* Neurotoxicology Division and
Experimental Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711; and
Research Triangle Institute, Research Triangle Park, North Carolina 27709
Received April 28, 2003; accepted July 30, 2003
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Key Words: Habers rule; physiologically based pharmacokinetic model; visual evoked potentials; neurotoxicity; organic solvents.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
An alternative approach to formula-based Habers rule for adjusting exposure concentrations and durations is the consideration of different exposure scenarios on the basis of target tissue concentrations (Andersen et al., 1987; Jarabek, 1995
). The advent of physiologically based pharmacokinetic (PBPK) models has enabled the prediction of target tissue concentrations resulting from a variety of exposure situations (Ramsey and Andersen, 1984). The issues then become understanding the extent to which tissue concentrations predict toxicity and determining appropriate and predictive measures of tissue dose. An assessment of the published literature for the behavioral effects of acute exposure to toluene found that blood concentrations at the time of testing, estimated using a PBPK model, consistently predicted behavioral deficits across several otherwise diverse experimental reports (Benignus et al., 1998
). Presumably, blood toluene concentration was a good surrogate for the more critical parameter, brain toluene concentration, which was linked to behavioral disruption. It is of interest to further study the relationships between exposure conditions, tissue concentrations, and neurological outcomes in an attempt to better understand the risks of exposure to differing concentrations and/or durations of volatile organic compounds (VOCs).
The exposure scenario is a particularly important consideration for neurological effects of exposure to VOCs, for which effects of acute exposure change quickly over time because of rapid absorption, distribution, and elimination of the compounds. The VOCs are widely used in industrial processes, and trichloroethylene (TCE) is among the leading VOCs in the United States, both in terms of industrial usage and environmental release. In a national air toxics assessment, the US Environmental Protection Agency estimated that 25,700 tons of TCE were emitted into the air in the United States in 1996 (USEPA, 2002a). The Toxics Release Inventory of the EPA cataloged over 10 million pounds of TCE released in 1999 (USEPA 2002b). There is concern that TCE, or its metabolites, may be carcinogenic (e.g., see reviews by Moore and Harringtion-Brock, 2000, and Wartenberg et al., 2000
). Chronic exposure to TCE has been shown to cause a variety of noncancer health effects, including neurotoxicity (Arlien-S
borg, 1992
; ATSDR, 1997
). Neurological effects of chronic or repeated acute TCE exposure include motor (Feldman et al., 1988
), cognitive (Grandjean et al., 1955
; Kulig, 1987
), and sensory deficits (Crofton and Zhao, 1993
; Crofton et al., 1994
; Jaspers et al., 1993
; Rebert et al., 1991
). Chronic occupational exposure to a variety of organic solvents or solvent mixtures, in some cases including TCE, has been linked to impaired visual contrast sensitivity or color perception (Broadwell et al., 1995
; Frenette et al., 1991
; Mergler et al., 1991
). Like many environmental neurotoxicants, the relationships between different TCE exposure scenarios and neurotoxicological outcomes are not well understood. Describing the relationship between exposure scenarios and the resultant effects on neurological function would help to assess the risks of many potential exposure situations, in particular those involving short-term or variable exposure conditions.
Previous attempts to link internal concentrations of VOC compounds with neurological or behavioral outcomes have been hampered by a combination of conditions including long-lasting behavioral test sessions, during which tissue VOC concentrations can be expected to change markedly. Brain concentrations can be expected to increase during testing if behavioral assessment occurs during exposure or decrease during testing if assessment occurs after exposure. When tissue concentrations differ markedly from the beginning to the end of the behavioral assessment interval, it is difficult to link a measured behavioral change to a value of tissue dose. To overcome these difficulties and to relate the estimates of internal doses of TCE and other VOC compounds more accurately to neurophysiological changes, we constructed a unique exposure and testing apparatus that allowed concurrent head-only inhalation exposure and assessment of visual function. Each assessment of visual function lasted only about 1 min, during which time blood or brain TCE concentrations remained relatively constant.
The current experiments evaluated the acute neurological effects of exposure to TCE as a function of changing exposure concentration and duration. Neurological changes were assessed with an electrophysiological measure of visual function, a type of sensory-evoked potential referred to as pattern-elicited visual-evoked potentials (VEPs), using a unique exposure assessment system suitable for this project. Several types of VEPs have been used to characterize the effects of exposure to potentially neurotoxic compounds (Boyes, 1994; Dyer, 1985
; Herr and Boyes, 1995
; Mattsson and Albee, 1988
; Rebert, 1983
). Pattern-elicited VEPs are related to psychophysical measurements of visual perception (Campbell and Maffei, 1970
) and are reported to change in response to several clinical neurological conditions (Boyes, 1993
) or neurotoxicological exposures (Boyes, 1992
). Different exposure scenarios were constructed so that the Haber product (C x t) was constant. The null hypothesis was that the exposure conditions with equivalent C x t products would show equal toxic effects. The magnitude of change in VEP amplitude was compared to different measures of TCE dose, including the C x t product; the area under the curve of TCE concentration in the brain estimated by the PBPK model; and the peak, or momentary, brain concentration of TCE at the time VEPs were recorded as estimated by the PBPK model.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Test animals.
Male Long-Evans rats (LE) (Crl:(LE)BR), approximately 60 days of age, were obtained from Charles River Laboratories (Raleigh, NC). The rats were housed individually in polycarbonate cages with wood-chip bedding, provided ad libitum access to tap water and rat chow (Ralston Purina Co., St. Louis, MO) and allowed to acclimate to the animal colony for at least one week. The animal colony had an ambient temperature and relative humidity of 22 ± 2E C and 50 ± 10%, respectively, and a 12:12 h light:dark cycle (lights on at 6:00 A.M.) with an illumination of approximately 800 lux, measured 1 m above the floor, during the light cycle. All aspects of the care and treatment of the laboratory animals were approved by the institutional laboratory animal care and use committee and were in compliance with applicable federal guidelines for laboratory animal experimentation.
Surgery.
The rats were anesthetized (sodium pentobarbital; 50 mg/kg i.p.) and placed in a standard stereotaxic device. Using aseptic surgical conditions, recording electrodes were implanted into the skull, epidurally, in the following locations: 1 mm anterior to lambda and 4 mm left of the midline overlying the primary visual cortex, and 2 mm anterior to bregma and 2 mm lateral left and right to the midline for ground and reference electrodes, respectively. The electrodes were constructed previously from stainless steel screws (0090 x 1/16 in) soldered to Nichrome wires. After implantation, the electrode wires were connected to a nine-pin connector (WirePro model 223-1609; Resource Electronics, Raleigh, NC), the assembly was encased in acrylic, and the wound was sutured. Approximately 1 week was allowed for surgical recovery before electrophysiological testing. Details of this preparation have been described in Herr et al. (1992).
Inhalation exposure.
Vapors of TCE were generated with a J-tubedesign inhalation system (McGee et al., 1994), constructed so that either all or a portion of the output could be directed into the chamber. Carbon and HEPA-filtered air was used as the generator sweep gas and introduced into the bottom of the J-tube at a flow rate of 6.6 l/min. The J-tube was heated to 80°C using heat tape. A syringe pump (Model 22; Harvard Apparatus, South Natick, MA) was used to meter TCE into the air stream of the J-tube. A 3-foot length of flexible, finned stainless steel tubing (3216-x; Cajon Co., Macedonia, OH) was used as a heat exchanger to cool the generator output to room temperature before introduction into the exposure chamber. Exposure atmospheres were monitored online with a long path-length dispersive infrared (IR) spectrophotometer (Miran, 1A; Foxboro Co., East Bridgewater, MA). The exposure monitor system was calibrated using a closed loop system, and TCE levels were confirmed using gas chromatography.
Exposure atmospheres were presented to the test subjects in a head-only exposure chamber as depicted in Figure 1. The interior dimensions of the head chamber space were 10 x 10 x 17 cm (width x depth x height). The chamber was constructed of stainless steel with the exception of the front and one side wall, which were constructed of glass to allow viewing of the stimulus screen by the rat, and the rat by the experimenter, respectively. The chamber wall facing the experimenter contained a small port, covered with a noninflated balloon, through which the experimenter could gently remove obstructing materials from the rats right eye with a cotton swab, if necessary, to maintain a clear visual field.
|
Visual stimuli.
Visual stimuli were presented on a video monitor (ViewSonic 15, model 1564M; Walnut, CA) located approximately 15 cm from the rats eyes outside the glass face of the exposure chamber. Visual stimuli were generated with a computer-based system described in detail in Hamm et al. (2000) operated in a "stand-alone" mode. Briefly, video stimulus patterns were provided to the memory of a super-VGA graphics display card operated at 640 x 480 resolution, 256 colors in Video Electronics Standards Association (VESA) mode 101 hexadecimal. The "green" video card output signal was then processed with analog circuitry. Analog multipliers were used to set the contrast, apply the temporal modulation, and set the overall luminance. Sixteen-bit D/A converters were used to generate the percent contrast and luminance control signals. A temperature-regulated diode gamma correction stage was used to help linearize the monitor response curve. The video board was operated at a vertical refresh rate of 72.8 Hz. A separate higher frequency time base (unsynchronized to the vertical refresh clock) was used for sine wave temporal modulation. The visual system provided a synchronization pulse to trigger the data collection system at the beginning of the pattern appearance cycle. Calibration of the brightness and contrast of the display monitor and the video subsystem was accomplished using a separate calibration program and the digitized output of a photometer (EG&G model 450 photometer, model 550-2), a multiprobe with model B1219 photometric filter, or a fiber-optics probe (EG&G model 550-14; Salem, MA). Percent contrast refers to the difference between the light and dark portions of the stimulus pattern, defined as (Lmax - Lmin)/(Lmax + Lmin) x 100, with Lmax and Lmin reflecting the maximum and minimum luminance portions of the stimulus pattern, respectively. Calibration of the video monitor showed a linear relationship between the input signal voltage and percent contrast up to approximately 80% contrast.
The visual stimulus pattern was a vertical grating with a sinusoidal spatial luminance profile. The mean stimulus luminance was approximately 10 cd/m2. The primary stimulus parameters determining the response to visual pattern modulation are spatial frequency and contrast. Spatial frequency refers to the size of the visual pattern and is expressed as the number of stimulus cycles/degree visual angle (cpd). Pilot experiments indicated that the effects of acute TCE inhalation on VEP amplitudes were independent of the spatial frequency or contrast of the stimulus pattern, and therefore a single stimulus pattern was selected for the current experiment. The stimulus employed had a spatial frequency of 0.16 cpd and a visual contrast of 60%. A value of 0.16 cpd was selected because it fell near the peak of the contrast sensitivity function of the pigmented rat. A value of 60% contrast was selected because it fell well within the linear portion of the video monitor response function and yet yielded a strong evoked potential. The stimulus was temporally modulated in an onoff fashion with a 5-Hz sinusoid.
Evoked potential recording.
The electrophysiological potentials were amplified, bandpass-filtered (1100 Hz; roll off = 12 dB/octave), and sampled in 1-s epochs (512 data points per epoch) using a Pathfinder II signal averager (Nicolet Biomedical, Madison, WI), so that each sample epoch contained five cycles of the stimulus temporal modulation. Averaged evoked potentials were constructed from 50 epochs and then submitted to spectral analysis (SPECTM Program; Nicolet Biomedical, Madison, WI). The spectral amplitude at the stimulus rate (F1) and twice that rate (F2) were recorded as dependent variables.
Experimental design.
The rats (n = 9 or 10/group) were exposed to either clean air or trichloroethylene (TCE) under one of four exposure conditions designed to have a constant C x t product of 4000 ppm-h. The exposure scenarios were either 0 ppm for 4 h, 1000 ppm for 4 h, 2000 ppm for 2 h, 3000 ppm for 1.3 h, or 4000 ppm for 1 h. The VEPs were recorded during the last minute of exposure. A single test duration was selected for the group exposed to the control air to avoid the necessity of four separate control groups. The test duration of 4 h was selected for the control group because we have occasionally observed a slight reduction in VEP amplitude over the course of several h recording, perhaps related to cumulative fatigue or stress due to the necessary restraint for head-only exposure and recording. By maximizing the test duration and amplitude decline of the control group, we were introducing a biasif anyin the direction away from being able to detect VEP amplitude reductions caused by exposure to the test compound. With regard to comparing the dose groups to one another, a bias, if any, caused by the different exposure durations would tend to favor maximizing the amplitude reductions in the longer duration (lower TCE concentration) groups relative to those groups with shorter durations and higher concentrations.
Statistical analysis.
The two dependent variables, the amplitudes of the F1 and F2 spectral components, were analyzed using a multivariate analysis of variance available in PROC GLM of SAS (1989). The independent variable was the dose or treatment group; a between-subjects factor with five levels. Statistical significance was defined as 0.05 divided by the number of dependent variables, or 0.05/2 = 0.025. For dependent variables demonstrating a statistically significant effect of treatment, univariate contrasts between the individual dose groups were examined using a value of p < 0.05 as the criterion of significant difference. Body temperature data were examined as a control procedure using an analysis of variance, because body temperature changes can complicate the interpretation of electrophysiological data. Body temperature changes, however, were not considered to be a part of the formal hypothesis testing.
Tissue TCE concentrations.
To determine the tissue concentrations of TCE under a variety of exposure conditions, the rats were exposed by inhalation to 200, 2000, or 4000 ppm of TCE for durations of 5, 20, or 60 min. In addition, some rats were exposed to TCE at the above concentrations for 60 min and then clean air for 60 min to evaluate clearance of the compound. The procedures for exposing the rats to TCE vapors in dynamic flow-through chambers, for rapidly removing tissues from the experimental animals, and for analysis of the tissues for TCE concentration using gas chromatography with electron capture detection were described in Simmons, et al. (2002). The primary data from these experiments were also presented in Simmons et al. (2002)
in association with evaluating the performance of the PBPK model used here. In the present paper, an additional analysis of these data is presented examining the relationship between simultaneous concentrations of TCE in the blood and brain. A linear regression (y = b[0] + b[1]x) was fit to the blood versus brain data using a standard computer program (SigmaPlot 2000 for Windows version 6.00).
PBPK model.
A five-compartment (brain, liver, fat, slowly perfused, and rapidly perfused) PBPK model developed for TCE in the LE rat (Simmons et al., 2002) was used to estimate the concentration of TCE in the brain under different exposure conditions. Values were derived for the volumes of the liver, brain, and fat compartments from measurements made in LE rats. Partition coefficients were measured in tissue homogenates from LE rats by the vial equilibration methodology described by Gargas et al. (1989)
. LE rats were exposed individually to TCE in a closed-uptake vapor chamber with O2 supplied as needed and CO2 removed. The model was used to estimate the metabolic rate constants Vmax and Km by optimization with Simusolv (Version 3.0, 1993) to obtain the best fit to chamber concentrations curves. Simulations were performed with this model to estimate the brain, blood, liver, and fat concentrations of TCE during and after constant-concentration inhalation exposures. These model simulations provided a reasonable fit to experimentally measured brain, blood, liver, and fat concentrations of TCE obtained from rats exposed by inhalation in flow-through chambers to TCE. A full description of the model, along with the experimental data used to evaluate model performance, is presented in Simmons et al. (2002)
. Estimated values derived from the PBPK model for comparisons to VEP outcome measures included the brain concentrations at the moment of VEP recording and the area under the brain concentration curve (AUC) measured from the onset of exposure to the moment of VEP recording.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Brain concentrations of TCE estimated from the PBPK model for the different exposure scenarios are presented in Figure 2. Two values from each curve were examined for the ability to predict evoked potential deficits, including (1) the area under the curve and (2) the momentary value obtained at the time of VEP testing. Among the exposure groups, estimated values of AUC were higher at the longer exposure durations (e.g.,1000 ppm x 4 h) whereas estimated values of momentary concentration were higher at the higher air concentrations of TCE (e.g., 4000 ppm x 1 h), as shown in Figure 2
.
|
|
|
|
It was of interest to evaluate the differences between exposure duration extrapolations based on Habers rule and those based on equivalent target tissue concentrations as predicted by the PBPK model. To do this, a point of departure of 25-mg/l TCE in brain tissue was selected from Figure 5C. This value was selected for illustrative purposes only, as being halfway between the lowest dose given, which was significantly different from clean air controls, and the control value. The exposure durations predicted to give rise to a brain concentration of 25 mg/l following exposure to 1000, 2000, 3000, or 4000 ppm were then determined from the PBPK model and are plotted as the data points in Figure 6
. Next, a line depicting Habers rule extrapolation according the formula C x t = K was fit to the 25-mg/l point for 4000 ppm exposure (diagonal dashed line in Fig. 6
). Finally, a horizontal and two vertical lines were added to illustrate the differences between extrapolation along either the Habers rule functions and the predictions of effects based on equal brain concentrations of 25 mg/l. When using Habers rule to extrapolate from 4000 ppm to 1000 ppm, Habers rule predicts that at 1000 ppm an exposure duration of approximately 0.14 h would produce the same effect, whereas the PBPK model indicates that an exposure duration of 0.875 h would be required to reach the same brain concentration. Thus, in this example, when Habers rule was used to predict across a 4-fold difference in exposure concentration, the result was over a 6-fold error in exposure duration.
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The current finding that a traditional linear expression of Habers rule (i.e., C x t = k) was inadequate to predict toxicity across exposure durations is consistent with the other neurotoxicological outcomes examined following inhalation exposure to TCE. This study adds to evidence regarding the neurotoxicological effects of TCE over changing exposure conditions (Boyes et al., 2000). Crofton and Zhao (1997)
examined hearing loss in LE rats following exposure to TCE concentrations ranging from 800 to 8000 ppm and exposure durations ranging from 1 day (6 h) to 13 weeks (6 h/day, 5 days/week). Bushnell (1997)
examined deficits in signal detection behavior in LE rats during exposure to TCE concentrations ranging from 400 to 2400 ppm and durations ranging from 20 to 60 min. In both cases, default extrapolations across exposure durations based on Habers rule lead to incorrect estimates of the minimally toxic concentration, with the direction of the error dependent on the direction of the extrapolation. Extrapolation by Habers rule from shorter to longer duration exposures resulted in overestimates of risk. In the opposite case, the use of Habers rule to extrapolate from longer to shorter duration exposures resulted in the underestimation of risk (Boyes et al., 2000
). Rozman (1999)
contends that Habers rule holds under conditions of toxicokinetic steady state and/or irreversible damage. Neither of those conditions hold for the acute reversible effects of TCE such as examined here or reported by Bushnell (1997)
. Habers rule did not hold for the irreversible hearing loss caused by repeated exposure to TCE (Crofton and Zhao, 1997
) or for the peripheral neuropathy caused by exposure to acrylamide (Crofton et al., 1996
). These examples illustrate that a criterion of irreversible effects must exclude factors such as tolerance or repair for application of Habers rule. Clearly, other means of extrapolation of toxic effects across exposure durations are needed to predict and assess the risks of untested exposure scenarios where conditions of toxicokinetic steady state or permanent damage are not met.
Although many risk assessors are aware that there may be errors in extrapolation when using Habers rule, there is a general belief that the errors introduced are minor in comparison to other uncertainties in typical risk assessments. The illustrative analysis presented here in Figure 6 shows that the magnitude of error possible when using Habers rule can be substantial. A greater then 6-fold error in duration estimation was introduced when extrapolating over only a 4-fold change in concentration. Obviously, the magnitude of the error of estimation will increase with increases in the extrapolation range, since the Habers and PBPK functions diverge from a common point. In any application, the magnitude of error introduced by use of Habers rule will be a function of many factors, including the difference in slope between Habers rule and the "true" function and the extent of the extrapolation interval. When extrapolating from short- to long-duration exposures, Habers rule was overprotective in that the predicted exposure interval underestimated the point of equal tissue dose. If the extrapolation were to be conducted in the direction from long to short durations, Habers rule could have predicted "safety" at exposure durations associated with toxic brain concentrations. The underprotective nature of Habers rule when extrapolating from long- to short-duration exposures is generally known among risk assessors, and therefore Habers rule is typically not used for this purpose. Our results illustrate the potential magnitude of such errors.
Different exposure scenarios result in different toxicities because the target tissue doses achieved are not equal. It follows that a better way to predict toxicity for different exposure situations is to understand the target tissue concentrations achieved for each exposure situation and the relationships between target tissue concentrations and the expression of toxicity. Andersen and co-workers (1987) proposed that PBPK models be used to predict target tissue dose and that this approach form the basis of extrapolations across exposure durations. They argued that, in most cases, toxicity would be related to an integrated measure of tissue dose, such as the area under the curve, but peak concentration would be an appropriate measure for situations such as a reversible interaction of the compound with a tissue protein with rapid clearance of the compound and no residual damage. The area under the curve can be considered an equivalent measure of Habers C x t product, only based on absorbed dose rather than exposure. It is interesting that, even when based on absorbed dose, the integrated dose measure did not describe the change in toxicity across exposure scenarios. Rozman (1999)
concluded that if the toxicokinetic half-life of a compound is longer than its toxicodynamic half-life, then the toxicokinetics will be rate-determining. In the case here, TCE can be assumed to have a rapid and reversible interaction with the neural tissue and therefore to fit the case where the toxicokinetics is rate-determining. In a review of the published literature of the acute behavioral effects of exposure to toluene in rat and human subjects, Benignus et al. (1998)
found that plotting the outcomes of several studies as a function of peak toluene level in blood estimated from a PBPK model yielded consistent doseeffect curves across a variety of different laboratories. Thus, for the two organic solvents TCE and toluene, the appropriate measure of tissue dose for describing the acute neurological effects appears to be the tissue concentration at the moment of testing.
Some of the data in this manuscript were included in a previous summary report of the EPAs evaluation of the scientific basis for reassessment of TCE risks (Boyes et al., 2000). The previous report compared VEP outcomes to blood TCE concentrations estimated from a preliminary version of the PBPK model. The current analysis employed a revised version of the PBPK model that included a separate brain tissue compartment not previously available (Simmons et al., 2002
). The brain is presumably the relevant target tissue for VEP outcomes, and the current PBPK model predicts sampled brain concentrations better than it does blood concentrations. We have also demonstrated a relationship between blood and brain TCE concentrations. Finally, we present an example of a duration adjustment that might be used in risk assessment to compare the results using an approach based on Habers rule with one based on maintaining equivalent tissue dose levels.
In consideration of the proper metric of tissue dose, it is important to determine if the effects are caused by the parent compound or by one or more of its metabolites. Among the metabolites of TCE, chloral hydrate is a known sedative-hypnotic and trichloroethanol is likely the active metabolite of chloral hydrate (Krasowski et al., 1998; Peoples and Weight, 1998
; Rall, 1990
). The PBPK model indicated that TCE metabolism becomes saturated at atmospheric concentrations above about 400 ppm. The fact that a doseresponse relationship was observed at concentrations above metabolic saturation suggests that the effects observed here were attributable primarily to the parent compound.
Sensory-evoked potentials, as recorded here with a large extradural electrode, reflect the combined activity of a large number of visual cortex neurons firing in response to the patterned visual stimulus. The relationship between a multicellular recording like the VEP as recorded here and the activity of individual visual neurons cannot be known with certainty, but there are features of prominent visual cell types that resemble the activity observed in pattern appearance/disappearance VEPs. Individual retinal ganglion cells have characteristic "linear" or "nonlinear" temporal response profiles. Linear cells respond primarily at the stimulation rate (F1) for stimuli that are sinusoidally modulated in time (Davson, 1990; Lennie and Perry, 1981
; So and Shapley, 1979
). Nonlinear cells, in contrast, show more complex temporal response profiles with activity at both F1 and F2. In both rats and humans, the pattern appearance/disappearance VEP F1 component shows a bandpassspatial frequency profile (Hudnell and Boyes, 1991
) similar to the F1 response of linear retinal ganglion cells. The VEP F2 response shows a low-pass spatial frequency profile (Hudnell and Boyes, 1991
) similar to the F2 response of nonlinear retinal ganglion cells. The temporal response profiles of the retinal ganglion cells are passed on to cells in the lateral geniculate nucleus and then to the visual cortex. In the cortex, the cells maintain linear and nonlinear temporal response characteristics, although the set of response profiles becomes more complex than at earlier stages of the visual pathway. The similarities in spatial/temporal response profiles makes it reasonable to presume that the VEP F1 component arises from the summed activity of the linear visual cells, and VEP F2 arises from the summed activity of the nonlinear visual cells, originally encoded in the retina and preserved along the visual projections to be recorded at the visual cortex.
In summary, these data indicate that (1) acute inhalation of TCE can alter visual function in rats and (2) a linear application of Habers rule to the data does not accurately describe the change in toxicity across different exposure durations, but (3) the magnitude of the effects could be accurately predicted by the brain TCE concentration at the time of visual function evaluation. The results suggest that risk assessment decisions requiring the extrapolation of effects across exposure durations would more accurately reflect risk if they were based on the determination of the equivalent target tissue concentrations than if based on the assumptions of Habers rule.
![]() |
ACKNOWLEDGMENTS |
---|
![]() |
NOTES |
---|
* To whom correspondence should be addressed at MD B105-03, Neurotoxicology Division, US Environmental Protection Agency, Research Triangle Park, NC 27711. Fax: 919-541-4849.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Andersen, M. E., MacNaughton, M. G., Clewell, H. J., III, and Paustenbach, D. J. (1987). Adjusting exposure limits for long and short exposure periods using a physiological pharmacokinetic model. Am. Ind. Hyg. Assoc. J. 48, 335343.[ISI][Medline]
Arlien-Sborg, P. (1992). Solvent Neurotoxicity. CRC Press, Boca Raton, FL.
Atherley, G. (1985). A critical review of time-weighted average as an index of exposure and dose, and of its key elements. Am. Ind. Hyg. Assoc. J. 46, 481487.[ISI][Medline]
Benignus, V. A., Boyes, W. K., and Bushnell, P. (1998). A dosimetric analysis of acute toluene exposure in rats and humans. Toxicol. Sci. 43, 186195.[Abstract]
Boyes, W. K. (1992). Testing visual system toxicity using visual evoked potential technology. In The Vulnerable Brain and Environmental Risk: Volume I, Malnutrition and Hazard Assessment (R. L. Isaacson and K. F. Jensen, Eds.), pp. 193222. Plenum Press, New York.
Boyes, W. K. (1993). Sensory evoked potentials: measures of neurotoxicity. In Assessing the Toxicity of Drugs of Abuse (L. Erinoff, Ed.), NIDA Research Monograph 136, NIH Publication No. 93-3644, pp. 63100. National Institute on Drug Abuse: Alcohol, Drug Abuse, and Mental Health Administration, Washington, DC.
Boyes, W. K. (1994). Rat and human evoked potentials and the predictability of human neurotoxicity from rat data. Neurotoxicology 15, 569578.[ISI][Medline]
Boyes, W. K., Bushnell, P. J., Crofton, K. M., Evans, M., and Simmons, J. E. (2000). Neurotoxic and pharmacokinetic responses to trichloroethylene as a function of exposure scenario. Environ. Health. Perspect. 108(Suppl. 2), 317322.[ISI][Medline]
Broadwell, D. K., Darcey, D. J., Hudnell, H. K., Otto, D. A., and Boyes, W. K. (1995). Work-site neurobehavioral assessment of solvent exposed microelectronic workers. Amer. J. Ind. Med. 27, 677698.[ISI][Medline]
Burke, W., Cottee, L. J., Hamilton, K., Kerr, L., Kyriacou, C., and Milosavljevic, M. (1987). Function of Y-optic nerve fibers in the cat: Do they contribute to acuity and ability to discriminate fast motion? J. Physiol. 217, 473497.
Bushnell, P. J. (1997). Concentration-time relationships for the effects of inhaled trichloroethylene on signal detection behavior in rats. Fundam. Appl. Toxicol. 36, 3038.[CrossRef][ISI][Medline]
Campbell, F. W., and Maffei, L. (1970). Electrophysiological evidence for the existence of orientation and size detectors in the human visual system. J. Physiol. 207, 635652.[ISI][Medline]
Crofton, K. M., Lassiter, T. L., and Rebert, C. R. (1994). Solvent-induced ototoxicity in rats: An atypical selective mid-frequency hearing deficit. Hear. Res. 80, 2530.[CrossRef][ISI][Medline]
Crofton, K. M., Tilson, H. A., Padilla, S., Anthony, D. C., Raymer, J. H., and MacPhail, R. C. (1996). The impact of dose rate on the neurotoxicity of acrylamide: The interaction of administered dose, target tissue concentration, tissue damage and functional effects. Toxicol. Appl. Pharmacol. 139, 163176.[CrossRef][ISI][Medline]
Crofton, K. M., and Zhao, X. (1993). Mid-frequency hearing loss in rats following inhalation exposure to trichloroethylene: Evidence from reflex modification audiometry. Neurotoxicol. Teratol. 15, 413423.[CrossRef][ISI][Medline]
Crofton, K. M., and Zhao, X. (1997). The ototoxicity of trichloroethylene: Extrapolation and relevance of high-concentration, short-duration animal exposure data. Fundam. Appl. Toxicol. 38, 101106.[CrossRef][ISI][Medline]
Dallas, C. E., Gallo, J. M., Ramanathan, R., Muralidhara, S., and Bruckner, J. V. (1991). Physiological pharmacokinetic modeling of inhaled trichloroethylene in rats. Toxicol. Appl. Pharmacol. 110, 303314.[ISI][Medline]
Davson, H. (1990). Physiology of the Eye. Pergamon Press, New York.
Dyer, R. S. (1985). The use of sensory evoked potentials in toxicology. Fundam. Appl. Toxicol. 5, 2440.[ISI][Medline]
Feldman, R. G., Chirico-Post, J., and Proctor, S. P. (1988). Blink reflex latency after exposure to trichloroethylene in well water. Arch. Environ. Health. 43, 143147.[ISI][Medline]
Frenette, B., Mergler, D., and Bowler, R. (1991). Contrast sensitivity loss in a group of former microelectronic workers with normal visual acuity. Optom. Vis. Sci. 68, 556560.[ISI][Medline]
Gargas, M. L., Burgess, R. J., Voisard, D. E., Cason, G. H., and Andersen, M. E. (1989). Partition coefficients of low-molecular weight volatile chemicals in various liquids and tissues. Toxicol. Appl. Pharmacol. 98, 8799.[ISI][Medline]
Grandjean, E., Muchinger, R., Turrian, V., Hass, P. A., Knoepfel, H. K., and Rosemund, H. (1955). Investigations into the effect of exposure to trichloroethylene in mechanical engineering. Br. J. Ind. Med. 12, 131142.
Hamm, C. W., Ali, J. S, and Herr, D. W. (2000). A system for simultaneous multiple subject, multiple stimulus modality, and multiple channel collection and analysis of sensory evoked potentials. J. Neurosci. Meth. 102, 95108.[CrossRef][ISI][Medline]
Herr, D. W., and Boyes, W. K. (1995). Electrophysiological analysis of complex brain systems: Sensory-evoked potentials and their generators. In Neurotoxicology: Approaches and Methods (L. W. Chang, and W. Slikker, Eds.), pp. 205221. Academic Press, San Diego, CA.
Herr, D. W., Boyes, W. K., and Dyer, R. S. (1992). Alterations in flash and pattern reversal evoked potentials after acute or repeated administration of carbon disulfide (CS2). Fundam. Appl. Toxicol. 18, 328342.[ISI][Medline]
Hudnell, H. K., and Boyes, W. K. (1991). The comparability of rat and human visual-evoked potentials. Neurosci. Biobehav. Rev. 15, 159164.[ISI][Medline]
Jarabek, A. M. (1995). Considerations of temporal toxicity challenges current default assumptions. Inhalat. Toxicol. 7, 927946.[ISI]
Jaspers, R. M. A., Muijser, H., Lammers, J. H. C. M, and Kulig, B. M. (1993). Mid-frequency hearing loss and reduction of acoustic startle responding in rats following trichloroethylene exposure. Neurotoxicol. Teratol. 15, 407412.[CrossRef][ISI][Medline]
Krasowski, M. D., Fin, S. E., Ye, Q., and Harrison, N. L. (1998). Trichloroethanol modulation of recombinant GABAA, glycine and GABA 1 receptors. J. Pharmacol. Exp. Therap. 284, 934942.
Kulig, B. M. (1987). The effects of chronic trichloroethylene exposure on neurobehavioral functioning in the rat. Neurotoxicol. Teratol. 9, 171178.[CrossRef][ISI][Medline]
Lehmkuhle, S., Kratz, K. E., Mangel, S. C., and Sherman, S. M. (1980). Spatial and temporal sensitivity of X- and Y-cells in dorsal lateral geniculate nucleus of the cat. J. Neurophysiol. 43, 520541.
Lennie, P., and Perry, V. H. (1981). Spatial contrast sensitivity of cells in the lateral geniculate nucleus of the rat. J. Physiol. 315, 6979.[Abstract]
Mattsson, J. L., and Albee, R. R. (1988). Sensory evoked potentials in neurotoxicology. Neurotoxicol. Teratol. 10, 435443.[CrossRef][ISI][Medline]
McGee, J. K., Evansky, P. A., Terrell, D., Walsh, L. C., Davies, D. W., and Costa, D. L. (1994). A new vapor and gas test atmosphere generator with broad concentration and flow output capabilities. In Measurement of Toxic and Related Air Pollutants: Proceedings of the 1994 U.S. EPA/AWMA International Symposium, Publication No. VIP-39, pp. 10111016. Air and Waste Management Association, Pittsburgh.
Mergler, D., Bowler, R., Frenette, B., and Cone, J. (1991). Visual dysfunction among former microelectronic assembly workers. Arch. Environ. Health. 46, 326334.[ISI][Medline]
Miller, F. J., Schlosser, P. M., and Janszen, D. B. (2000). Habers rule: A special case in a family of curves relating concentration and duration of exposure to a fixed level of response for a given endpoint. Toxicology 149, 2134.[CrossRef][ISI][Medline]
Moore, M. M., and Harrington-Brock, K. (2000). Mutagenicity of trichloroethylene and its metabolites: Implications for the risk assessment of trichloroethylene. Environ. Health. Perspect. 108(Suppl. 2), 215223.[ISI][Medline]
Moser, V. C., and Boyes, W. K. (1993). Prolonged neurobehavioral and visual effects of short-term exposure to 3,3'- iminodiproprionitrile (IDPN) in rats. Fundam. Appl. Toxicol. 21, 277290.[CrossRef][ISI][Medline]
Peoples, R. W., and Weight, F. F. (1998). Inhibition of excitatory amino acid-activated currents by trichloroethanol and trifluoroethanol in mouse hippocampal neurones. Br. J. Pharmacol. 124, 11591164.[Abstract]
Rall, T. W. (1990). Hypnotics and sedatives; ethanol. In Goodman and Gilmans The Pharmacological Basis of Therapeutics (A. G. Goodman, T. W. Rall., A. S. Nies, and P. Taylor, Eds.), pp. 345382. Pergamon Press, New York.
Ramsey, J. C., and Anderson, M. E. (1984). A physiologically-based description of the inhalation pharmacokinetics of styrene in rats and humans. Toxicol. Appl. Pharmacol. 73, 159175.[ISI][Medline]
Rebert, C. S. (1983). Multisensory evoked potentials in experimental and applied neurotoxicology. Neurobehav. Toxicol. Teratol. 5, 659671.[ISI][Medline]
Rebert, C. S., Day, V. L., Matteucci, M. J., and Pryor, G. T. (1991). Sensory-evoked potentials in rats chronically exposed to trichloroethylene: Predominant auditory dysfunction. Neurotoxicol. Teratol. 13, 8390.[CrossRef][ISI][Medline]
Rozman, K. K. (1999). Delayed acute toxicity of 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin (HpCDD), after oral administration, obeys Habers rule of inhalation toxicology. Toxicol. Sci. 49, 102109.[Abstract]
SAS Institute Inc. (1989). SAS/STAT Users Guide, Version 6. 4th ed., Vol. 2. SAS Institute Inc., Cary, NC.
Simmons, J. E., Boyes, W. K., Bushnell, P. J., Raymer, J. H., Limsakun, T., McDonald, A., Sey, Y. M. and Evans, M. V. (2002). A physiologically-based pharmacokinetic model for trichloroethylene in the male Long-Evans rat. Toxicol. Sci. 69, 315.
So, Y. T., and Shapley, R. (1979). Spatial properties of X and Y cells in the lateral geniculate nucleus of the cat and conduction velocities of their inputs. Exp. Brain Res. 36, 533550.[ISI][Medline]
ten Berge, W. F., Zwart, A., and Appelman, L. M. (1986). Concentration-time mortality response relationship of irritant and systemically acting vapours and gases. J. Haz. Mat. 13, 301309.[CrossRef][ISI]
Wartenberg, D., Reyner, D., and Scott, C. S. (2000). Trichloroethylene and cancer: The epidemiological evidence. Environ. Health. Perspect. 108(Suppl. 2), 161176.[ISI][Medline]
Wu, C., Becker, J., and Schaum, J. (2000). Major findings of the exposure assessment of trichloroethylene (TCE) and related compounds. Environ. Health. Perspect. 108(Suppl. 2), 359363.[ISI][Medline]