6.0 ACCURACY OF THE 3T3 AND NHK NRU TEST METHODS 6-3

6.1 Accuracy of the 3T3 and NHK NRU Test Methods for Predicting Acute
Oral Systemic Toxicity 6-5

> 6.1.1 Linear Regression Analyses for the Prediction of *In Vivo*
> Rodent LD~50~ Values from *In Vitro* NRU IC~50~ Values 6-5
>
> 6.1.2 Comparison of Combined-Laboratory 3T3 and NHK NRU Regressions to
> the RC Millimole Regression 6-6

6.2 Improving the Prediction of *In Vivo* Rodent LD~50~ Values from *In
Vitro* NRU IC~50~ Data 6-8

> 6.2.1 The RC Rat-Only Regression in Millimolar Units 6-9
>
> 6.2.2 The RC Rat-Only Regression in Weight Units 6-10
>
> 6.2.3 The RC Rat-Only Regression in Weight Units Excluding Substances
> with Specific Mechanisms of Toxicity 6-12

6.3 Accuracy of the 3T3 and NHK NRU Test Methods for Toxicity Category
Predictions 6-14

> 6.3.1 Prediction of Toxicity Category by the 3T3 and NHK NRU
>
> Test Methods Using the RC Millimole Regression 6-17
>
> 6.3.2 Prediction of Toxicity Category by the 3T3 and NHK NRU\
> Test Methods Using the RC Rat-Only Weight Regression 6-22
>
> 6.3.3 Prediction of Toxicity Category by the 3T3 and NHK NRU\
> Test Methods with the RC Rat-Only Weight Regression Excluding
> Substances with Specific Mechanisms of Toxicity 6-26
>
> 6.3.4 Summary of the Regressions Evaluated 6-30

6.4 Strengths and Limitations of the *In Vitro* NRU Test Methods for *In
Vivo* Toxicity Prediction 6-31

6.5 Salient Issues of Data Interpretation 6-35

6.6 Comparison to Established Performance Standards 6-36

6.7 Summary 6-41

***\[This Page Intentionally Left Blank\]***

**6.0 ACCURACY OF THE 3T3 AND NHK NRU TEST METHODS**

This section discusses the accuracy of the 3T3 and NHK NRU test methods
for predicting acute oral systemic toxicity. Accuracy, the agreement
between a test method result and an accepted reference value, is a
critical component of the ICCVAM evaluation of the validation status of
a test method (ICCVAM 2003). Although the 3T3 and NHK NRU test methods
are not suitable as replacements for acute systemic toxicity assays, the
ability of these assays to correctly predict LD~50~ values are used to
evaluate their accuracy. The rationale for evaluating the accuracy of
LD~50~ predictions is that the animal savings produced by using these
*in vitro* test methods to predict starting doses for acute systemic
toxicity assays will be greatest when the starting dose is as close as
possible to the "true" LD~50~ value (see **Section 10** for the
evaluation of animal savings).

The ability of the 3T3 and NHK NRU test methods to correctly predict
rodent acute oral systemic toxicity is based on the validity of the *in
vitro* -- *in vivo* regression model. It is the *in vivo* -- *in vitro*
regression that establishes the relationship between the 3T3 and NHK NRU
IC~50~ values and the predicted LD~50~ values that are to be used to set
the starting doses for the acute oral systemic toxicity assays in this
study.

Upon review of these regressions, it became apparent that the regression
model could be improved. This section discusses the evolution of these
improvements. Initially, since the regressions generated by the three
laboratories were not statistically different, the data were combined
(using a geometric mean IC~50~ of the three individual laboratory
geometric mean IC~50~ values) into a single regression for each test
method (3T3 and NHK). These regressions, in millimole units, were then
compared to the RC millimole regression that was created using rat and
mouse oral LD~50~ values from RTECS^®^ and IC~50~ values from *in vitro*
cytotoxicity assays using multiple cell lines and cytotoxicity endpoints
for 347 substances with known molecular weights (Halle 1998). Because
the 3T3 and NHK NRU test method regressions were not statistically
different from the RC regression, the RC regression was chosen to
predict the LD~50~ values from the NRU generated IC~50~ values because
it is based on a much larger database.

The next steps taken were to improve upon the RC millimole regression's
ability to accurately predict LD~50~ values from IC~50~ values, and to
make the approach relevant to the testing of mixtures and substances
without a known molecular weight in rats, the preferred species for
acute oral toxicity testing (EPA 2002b; OECD 2001a; OECD 2001d). To
achieve this goal, three new regressions are presented.

The first regression \-- a RC rat-only millimole regression \-- utilizes
only the 282 substances in the RC dataset of 347 substances that had a
reported rat LD~50~ value. The next step was to transform this RC
rat-only millimole regression to one based on a weight basis (mg/kg body
weight for LD~50~ and µg/mL for IC~50~) in order to make the regression
more generally applicable to the testing of mixtures and substances
without a known molecular weight.

Upon review of this rat-only weight regression, it became apparent that
many of the substances with underpredicted toxicity had mechanisms of
toxicity that could not be expected to be detected in the 3T3 and NHK
cell lines. These mechanisms included neurotoxic and cardiotoxic
mechanisms, interference with energy utilization, and agents that
alkylate macromolecules. Therefore, the third improved regression
presented is based on an RC dataset of 232 substances that have rat
LD~50~ data and that excludes the 50 substances which are reported to
induce toxicity via one of the above mentioned mechanisms of action.

The ability of the 3T3 and NHK NRU IC~50~ data to correctly predict rat
acute oral LD~50~ values, based on using the RC millimole regression and
two of the modified regressions (RC rat-only weight regression and RC
rat-only weight regression excluding substances with specific mechanisms
of toxicity), was evaluated by determining the extent to which the
appropriate GHS acute oral toxicity category was identified for each
reference substance. This approach permits an assessment of accuracy
specific to each GHS hazard classification category. The results of
these analyses are presented in **Section 6.3**. The discordant
reference substances from the predictions of GHS acute oral toxicity
category are presented in **Appendix L-2**.

The remainder of **Section 6** discusses physical, chemical, and
biological characteristics of substances that may have an impact on the
accuracy of the 3T3 and NHK NRU test methods.

**6.1 Accuracy of the 3T3 and NHK NRU Test Methods for Predicting Acute
Oral Systemic Toxicity**

Rodent LD~50~ values are used as the reference values for assessing the
ability of the 3T3 and NHK NRU test methods to accurately predict acute
oral systemic toxicity. The accuracy of the two *in vitro* cytotoxicity
test methods is assessed in two ways: (1) by the goodness of fit of the
*in vitro* NRU IC~50~ data to the rodent LD~50~ data in linear
regression analyses, and (2) by the concordance (i.e., extent of
agreement) between the GHS acute oral toxicity categories (UN 2005)
assigned based on rodent LD~50~ data and those predicted using *in
vitro* NRU IC~50~ data.

6.1.1 [Linear Regression Analyses for the Prediction of *In Vivo* Rodent
LD50 Values from *In Vitro* NRU IC50 Values]{.underline}

As described in **Section 5.3.4**, linear regressions for each test
method were calculated using log IC~50~ values (mM) versus the
corresponding reference log LD~50~ values (mmol/kg) identified in
**Table 4-2**. The slopes for all regressions were statistically
significantly different from zero (p \< 0.0001), which indicates a
significant relationship between *in vitro* IC~50~ values and the
corresponding rodent LD~50~ values.

Comparison of the individual laboratory regressions to one another with
the goodness of fit F-test described in **Section 5.3.3** (under
"Generation of Other Linear Regressions") indicated that the
laboratory-specific regressions for either *in vitro* NRU cytotoxicity
test method were not significantly different from one another (see
**Section 7.0** for a more detailed discussion of the results of this
analysis). Because the individual laboratory regressions were not
significantly different, data were combined into a single regression for
each test method using the geometric mean of the mean IC~50~ values
determined by each laboratory for each substance (see the
"Combined-laboratory" regressions in **Table 6-1** and **Figure 6-1**).
The combined-laboratory 3T3 regression yielded a better fit to the
reference LD~50~ data (adjusted R^2^ = 0.524) than the
combined-laboratory NHK regression (adjusted R^2^ = 0.455).

**Table 6-1 Linear Regression Analyses of** **the 3T3 and NHK NRU and
*In Vivo* Rodent LD~50~ Test Results^a^**

  -------------------------- ---------- ----------- --------------- -------------
  **Laboratory**             **N^b^**   **Slope**   **Intercept**   **Adjusted
                                                                    R^2^**

  **3T3 NRU Test Method**                                           

  ECBC^c^                    69         0.580       0.467           0.531

  FAL^c^                     67         0.543       0.287           0.432

  IIVS^c^                    69         0.585       0.467           0.534

  Combined-laboratory^d^     70         0.589       0.425           0.524

  **NHK NRU Test Method**                                           

  ECBC^c^                    69         0.507       0.405           0.446

  FAL^c^                     69         0.466       0.427           0.411

  IIVS^c^                    70         0.513       0.439           0.454

  Combined-laboratory^d^     71         0.510       0.452           0.455
  -------------------------- ---------- ----------- --------------- -------------

^a^Log IC~50~ in mM; log LD~50~ in mmol/kg.

^b^Number of substances used to calculate regression.

^c^Regression based on a single point per substance (i.e., the geometric
mean of the within laboratory replicate IC~50~ values and the reference
rodent oral LD~50~ from **Table 4-2**).

^d^Regression based on a single point per substance (i.e., the geometric
mean of the geometric mean IC~50~ values obtained for each laboratory
and the reference rodent oral LD~50~ from **Table 4-2**). Data for 70
substances in the 3T3 assay and 71 substances in the NHK assay. No
laboratory achieved sufficient toxicity for calculation of an IC~50~ for
carbon tetrachloride or methanol in the 3T3 NRU test method or for
carbon tetrachloride in the NHK NRU test method.

Abbreviations: ECBC -- US Army Edgewood Chemical Biological Center; FAL
-- FRAME Alternatives Laboratory; IIVS -- Institute for *In Vitro*
Sciences

6.1.2 [Comparison of the Combined-Laboratory 3T3 and NHK NRU Regressions
to the RC Millimole Regression]{.underline}

The NICEATM/ECVAM validation study tested 58 RC substances (see **Figure
3-1**). A comparison of the regression developed for the 3T3 and NHK NRU
test results to the RC millimole regression was made to test the
assumption of the *Guidance Document* that the RC millimole regression
can be obtained with a basal cytotoxicity test method using a single
cell type and cytotoxicity endpoint (ICCVAM 2001b). The regression for
the 58 substances calculated using the RC IC~50~ and LD~50~ data points
is shown in **Figure 6-2**. A graphical comparison of the RC millimole
regression for the 58 substances to the 3T3 and NHK combined-laboratory
regressions is shown in **Figure 6-3**. A statistical comparison of
slope

**Figure 6-1 Combined-Laboratory 3T3 and NHK Regressions**

![](media/image1.pct){width="6.249305555555556in"
height="8.11111111111111in"}

Solid lines show the combined-laboratory regressions for each test
method (see **Table 6-1**).

and intercept (simultaneously) using an F test showed that neither the
3T3 regression (p = 0.929) nor the NHK regression (p = 0.144) was
different from the 58 RC substance regression.

**Figure 6-2 Regression for the 58 RC Substances Using RC Data**

![](media/image2.pct){width="6.247916666666667in"
height="4.120833333333334in"}

**6.2 Improving the Prediction of *In Vivo* Rodent LD~50~ Values from
*In Vitro* NRU IC~50~ Data**

Since the RC and the 3T3 and NHK NRU IC~50~ -- rodent acute oral LD~50~
regressions were not significantly different, the next step was an
attempt to improve upon the RC millimole regression for the prediction
of LD~50~ from IC~50~. The RC data were used to develop three new
regressions. For reference, the original RC millimole regression, log
LD~50~ (mmol/kg) = 0.435 x log IC~50x~ (mM) + 0.625 (Halle 1998), is
shown in **Table 6-2** and **Figure 6-4**.

**Figure 6-3 Regression for the 58 RC Substances with the 3T3 and NHK
NRU Regressions**

![](media/image3.pct){width="6.089583333333334in"
height="4.1409722222222225in"}

Regression for the 58 RC substances using RC data is log LD~50~ = 0.659
log IC~50~ + 0.323 (adjusted R^2^ = 0.683). The combined-laboratory 3T3
NRU regression, which uses data for 70 substances, is log LD~50~ = 0.589
log IC~50~ + 0.425 (adjusted R^2^ = 0.524) (from **Table 6-1**). The
combined-laboratory NHK NRU regression, which uses data for 71
substances, is log LD~50~ = 0.510 log IC~50~ + 0.452 (adjusted R^2^ =
0.455) (from **Table 6-1**). No laboratory achieved sufficient toxicity
for calculation of an IC~50~ for carbon tetrachloride or methanol in the
3T3 NRU test method or for carbon tetrachloride in the NHK NRU test
method.

6.2.1 [The RC Rat-Only Regression in Millimolar Units]{.underline}

The first regression used the RC data only for the 282 substances with
rat LD~50~ data (i.e., the regression excluded the substances with mouse
LD~50~ data) using the original units of mM for IC~50~ and mmol/kg for
LD~50~ (see **Table 6-2** and **Figure 6-4**). Rat data only were used
because:

-   rats and mice may not have the same sensitivity to individual
    substances, regardless of the high correlation of a subset of 173 RC
    substances with both rat and mouse LD~50~ data (r~s~ = 0.88; p \<
    0.0001) (see **Section 4.1.4**)

-   the majority of LD~50~ data used in the RC millimole regression were
    from studies using rats (282 rat data points and 65 mouse data
    points) (Halle 1998)

-   the great majority of acute oral systemic toxicity testing is
    performed with rats

**Table 6-2 Linear Regression Analyses to Improve the Prediction of
Rodent LD~50~ from *In Vitro* NRU IC~50~ Using the RC Regression^a^**

  ---------------------------------------- ----------- --------------- ------------
  **Data Used**                            **Slope**   **Intercept**   **Adjusted
                                                                       R^2^**

  347 RC substances with rat and mouse     0.435       0.625           0.450^d^
  LD~50~ data -- millimole units^c^                                    

  282 RC substances with rat LD~50~ data   0.439       0.621           0.451
  -- millimole units^c^                                                

  282 RC substances with rat LD~50~ data   0.372       2.024           0.322
  -- weight units^e^                                                   

  232 RC substances with rat LD~50~ data   0.357       2.194           0.353
  (excluded 50 substances with specific                                
  mechanisms of action^f^) -- weight                                   
  units^e^                                                             
  ---------------------------------------- ----------- --------------- ------------

^a^Slopes of all regressions were significantly different (p \< 0.05)
from zero at p \< 0.0001.

^b^Simultaneous comparison of slopes and intercepts using an F test.
Significance denoted by p \< 0.05.

^c^IC~50~ in mM; LD~50~ in mmol/kg.

^d^Calculated from RC data (i.e., not reported by Halle \[1998\]).

^e^IC~50~ in μg/mL; LD~50~ in mg/kg.

^f^See the text for the applicable mechanisms and **Appendix K-3** for
the identified substances.

**Table 6-2** shows that the regression using rat LD~50~ data only was
almost identical to the original RC millimole regression which used both
rat and mouse LD~50~ data. The slope changed from 0.435 for the RC
millimole regression to 0.439 and the intercept changed from 0.625 to
0.621.

6.2.2 [The RC Rat-Only Regression in Weight Units]{.underline}

The second regression used the same RC data for the 282 substances with
rat LD~50~ data, but was calculated with weight units rather than
millimolar units (see **Table 6-2** and **Figure 6-5**). Weight units
(i.e., mg/kg for the LD~50~ and μg/mL for the IC~50~) were selected for
the units of measurement because

-   millimole units are not applicable to mixtures and unknown
    substances

-   they are most practical \[i.e., in all regulatory systems, hazard
    classification is based on LD~50~ values expressed in mg/kg (see
    **Table 1-2**)\]

    **Figure 6-4 RC Regression (a) and RC Rat Regression (b) Using
    Millimole Units**

    ![](media/image4.pct){width="5.035416666666666in"
    height="8.216666666666667in"}

    6.2.3 [The RC Rat-Only Regression in Weight Units Excluding
    Substances with Specific Mechanisms of Toxicity]{.underline}

    The third regression was a further refinement on the weight-unit
    regression developed from the 282 RC substances with rat LD~50~
    data. It excluded the RC substances for which the mechanisms of
    toxic action were not expected to be active in the 3T3 and NHK cell
    cultures. This reduced the number of data points from 282 to 232 RC
    substances for the calculation of the regression (see **Table 6-2**
    and **Figure 6-5**). The third regression was significantly
    different (p \< 0.05) from the RC rat-only weight regression when
    slopes and intercepts were simultaneously compared (F test; p =
    0.0063). The idea for the further refinement for the rat RC
    millimole regression came from the evaluation of discordant
    substances (i.e., those greater than 0.699 or 0.5 log from the
    regression) when the 3T3 and NHK NRU data were used with the RC
    millimole regression (see **Appendix L-1**). For the 3T3 NRU, 13/30
    (43%) of the discordant substances had mechanisms of toxicity that
    were not expected to be active in the 3T3 cell cultures. For the NHK
    NRU, 13/31 (42%) of the discordant substances had mechanisms of
    toxicity that were not expected to be active in the NHK cell
    cultures.

    *Development of the RC Rat-Only Weight Regression Excluding
    Substances with Specific Mechanisms of Toxicity*

    Mechanism of action data for the 282 RC substances with rat LD~50~
    values were obtained from *Casarett & Doull's* *Toxicology*
    (Casarett et al. 2001) and the following Internet sources: HSDB (NLM
    2005); Haz-Map (NLM 2005); Pesticide Action Network \[PAN\]
    Pesticides Database (PAN North America 2005); and IPCS INTOX
    Database (Canadian Centre for Occupational Health and Safety 2005)
    (see **Appendix K-3**). Mechanism of action information could not be
    found for all substances. For 35 of the 282 (12%) substances, only
    the product class could be identified; for seven (3%) substances, no
    information was found. Examination of the RC rat database revealed
    the following.

```{=html}
<!-- -->
```
-   Of the 282 substances, 73 (26%) were outliers[^1] (i.e., log
    observed -- log predicted LD~50~ \> 0.699 as defined for the RC
    millimole regression).

    **Figure 6-5 RC Rat-Only Regression (a) and RC Rat-Only Regression
    after Excluding 50 Substances with Specific Mechanisms of
    Toxicity (b) Using Weight Units**

    ![](media/image5.pct){width="4.877083333333333in"
    height="8.002083333333333in"}

```{=html}
<!-- -->
```
-   For 40 (55%) of the 73 substances, *in vivo* toxicity was
    underpredicted; for 33 (45%) of the 73 substances, *in vivo*
    toxicity was overpredicted.

-   All underpredicted substances were very toxic, with LD~50~ ≤ 200
    mg/kg.

-   The discordant status of 65% (26/40) of the underpredicted
    substances could be explained by four general mechanisms

    -   neurotoxic (i.e., cholinesterase inhibitor, affects CNS
        nicotinic receptor, or otherwise neurotoxic by a mechanism other
        than membrane destabilization such as that produced by a
        solvent)

    -   interferes with energy utilization (i.e., interferes with ATP
        synthesis, inhibits ADP phosphorylation, or uncouples oxidative
        phosphorylation, or is a metabolic poison)

    -   cardiotoxic via specific mechanisms (i.e., positive inotropic
        action, calcium channel blocker)

    -   alkylates cellular proteins and other macromolecules (i.e.,
        covalently binds to enzymes and other proteins to disrupt normal
        function)

        Substances with such mechanisms would not be expected to exert
        their toxic mechanisms in the 3T3 and NHK cells and thus, they
        would be expected to fit the RC millimole regression poorly, as
        evidenced by their discordant status. A new regression was
        calculated after the exclusion of all substances in the RC
        database known to act by these four mechanisms; this included
        the 26 underpredicted substances and 24 other substances that
        were not identified as outliers. The substances excluded from
        the RC rat weight regression are identified in **Appendix K-3**.

        **6.3 Accuracy of the 3T3 and NHK NRU Test Methods for Toxicity
        Category Predictions**

        The 3T3 and NHK NRU test methods are not suitable as
        replacements for acute oral systemic toxicity assays. However,
        the use of *in vitro* NRU test methods to reduce animal use for
        acute oral systemic toxicity assays depends upon their accuracy
        for the prediction of LD~50~ values. NRU-predicted LD~50~ values
        were determined by using the *in vitro* NRU IC~50~ values in the
        regressions presented in **Table 6-2.** The predicted LD~50~
        values were then used to assign each substance to a predicted
        GHS acute oral category (UN 2005). The accuracy of the 3T3 and
        NHK NRU test methods for predicting GHS toxicity categories was
        determined by comparison with categorization based on *in vivo*
        rodent LD~50~ data. This accuracy evaluation approach was used
        because the regulatory use of acute systemic toxicity test
        results is for the purpose of hazard classification and
        labelling of products to protect handlers and consumers.

        The following regressions from **Section 6.2** were evaluated
        for accuracy of GHS oral toxicity category predictions:

```{=html}
<!-- -->
```
-   RC millimole regression

-   RC rat-only weight regression

-   RC rat-only weight regression excluding substances with specific
    mechanisms of toxicity

    The regression calculated using the rat RC data in millimole units
    (**Section 6.2.1**) was not evaluated separately since it was very
    similar to the original RC millimole regression, which used both rat
    and mouse data. As **Table 6-2** shows, the slopes and intercepts
    varied only in the thousandths digits.

    Data for the same reference substances were evaluated for each
    regression. Forty-six substances were evaluated for the 3T3 NRU test
    method and 47 substances were evaluated for the NHK NRU test method.
    Of the original 72 substances tested, epinephrine bitartrate,
    colchicine, and propylparaben were excluded because they were
    removed from the calculation of the RC rat-only weight regression
    due to the lack of rat oral reference LD~50~ data. The 21 substances
    with specific mechanisms of toxicity in **Table 6-3** were excluded
    from all analyses to be consistent with those removed from the RC
    rat-only weight regression excluding substances with specific
    mechanisms of toxicity. These substances have known mechanisms of
    toxicity that are not expected to be active in the 3T3 and NHK cell
    cultures.

    Table 6-3 Substances Deleted from the Evaluations of the 3T3 and NHK
    NRU Test Methods and Regressions Due to Mechanisms of Toxicity Not
    Expected to Be Active in the 3T3 and NHK Cell Cultures

  ----------------- -------------------------------------------------------
  **Substance**     **Mechanism of Toxicity^1^**

  **Neurotoxic**    

  Amitriptyline HCl Blocks norepinephrine, 5-hydroxytryptamine, and
                    dopamine presynaptic uptake; prevents reuptake of heart
                    norepinephrine.

  Atropine sulfate  Antimuscarinic, anticholinergic action; competitive
                    antagonism of anticholinesterase at cardiac & CNS
                    receptor sites.

  Caffeine          Inhibition of phosphodiesterase leading to AMP
                    accumulation, translocation of intracellular Ca^++^,
                    adenosine receptor antagonism, neurotoxic.

  Carbamazepine     Therapeutically decreases firing of noradrenergic
                    neurons.

  Chloral hydrate   Potentiation of GABA~A~ receptor activity, inhibition
                    of N-methyl-D-aspartate activity, & modulation of
                    5-hydroxytryptamine~3~ receptor-mediated depolarization
                    of the vagas nerve^2^.

  Dichlorvos        Inhibition of acetylcholinesterase resulting in
                    acetylcholine accumulation in CNS & effector organs.

  Disulfoton        Inhibition of acetylcholinesterase resulting in
                    acetylcholine accumulation in CNS & effector organs.

  Endosulfan        Affects brain neurotransmitter levels^3^.

  Fenpropathrin     Delays closure of sodium channel causing persistent
                    depolarization of membrane.

  Glutethimide      CNS depression, anticholinergic activity.

  Haloperidol       Blocks dopamine receptors.

  Lindane           CNS depression through inhibition of GABA receptor
                    linked chloride channel at the picrotoxin binding site,
                    leading to blockade of chloride influx into neurons.

  Nicotine          Cholinergic block causing polarization of CNS and PNS
                    synapses.

  Parathion         Inhibition of acetylcholinesterase resulting in
                    acetylcholine accumulation in CNS & effector organs.

  Phenobarbital     CNS depression through inhibition of GABA synapses,
                    inhibits hepatic NADH cytochrome oxidoreductase.

  Physostigmine     Inhibition of acetylcholinesterase resulting in
                    acetylcholine accumulation in CNS & effector organs.

  Strychnine        Increases glutamic acid in the CNS.

  **Interferes with 
  Energy            
  Utilization**     

  Paraquat          Multisystem failure due to depletion of superoxide
                    dismutase, with formation of free radicals & lipid
                    peroxidation; lung fibrosis due to accumulation.

  Potassium cyanide General enzyme inhibition, high affinity for Fe^+++^,
                    inhibits cell respiration by inhibition of cytochrome
                    oxidase.

  **Cardiotoxic**   

  Procainamide HCl  Slows impulse conduction in the heart^4^.

  Verapamil HCl     Inhibition of transmembrane Ca^++^ flux in excitatory
                    tissues, alpha-adrenergic blockade.
  ----------------- -------------------------------------------------------

Abbreviations: NA = not available or information not found; CNS =
central nervous system; GABA = gamma aminobutyric acid; PNS = peripheral
nervous system; NADH = nicotine adenine dinucleotide (reduced).

^1^Ekwall et al. (1998) or Hazardous Substances Data Bank (NLM 2001,
2002) unless otherwise noted.

^2^EPA (2000b).

^3^ATSDR (2000a).

^4^Hardman et al. (1996).

Carbon tetrachloride and methanol were excluded from the 3T3 NRU
evaluations because no laboratory attained sufficient toxicity in any
test for the calculation of an IC~50~. Carbon tetrachloride was also
excluded from the NHK NRU evaluations because no laboratory attained
sufficient toxicity in any test for the calculation of an IC~50.~

The tables providing accuracy information in this section (**Tables
6-4** to **6-6**) are divided into top and bottom parts that provide
accuracy data for the 3T3 and NHK NRU test methods, respectively. For
each part, the toxicity categories corresponding to the *in vivo* LD~50~
data are provided in rows that are labeled on the far left side of the
table. The toxicity categories predicted by the *in vitro* NRU assays
(and associated regressions) are provided in columns that are labeled
across the top of each part (i.e., 3T3 or NHK NRU-predicted toxicity
category) of the table. The numbers at the intersections of the *in
vivo* LD~50~ rows and 3T3 or NHK NRU-predicted toxicity category columns
are the numbers of substances with *in vitro* category predictions that
correspond to the various *in vivo* categories. The right sides of the
tables also provide summaries containing, for each *in vivo* toxicity
category and for the total number of substances evaluated: number of
substances, the accuracy of the 3T3 or NHK NRU prediction, and the
percentage of substances for which toxicity has been overpredicted and
underpredicted by the *in vitro* NRU methods. In each of the 3T3 NRU and
NHK NRU sections of the table, a summary of predictivity[^2] is also
provided for each predicted toxicity category along with the percentage
of substances with category (i.e., toxicity) underpredicted and
overpredicted.

6.3.1 [Prediction of]{.underline} [Toxicity Category by the 3T3 and NHK
NRU Test Methods Using the RC Millimole Regression]{.underline}

**Table 6-4** shows the concordance of the observed (i.e., *in vivo*)
and predicted GHS acute oral toxicity categories (UN 2005) for each *in
vitro* NRU cytotoxicity test method using the geometric mean IC~50~
values (of the three laboratories) in the RC millimole regression, log
LD~50~ (mmol/kg) = 0.435 x log IC~50~ (mM) + 0.625. Accuracy is the
agreement of the

**Table 6-4 Prediction of GHS Toxicity Category^1^ by the 3T3 and NHK
NRU Test Methods and the RC Millimole Regression^2^**

<table>
<colgroup>
<col style="width: 11%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 9%" />
<col style="width: 10%" />
<col style="width: 6%" />
<col style="width: 5%" />
<col style="width: 8%" />
<col style="width: 12%" />
<col style="width: 12%" />
</colgroup>
<tbody>
<tr class="odd">
<td rowspan="2"><strong>Reference Rodent
LD<sub>50</sub><sup>3</sup></strong></td>
<td colspan="6"><strong>3T3 NRU-Predicted Toxicity
Category</strong></td>
<td rowspan="2"><strong>Total</strong></td>
<td rowspan="2"><strong>Accuracy</strong></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Overpredicted</strong></p></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Underpredicted</strong></p></td>
</tr>
<tr class="even">
<td><strong>&lt; 5</strong></td>
<td><strong>5 – 50</strong></td>
<td><strong>50 – 300</strong></td>
<td><strong>300 – 2000</strong></td>
<td><strong>2000 – 5000</strong></td>
<td><strong>&gt; 5000</strong></td>
</tr>
<tr class="odd">
<td>&lt; 5</td>
<td>0</td>
<td>3</td>
<td>1</td>
<td>3</td>
<td>0</td>
<td>0</td>
<td>7<sup>4</sup></td>
<td>0%</td>
<td>0%</td>
<td>100%</td>
</tr>
<tr class="even">
<td>5 –50</td>
<td>0</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>0</td>
<td>6<sup>5</sup></td>
<td>17%</td>
<td>0%</td>
<td>83%</td>
</tr>
<tr class="odd">
<td>50 – 300</td>
<td>0</td>
<td>0</td>
<td>4</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>6<sup>6</sup></td>
<td>67%</td>
<td>0%</td>
<td>33%</td>
</tr>
<tr class="even">
<td>300 – 2000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>6</td>
<td>0</td>
<td>0</td>
<td>6<sup>7</sup></td>
<td>100%</td>
<td>0%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>2000 – 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>11</td>
<td>0</td>
<td>0</td>
<td>11<sup>8</sup></td>
<td>0%</td>
<td>100%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>&gt; 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>6</td>
<td>3</td>
<td>1</td>
<td>10<sup>9,10</sup></td>
<td>10%</td>
<td>90%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>0</td>
<td>4</td>
<td>8</td>
<td>29</td>
<td>4</td>
<td>1</td>
<td>46</td>
<td>26%</td>
<td>43%</td>
<td>30%</td>
</tr>
<tr class="even">
<td>Predictivity</td>
<td>0%</td>
<td>25%</td>
<td>50%</td>
<td>21%</td>
<td>0%</td>
<td>100%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Category Underpredicted</td>
<td>0%</td>
<td>0%</td>
<td>0%</td>
<td>59%</td>
<td>75%</td>
<td>0%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Category Overpredicted</td>
<td>0%</td>
<td>75%</td>
<td>50%</td>
<td>21%</td>
<td>25%</td>
<td>0%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="2"><strong>Reference Rodent
LD<sub>50</sub><sup>3</sup></strong></td>
<td colspan="6"><strong>NHK NRU-Predicted Toxicity
Category</strong></td>
<td rowspan="2"><strong>Total</strong></td>
<td rowspan="2"><strong>Accuracy</strong></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Overpredicted</strong></p></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Underpredicted</strong></p></td>
</tr>
<tr class="even">
<td><strong>&lt; 5</strong></td>
<td><strong>5 – 50</strong></td>
<td><strong>50 – 300</strong></td>
<td><strong>300 – 2000</strong></td>
<td><strong>2000 – 5000</strong></td>
<td><strong>&gt; 5000</strong></td>
</tr>
<tr class="odd">
<td>&lt; 5</td>
<td>0</td>
<td>1</td>
<td>3</td>
<td>2</td>
<td>1</td>
<td>0</td>
<td>7<sup>4</sup></td>
<td>0%</td>
<td>0%</td>
<td>100%</td>
</tr>
<tr class="even">
<td>5 – 50</td>
<td>0</td>
<td>3</td>
<td>3</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>6<sup>5</sup></td>
<td>50%</td>
<td>0%</td>
<td>50%</td>
</tr>
<tr class="odd">
<td>50 – 300</td>
<td>0</td>
<td>1</td>
<td>3</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>6<sup>6</sup></td>
<td>50%</td>
<td>17%</td>
<td>33%</td>
</tr>
<tr class="even">
<td>300 – 2000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>6</td>
<td>0</td>
<td>0</td>
<td>6<sup>7</sup></td>
<td>100%</td>
<td>0%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>2000 – 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>10</td>
<td>1</td>
<td>0</td>
<td>11<sup>8</sup></td>
<td>9%</td>
<td>91%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>&gt; 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>6</td>
<td>5</td>
<td>0</td>
<td>11<sup>10</sup></td>
<td>0%</td>
<td>100%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>0</td>
<td>5</td>
<td>9</td>
<td>26</td>
<td>7</td>
<td>0</td>
<td>47</td>
<td>28%</td>
<td>47%</td>
<td>26%</td>
</tr>
<tr class="even">
<td>Predictivity</td>
<td>0%</td>
<td>60%</td>
<td>33%</td>
<td>23%</td>
<td>14%</td>
<td>0%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Category Underpredicted</td>
<td>0%</td>
<td>20%</td>
<td>0%</td>
<td>62%</td>
<td>71%</td>
<td>0%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Category Overpredicted</td>
<td>0%</td>
<td>20%</td>
<td>67%</td>
<td>15%</td>
<td>14%</td>
<td>0%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

^1^GHS-Globally Harmonized System of Classification and Labelling of
Chemicals with LD~50~ in mg/kg (UN 2005).

\< 5: LD~50~ ≤ 5 mg/kg

5 -- 50: 5 \< LD~50~ ≤ 50 mg/kg

50 -- 300: 50 \< LD~50~ ≤ 300 mg/kg

300 -- 2000: 300 \< LD~50~ ≤ 2000 mg/kg

2000 -- 5000: 2000 \< LD~50~ ≤ 5000 mg/kg

\> 5000: LD~50~ \> 5000 mg/kg

^2^The RC millimole regression is log LD~50~ (mmol/kg) = log IC~50~ (mM)
X 0.435 + 0.625. Numbers in table represent number of substances.

^3^Reference oral LD~50~ values from **Table 3-2**.

^4^Epinephrine bitartrate excluded because no rat LD~50~ was identified.
Disulfoton, parathion, strychnine and physostigmine excluded based on
mechanism of toxicity (see **Table 6-3**).

^5^Colchine excluded because no rat LD~50~ was identified. Dichlorvos,
endosulfan, fenpropathrin, nicotine, and potassium cyanide excluded
based on mechanism of toxicity (see **Table 6-3**).

^6^Caffeine, haloperidol, lindane, paraquat, phenobarbital, and
verapamil HCl excluded based on mechanism of toxicity (see **Table
6-3**).

^7^Amitriptyline, atropine sulfate, carbamazepine, chloral hydrate,
glutethimide, and procainamide HCl excluded based on mechanism of
toxicity (see **Table 6-3**).

^8^Carbon tetrachloride excluded because no laboratory attained
sufficient toxicity for the calculation of an IC~50~.

^9^Methanol excluded because no laboratory attained sufficient toxicity
for the calculation of an IC~50~.

^10^Propylparaben excluded because no rat LD~50~ was identified.

category predictions with those based on the reference rodent LD~50~
values in **Table 3-2**, which are the values used for the original
classification of the test substances. Substances for which the *in
vitro* toxicity category prediction does not match the *in vivo*
determined toxicity category are considered discordant substances for
the GHS toxicity category predictions.

*In Vitro -- In Vivo Concordance Using the RC Millimole Regression*

The overall accuracy of the 3T3 NRU test method for correctly predicting
GHS toxicity classification category using the RC millimole regression
was 26% (12/46 substances) (**Table 6-4**). *In vivo* toxicity was
overpredicted for 43% (20) and underpredicted for 30% (14) of the 46
substances. For this analysis, in terms of each GHS toxicity
classification category:

-   0 (0%) of seven substances with LD~50~ \< 5 mg/kg was correctly
    predicted

-   1 (17%) of six substances in the 5 \< LD~50~ ≤ 50 mg/kg category was
    correctly predicted

-   4 (67%) of six substances in the 50 \< LD~50~ ≤ 300 mg/kg category
    were correctly predicted

-   6 (100%) of six substances in the 300 \< LD~50~ ≤ 2000 mg/kg
    category were correctly predicted; however, this toxicity category
    was also predicted for 23 other substances (79%; 23/29) that did not
    match this category *in vivo*. Thus, the predictivity for this
    category was 21% (6/29 substances predicted for this category
    matched the *in vivo* category).

-   0 (0%) of the 11 substances in the 2000 \< LD~50~ ≤ 5000 mg/kg
    category were correctly predicted

-   1 (10%) of the 10 substances in the LD~50~ \> 5000 mg/kg range was
    correctly predicted

The overall accuracy of the NHK NRU cytotoxicity test method for
correctly predicting the GHS toxicity classification, when the
prediction was based on the RC millimole regression, was 28% (13/47
substances) (see **Table 6-4**). Toxicity was overpredicted for 47% (22)
and underpredicted for 26% (12) of the 47 substances. The pattern of
concordance between *in vitro* and *in vivo* results for the NHK assay
with the RC millimole regression was similar to the 3T3 results with the
exception that two more substances were correctly in the 5 \< LD~50~ ≤
50 mg/kg category. For this analysis, in terms of each GHS toxicity
classification category:

-   0 (0%) of seven substances with LD~50~ \< 5 mg/kg were correctly
    predicted

-   3 (50%) of six substances in the 5 \< LD~50~ ≤ 50 mg/kg and in the

> 50 \< LD~50~ ≤ 300 mg/kg categories were correctly predicted

-   6 (100%) of six substances in the 300 \< LD~50~ ≤ 2000 mg/kg
    category were correctly predicted; however, this toxicity category
    was also predicted for 20 (77%; 20/26) other substances with *in
    vivo* data that did not match the category. Thus, the predictivity
    for this category was 23%.

-   1 (9%) of 11 substances in the 2000 \< LD~50~ ≤ 5000 mg/kg category
    was correctly predicted

-   0 (0%) of 11 substances in the LD~50~ \> 5000 mg/kg range were
    correctly predicted.

For both *in vitro* NRU cytotoxicity test methods, when predicted GHS
toxicity categories did not match the reference rodent GHS toxicity
category, the RC millimole regression generally underpredicted toxicity
for substances in the highest toxicity (i.e., lowest LD~50~) categories
and overpredicted toxicity for substances in the lowest toxicity (i.e.,
highest LD~50~) categories (see **Table 6-4**). While substances at the
very low and very high ends of the toxicity range were poorly predicted,
those in the middle of the toxicity range (i.e., 300 \< LD~50~ ≤ 2000
mg/kg) were predicted quite well.

*Discordant Substances for Prediction of Toxicity Category by the 3T3
and NHK NRU Test Methods and the RC Millimole Regression*

**Appendix L-2** identifies the discordant substances for which the *in
vitro* predicted GHS toxicity category did not match the GHS toxicity
category assigned based on the reference rodent LD~50~ data in **Table
3-2**. For the 3T3 NRU test method, the toxicity was underpredicted for
14 (30%) and overpredicted for 20 (43%) of the 34 discordant substances.
For the NHK NRU test method, toxicity was underpredicted for 12 (35%)
and overpredicted for 22 (65%) of the 34 discordant substances. The fact
that there were more substances for which toxicity was overpredicted is
a result of the removal of substances with specific mechanisms of
toxicity that were not expected to be active in the 3T3 and NHK cell
cultures. The toxicity for most of these substances would have been
underpredicted. **Figure 3-1** shows that most of the RC substances
selected for testing in the NICEATM/ECVAM validation study are below the
RC millimole regression line. Thus, the RC millimole regression is
expected to predict lower toxicity (i.e., a higher LD~50~) for these
substances.

6.3.2 [Prediction of Toxicity Category by the 3T3 and NHK NRU Test
Methods Using the RC Rat-Only Weight Regression]{.underline}

**Table 6-5** shows the concordance of the observed and predicted GHS
acute oral toxicity categories for each *in vitro* NRU test method using
the geometric mean IC~50~ values (of the three laboratories) and the RC
rat only weight regression from **Table 6-2**. The regression formula
for the RC rat-only weight regression was log LD~50~ (mg/kg) = log
IC~50~ (μg/mL) x 0.372 + 2.024. Accuracy is the agreement of the *in
vitro* NRU cytotoxicity GHS toxicity category predictions with those
based on the reference rat oral LD~50~ values from **Table 4-2**.

*In Vitro -- In Vivo Concordance with the RC Rat-Only Weight Regression*

The overall accuracy of the 3T3 NRU test method with the RC rat-only
weight regression was 35% (16) for the results from 46 substances
(**Table 6-5**). The toxicity was overpredicted for 41% (19) and
underpredicted for 24% (11) of the 46 substances. For this analysis, in
terms of each GHS toxicity classification category:

-   0 (0%) of four substances with LD~50~ \< 5 mg/kg were correctly
    predicted

-   1 (14%) of seven substances in the 5 \< LD~50~ ≤ 50 mg/kg GHS
    toxicity category was correctly predicted

-   4 (80%) of five substances in the 50 \< LD~50~ ≤ 300 mg/kg GHS
    toxicity category were correctly predicted; however, since seven
    other substances were also predicted for this category, the
    predictivity was 36% (4/11)

-   7 (78%) of nine substances in the 300 \< LD~50~ ≤ 2000 mg/kg GHS
    toxicity category were predicted correctly. Since a total of 22
    substances were predicted for this category, the predictivity was
    32% (7/22).

**Table 6-5 Prediction of GHS Toxicity Category^1^ by the RC Rat-Only
Weight Regression^2^**

<table style="width:100%;">
<colgroup>
<col style="width: 11%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 7%" />
<col style="width: 9%" />
<col style="width: 9%" />
<col style="width: 8%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 11%" />
<col style="width: 12%" />
</colgroup>
<tbody>
<tr class="odd">
<td rowspan="2"><strong>Reference Rodent
LD<sub>50</sub><sup>3</sup></strong></td>
<td colspan="6"><strong>3T3 NRU-Predicted Toxicity
Category</strong></td>
<td rowspan="2"><strong>Total</strong></td>
<td rowspan="2"><strong>Accuracy</strong></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Overpredicted</strong></p></td>
<td rowspan="2"><strong>Toxicity Underpredicted</strong></td>
</tr>
<tr class="even">
<td><strong>&lt; 5</strong></td>
<td><strong>5 – 50</strong></td>
<td><strong>50 – 300</strong></td>
<td><strong>300-2000</strong></td>
<td><strong>2000-5000</strong></td>
<td><strong>&gt; 5000</strong></td>
</tr>
<tr class="odd">
<td>&lt; 5</td>
<td>0</td>
<td>0</td>
<td>2</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>4<sup>4</sup></td>
<td>0%</td>
<td>0%</td>
<td>100%</td>
</tr>
<tr class="even">
<td>5 – 50</td>
<td>0</td>
<td>1</td>
<td>4</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>7<sup>5</sup></td>
<td>14%</td>
<td>0%</td>
<td>86%</td>
</tr>
<tr class="odd">
<td>50 – 300</td>
<td>0</td>
<td>0</td>
<td>4</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>5<sup>6</sup></td>
<td>80%</td>
<td>0%</td>
<td>20%</td>
</tr>
<tr class="even">
<td>300 – 2000</td>
<td>0</td>
<td>1</td>
<td>1</td>
<td>7</td>
<td>0</td>
<td>0</td>
<td>9<sup>7</sup></td>
<td>78%</td>
<td>22%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>2000 – 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>5</td>
<td>4</td>
<td>0</td>
<td>9<sup>8</sup></td>
<td>44%</td>
<td>56%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>&gt; 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>5</td>
<td>7</td>
<td>0</td>
<td>12<sup>9,10</sup></td>
<td>0%</td>
<td>100%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>0</td>
<td>2</td>
<td>11</td>
<td>22</td>
<td>11</td>
<td>0</td>
<td>46</td>
<td>35%</td>
<td>41%</td>
<td>24%</td>
</tr>
<tr class="even">
<td>Predictivity</td>
<td>0%</td>
<td>50%</td>
<td>36%</td>
<td>32%</td>
<td>36%</td>
<td>0%</td>
<td> </td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Category Underpredicted</td>
<td>0%</td>
<td>50%</td>
<td>9%</td>
<td>46%</td>
<td>64%</td>
<td>0%</td>
<td> </td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Category Overpredicted</td>
<td>0%</td>
<td>0%</td>
<td>55%</td>
<td>23%</td>
<td>0%</td>
<td>0%</td>
<td> </td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="2"><strong>Reference Rodent
LD<sub>50</sub><sup>3</sup></strong></td>
<td colspan="6"><strong>NHK NRU-Predicted Toxicity
Category</strong></td>
<td rowspan="2"><strong>Total</strong></td>
<td rowspan="2"><strong>Accuracy</strong></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Overpredicted</strong></p></td>
<td rowspan="2"><strong>Toxicity Underpredicted</strong></td>
</tr>
<tr class="even">
<td><strong>&lt; 5</strong></td>
<td><strong>5 – 50</strong></td>
<td><strong>50 – 300</strong></td>
<td><strong>300 – 2000</strong></td>
<td><strong>2000 – 5000</strong></td>
<td><strong>&gt; 5000</strong></td>
</tr>
<tr class="odd">
<td>&lt; 5</td>
<td>0</td>
<td>1</td>
<td>2</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>4<sup>4</sup></td>
<td>0%</td>
<td>0%</td>
<td>100%</td>
</tr>
<tr class="even">
<td>5 – 50</td>
<td>0</td>
<td>1</td>
<td>4</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>7<sup>5</sup></td>
<td>14%</td>
<td>0%</td>
<td>86%</td>
</tr>
<tr class="odd">
<td>50 – 300</td>
<td>0</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>5<sup>6</sup></td>
<td>60%</td>
<td>20%</td>
<td>20%</td>
</tr>
<tr class="even">
<td>300 – 2000</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>8</td>
<td>0</td>
<td>0</td>
<td>9<sup>7</sup></td>
<td>89%</td>
<td>11%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>2000 – 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>8</td>
<td>1</td>
<td>0</td>
<td>9<sup>8</sup></td>
<td>11%</td>
<td>89%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>&gt; 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>6</td>
<td>6</td>
<td>1</td>
<td>13<sup>10</sup></td>
<td>8%</td>
<td>92%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>0</td>
<td>4</td>
<td>9</td>
<td>26</td>
<td>7</td>
<td>1</td>
<td>47</td>
<td>30%</td>
<td>47%</td>
<td>23%</td>
</tr>
<tr class="even">
<td>Predictivity</td>
<td>0%</td>
<td>25%</td>
<td>33%</td>
<td>31%</td>
<td>14%</td>
<td>100%</td>
<td> </td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Category Underpredicted</td>
<td>0%</td>
<td>50%</td>
<td>0%</td>
<td>54%</td>
<td>86%</td>
<td>0%</td>
<td> </td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Category Overpredicted</td>
<td>0%</td>
<td>25%</td>
<td>67%</td>
<td>15%</td>
<td>0%</td>
<td>0%</td>
<td> </td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

^1^Globally Harmonized System of Classification and Labelling of
Chemicals with LD~50~ in mg/kg (UN 2005).

\< 5: LD~50~ ≤ 5 mg/kg

5 -- 50: 5 \< LD~50~ ≤ 50 mg/kg

50 -- 300: 50 \< LD~50~ ≤ 300 mg/kg

300 -- 2000: 300 \< LD~50~ ≤ 2000 mg/kg

2000 -- 5000: 2000 \< LD~50~ ≤ 5000 mg/kg

\> 5000: LD~50~ \> 5000 mg/kg

^2^The RC rat-only weight regression is log LD~50~ (mg/kg) = log IC~50~
(μg/mL) X 0.372 + 2.024.

^3^Reference rodent LD~50~ values from **Table 4-2**.

^4^Epinephrine bitartrate excluded because no rat LD~50~ was identified.
Disulfoton and physostigmine excluded based on mechanism of toxicity
(see **Table 6-3**).

^5^Colchine excluded because no rat LD~50~ was identified. Endosulfan,
parathion, potassium cyanide, and strychnine excluded based on mechanism
of toxicity (see **Table 6-3**).

^6^Dichlorvos, fenpropathrin, lindane, paraquat, phenobarbital,
nicotine, and verapamil HCl excluded based on mechanism of toxicity (see
**Table 6-3**).

^7^Amitriptyline, atropine sulfate, caffeine, chloral hydrate,
glutethimide, haloperidol, and procainamide HCl excluded based on
mechanism of toxicity (see **Table 6-3**).

^8^Carbon tetrachloride excluded because no laboratory attained
sufficient toxicity for the calculation of an IC~50~. Carbamazepine
excluded based on mechanism of toxicity (see **Table 6-3**).

^9^Methanol excluded because no laboratory attained sufficient toxicity
for the calculation of an IC~50~.

^10^Propylparaben excluded because no rat LD~50~ was identified.

-   4 (44%) of nine substances in the 2000 \< LD~50~ ≤ 5000 mg/kg GHS
    toxicity category were correctly predicted; however, since a total
    of 11 substances were predicted for this category, the predictivity
    was 36% (4/11).

-   0 (0%) of 12 substances with LD~50~ \> 5000 mg/kg were correctly
    predicted

The overall accuracy of the NHK NRU test method with the RC rat-only
weight regression was 30% \[14 /47\]) (**Table 6-5**). Toxicity was
overpredicted for 47% (22) and underpredicted for 23% (11) of the 47
substances, compared with *in vivo* toxicity categories (i.e., the GHS
categories for the reference LD~50~ values in **Table 4-2**). For this
analysis, in terms of each GHS toxicity classification category:

-   0 (0%) of four substances with LD~50~ \< 5 mg/kg were correctly
    predicted

-   1 (14%) of seven substances in the 5 \< LD~50~ ≤ 50 mg/kg GHS
    toxicity category was correctly predicted

-   3 (60%) of five substances in the 50 \< LD~50~ ≤ 300 mg/kg GHS
    toxicity category were correctly predicted; however, since six other
    substances were also predicted for this category, the predictivity
    was 33% (3/9)

-   8 (89%) of nine substances in the 300 \< LD~50~ ≤ 2000 mg/kg GHS
    toxicity category were predicted correctly; however, since 18 other
    substances were also predicted for this category, the predictivity
    was 31% (8/26)

-   1 (11%) of nine substances in the 2000 \< LD~50~ ≤ 5000 mg/kg GHS
    toxicity category was correctly predicted

-   1 (8%) of 13 substances with LD~50~ \> 5000 mg/kg was correctly
    predicted

*Discordant Substances for Prediction of Toxicity Category by the 3T3
and NHK NRU Test Methods and the RC Rat-Only Weight Regression*

**Appendix L-2** shows the discordant substances for which the *in
vitro* predicted GHS toxicity category did not match that based on the
reference rodent LD~50~ data using the RC rat-only weight regression.
The two *in vitro* NRU cytotoxicity test methods over- and
under-predicted the GHS toxicity category for a similar number of
substances, compared with the GHS toxicity categories for the reference
LD~50~ values in **Table 4-2**. For the 3T3 NRU test method, the GHS
toxicity category of 19 (63%) of 30 discordant substances was
overpredicted and the GHS toxicity category of 11 (37%) substances was
underpredicted. For the NHK NRU test method, the GHS toxicity category
of 22 (67%) of 33 discordant substances was overpredicted and the
toxicity of 11 (33%) discordant substances was underpredicted.

6.3.3 [Prediction of Toxicity Category by the 3T3 and NHK NRU Test
Methods with the RC Rat-Only Weight Regression Excluding Substances with
Specific Mechanisms of Toxicity]{.underline}

**Table 6-6** shows the concordance of the observed and predicted GHS
acute oral toxicity categories for each *in vitro* NRU test method using
the geometric mean IC~50~ values (of the three laboratories) and the RC
rat-only weight regression after excluding substances with specific
mechanisms of toxicity (see **Table 6-3**). The formula for this
regression was log LD~50~ (mg/kg) = log IC~50~ (μg/mL) x 0.357 + 2.194.
Accuracy is the agreement of the *in vitro* predicted GHS toxicity
categories with those based on the reference rat oral LD~50~ values from
**Table 4-2**.

*In Vitro -- In Vivo Concordance for the 3T3 and NHK NRU Test Methods
with the RC Rat- Only Weight Regression Excluding Substances with
Specific Mechanisms of Toxicity*

-   The overall accuracy of the 3T3 NRU test method with the RC rat-only
    weight regression after excluding substances with specific
    mechanisms of toxicity was 46% (21/46 substances) (**Table
    6**-**6**), compared to 35% (16/46 substances) when the complete RC
    rat-only weight regression was used (**Section 6.3.2** and **Table
    6-5**). **Table 6-6** shows that GHS toxicity category was
    overpredicted for 24% (19) and underpredicted for 30% (11) of the 46
    substances compared with the *in vivo* GHS toxicity categories for
    the reference LD~50~ values in **Table 4-2**.

In terms of each GHS toxicity classification category:

-   0 (0%) of the four substances with LD~50~ \< 5 mg/kg were correctly
    predicted

-   1 (14%) of seven substances in the 5 \< LD~50~ ≤ 50 mg/kg GHS
    toxicity category was correctly predicted

**Table 6-6 Prediction of GHS Toxicity Categories^1^ by** **RC Rat-Only
Weight Regression Excluding Substances with Specific Mechanisms of
Toxicity^2^**

<table style="width:100%;">
<colgroup>
<col style="width: 11%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 7%" />
<col style="width: 9%" />
<col style="width: 9%" />
<col style="width: 8%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 11%" />
<col style="width: 12%" />
</colgroup>
<tbody>
<tr class="odd">
<td rowspan="2"><strong>Reference Rodent
LD<sub>50</sub><sup>3</sup></strong></td>
<td colspan="6"><strong>3T3 NRU-Predicted Toxicity
Category</strong></td>
<td rowspan="2"><strong>Total</strong></td>
<td rowspan="2"><strong>Accuracy</strong></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Overpredicted</strong></p></td>
<td rowspan="2"><strong>Toxicity Underpredicted</strong></td>
</tr>
<tr class="even">
<td><strong>&lt; 5</strong></td>
<td><strong>5 – 50</strong></td>
<td><strong>50 – 300</strong></td>
<td><strong>300-2000</strong></td>
<td><strong>2000-5000</strong></td>
<td><strong>&gt; 5000</strong></td>
</tr>
<tr class="odd">
<td>&lt; 5</td>
<td>0</td>
<td>0</td>
<td>2</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>4<sup>4</sup></td>
<td>0%</td>
<td>100%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>5 – 50</td>
<td>0</td>
<td>1</td>
<td>4</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>7<sup>5</sup></td>
<td>14%</td>
<td>86%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>50 – 300</td>
<td>0</td>
<td>0</td>
<td>4</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>5<sup>6</sup></td>
<td>80%</td>
<td>20%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>300 – 2000</td>
<td>0</td>
<td>1</td>
<td>1</td>
<td>7</td>
<td>0</td>
<td>0</td>
<td>9<sup>7</sup></td>
<td>78%</td>
<td>0%</td>
<td>22%</td>
</tr>
<tr class="odd">
<td>2000 – 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>3</td>
<td>6</td>
<td>0</td>
<td>9<sup>8</sup></td>
<td>67%</td>
<td>0%</td>
<td>33%</td>
</tr>
<tr class="even">
<td>&gt; 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>5</td>
<td>4</td>
<td>3</td>
<td>12<sup>9,10</sup></td>
<td>25%</td>
<td>0%</td>
<td>75%</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>0</td>
<td>2</td>
<td>11</td>
<td>20</td>
<td>10</td>
<td>3</td>
<td>46</td>
<td>46%</td>
<td>24%</td>
<td>30%</td>
</tr>
<tr class="even">
<td>Predictivity</td>
<td>0%</td>
<td>50%</td>
<td>36%</td>
<td>35%</td>
<td>60%</td>
<td>100%</td>
<td> </td>
<td> </td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Category Underpredicted</td>
<td>0%</td>
<td>50%</td>
<td>9%</td>
<td>40%</td>
<td>40%</td>
<td>0%</td>
<td> </td>
<td> </td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Category Overpredicted</td>
<td>0%</td>
<td>0%</td>
<td>55%</td>
<td>25%</td>
<td>0%</td>
<td>0%</td>
<td> </td>
<td> </td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="2"><strong>Reference Rodent
LD<sub>50</sub><sup>3</sup></strong></td>
<td colspan="6"><strong>NHK NRU-Predicted Toxicity
Category</strong></td>
<td rowspan="2"><strong>Total</strong></td>
<td rowspan="2"><strong>Accuracy</strong></td>
<td rowspan="2"><p><strong>Toxicity</strong></p>
<p><strong>Overpredicted</strong></p></td>
<td rowspan="2"><strong>Toxicity Underpredicted</strong></td>
</tr>
<tr class="even">
<td><strong>&lt; 5</strong></td>
<td><strong>5 – 50</strong></td>
<td><strong>50 – 300</strong></td>
<td><strong>300 – 2000</strong></td>
<td><strong>2000 – 5000</strong></td>
<td><strong>&gt; 5000</strong></td>
</tr>
<tr class="odd">
<td>&lt; 5</td>
<td>0</td>
<td>0</td>
<td>2</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>4<sup>4</sup></td>
<td>0%</td>
<td>100%</td>
<td>0%</td>
</tr>
<tr class="even">
<td>5 – 50</td>
<td>0</td>
<td>1</td>
<td>4</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>7<sup>5</sup></td>
<td>14%</td>
<td>86%</td>
<td>0%</td>
</tr>
<tr class="odd">
<td>50 – 300</td>
<td>0</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>5<sup>6</sup></td>
<td>60%</td>
<td>20%</td>
<td>20%</td>
</tr>
<tr class="even">
<td>300 – 2000</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>8</td>
<td>0</td>
<td>0</td>
<td>9<sup>7</sup></td>
<td>89%</td>
<td>0%</td>
<td>11%</td>
</tr>
<tr class="odd">
<td>2000 – 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>5</td>
<td>4</td>
<td>0</td>
<td>9<sup>8</sup></td>
<td>44%</td>
<td>0%</td>
<td>56%</td>
</tr>
<tr class="even">
<td>&gt; 5000</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>4</td>
<td>7</td>
<td>2</td>
<td>13<sup>10</sup></td>
<td>15%</td>
<td>0%</td>
<td>85%</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>0</td>
<td>3</td>
<td>9</td>
<td>22</td>
<td>11</td>
<td>2</td>
<td>47</td>
<td>38%</td>
<td>23%</td>
<td>38%</td>
</tr>
<tr class="even">
<td>Predictivity</td>
<td>0%</td>
<td>33%</td>
<td>33%</td>
<td>36%</td>
<td>36%</td>
<td>100%</td>
<td> </td>
<td> </td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Category Underpredicted</td>
<td>0%</td>
<td>67%</td>
<td>0%</td>
<td>41%</td>
<td>64%</td>
<td>0%</td>
<td> </td>
<td> </td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Category Overpredicted</td>
<td>0%</td>
<td>33%</td>
<td>67%</td>
<td>23%</td>
<td>0%</td>
<td>0%</td>
<td> </td>
<td> </td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

^1^Globally Harmonized System of Classification and Labelling of
Chemicals with LD~50~ in mg/kg (UN 2005).

\< 5: LD~50~ ≤ 5 mg/kg

5 -- 50: 5 \< LD~50~ ≤ 50 mg/kg

50 -- 300: 50 \< LD~50~ ≤ 300 mg/kg

300 -- 2000: 300 \< LD~50~ ≤ 2000 mg/kg

2000 -- 5000: 2000 \< LD~50~ ≤ 5000 mg/kg

\> 5000: LD~50~ \> 5000 mg/kg

^2^The RC rat-only weight regression excluding substances with specific
mechanisms of toxicity is log LD~50~ (mg/kg) = log IC~50~ (μg/mL) X
0.357 + 2.194.

^3^Reference rodent LD~50~ values from **Table 4-2**.

^4^Epinephrine bitartrate excluded because no rat LD~50~ was identified.
Disulfoton and physostigmine excluded based on mechanism of toxicity
(see **Table 6-3**).

^5^Colchine excluded because no rat LD~50~ was identified. Endosulfan,
parathion, potassium cyanide, and strychnine excluded based on mechanism
of toxicity (see **Table 6-3**).

^6^Dichlorvos, fenpropathrin, lindane, paraquat, phenobarbital,
nicotine, and verapamil HCl excluded based on mechanism of toxicity (see
**Table 6-3**).

^7^Amitriptyline, atropine sulfate, caffeine, chloral hydrate,
glutethimide, haloperidol, and procainamide HCl excluded based on
mechanism of toxicity (see **Table 6-3**).

^8^Carbon tetrachloride excluded because no laboratory attained
sufficient toxicity for the calculation of an IC~50~. Carbamazepine
excluded based on mechanism of toxicity (see **Table 6-3**).

^9^Methanol excluded because no laboratory attained sufficient toxicity
for the calculation of an IC~50~.

^10^Propylparaben excluded because no rat LD~50~ was identified.

-   4 (80%) of five substances in the 50 \< LD~50~ ≤ 300 mg/kg GHS
    toxicity category were correctly predicted. Since seven other
    substances were also predicted for this category, predictivity was
    36% (4/11).

-   7 (78%) of nine substances in the 300 \< LD~50~ ≤ 2000 mg/kg GHS
    toxicity category were predicted correctly. Since a total of 20
    substances were predicted for this category, the predictivity was
    35% (7/20).

-   6 (67%) of nine substances in the 2000 \< LD~50~ ≤ 5000 mg/kg GHS
    toxicity category were correctly predicted; the predictivity of this
    category was 60% (6/10)

-   3 (25%) of 12 substances with LD~50~ \> 5000 mg/kg were correctly
    predicted. Since no other substances were predicted for this
    category, the predictivity was 100% (3/3).

**Table 6-6** shows that the accuracy of the NHK NRU test method with
the RC rat-only weight regression excluding substances with specific
mechanisms of toxicity was 38% (18/47), compared to the 30% (14/47)
accuracy when the complete RC rat-only weight regression was used (see
**Table 6-5**). Toxicity was overpredicted for 23% (11) and
underpredicted for 38% (19) of the 47 substances compared with the *in
vivo* GHS categories for the reference LD~50~ values in **Table 4-2**.
In terms of each GHS toxicity classification category:

-   0 (0%) of the four substances with LD~50~ \< 5 mg/kg were correctly
    predicted

-   1 (14%) of seven substances in the 5 \< LD~50~ ≤ 50 mg/kg GHS
    toxicity category was correctly predicted

-   3 (60%) of five substances in the 50 \< LD~50~ ≤ 300 mg/kg GHS
    toxicity category were correctly predicted. Since six other
    substances were also predicted for this category, predictivity was
    33% (3/9).

-   8 (89%) of nine substances in the 300 \< LD~50~ ≤ 2000 mg/kg GHS
    toxicity category were predicted correctly. Since 14 other
    substances that did not match this category were also predicted,
    predictivity was 36% (8/22).

-   4 (44%) of nine substances in the 2000 \< LD~50~ ≤ 5000 mg/kg GHS
    toxicity category were correctly predicted; the predictivity of this
    category was 36% (4/11)

-   2 (15%) of 13 substances with LD~50~ \> 5000 mg/kg were correctly
    predicted. Since no other substances were predicted for this
    category, the predictivity was 100% (2/2).

*Discordant Substances for the Prediction of Toxicity Category by the
3T3 and NHK NRU Test Methods and the RC Rat-Only Weight Regression
Excluding Substances with Specific Mechanisms of Toxicity*

**Appendix L-2** shows the discordant substances for which the *in
vitro* NRU predicted toxicity category did not match that based on the
reference rodent LD~50~ data. The NHK NRU test method had four more
discordant substances than the corresponding assay using 3T3 cells when
the IC~50~ results were applied to the RC rat-only weight regression
excluding substances with specific mechanisms of toxicity. For the 3T3
NRU test method, the GHS toxicity category of 19 (63%) of 30 discordant
substances was overpredicted while the toxicity of 11 (37%) of 30
discordant substances was underpredicted compared with the *in vivo* GHS
toxicity categories for the reference LD~50~ values in **Table 4-2**.
For the NHK NRU test method, the toxicity of 22 (65%) of 34 discordant
substances was overpredicted while the toxicity of 12 (35%) of 34
discordant substances was underpredicted.

6.3.4 [Summary of the Regressions Evaluated]{.underline}

**Table 6-7** summarizes the regressions evaluated in **Section 6.3**
for accuracy in predicting the GHS acute oral toxicity categories (UN
2005) and the proportion of discordant substances for *in vitro*
predictions of GHS toxicity categories. Accuracy for the 3T3 NRU test
method was slightly lower than that for the NHK NRU test method for the
RC millimole regression (i.e., 26% vs. 28%). Accuracy for the 3T3 NRU
test method was higher than that for the NHK NRU test method for the RC
rat-only weight regression (i.e., 35% vs. 30%) and the RC rat-only
weight regression excluding substances with specific mechanisms of
toxicity (i.e., 46% vs. 38%). The proportion of discordant substances
for the 3T3 NRU test method was higher for the RC millimole regression
(74%) than it was for the RC rat-only weight (65%) regression and the RC
rat-only weight regression excluding substances with specific mechanisms
of toxicity (65%). The proportion of discordant substances for the NHK
NRU test method was similar for each regression (i.e., 70-72%). **Table
6-7** shows that the difference between the proportions of discordant
substances for the 3T3 and NHK NRU test methods widened with each
subsequent regression (74% vs. 72% for the RC millimole regression, 65%
vs. 70% for the RC rat-only weight regression, and 65% vs. 72% for the
RC rat-only weight regression excluding substances with specific
mechanisms of toxicity).

**Table 6-7 Comparison of Regressions and *In Vitro* NRU Test Methods
for Performance in Predicting GHS^a^ Toxicity Categories**

+--------------------------+---+---------+-----------+-----------------+
| **Regression**           | * | **A     | **A       | **Discordant    |
|                          | * | djusted | ccuracy** | Substances^c^** |
|                          | N | R^2^**  |           |                 |
|                          | ^ |         |           |                 |
|                          | b |         |           |                 |
|                          | ^ |         |           |                 |
|                          | * |         |           |                 |
|                          | * |         |           |                 |
+--------------------------+---+---------+-----------+-----------------+
| RC --millimole units     | 3 | 0       | 3T3 --    | 3T3 -- 34/46    |
|                          | 4 | .450^d^ | 26%       | (74%)           |
|                          | 7 |         |           |                 |
|                          |   |         | NHK --    | NHK -- 34/47    |
|                          |   |         | 28%       | (72%)           |
+--------------------------+---+---------+-----------+-----------------+
| RC rat only --weight     | 2 | 0.322   | 3T3 --    | 3T3 -- 30/46    |
| units^e^                 | 8 |         | 35%       | (65%)           |
|                          | 2 |         |           |                 |
|                          |   |         | NHK --    | NHK -- 33/47    |
|                          |   |         | 30%       | (70%)           |
+--------------------------+---+---------+-----------+-----------------+
| RC rat only excluding    | 2 | 0.353   | 3T3 --    | 3T3 -- 30/46    |
| substances with specific | 3 |         | 46%       | (65%)           |
| mechanisms of action     | 2 |         |           |                 |
| --weight units^e^        |   |         | NHK --    | NHK -- 34/47    |
|                          |   |         | 38%       | (72%)           |
+--------------------------+---+---------+-----------+-----------------+

^a^Globally Harmonized System of Classification and Labelling of
Chemicals with LD~50~ in mg/kg (UN 2005).

^b^Number of substances used in regression.

^c^Proportion of substances evaluated.

^d^Calculated from RC data (i.e., regression not reported by Halle
\[1998\]).

^e^From **Table 6-1**.

The highest accuracy for both *in vitro* NRU cytotoxicity test methods
was attained when using the RC rat-only weight regression excluding
substances with specific mechanisms of toxicity. The accuracy for the
3T3 NRU test method was 46%, which was greater than the accuracy of the
3T3 NRU with the RC millimole regression (26%) and with the RC rat-only
weight regression (35%). The accuracy for the NHK NRU test method was
38% for the RC rat-only weight regression excluding substances with
specific mechanisms of toxicity, 28% with the RC millimole regression,
and 30% with the RC rat-only weight regression.

**6.4 Strengths and Limitations of the *In Vitro* NRU Cytotoxicity Test
Methods for *In Vivo* Toxicity Prediction**

For each regression evaluated, the NRU basal cytotoxicity test methods
tended to underpredict the toxicity of the most toxic substances and to
overpredict the toxicity of the least toxic substances. The 3T3 and NHK
NRU test methods were better at predicting the toxicity of substances
with 50 \< LD~50~ ≤ 300 mg/kg and 300 \< LD~50~ ≤ 2000 mg/kg than
predicting the toxicity of substances with higher or lower LD~50~
values. The accuracy for the RC millimole regression and the RC rat-only
weight regression for these toxicity categories was 67-100% for the 3T3
NRU and 33-83% for the NHK NRU data. Substances with higher or lower
LD~50~ values were infrequently predicted correctly. The accuracy for
substances with LD~50~ ≤ 50 mg/kg (GHS toxicity categories for LD~50~ ≤
5 mg/kg and 5 \< LD~50~ ≤ 50 mg/kg) was 0-17% for the 3T3 NRU and 0-50%
for the NHK NRU with the same regressions. Accuracy for substances in
the 2000 \< LD~50~ ≤ 5000 and LD~50~ \> 5000 mg/kg toxicity categories
was 0-44% for the 3T3 NRU and 0-11% for the NHK NRU.

The RC rat-only weight regression calculated after removal of substances
with specific mechanisms of toxicity improved the accuracy of GHS
toxicity category predictions for substances with LD~50~ \> 2000 mg/kg
compared with the accuracy for the other regressions. The accuracy for
substances in these categories was 25-67% for the 3T3 NRU and 15-44% for
the NHK NRU. The RC rat-only weight regression excluding substances with
specific mechanisms of toxicity did not increase the accuracy for
substances with LD~50~ \< 2000 mg/kg. However, the accuracy for
substances in the 50 \< LD~50~ ≤ 300 mg/kg and 300 \< LD~50~ ≤ 2000
mg/kg categories using the RC millimole regression and the RC rat-only
weight regression was already quite high. The accuracy for predicting
these categories using the RC rat-only weight regression excluding
substances with specific mechanisms of toxicity was 78-80% for the 3T3
NRU and 60-89% for the NHK NRU. The accuracy for predicting the toxicity
categories for LD~50~ ≤ 5 mg/kg and 5 \< LD~50~ ≤ 50 mg/kg was 0-14% for
both the 3T3 NRU test methods when using the RC rat-only weight
regression excluding substances with specific mechanisms of toxicity.

The analysis of the 30 (3T3 NRU) to 31 (NHK NRU) discordant substances
for the RC millimole regression to determine the physical, chemical, and
biological characteristics associated with the discordant substances is
presented in **Appendix L-1**. The analysis showed that 3 of 3 (100%)
organophosphates were discordant in both test methods (10% of the 30
\[3T3 NRU)\] to 31 \[NHK NRU\] discordant substances). Other
characteristics that seemed promising for characterizing RC millimole
regression outliers were boiling point, molecular weight, and log K~ow~.
For boiling points \> 200°C, 9/13 substances (69%) were outliers for
both the 3T3 results NHK NRU results (29 and 26% of the outliers,
respectively). The toxicity of seven of the nine (78%) outliers with
boiling points \> 200°C was underpredicted by the RC millimole
regression and the toxicity of the other two (22%) substances was
overpredicted. For molecular weight \> 400 g/mole, 5/7 (71%) substances
were outliers using the 3T3 data and 3/7 (43%) were outliers using the
NHK data (17 and 10% of the outliers, respectively). The toxicity of all
the outliers with molecular weight \> 400 g/mole was underpredicted by
the RC millimole regression (5/5 \[100%\] for the 3T3 NRU and 3/3
\[100%\] for the NHK NRU). For log K~ow~ \> 3, 9/12 (75%) substances
were outliers using the 3T3 data (30% of the outliers) and 8/12 (67%)
substances were outliers using the NHK data (26% of the outliers). The
toxicity of 7/9 (78%) outliers (with

log K~ow~ \> 3) for the 3T3 NRU assay was underpredicted by the RC
millimole regression and the toxicity of 2/9 (22%) outliers was
overpredicted. The toxicity of 6/8 (75%) outliers (with log K~ow~ \> 3)
for the NHK NRU assay was underpredicted by the RC millimole regression
and the toxicity of 2/8 (25%) outliers was overpredicted. Of the 21
substances with specific mechanisms of toxicity that were not expected
to be active in the 3T3 and NHK cell cultures, 13 (62%) were outliers.
These substances represented 13/30 (43%) of the discordant substances
for the 3T3 NRU and 13/31 (42%) for the NHK NRU.

The lack of fit of individual substances to the regressions was not
consistently related to their insolubility in the 3T3 or NHK medium. Of
the 25 substances that exhibited precipitates in the 3T3 NRU assay, 11
(44%) substances were discordant (see **Table 5-8** for substances that
had precipitates and **Appendix L-1** for the analysis of discordant
substances). The toxicity of nine of the 11 (82%) substances was
underpredicted by the RC millimole regression and the toxicity of two of
the 11 (18%) substances was overpredicted by the RC millimole
regression. Of the 24 substances that exhibited precipitates in the NHK
NRU assay, 11 (46%) substances were outliers. The toxicity of nine of
the 11 (82%) substances was underpredicted by the RC millimole
regression and the toxicity of two of the 11 (18%) substances was
overpredicted by the RC millimole regression.

Additionally, the lack of fit of individual substances to the RC
millimole regression was not consistently related to the fact that the
test method systems had little to no metabolic capability. Such a system
would be expected to underestimate the toxicity of substances with
active metabolites. However, the toxicity of substances known to produce
active metabolites *in vivo* (listed in **Table 3-7**) was not
necessarily underpredicted by the NRU assays. Of the 19 substances known
to produce active metabolites *in vivo*, ten were discordant in the 3T3
NRU test method. Of these ten discordant substances, the toxicity of six
(60%) was underpredicted while the toxicity of four (40%) was
overpredicted by the 3T3 NRU test method. These ten discordant
substances accounted for 33% of the 30 discordant substances identified
for the 3T3 NRU test method. Nine of the 19 substances known to produce
active metabolites *in vivo* were discordant for the NHK NRU test
method. Of these nine discordant substances, the NHK NRU assay
underpredicted the toxicity of five (56%) substances and overpredicted
the toxicity of four (44%) substances. These nine discordant substances
accounted for 29% of the 31 discordant substances identified for the NHK
NRU test method.

Similarly, Halle (1998) noted that the RC substances that required
metabolic activation to produce *in vivo* toxicity were not necessarily
discordant substances (with respect to fit to the RC millimole
regression). Halle (1998) found that eight (50%) of the 16 substances
that required metabolic activation to product toxicity were discordant
substances while eight (50%) were not discordant (see **Table L3-3** in
**Appendix L3**).

Some substances with low toxicity and low solubility could not be tested
in the *in vitro* NRU cytotoxicity assays because the amount of
dissolved substance was inadequate to obtain an IC~50~ value. In the 3T3
NRU test method, none of the laboratories obtained adequate toxicity in
any experiment with carbon tetrachloride and methanol. At least one
laboratory failed to achieve adequate toxicity with gibberellic acid and
xylene. In the corresponding NHK assay, no laboratory achieved adequate
toxicity in any experiment with carbon tetrachloride, and at least one
laboratory could not achieve adequate toxicity with methanol,
1,1,1-trichloroethane, and xylene.

Although the accuracy of the 3T3 and NHK NRU test methods for predicting
*in vivo* toxicity category was rather low when used with the RC
millimole regression and the RC rat-only weight regression, it was
improved by removing substances with specific mechanisms of toxicity
that were not expected to be active in the 3T3 and NHK cell cultures.
The evaluation of these *in vitro* NRU cytotoxicity test methods for
predicting starting doses for acute systemic toxicity testing, thereby
reducing and refining animal use, is provided in **Section 10**.

**6.5 Salient Issues of Data Interpretation**

One of the most important considerations for the 3T3 and NHK NRU test
methods is getting good dose-response results. In addition to technical
difficulties with these methods, such as occasional poor cell growth and
the formation of NRU crystals, this validation study yielded
observations of unusual dose-responses for certain substances.

The experimenter must be aware of dose-response anomalies and their
causes in order to determine whether the dose-response can be better
defined. For example, for substances such as aminopterin, which
generally produced a biphasic dose-response using the log-dose spacing
of the range-finder test, the experimenter must focus on the lowest
concentration at which the substance produced 50% toxicity in order to
perform the definitive testing with more closely spaced concentrations.
In the definitive tests of such substances, the toxic response may
plateau before producing 100% toxicity (i.e., 0% viability). The method
used for the calculation of the IC~50~ must reflect an IC~50~ that is
50% inhibition of the control values rather than the midpoint of the
highest and lowest response (as provided by the standard Hill function
analysis).

Some substances, because of their low toxicity and/or low solubility, do
not provide sufficient toxicity for the calculation of an IC~50~ value.
Carbon tetrachloride, methanol, xylene, gibberellic acid, lithium
carbonate and 1,1,1-trichloroethane failed to yield acceptable IC~50~
results in at least one laboratory due to insufficient
toxicity/insolubility. All of these substances, with the exception of
methanol, were reported to produce precipitate in the cell culture
medium.

**6.6 Comparison to Established Performance Standards**

The *Guidance Document* method of evaluating basal cytotoxicity assays
for use in predicting starting doses for acute oral toxicity assays
provides the existing performance standard (ICCVAM 2001b) for the 3T3
and NHK NRU cytotoxicity test methods. The *Guidance Document*
recommends testing10 to 20 reference substances from the RC in a
candidate *in vitro* basal cytotoxicity assay to be used for predicting
starting doses (ICCVAM 2001b). The substances should cover a wide range
of toxicity and fit the RC prediction model (i.e., the linear regression
line) as closely as possible. The IC~50~ results for the selected
reference substances are used to calculate a new regression line with
the LD~50~ values used by the RC. If the resulting regression is
parallel to the RC millimole regression and within the ± log 5 (i.e., ±
0.699) prediction interval for the RC, the *Guidance Document*
recommends using the cytotoxicity assay to predict starting doses for
unknown substances to be tested in acute oral systemic toxicity assays.

One goal of the coded substance testing in Phases Ib and II of this
study was to establish whether the results from the 3T3 and NHK NRU
cytotoxicity test methods were consistent with the RC millimole
regression. As discussed in **Section 3.4.1**, two of the major criteria
for selecting the 12 coded substances tested in these phases from the 72
substances to be tested were (a) two substances must be included from
each of the unclassified and classified GHS acute oral toxicity
categories and (b) the substances must fit as closely to the RC
millimole regression as possible. Unfortunately, the SMT could not
identify 12 substances that fit both criteria since there was only one
substance, aminopterin, in the LD~50~ \< 5 mg/kg category that fit the
RC millimole regression. The other substance chosen for testing for that
toxicity category was sodium selenate. Since sodium selenate was not
included in the RC, the SMT did not know how closely it would fit the RC
millimole regression and it was not included in the regression analyses
for Phases Ib and II.

The geometric mean log IC~50~ values from the 3T3 and NHK test methods
from each laboratory were used with the oral log LD~50~ values from the
RC (see **Appendices J-1 and J-3**) for the calculation of least squares
linear regression analyses (see **Section 5.3**) for the substances
tested in Phases Ib and II. The slopes for all regressions were
significantly different from zero with p \< 0.0001. The adjusted R^2^
values for the regressions from each laboratory, shown in **Table 6-8**,
indicate that the 3T3 NRU test method produced better fitting
regressions than the corresponding NHK assay (adjusted R^2^ = 0.934 --
0.947 vs. 0.530 -- 0.579). The relatively low adjusted R^2^ values for
the NHK assay were attributed to the much lower toxicity of aminopterin
in that assay (see **Figures 6-6** to **6-8** and **Table 5-4**). The
regressions were consistent with the RC millimole regression. **Table
6-8** shows that

p \> 0.01, the level of statistical significance, for all joint
comparisons of slopes and intercepts with the RC millimole regression.
The RC millimole regression slope and intercept were assumed to be
constants for the comparison. A graphical comparison of the regressions
with the RC millimole regression as suggested by the *Guidance Document*
(ICCVAM 2001b) examples demonstrated that the regressions were generally
within the RC millimole regression acceptance limits (see **Figures
6-6** to **6-8**). According to the *Guidance Document* (ICCVAM 2001b),
basal cytotoxicity assays providing such consistency with the RC
millimole regression are acceptable for predicting starting doses for
*in vivo* acute oral systemic toxicity assays.

Table 6-8 Linear Regressions for Substances Tested in Phases Ib and II

  ---------------- --------------- ----------- ------------ --------------- ----------- --------------
                   **3T3 Millimole                          **P-Values for              
                   Regression**                             Test Against RC             
                                                            Millimole                   
                                                            Regression**                

  **Laboratory**   **Intercept**   **Slope**   **Adjusted   **Intercept**   **Slope**   **Joint^1^**
                                               R^2^**                                   

  ECBC             0.793           0.584       0.934        0.202           0.014       0.040

  FAL              0.709           0.598       0.947        0.497           0.008       0.024

  IIVS             0.710           0.584       0.943        0.508           0.014       0.041

                   **NHK Millimole                          **P-Values for              
                   Regression**                             Test Against RC             
                                                            Millimole                   
                                                            Regression**                

  **Laboratory**   **Intercept**   **Slope**   **Adjusted   **Intercept**   **Slope**   **Joint^1^**
                                               R^2^**                                   

  ECBC             0.401           0.530       0.530        0.484           0.547       0.620

  FAL              0.429           0.548       0.579        0.519           0.450       0.569

  IIVS             0.373           0.549       0.544        0.426           0.475       0.538
  ---------------- --------------- ----------- ------------ --------------- ----------- --------------

^1^Simultaneous comparison of slope and intercept. The RC slope and
intercept were assumed to be constants.

ECBC -- US Army Edgewood Chemical Biological Center; FAL -- FRAME
Alternatives Laboratory; IIVS -- Institute for *In Vitro* Sciences

**Figure 6-6 *In Vitro* -- *In Vivo* Regressions^1^ for Phases Ib and II
for ECBC**

![](media/image6.pct){width="5.727777777777778in"
height="7.634722222222222in"}

**^\_\_\_\_\_\_\_^** Solid Lines: RC millimole regression and acceptance
limits **- - - - -** Dashed Line: Study Regression

^1^Regressions of substances tested in Phases Ib and II do not include
sodium selenate since it was not included in the RC.

ECBC: U.S. Army Edgewood Chemical Biological Command

**Figure 6-7 *In Vitro* -- *In Vivo* Regressions^1^ for Phases Ib and II
for FAL**

![](media/image7.pct){width="5.557638888888889in"
height="7.475694444444445in"}

**^\_\_\_\_\_\_\_^** Solid Lines: RC millimole regression and acceptance
limits **- - - - -** Dashed Line: Study Regression

^1^Regressions of substances tested in Phases Ib and II do not include
sodium selenate since it was not included in the RC.

FAL: Fund for the Replacement of Animals in Medical Experiments
Alternatives Laboratory

**Figure 6-8 *In Vitro* -- *In Vivo* Regressions^1^ for Phases Ib and II
for IIVS**

![](media/image8.pct){width="5.5in" height="7.081944444444445in"}

**^\_\_\_\_\_\_\_^** Solid Lines: RC millimole regression and acceptance
limits **- - - - -** Dashed Line: Study Regression

^1^Regressions of substances tested in Phases Ib and II do not include
sodium selenate since it was not included in the RC.

IIVS: Institute for In Vitro Sciences

**6.7** **Summary**

The millimole regressions developed using the NICEATM/ECVAM IC~50~ and
LD~50~ data were not significantly different from a regression for the
58 RC substances calculated using the RC data (F test; p = 0.929 for the
3T3 NRU regression and p = 0.144 for the NHK NRU regression). To improve
the RC millimole regression with respect to the prediction of LD~50~
values by *in vitro* NRU IC~50~ values, regressions were developed using
the RC data in weight units to exclude (1) mouse data (i.e., the RC
rat-only weight regression) and (2) substances with mechanisms of
toxicity that were not expected to be active in the 3T3 and NHK cell
cultures (i.e., the RC rat-only regression excluding substances with
specific mechanisms of toxicity regression).

Accuracy in predicting GHS acute toxicity category using these *in
vitro* NRU cytotoxicity test methods was 26% (12/46) for the 3T3 NRU and
28% (13/47) for the NHK NRU with the RC millimole regression. Accuracy
with the RC rat-only weight regression improved to 35% (16/46) for the
3T3 NRU and 30% (14/47) for the NHK NRU. Accuracy was higher for
substances with 50 \< LD~50~ ≤ 2000 mg/kg compared to substances with
higher or lower toxicity. For these two regressions, the accuracy of
predicting the 50 \< LD~50~ ≤ 300 mg/kg and 300 \< LD~50~ ≤ 2000 mg/kg
categories for the 3T3 and NHK NRU was 67-100% and 50-100%,
respectively. The accuracy of predicting the LD~50~ ≤ 5 mg/kg and 5 \<
LD~50~ ≤ 50 mg/kg categories was 0-17% for the 3T3 NRU and 0-50% for the
NHK NRU. The accuracy for substances with 2000 \< LD~50~ ≤ 5000 mg/kg
and LD~50~ \> 5000 mg/kg was 0-67% and

0-44% for the 3T3 and NHK NRU data, respectively.

Examination of outliers for the RC millimole regression by chemical
class showed that 3 of 3 (100%) organophosphates were outliers in both
test methods. Other characteristics that seemed promising for
characterizing RC outliers were boiling point, molecular weight, and log
K~ow~. For boiling points \> 200°C, 9/13 (69%) substances were outliers
for both the 3T3 and NHK NRU results. For molecular weight \> 400
g/mole, 5/7 (71%) substances were

outliers using the 3T3 data and 3/7 (43%) were outliers using the NHK
data. For log K~ow~ \> 3, 9/12 (75%) substances were outliers using the
3T3 data and 8/12 (67%) substances were outliers using the NHK data.

The lack of fit of individual substances to the RC millimole regression
was not consistently related to substance insolubility in the 3T3 or NHK
medium or to the fact that the test method systems had little to no
metabolic capability. Of the substances that exhibited precipitates,
11/25 (44%) substances were discordant with the 3T3 NRU assay and 11/24
(46%) were discordant with the NHK NRU assay. Also, although the 3T3 and
NHK cells have little to no metabolic capability, the toxicity of
substances known to produce active metabolites *in vivo* was not
necessarily underpredicted by these assays. Of the 19 substances known
to produce active metabolites *in vivo*, ten (53%) were discordant in
the 3T3 NRU test method. Of these ten discordant substances, the
toxicity of six (60%) was underpredicted while the toxicity of four
(40%) was overpredicted by the 3T3 NRU test method. These ten discordant
substances accounted for 33% of the 30 discordant substances identified
for the 3T3 NRU test method. Similarly, nine (47%) of the 19 substances
known to produce active metabolites *in vivo* were discordant for the
NHK NRU test method. Of these nine discordant substances, the NHK NRU
assay underpredicted the toxicity of five (56%) substances and
overpredicted the toxicity of four (44%) substances. These nine
discordant substances accounted for 29% of the 31 discordant substances
identified for the NHK NRU test method.

The examination of outliers based on mechanism of toxicity lead to the
development the RC rat-only weight regression excluding substances with
specific mechanisms of toxicity. Of the 21 substances with specific
mechanisms of toxicity that were not expected to be active in the 3T3
and NHK cell cultures, 13 (62%) were outliers. These substances
represented 13/30 (43%) of the discordant substances for the 3T3 NRU
test method and 13/31 (42%) for the NHK NRU test method. The RC rat-only
weight regression excluding substances with specific mechanisms of
toxicity improved the accuracy from 26% (12/46) for the RC millimole
regression to 46% (21/46) for the 3T3 NRU test method and from 28%
(13/47) to 38% (18/47) for the NHK NRU test method.

The RC rat-only weight regression calculated after removal of substances
with specific mechanisms of toxicity improved the accuracy (compared
with the RC millimole regression) for predicting most toxicity
categories. It did not improve the accuracy of category prediction for
substances with LD~50~ \< 50 mg/kg or for substances with 300 \< LD~50~
≤ 2000 mg/kg. The following changes in accuracy for the various toxicity
categories, compared with the RC millimole regression, occurred:

-   LD~50~ ≤ 5 mg/kg-- 0% to 0% for both 3T3 and NHK NRU

-   5 \< LD~50~ ≤ 50 mg/kg -- 17% to 14% for the 3T3 NRU and 50% to 14%
    for the NHK NRU

-   300 \< LD~50~ ≤ 2000 mg/kg -- 100% to 78% for the 3T3 NRU and 100%
    to 89% for the NHK NRU

-   2000 \< LD~50~ ≤ 5000 mg/kg -- 0% to 67% for the 3T3 NRU and 9% to
    44% for the NHK NRU

-   LD~50~ \> 5000 mg/kg -- 10% to 25% for the 3T3 NRU and 0% to 15% for
    the NHK NRU

***\[This Page Intentionally Left Blank\]***

[^1]: Substance "outliers" are often referred to as discordant
    chemicals. Substance outliers are different from the replicate
    "outliers" described in **Sections 5.2** and **5.3**, which were
    extreme values in a set of replicate data. See **Section 13** for
    definitions.

[^2]: Proportion of *in vivo* category matches for all substances with
    *in vitro* predictions for a particular category. Predictivity is an
    indicator of test accuracy (ICCVAM 2003).