, and measured
the dose-response curves of these mixtures.
The
xenoestrogens they used were:
- 2´,3´,4´,5´-tetrachlorobiphenyl-4-ol
- 2´,5´-dichlorobiphenyl-4-ol
- 4´-chlorobiphenyl-4-ol
- genistein
- 2,4-dihydroxbenzophenone
- benzyl-4-hydroxyparabene
- 2,3,4,5-tetrachlorobiphenyl
- bisphenol
A
- resorcinol
monobenzoate
- 2,3,4-trichlorobiphenyl
- phenyl
salicylate
What
did they find? In the first phase of their work, Rajapaske
et al. studied each compound individually to determine relationships
between dose and response specific to each chemical.
Each
xenoestrogen had a characteristic dose-response curve describing
its ability to provoke an estrogenic response. As expected, all
xenoestrogens were significantly weaker than natural estrogen, 17ß-estradiol.
Absorbance
curves for 17ß
estradiol and 11 xenoestrogens. Weaker compounds plot farther
to the right.
adapted
from Rajapaske et al. 2002 |
|
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Each
absorbance curve shows the degree of estrogenic response elicited
by a given compound at a given concentration. Each of this curves
is sigmoidal (S-shaped) in shape. Beneath a certain level (different
for each compound), no response is observed. As the concentration
of that compound is increased (moving right and upward along a particular
curve, the response increases, until it reaches a maximum and then
levels out at a plateau. Increasing the concentration then has no
additional effect on absorbance.
To
compare estrogenic potency among compounds, Rajapaske et al.
compare the concentration of the compound necessary to produce 1%
of the maximum response observed in the assay.
17ß-estradiol
is the strongest, with 1% of the response reached by 2.3 x 10-5
µM (micromolar, a standard unit of concentration). Phenyl
salicylate (labeled 12 in the graph) is the weakest. Its concentration
must be roughly 400,000 higher than estradiol's to elicit the same
level of response. Bisphenol A (#9) is roughly 28,000 times weaker
than estradiol. The strongest xenoestrogen used, 2´,3´,4´,5´-tetrachlorobiphenyl-4-ol
(#2), is 350 times weaker than estradiol. Clearly all the xenoestrogens
in this experiment are dramatically less potent than natural estrogen
at provoking estrogenic responses.
This
first round of experiments then set up the second phase.
Rajapaske et al. worked out dose-response curves for mixtures
of the 11 xenoestrogens in combination with 17ß-estradiol.
They did this first by combining each xenoestrogen in a xenoestrogen
stock mixture with each compound present in proportion to its concentration
level necessary to provoke 1% of the maximum response. They then
combined that xenoestrogen mixture in solutions in combination with
a 17ß-estradiol stock mixture, also at its 1% level. The key
variable here was that the ratio of the xenoestrogen stock to 17ß-estradiol
stock varied from 25,000 to 1 to 100,000 to 1.
Their
interest in these combinations was to test whether it was possible
to predict, on the basis of the dose-response curves of the compounds
by themselves, what the dose-response curve of the mixture would
be.
They
found excellent agreement between observed effects and their calculated
predictions, using a method called "combination-addition"
that is designed specifically for using with sigmoidal dose-response
(S-shaped) curves.
They
also found that a more traditional method, "effect-addition"
led to erroneous predictions that would have been interpreted wrongly
as synergistic. In other words, the "effect-addition"
approach underestimated the true effect of the mixture, whereas
the "combination-addition" approach predicted it correctly.
Both these approaches assumed additivity of the compounds, but they
were based upon different models of additivity, one corrected for
the sigmoidal nature of the dose response curve. They concluded,
therefore, that the effect of the combination of estradiol and xenoestrogens
in a mixture was additive, not synergistic.
In
a final and profoundly important step in their analysisof the importance
of mixtures, they looked at set of these experiments in which the
levels of the xenobiotics were so low that individually they would
not cause any effect.
For
each xenoestrogen they had determined a "no-effect" level,
that is, the highest concentration of that compound that caused
no statistically-significant effect. In other words, at that level
or below, the compound's effect was not any different than having
no compound.
If
the criticism, above, of endocrine disruption
is correct, then the effect of these contaminants in combination
on the assay should be trivial. After all, each of them are present
at a level that causes no effect. This is the way that toxicological
standards are currently developed: each in isolation.
They
then looked again, with a different lens, at the mixture experiments
and specifically at a case in which the xenoestrogens were all beneath
"no effect" levels and 17ß-estradiol concentrations
were in the mid-range of its dose response curve.
In
this case, they knew what 17ß-estradiol by itself would do
at that concentration because they had begun the work with compounds
individually (above). They then predicted
what the mixture would do based on their two prediction models,
"combination-addition" and "effect-addition"
(CA and ES in the figure below).
They
found (observed effect, figure below) that the mixture more than
doubled the effect of 17ß-estradiol. Their CA prediction matched
the model, whereas the ES prediction grossly underestimated the
mixtures' impact.
|
 |
Effect
of adding 11 xenoestrogens, each at levels beneath effect
levels, to a solution with 17ß-estradiol. The effect
of the mixture more than doubled the effect of 17ß-estradiol
alone. The predictions using a "combination-addition"
method were accurate whereas the "effect-addition"
model grossly underestimated the observed effect. |
What
does it mean? Rajapaske et al.'s results are important
for three reasons.
First,
they clearly and elegantly demonstrate that xenoestrogens at very
low levels can dramatically increase the responsivity of a system
to endogenous 17ß-estradiol. These results definitively
refute the claim that the low potency and low concentrations of
xenoestrogens, compared to 17ß-estradiol, make them insignificant
players in the control of hormone systems. According to
Rajapaske et al.:
|
"...by
not taking combination effects into account, significant underestimations
of the effects associated with exposure to xenoestrogens are
likely. In our experimental model, we have demonstrated in
principle that every xenoestrogen, however weak, may add incrementally
to the total estrogenic effect, even at very low concentrations,
and even in the presence of potent endogenous steroidal estrogens.
...
"Considered
in isolation, the contribution of individual xenoestrogens
at the concentrations found in wildlife and human tissues
will always be small. However, such reasoning cannot be used
to support claims of negligible health risks from weak xenoestrogens,
because the number of xenoestrogens present in wildlife and
humans is unknown but likely to be very large." |
|
Second,
the comparisons of the two different types of predictions, "combination-addition"
vs. "effect-addition" show that it is important to use
the correct predictive models to distinguish between "additive"
vs. "synergistic" effects. Both of these predictions were
based on assumptions of additivity. One, the CA model, was developed
specifically for sigmoidal curves. The "effect-addition"
model, however, has been more widely used in toxicology. It may
have led to erroneous conclusions about synergy.
Third,
they show conclusively that working with mixtures is vital to anticipating
the effects of endocrine disrupting compounds. Their mixtures caused
effects that normal regulatory science would miss completely, because
it is based on work with compounds one-by-one. These results
mean it is highly likely—if not virtually certain—that
current standards, based on traditional regulatory science, are
not strong enough to protect human health.
Many
questions remain to be followed-up. Foremost among these is the
fact that while some contaminants have estrogenic activity, others
have anti-estrogenic impacts. And multiple contaminants of both
types are probably ubiquitous in most living organisms. How do they
interact in mixtures? |