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TABLE 7.2 Structural Alerts Leading to High Risks of Carcinogenicity
Number
Structural Motif Alerting
for Carcinogenicity Metabolic Activation (if any)
1 Primary alky halides
a
2 Aryl amines and alkylated aryl amines In situ formation of nitrenium ion
3 Aromatic nitro (and some aliphatic nitro) In situ formation of nitrenium ion
4 Azo compounds
b
Via reduction and in situ
formation of diazonium ion
5 Unsymmetrical hydrazines
b
In situ formation of carbocation
or diazonium species
6 Di-substituted hydrazines
b
Via reduction and in situ
formation of diazonium ion
7 Alkyl aldehydes
8 Epoxides (both alkyl and aryl)
c
9 Aziridines (both alkyl and aryl)
c
10 N-nitroso Via formation of carbocation or
diazonium species
11 Esters of sulfonic and phosphonic acids
(both alkyl and aryl)
a
12 Aza N-oxides, Aryl N-oxides
13 N-chloro amines
a
Accumulation possible
14 Michael reagents (amides, e.g., acrylamide,
nitriles, a,b-unsaturated esters)
b
15 Carbamate derivatives (urethanes)
16 N-methyol derivatives
17 N- and S-mustards (b-haloethyl)
18 Propiolactones (and their thiolated
derivatives, propiosultones)
19 Monohaloalkenes
20 Heavy metal compounds
d
21 Polycyclic amines In situ formation of nitrenium ion
22 Organophosphorous compounds
d
23 Aflatoxin-like compounds
c
In situ formation of epoxide
24 Azoxy compounds
b
Via reduction and in situ
formation of diazonium ion
25 Benzidine compounds
e
In situ formation of nitrenium ion
26 Steroid-like compounds
f
27 Tetra-halogenated dibenzodioxins and
dibenzofurans
f
Accumulation possible
28 Vinyl-containing compounds
c
In situ formation of epoxide
29 N-hydroxy aminoaryls
30 N-acetylated aminoaryls
a
Part of miscellaneous Ashby alerts [21].
b
Part of Cheeseman’s original hydrazine grouping [20].
c
Part of Cheeseman’s original strained ring grouping [20].
d
Often viewed separately under neurotoxic classification [21].
e
Benzidine is often included in polycyclic amine grouping [21].
f
Part of Cheeseman’s original endocrine disrupters grouping [20].
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The N-nitroso compounds would be expected to demonstrate toxicity via meta-
bolic activation to either the carbocation or diazonium ions via similar pathways.
These compounds are about 100-fold more potent (MALV ¼ 6.79) compared to the
nonalerting elements in the database.
The endocrine disruptors include estrogen mimetics that disrupt endocrine-
mediatedbiologicalpathways and include substituted dibenzodioxins,tetra-substituted
dibenz ofurans, ergot-derived alkaloids, flavenoids, and polychlorinated pesticides.
Other researchers have divided this original subgroup into four further
subgroups: steroids, highly chlorinated compounds, organotin compounds, and
tetra-halogenated dibenzodioxins and structurally similar compounds [21]. Some
members of this original endocrine disruptor class have the common 1,2cyclopentanophenathrene ring system which is characteristic of the steroids and
the structurally related motifs observed in the flavone ring systems and ergot
alkaloids. Other r elate d structural moieties include the multiple substituted dibenxodioxins and dibenzofurans and the polychlorinated ring sy st ems in some pesticides. These findings are in broad agreement with the structural motifs required to
bind to the endocrine receptor sites [24]. These compounds are about 40-fold more
potent (MALV ¼ 6.45) versus the nonalerting elements in the database.
The strained heterocyclic ring compounds include those that do not require
metabolic activation, for example, epoxides, aziridines, strained lactones, and alkyl
imines; as well as those undergoing bioactivation to produce these structures in vivo,
for example, polycyclic aromatic hydrocarbons and the extremely potent aflatoxins.
These compounds are about 10 times more potent (MALV ¼ 5.95) versus the
nonalerting elements in the database . Subsequently, other research groups have
subdivided this original class into epoxides and aziridines (strained ring systems)
and three additional groups of compounds that are established as expressing their
toxicology after metabolic activation via strained ring metabolites, for example,
aflatoxin-like compounds, polycyclic aromatic hydrocarbons, and compounds containing a vinyl structural moiety [21].
In a similar fashion to N-nitroso analytes, the aromatic nitro (and related aromatic
amine) compounds undergo metabolic activation to form the very reactive nitrenium
ions. Cheeseman and colleagues [20] narrowed this group to a nitro-substituted furan
ring with the substituent alpha to the heterocyclic oxygen and, for example, the related
nitrogen-substituted imidazole and sulfur-substituted thienyl compounds. However,
as other researchers have indicated, the whole class of aromatic nitro groups are highly
alerting for mutagenicity and carcinogenicity. The MALV (5.90) for this more
restricted class of nitroaromatics shows 10-fold greater potency than the nonalerting
elements in the database.
The heavy metals, exemplified by lead, cadmium, mercury, and some of the
precious metals, for example, platinum and palladium have been long recognized as
being highly toxic. It is also probable that this class also forms a significant subset of
the potent neurotoxins, as well as alerting for mutagenicity and carcinogenicity. The
MALV (5.91) is very similar to the previous class of alerting compounds.
Hydrazines, azoarenes, and related triazenes, azides, and azoxy compounds all
share a common structural alert and, similar to the N-nitroso group, demonstrate
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toxicity via metabolic activation to either the carbocation or diazonium ions. The
MALV (5.88) is again about 10 times more potent than the nonalerting elements in the
database. Latterly, other researchers have concluded that this grouping is too broad
and have recommended a subdivision into hydrazines, azoxy compounds, and azo
compounds [21].
Polycyclic amines, which include polyheterocyclic amines, biphenyl amines,
aromatic acetamides, benzidines as well as the ubiquitous polycyclic aromatic amines
are often encountered in nature via overcooked meats, fish, and poultry. Their toxicity
is via bioactivation to form the highly reactive nitrenium ion in a similar fashion to
nitroarenes. Benzidine is one of the more potent subgroups in this group causing
carcinogenicity after even limited exposure and is sometimes extracted as a separate
group. The MALV (5.82) is again about 10 times more potent than the nonalerting
elements in the database.
Finally, organophosphorous compounds are the least potent of this highly alerting
subgroup, with a MALVof 5.67. They act via their intrinsic ability to phosphorylate or
alkylate biomolecules. Similar to the heavy metals, organophosphorous compounds
are neurotoxic in nature and anticholinesterase substrates due to phosphorylation of
this neurotransmitter.
Different authors have advocated that heavy metals and organophosphorous
compounds should be excluded from any databases predicting solely for mutagenicity
and carcinogenicity. These compounds, particularly organophosphates, should be
extracted into a separate neurotoxicant database to avoid biases towards high potency
based on both numbers of compounds and high toxicity [21].
Kroes and colleagues [21] identified five different high concern groups: aflatoxinlike, N-nitroso, azoxy, steroids, and polyhalogenated compounds, for example,
halogenated dibenzo-p-dioxins and dibenzofurans and defined these as the “cohort
of concern”. Steroids and polyhalogenated compounds are considered to be nongenotoxic carcinogens with thresholded-toxicity.
In addition to the structurally alerting motif, metabolic, and other toxicokinetic
considerations should be evaluated. Besides the functional groups present, for
example, nitro, epoxide, and so on; the potential of that group to be metabolically
activated is critical [25]. This is exemplified by the decision tree used for the safety
evaluation of flavors [26, 27]. This identified structural motifs with a high potential for
toxicity. These included (1) safrole-like structural motifs, (2) unionized moieties
containing elements in addition to C,H,N, O, divalent S, for example, halide
substituted compounds, (3) strained ring systems, for example, aziridines, epoxides,
(4) a,b-unsaturated lactone, or fused lactones, (5) aliphatic secondary nitrile,
quaternary N, amino, N-nitroso, diazo, and so on, (6) aryl unsubstituted groups,
and (7) multifunctional groupings, excluding methoxy groups and classifying esters
and acids as one functional grouping.
Most structurally alerting compounds are rapidly metabolized and do not pose a
threat towards accumulation. However, halogenated molecules (where halogen is
directly substituted onto carbon atom) are poorly metabolized. These substituents
may block normal metabolism at that carbon or at the adjacent carbon atoms, for
example, a-C atom. Structural moieties that particularly alert for accumulation are
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multiple halogenated substituted alkyl groups, for example, CF3, CHF2, and so on,
and multisubstituted aryl halides, where either all ring substituents are halides or are
adjacent to halide-substituted ring carbon atoms, for example, C
6Cl6
,
hexachlorophene [27].
Some authors advocated additional structural groupings, for example, aromatic
amines and aromatic nitro groups based on structural alerts identified previously by
Ashby and Tennant [15]. As with all rule-based Expert systems, some of the generic
structural alerts appear to be quite nonspecific such as the generic alert for aromatic
amines [28]. It was proposed by other researchers that the various alkylating agents,
for example, N-chloro amines, alkyl chlorides, sulfonate, and phosphonate esters
were combined into a group labeled “miscellaneous Ashby alerts” [21].
Dobo and colleagues [11] evaluated 272 pharmaceutical starting materials and
intermediates using structure based analysis (DEREK for Windows [29]; TOXNET [30]; and SCIFINDER [31]). These compounds had already being tested for
mutagenicity using Ames tests. However, on a blinded basis, without the benefit of
this biological information, these materials were then classified into five separate
categories (see Table 7.3).
Based on the Ames test data there were 18% (48) mutagens contained in the data
set. The percentage concordance of the actual mutagenicity (based on Ames testing)
versus the predicted mutagenicity (based on stru cture–activity relationships) was then
evaluated for each of the five categories; with the exception of category 3, where no
predictions were undertaken. The data showed a very high degree of concordance,
with 100% predictivity for category 1 (n ¼ 2), 76% for category 2 (n ¼ 25), 96% for
category 4 (n ¼ 67), and 94% for category 5 (n ¼ 111). The overall predictivity within
the data set was 92%, with a higher predictivity value for negative concordance (94%)
versus positive concordance (74%).
Based on the available Ames test data for the category 3 data set (n ¼ 67), 25%
were actually mutagenic. The authors [11] assessed within each category the
occurrence of a particular alerting motif that is either known or suspected of
TABLE 7.3 Compound Classification with Respect to Mutagenicity or Carcinogenicity
Classification Mutagenicity or Carcinogenicity
1 Literature precedent for mutagenicity or carcinogenicity
2 Known mutagens, but with unknown carcinogenic potential or the chemical
structure gave a positive alert for mutagenicity and a closely related
structural analogue was identified as a known mutagen
3 Alerting structure for mutagenicity or carcinogenicity, without the necessary
supporting evidence to confirm/deny biological relevance of data
4 Alerting structure for mutagenicity or carcinogenicity, with data on related
compounds that demonstrate negative Ames tests and are consequently
considered to be nonmutagenic
5 No alerting structure for mutagenicity or carcinogenicity
Based on Ref. 11.
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bestowing mutagenicity (or carcinogenicity) onto the structure. In those cases where
more than one alerting moiety was present, then the motif that gave the highest alert
for mutagenicity (or carcinogenicity) was selected. The data are summarized in
Table 7.4.
Both of the category 1 structural motifs were aniline derivatives. In contrast, in
category 2 just over half of the alerting structures were comprised of aromatic nitro
TABLE 7.4 The Percentage Frequency of an Alerting Structural Motif in each Category
Category Alerting Structural Motif Percentage Frequency
1 Aromatic amine 0.74
2 Aromatic nitro 4.78
Aromatic amine 1.48
Secondary amine 1.48
Alkylating agent 0.37
a-Haloether 0.37
Acid halide 0.37
Hydrazine 0.37
3 Aromatic nitro 5.15
Alkylating agent 4.78
Aromatic amide 4.41
Aromatic amine 2.57
Halogenated quinoline 1.48
Michel acceptors 1.48
Aliphatic nitro 0.74
Halogenated heterocyclic 0.74
Pyridine dialdehyde 0.74
Acid halide 0.37
Alkyl ester 0.37
a-Haloether 0.37
Epoxide 0.37
Hydrazine 0.37
N-methyol derivative 0.37
Oxime 0.37
4 Secondary amine 9.93
Aromatic amide 4.41
Aromatic amine 2.94
Aldehyde 1.47
Alkyl aldehyde 1.47
Dialdehyde 1.10
Vinyl ketone 1.10
Aliphatic carboxylic acid 0.74
Hydroxamic acid 0.74
Halogenated alkene 0.37
Alkyl carbamate 0.37
5 No alerting structural motif 40.80
Based on Ref. 11.
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compounds (52%) and just under a third comprised of amines (30%). The rest were a
very diverse group of structural moieties (16 in total). In category 3, 21% of the
alerting structures were comprised of aromatic nitro compounds, with similar
representation from alkylating agents (19%) and aromatic amides (18%) and 10%
were aromatic amines. The remainder alerting structures were less common. Similarly, in category 4, 40% of the total comprised of secondary amines, 18% comprised
of aromatic amides and 12% were aromatic amines. The remainder of the group
comprised of less common structural motifs. Finally, category 5 compounds were
represented by aromatic/aliphatic ring systems with a wide ranging of carboxylic and
heterocyclic ring structures.
Dobo and colleagues [11] reported that although the concordance analysis gave
highly predictive outcomes, 25% of the compounds were allocated into category 3
where mutagenic potential was not easily predictable. This was attributed to the
complexity as well as the novelty of the compound with few literature precedent
structures. However, the authors indicated that this should not be considered
surprising. They cited the work of Shimizu et al. [32], who related the position of
substituents on the aromatic ring and Vance and Levin [33], who related the number of
ring systems to the overall mutage nicity of aromatic nitro groups. The authors
concluded that there was little literature precedence for nitro-substituted heterocycles. In a similar fashion, Ninomiya et al. [34] had previously demonstrated that the
structure of both the alkyl and the sulfonic acid moieties were intrinsically linked with
mutagenicity of alkyl sulfonates.
The FDA has initiated an Informatics and Computational Safety Analysis Staffing
(ICSAS) group within the Office of Pharmaceutica l Science (OPS) to provide QSAR
assessment of the safety implications of structural motifs (both known and unknown).
This group has constructed QSAR models for molecules where both individual and
groups of genotoxic and reproductive toxicity (reprotoxic) tests data are available and
correlated those results with the findings of rodent carcinogenicity bioassays [35, 36].
The group used in silico experiments based on MultiCASE, MC4PC software
program [37], which was selected because of proven predictability for correlating
mutagenicity with rodent carcinogenicity data [7, 8, 38]. This program identifies
QSAR motifs and/or molecular fragments that correlate with either enhanced or
reduced biological activity for groups of chemicals sharing a common structural alert.
Interestingly, the associated physicochemical attributes of the molecule, for example,
log P are considered to be fairly inconsequential in comparison to a significant
structural alert.
The authors utilized a database with a total of 7205 compounds, comprising of
4961 with genotoxic end points, 2173 with reprotoxic end points, and 2173 with
rodent carcinogenicity bioassay end points. The correlation of genotoxic and reprotox
end points with carcinogenicity data was evaluated using a correlation indicator (CI)
which is defined as the average of high specificity (SP), positive predictivity value
(PPV), and low false positive (FP) rates. Of the 27 genotoxic and reprotoxicity tests
evaluated, just over 50% (14/27) showed high CI values and 48% (13/27) were
suitable for QSAR modeling using the MC4PC program. The authors found four
alerting structures (aziridine, epoxide, N-nitroso, and sulfonate esters) that alerted in
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multiple genotoxic and reprotoxic animal models and termed these trans-genera
alerts. The majority of trans-genera alerts were the same types of polar electrophilic
molecules that are also identified in human expert systems [15, 19].
Although this exercise dem onstrated high correlation (80.9% CI, 83.1% SP, 78.6%
PPV, and 16.9% FP), mos t of the QSAR assessments were of low sensitivity. This was
based on two underlying factors: the relatively small size of the training data sets
coupled with the comparatively rigid expert rules within the MC4PC program.
Relaxing the expert rules of the system inevitably increases FP and lowers the
specificity. The latter can be addressed by increasing the size of the training data set,
using multiple validated QSAR programs to predict toxicity, and by combination of
the results of related toxicological endpoints to predict the overall toxicity of the
chemical.
Medicinal chemists are now actively encouraged to avoid structures with embedded genotoxic moieties. A good example of the changing perceptions of chemists can
be ascertained by looking at the structural evolution of the dihydropyridine (DHP)
calcium channel blockers. The first generation of DHPs, for example, nifedipine (2,6dimethyl-4-(2-nitrophenyl)-1,4-dihydropyridine-3,5-dicarboxylate) had a 2-subsituted aryl nitro group. Pharmacological activity is linked to noncoplanarity of the
two ring systems. Initially bulky nitro groups in the ortho position were utilized to
facilitate this requirement, but this does have a structural alert for genotoxicity.
In contrast, the third gener ation DHPs, for example, amlodipine (3-ethyl-5-methyl
(4RS)-2-[(2-aminoethoxy)methyl]-4-(2-chlorophenyl)-6-methyl-1,4-dihydropyridine-3,5-dicarboxylate) replaced the alerting structural m otif with a 2-chlorophenyl moiety. This substitution was still bulky to prevent coplanarity but without the
genotoxic liability.
7.5 GENOTOXICITY ASSAYS FOR SCREENING
In this section, regulatory genotoxicity assays are briefly introduced in the context
of early profiling and are contrasted with the distinctive properties of discoverystage screening assays. Table 7.5 provides a summary of the properties of tests
suitable for early screening. The regulatory assays are summarized and the reader
is referred to published guidelines for detailed information. The various regulatory
and screening tests are considered in three basic classes according to the endpoint
of the assay: (1) gene mutation; (2) chromosome damage, including single and
double-strand breaks and aneugenesis; and (3) genotoxin-induced gene expression. Screening assays that have been derived from regulatory assays are described
noting protocol differences from the parent assays, as well as the strengths and
weaknesses of all the assays, which might be considered for use in the profiling
context.
The current ICH tripartite guideline for genotoxicity testing of pharmaceuticals
(S2B) defines a battery of standard in vitro and in vivo tests. The 3-test battery consists
of the following:
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1. bacterial gene mutation test (Ames)
2. in vitro mammalian cell test (cytogenetic evaluation of chromosomal damage
or mouse lymphoma forward mutation thymidine kinase (TK) assay)
3. in vivo test for genetic damage.
The in vivo assays suggested to be most useful are based in rodent hematopoietic cells and comprise either assessment of chromosomal damage in bone
marrow cells or micronuclei in bone marrow or peripheral blood erythrocytes. If all
the tests in the battery give negative results then this is usually considered sufficient
to demonstrate the lack of potential human genotoxic activity. However, according
to the current guideline, compounds yielding positive results may require more
extensive testing, provided in the form of the ICH S2A “Guidance on Specific
Aspects of Regulatory Genotoxicity Tests for Pharmaceuticals.” This current
guidance suggests that for a compound producing biologically relevant positive
results in one or more in vitro tests, further useful information may be obtained by
performing another in vivo test in an alternative tissue. The target tissue for the
compound and the endpoint measured in the in vitro test both impact on the choice
of the additional in vivo test.
In 2008, the ICH proposed revisions to its S2 battery guideline and issued guidance
on specific aspects [9]. In the combined revision, S2(R1), there is a new option for
regulatory submissions in which the bacterial mutation assay is the only required
in vitro test, but two in vivo endpoints are required [9]. Table 7.1 provides a summary
of existing and proposed testing regimes (see also Section 7.9). Further advice is
TABLE 7.5 Properties of Tests Suitable for Profiling
Test Endpoint Category
Validation:
Transfer Application Throughput
Bacterial mutation Ames II Yes: Yes Candidates/
leads
50/week;
2,500/year
Bacterial mutation Biolum Yes: No Candidates 20/week;
1,000/year
Bacterial reporter Escherichia SOS Yes: Yes Candidates/
leads
180/week;
9,000/year
Yeast deletion DEL Yes: No Candidates Unknown
Yeast reporter RAD54-GFP Yes: Yes Candidates/
leads
160/week;
8,000/year
Mammalian DNA
damage
MNT imaging Yes: Yes Candidates 55/week;
2,750/year
Mammalian DNA
damage
MNT flow Yes: Yes Candidates 40/week;
2,000/year
Mammalian DNA
damage
Comet Yes: Yes Candidates 30/week;
1,500/year
Mammalian reporter GADD45a-GFP Yes: Yes Candidates/
leads
200/week;
10,000/year
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offered on the choice of in vivo test and top conce ntration for testing. ICH guidelines
do not intend to affect the development of an effectiveearly screening strategy. It is not
anticipated that early profiling and screening will replace the regulatory battery in the
near future. Instead, they are complimentary strategies that allow for safer compounds
to be promoted from discovery to regulatory testing stage.
7.5.1 Bacterial Gene Mutation Assays
The bacterial and mammalian cell assays for gene mutation were developed to
measure statistically significant increases in mutation rate. Mutations are infrequent
(< 10
–5
) so many millions of exposed cells must be plated out to provide robust
assessment. This generates hundreds of Petri dishes for counting cells and is not
practical for profiling. The Salmonella typhimurium reverse mutation assay (“Ames”
test) is carried out in a variety of different mutant strains selected to identify the
various classes of mutation. For example, TA98and TA1537for frameshift mutations,
TA100 for base pair mutations, TA102 for oxidative damage, and TA1535 for base
pair substitution.
The “mouse lymphoma assay” (MLA) is one of several mammalian cell mutations
assays. MLA assesses mutation at the TK locus in murine lymphoma cells (L5178Y),
though TK mutation data are produced in the human lymphoblastoid cell line TK6,
and from the HGPRT (hypoxanthine–guanine phosphoribosyl-transferase) locus in
Chinese Hamster ovary or lung (V79) cells and mouse lymphoma cells. Since Ames
mutation data and often mammalian cell mutation data may cause the abando nment of
a drug, efforts have been made to increase throughput of the tests and apply them
appropriately during the candidate selection stage.
The Ames Test Variants Many laboratories use streamlined versions of the regulatory Ames test in which a reduced set of strains is used, and a smaller number of
colonies may be counted to estimate mutation rate [39]. While practical for candidate
selection, this is still not suitable for profiling assessments. A more recent development is the Pfizer “BioLum” test [1]. In this assay, bacterial mutation assays are
incorporated into the Salmonella tester strains of a gene encoding a light-emitting
protein under the control of the constitutively expressed kanamycin resistance gene.
Cells that can form even small colonies (for the Ames test, revertant mutant colonies
selected for histidine prototrophy) will emit light and are readily counted using
electronic imaging systems. This allows highe r cell densities to be plated in microplate format with the concomitant reduction in use of plastic ware and test article [1].
Initial validation work was performed in 24-well microplates with a throughput of
around 20 compounds per week in the authors’ laboratories. The authors believe that
the simple protocol and amenability to automation should mean that significantly
higher throughputs are possible.
Fluctuation Tests Mutation rate can also be estimated using fluctuation tests
(cited in OECD 471). Multiple wells containing a specified number of cells are
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scored for growth in selective media. The mutation rates are derived from the
number of cells whe re growth occurs. A further improvement in the efficiency of
these methods is the use of “mixes” of tester strains in addition to single strains.
After a series of handling and treatment steps in larger volumes, cells are
transferred to 384-well microplates for incubation and scoring. Assay preparation
can be efficiently handled robotically. Cell proliferation can be detected with
colorimetric indicators of cell growth (e.g., purple to yellow change), permitting
the use of spectrophotometric plate readers for data collection. The method can be
established independently, but is also available commercially as either “Ames
MPF” or “Ames II” (Xenometrix by Endotell GmbH). The difference
between the two commercially available assays lies in the Salmonella strains
employed: Ames MPF uses up to 4 of the classical TA strains (98, 100, 1535, and
1537). Ames II uses TA98 and then a TA mix, consisting of the TA700X series of
strains.
The Ames II test is reported to use at least threefold less test compound and sixfold
less plastic ware than the traditional Ames test. This assay may be developed into a
rather high-throughput test: one study reported screening 2698 compounds in a little
more than a year [40]. An interlaboratory “ring trial” with 19 coded compounds
concluded that the method provides an effective reproducible and reliable higher
throughput screening alternative to the standard Ames test [41].
7.5.2 Mammalian Cell Mutatio n Assays
The mouse lymphoma assay detects gene mutations as well as chromos omal mutations with the difference inferred from colony size and confirmed by secondary
assays. Both colony-counting and well-counting methods are used in microplate
format for the calculation of mutant frequency. Cells are first exposed to the test
chemical, then after a wash step are allowed an expression/recovery phase before an
estimate of cell growth. This is followed by another wash step, plating for growth,
colony counting, and derivation of mutation rate. Small and large colony types are
identified by inspection. The assay requires weeks rather than hours or days. The
slower growth of mammalian cells coupled with the complex handling protocols
make these the least promising assays to adapt for use in hit and lead profiling. At
present there are no reports of these assays being used routinely earlier than
preclinical drug candidate selection.
7.5.3 Saccharomyces cerevisiae (“Yeast”) Mutation Assays The use of yeast
cells, as a eukaryotic complement to the prokaryotic Ames test led to the development
of several new protocols for the detection of mutation, gene conversion, and
recombination. The formal introduction of methods [42], followed by much
development work from Dr. Zimmermann’s laboratory, led to large systematic
studies [43, 44] and OECD guidelines for the test battery (OECD 480, 481). However,
those early assays are now rarely used partially due to concerns over low sensitivity
presumably from limited permeability of the cell wall.
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