Download FORUM SERIES Genetic Toxicity Assessment

TOXICOLOGICAL SCIENCES 96(1), 16–20 (2007)
doi:10.1093/toxsci/kfl191
Advance Access publication December 28, 2006
FORUM SERIES
Genetic Toxicity Assessment: Employing the Best Science
for Human Safety Evaluation Part I: Early Screening for Potential
Human Mutagens
David Jacobson-Kram*,1 and Joseph F. Contrera†
*Office of New Drugs; and †Office of Pharmaceutical Science, Center for Drug Evaluation and Research,
U.S. Food and Drug Administration, Silver Spring, Maryland 20993
Received September 18, 2006; accepted November 28, 2006
Results of genetic toxicology tests are used by FDA’s Center for
Drug Evaluation and Research as a surrogate for carcinogenicity
data during the drug development process. Mammalian in vitro
assays have a high frequency of positive results which can impede
or derail the drug development process. To reduce the risk of such
delays, most pharmaceutical companies conduct early non-GLP
(good laboratory practices) studies to eliminate drug candidate
with mutagenic or clastogenic activity. Early screens include in
silico structure activity assessments and various iterations of the
ultimate regulatory mandated GLP studies.
Key Words: mutagenesis; clastogenesis; structure activity; bacterial mutation.
Phase 1 clinical trails for new drugs are often performed in
healthy volunteers. For people involved in these early trials,
there is no risk/benefit assessment as would exist for patients
given an approved drug or patients in later clinical trials. Risks
to healthy subjects must be carefully assessed and must be
exceedingly low. With only rare exceptions, carcinogenicity
data for a new drug are submitted with the New Drug
Application. The consequence of this timing is that hundreds
or thousands or individuals will have been exposed to the drug,
generally on a repeated basis and at pharmacological doses
without understanding potential carcinogenicity. To protect
subjects in clinical trials from exposure to potential carcinogens, Food and Drug Administration (FDA) requires sponsors
to submit data from genetic toxicology studies prior to
beginning ‘‘first in human (FIH)’’ studies. The required tests
are delineated in International Committee on Harmonization
(ICH) guidelines S2A and S2B which inform on methods for
1
To whom correspondence should be addressed at the Center for Drug
Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD
20993. Fax: (301) 796-9856. E-mail: [email protected].
Published by Oxford University Press 2006.
conducting the assays and the required battery of tests,
respectively (ICH S2A, 1995; ICH S2B, 1997). S2B specifies
that prior to FIH studies, a bacterial gene mutation assay and an
assay for chromosomal damage in mammalian cells in vitro are
required. A test for chromosome damage in rodent bone
marrow is required prior to phase 2. In practice, however,
most drug sponsors perform all three assays prior to phase 1.
A typical battery would consist of an (1) Salmonella mutagenicity test, (2) a chromosomal aberration assay performed
either in CHO cells or human peripheral blood lymphocytes or
the mouse lymphoma thymidine kinase gene mutation assay,
and (3) a bone marrow micronucleus test in mouse or rat bone
marrow.
One could rightfully argue that this battery will not detect all
carcinogens. If fact, approximately two-thirds of drugs which
give positive responses in rodent cancer bioassays are negative
in genetic toxicology studies (Snyder and Green, 2001).
Examination of the list of drugs judged to be nongenotoxic
carcinogens shows that most induce their effects via exaggerated pharmacological responses, immune suppression, or
hormonal imbalance. Nongenotoxic carcinogens generally
require chronic exposure to induce tumors, and cessation of
exposure generally is sufficient to prevent tumor development.
Because so many phase 1 clinical trials are performed in
healthy subjects, a positive response in a genetic toxicology
assay can be a serious liability in the progression of a drug’s
clinical development. This often leads to the imposition of
a clinical hold for repeated dose clinical studies unless or until
the sponsor is able to demonstrate that human exposure does
not present a risk to trial participants. Even if the trial moves
forward, the positive genotoxicity result will be shown in the
informed consent form and ultimately in the drug label. In early
2006, the Center for Drug Evaluation and Research (CDER)
published a guidance entitled ‘‘Guidance for Industry and
Review Staff- Recommended Approaches to Integration of
Genetic Toxicology Study Results’’ (http://www.fda.gov/cder/
EARLY SCREENING FOR POTENTIAL HUMAN MUTAGENS
guidance/6848fnl.htm). The guidance discusses various approaches for dealing with a positive study and these include
assessment of the weight of evidence, consideration of the
mode-of-action, and performance of additional tests to clarify
a positive or equivocal result.
Clearly, the best situation from a public health perspective
and from an efficient drug development model is to move
forward with drugs that are clearly negative for potential
genetic toxicity. Most pharmaceutical companies have long
recognized this and have developed strategies to avoid the
liabilities associated with positive genotoxicity responses.
17
and development. QSAR methods are also being used to help
qualify and assess the potential risk of contaminants and
degradants in drug products at FDA-CDER, the safety of food
additives and food contact substances at FDA-Center for Food
Safety and Applied Nutrition (Bailey et al., 2005), and environmental hazards (Environmental Protection Agency [EPA],
2006). From a resource, cost, and timeliness perspective, it is
not feasible to experimentally test the millions of chemicals in
industry combinatorial databases, and QSAR screening can
provide a ‘‘virtual’’ alternative to laboratory testing.
The ‘‘In Silico First’’ Paradigm for Pharmaceuticals
STRUCTURE-ACTIVITY SCREENING
Recent advances in the computer technology and chemoinformatics have made available additional resources for safety
assessment such as structure similarity searching and improved
quantitative structure-activity relationship (QSAR) software
models. Essential for effective and predictive QSAR models
is a chemically diverse training data set of toxicological
information linked to chemical structures. The use of a digital
representation of chemical structure (e.g., the MDL molfile or
Simplified Molecular Input Line Entry System code) as the
primary substance identification in the training database makes
possible the identification of clusters of substances having
similar chemical structure/substructural features and similar
pharmacological or toxicological activities. Toxicological information may be available for compounds that are related to
a test compound and may provide some insight on the possible
properties of the test compound (Fig. 1). The application of
these methods is rapidly expanding and gaining in credibility.
In silico QSAR predictive toxicology offers a rapid and cost
effective initial screening for potential drug candidate molecules to identify and prioritize compounds for further testing
A paradigm shift to utilize in silico testing first to determine
a biological testing priority is underway (McGee, 2005). In the
pharmaceutical industry, computational toxicology is now
being used as a sentinel tool for the early assessment of the
toxicological potential of candidate molecules in lead selection
and drug discovery (Pearl et al., 2001). For pharmaceuticals,
in silico methods are useful decision support tools that compliment traditional animal and laboratory based testing. In the
case of pharmaceutical impurities that impart no benefit to the
consumer, in silico screening for mutagenicity, carcinogenicity,
and developmental toxicity is a useful tool to assess potential
toxicological risk, whether further testing is necessary, and the
nature of confirmatory laboratory studies that would be needed
(Kruhlak et al., 2006).
The FDA-CDER, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff (ICSAS) is
an applied regulatory research unit within the FDA that has
been developing comprehensive electronic relational databases
of FDA regulated substances that are linked to chemical
structure. (http://www.fda.gov/cder/Offices/OPS_IO/default.htm).
To achieve this goal, ICSAS is engaged in Cooperative Research
and Development Agreements (CRADAs) with a number of
software developers (MultiCASE, Inc., Multicase.com, MDL
Information Systems, Inc., MDL.com and Leadscope, Inc.,
LeadScope.com) to extract and compile toxicology data into an
organized, electronically accessible format and to improve the
predictive performance of computational toxicology software to
meet the goals of the FDA Critical Path initiative to improve
regulatory and drug development tools (U.S. FDA, 2004).
MultiCASE MC4PC and MDL-QSAR software modules are
being developed and distributed publicly by the CRADA
partners. These software platforms are being used by the
pharmaceutical industry as an initial screen in the compound
selection process in drug development. QSAR modules developed under these CRADAs are now available for many
endpoints including mutagenicity, rodent carcinogenicity, reproductive and developmental toxicity.
Structural Alerts and In Silico Prediction of Mutagenicity
FIG. 1. Identifying a Test Compound and Estimating Potential Toxicity
Using Structural Similarity.
Structural alerts are defined as molecular functionalities
(structural features) that are associated with toxicity, and their
18
JACOBSON-KRAM AND CONTRERA
presence in a molecular structure alerts the investigator to the
potential hazard of a chemical. Structural alerts have historically been identified and applied by human experts in the field
of toxicology. The structural alerts or toxicophores for
Salmonella positive mutagenic compounds developed by
Ashby and Tennant (1991) can be considered human expert
generated structure-activity relationships (SAR). Predictive
toxicology software can be classified as either qualitative
rule-based SAR or QSAR software. Softwares such as DEREK
(Deductive Estimation of Risk from Existing Knowledge) and
Oncologic are SAR programs that incorporate rules associating
toxicophores with mutagenicity that were developed by panels
of human experts. In contrast, QSAR softwares are statistically
based programs that produce computer generated equations
(models) relating chemoinformatic information such as molecular descriptors, physical chemical attributes and other substructural molecular attributes with toxicity. This approach is
multidimensional and considers many more molecular parameters than simple toxicophores in a molecule. Major QSAR
softwares currently used by the pharmaceutical and chemical
industry include MC4PC (MultiCASE Inc.), MDL-QSAR
(MDL Information Systems), and TOPKAT (Accelrys, Inc.,
Accelrys.com).
Predictive Models for Mutagenicity
FDA-CDER–developed genetic toxicity models are based
upon results of toxicology studies conducted by the National
Institute of Environmental Health Sciences National Toxicology Program (NTP) (Gold and Zeiger, 1997), studies in FDA/
CDER archives, and study data collected by the EPA and
incorporated into the National Institutes of Health, National
Library of Medicine (2005) GENETOX database.
A weight-of-evidence scoring system was employed for
mutagenicity and other genetic toxicity data where study
results were assigned activity scores ranging from a low of
10 units to a high of 80 units. Chemicals with multiple study
results for a single test system were assigned a cumulative
activity score based on the degree of concordance between
those results (Matthews et al., 2006a,b). For example, a test
compound that was tested five times in a particular assay, and
four of those results were positive but one was negative, would
be assigned a relatively high activity score (55 units) reflecting
the high degree of confidence in the positive findings and low
degree of confidence in the single negative finding. This is
consistent with the estimated interlaboratory error rate for
reproducibility of Salmonella test data from the NTP that was
estimated to be 85% (Piegorsch and Zeiger, 1991). For QSAR
modeling purposes, weight-of-evidence scoring allows the
computer to more readily distinguish between the varying
degrees of confidence in training set data and results in
increased overall predictive performance compared to other
approaches. In support of the applicability of QSAR models,
essentially all of the Salmonella positive mutagenic com-
pounds with structurally alerting molecular features identified
by Ashby and Tennant (1991) were correctly predicted as highrisk mutagens by MDL-QSAR employing E-state descriptors
(Contrera et al., 2005a), and MC4PC using molecular fragment
structural features (Matthews et al., 2006b). QSAR offers many
of the benefits of conventional human expert structural alert
analysis with greatly expanded potential. QSAR can perform
highly rapid analyses of large numbers of chemicals and can
process multiple toxicological and physical chemical parameters. Validation studies have been performed on both the
MC4PC and MDL-QSAR Salmonella mutagenicity modules
(Contrera et al., 2005a; Matthews et al., 2006b). The modules
have good sensitivity and have been optimized by FDA/CDER
to provide high specificity (> 75%) and low false positive rates
(< 15%), Salmonella mutagenicity, and other endpoints.
The utility and accuracy of a model are dependent on the
domain of applicability or ‘‘coverage’’ of a particular model
that is a function of the molecular characteristics of the test
molecule relative to the molecules in the training data set. If
a test molecule is not well represented in the training data
molecular library, the test molecule will be outside of the
domain of the model and will have a poor statistical probability
of an accurate prediction. Various metrics have been developed
to define the position of a test chemical relative to a model’s
chemical space (Tropsha et al., 2003). Another QSAR
limitation is related to the molecular attributes of a test
molecule. Molecules such as inorganic chemicals, large
organic molecules (> 1200 Da) or polymers, fibers, salts,
organometallic chemicals, gases, and complex mixtures of
chemicals are currently unsuitable for QSAR analysis. These
are not limiting factors for human expert based SAR methods
such as DEREK.
FDA-CDER Pilot New Molecular Entity Screening Program
A large-scale external validation study is underway in which
all pharmaceuticals submitted to CDER in 2005 including new
molecular entities will undergo QSAR predictive toxicology
screening using CDER-developed MC4PC and MDL-QSAR
models. The predictive performance of the QSAR models will
be evaluated to determine their utility as additional decision
support tools for reviewers on toxicological endpoints such as
carcinogenicity, genetic toxicity, and reproductive and developmental toxicity.
SCREENING IN BACTERIAL MUTATION ASSAYS
Most pharmaceutical companies screen drug candidates
early in development for potential mutagenicity in a bacterial
reverse mutation assay. Positive results in such an assay are
generally sufficient to forgo further development. Often,
pharmaceutical companies will have back-up molecules in
case the lead structure is found to have toxic potential. These
early tests are non Good Laboratory Practices (GLP) and
EARLY SCREENING FOR POTENTIAL HUMAN MUTAGENS
require only small amounts of test material, often on the order
of 20–150 mg. Different test strategies have evolved. For
example, many companies will screen using two bacterial
tester strains, most often TA98 and TA100. The assay is
performed in the presence and in the absence of rat liver S9 up
to 5 mg of test material per plate, often using single plates.
Generally, these two strains are sufficient to detect most
Salmonella mutagenicity positive chemicals. Other approaches
include use of all tester strains, with and without S9 but with
a lower top dose, for example 500 lg per plate. This strategy
conserves test material but risks observing a positive response
at higher doses in the GLP study. Some companies use all tester
strains with doses up to 5 mg per plate but only in the presence
of S9 since compounds uniquely positive in the absence of S9
are rare (E. Zeiger, unpublished observations). Use of the ‘‘spot
test’’ is a qualitative approach to screening for bacterial
mutation. The solubulized test material is applied in a small
well in the center of a plate containing selective agar and the
tester strain. The material diffuses out into the agar and forms
a concentration gradient. If the chemical induces reverse
mutations, a ring of revertants will form around the well. In
this way, only two plates (with and without S9) are required for
each tester strain. Yet another approach involves the use of the
Ames II assay. This variation involves the use of a liquid
microtiter system and uses the traditional strain TA98 to detect
frame shift mutations and ‘‘TAMix’’ (Fluckiger-Isler et al.,
2004). The latter is a mixture of six Salmonella strains used to
detect base pair substitutions; these strains are patented and
must be licensed for use.
While the strategies between companies can vary, all these
approaches have been found to be highly predictive of the
ultimate ICH/GLP compliant bacterial mutation assay. This is
not terribly surprising since the screening tests generally utilize
the same tester stains as the regulatory-required test. A
confounding variable during this early phase of screening is
that the candidate drug substance can contain significant levels
of impurities. A ‘‘false positive’’ could result from a mutagenic
impurity resulting in the discontinuation of development of
a potentially valuable drug.
SCREENING FOR CLASTOGENIC ACTIVITY
Positive results for bacterial mutation and in vivo chromosome damage assays are relatively rare in Investigational
New Drug submissions to CDER. Salmonella mutagenicity–
positive compounds are effectively weeded out using the
approaches described above. In addition, both the bacterial
mutation assay and the in vivo chromosome damage assays
have relatively high specificities and low sensitivities (low
frequencies false positives) so that positive results are less
common (Kirkland et al., 2005). The in vitro mammalian cell
assays, chromosomal aberration and mouse lymphoma, have
high sensitivities and low specificities so that positive results
19
are much more common. Nevertheless, even though many of
these results may be ‘‘false positives’’ in that they are later
found not to be carcinogenic or perhaps carcinogenic for a
different reason, a positive result in an in vitro mammalian cell
assay can delay or possibly derail a drug development program.
Non-GLP screening for potential clastogenic activity is also
commonly done by many pharmaceutical companies, especially large pharma. Performing an abbreviated, non-GLP
in vitro metaphase assay is an option but requires significant
amounts of test material (100–500 mg), is relatively expensive
($4000–$5000), and is time (3–4 weeks) and labor intensive.
More commonly, drug developers rely on in vitro assessment of
micronucleus induction using CHO cells as the target. In this
assay, cells are grown and treated in slide chambers and fixed in
situ. Treatment in the absence of S9 is generally for 24 h to
mimic the protracted exposure in the GLP chromosomal
aberration assay and 4 h in the presence of S9. Cells are also
treated with cytochalasin B to generate binucleated cells so that
only those cells that divided during the treatment are scored for
the presence of micronuclei. Unless limited by solubility or
toxicity, the high dose in such an assay is set at 5 mg/ml. While
still not a ‘‘high-throughput’’ assay, the in vitro micronucleus
assay can generally be performed with 50–100 mg of test
material.
Since the endpoint in this assay is different from that
evaluated in the GLP in vitro metaphase analysis, the
correlation of the two assays has been investigated (Miller
et al., 1997). The percentage concordance is approximately
79%. One reason for discordance is that the micronucleus assay
detects aneugenic materials while metaphase assays generally
do not.
HIGH-THROUGHPUT SCREENS
Advances in combinatorial chemistry have made possible
the generation of thousands of unique chemical structures in
very short time frames. While the assays described above are
useful, they cannot be considered high throughput. More facile
assays have been developed in an attempt to fill this gap. For
example, Billinton et al. (1998) developed a yeast assay in
which expression of a green fluorescent protein is linked to the
upregulation of the DNA damage responsive RAD54 gene.
Subsequent validation studies have assessed the utility of this
assay (Van Gompel et al., 2005). Recently, a variation of this
assay using a human cell line, ‘‘GreenScreen HC GADD45aGFP genotoxicity assay’’ has been published (Hastwell et al.,
2006). This test uses the human lymphoblastoid cell line TK6
in which the promotor for a DNA damage–inducible gene,
GADD45a, is linked a green fluorescent protein gene. The use
of the human cell lines permits testing both in the presence and
absence of metabolic activation. In a validation study of 75
well-characterized genotoxic and nongenotoxic chemicals, the
assay appeared to have good sensitivity and specificity.
20
JACOBSON-KRAM AND CONTRERA
OMIC STUDIES
ICH S2B. (1997). Genotoxicity: A standard battery for genotoxicity testing of
pharmaceuticals. Available at: www.ich.org. Accessed January 1, 2007.
Intuitively, one might expect that changes in gene expression
following exposure to a mutagenic agent would be a more
sensitive endpoint than the quantification of a specific locus
mutation. In fact, the Health and Environmental Sciences
Institute organized a collaborative study in which expression
arrays were examined after exposure to 12 direct acting
mutagens (Pennie et al., 2004). Contrary to expectations,
mutations at the thymidine kinase locus and levels of DNA
adducts were much more sensitive endpoints than changes in
gene expression. Indeed, significant changes in expression
were only seen after plating efficiency (a measure of cytotoxicity) declined to 1.3% of control. While these types of studies
may help shed light on mechanisms of mutagenesis, they
currently do not appear to be of value in drug screening.
Kirkland, D., Aardema, M., Henderson, L., and Muller, L. (2005). Evaluation
of the ability of a battery of three in vitro genotoxicity tests to discriminate
rodent carcinogens and non-carcinogens I. Sensitivity, specificity and
relative predictivity. Mutat. Res. 584, 1–256.
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