The Toxicologist - Society of Toxicology

pharmacokinetic and toxicological profile optimization. During the development
of a new lead compound, several in silico tools and models are normally used.
These are however built using compounds that may look quite different from conventional
drug compounds and/or natural products and have a different distribution
in their physicochemical properties. To understand how chemistry and pharmacokinetic
and toxicological profiles compare between natural products and
synthetic molecules and to determine the reliability of our current tools, we have
analyzed and compared a number of data sets from different sources. These were
the Clinical candidate database (9 800 compounds) and the Natural Product database
(121 600 compounds) from GVKBio, a set of 2 000 oral drugs and the KEGG
set of 11 700 compounds. For all datasets, we computed physicochemical properties
as well as standard DMPK and safety models such as Caco-2, Ames and hERG.
We reviewed the structural space overlap between the different sets using Principal
Component Analysis (PCA) and established usability of our current models for the
natural drug compounds. Physicochemical property models can be validated
against experimental data but safety models can mainly be assessed by space coverage
and model domain applicability as experimental data is not generally available
for natural products. When possible, validation against experimental data was
done. The results show that there are some significant differences between the distributions
of simple molecular properties and that the classes (synthetic drug/natural
drug) can be partially separated using PCA. The reliability of our current models
is also discussed in relation to these differences.
480 A FLEXIBLE METHOD FOR BUILDING AND USING
PREDICTIVE MODELS APPLIED TO SAFETY
ENDPOINTS.
O. Spjuth2 , L. Carlsson1 , M. Eklund2 , E. Ahlberg Helgee1 and S. Boyer1 .
1Computational Toxicology, AstraZeneca, Mölndal, Sweden and 2Department of
Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
We present a general method for building and using predictive in silico models. The
method can take any collection of compounds with activities as input, and automatically
produces several different predictive models; (1) Similarity searches using
InChI, signatures and fingerprints (exact matches and nearest neighbours) (2) A
classification or regression QSAR model using a support vector machine (SVM)
and interpretable signature descriptors (3) Discriminative signatures (structural
alerts or toxicophores). All models can be run simultaneously for novel chemical
structures and produce interpretable results with important substructures highlighted
in a chemical editor or spreadsheet, with the possibility to generate hypothesis
on how to avoid safety hazards by making changes to the chemical structure
and watch how this change affects the predictions. The method is available from
the Bioclipse workbench, can be run on desktop computers and does not necessarily
require a network connection. We demonstrate our method on publicly available
data for three endpoints: mutagenicity, carcinogenicity, and AhR inhibition.
The results indicate that combining local data with established and well tested
models together with interpretable results can aid scientists in making better informed
decisions in safety assessment.
481 ASSESSING DOSE-RESPONSE OF ENVIRONMENTAL
CHEMICALS INTERACTION WITH DNA USING A
SYSTEM BIOLOGY MODELING APPROACH.
J. M. Gavina, Y. Feng and A. Nong. Environmental Health Science and Research
Bureau, Health Canada, Ottawa, ON, Canada.
Genotoxicity of environmental pollutants is investigated at the molecular level for
DNA damage or instability, disruption of cellular metabolic processes, carcinogenicity,
or mutagenic modifications. Chemical interaction with cellular DNA can
be screened using genetic or proteomic assays, sensitive chromatography, or mass
spectrometry techniques. The difficulty with the in-vitro chemical screens at the
target receptor lies in the interpretation of these results for in-vivo or even human
internal dose levels. System biology modeling approaches integrate genomic and
proteomic data with biological mechanism of response. These models provide understanding
of the process as well for correlation in doses ranges of concern. In this
pilot study, a biological model or system motif was developed to described the kinetic
reaction of DNA adduct formation following exposure to different chemicals
(tetrachlorohydroquinone, styrene-7,8-oxide, methyl methane sulfonate, PGE, and
BPDE). A HPLC screening method was used to detect and quantify DNA products.
A simple experiment was designed to expose double stranded DNA oligonucleotides
under controlled conditions over time and various doses. DNA adducts
were then separated and measured respectively by chromatography and spectrometry.
The motif model parameters were then calibrated with the time series and validated
with the dose-response relationship data. The estimated rate constants of
these reactions are chemical dependent. The dose-response behaviour is linear,
where a Hill coefficient of 1 was estimated, for all chemicals except for tetrachlorohydroquinone.
This in-vitro system motif provides as basis for more complex biological
toxic response such as cellular DNA damage. Combined with internal
dosimetry models (e.g. PBPK), system biology models can serve as a useful tools to
predict the dose-response at the target receptor for environmental exposure risk assessment
of contaminants.
482 USING A MULTIPLE STAGE DECISION TREE TO MAKE
ACTIVITY CALLS IN QUANTITATIVE HIGH-
THROUGHPUT SCREENING (QHTS) DATA.
K. R. Shockley 1 , G. E. Kissling 1 , R. Huang 2 , M. Xia 2 , C.P.Austin 2 and R. R.
Tice 1 . 1 National Toxicology Program/National Inistitute of Environmental Health
Sciences, Research Triangle Park, NC and 2 NIH Chemical Genomic Center,
Bethesda, MD.
The goals of the Tox21 collaboration are to identify mechanisms of toxicity, prioritize
chemicals for in vivo testing and predict adverse responses to environmental
chemicals in humans. Quantitative high throughput screening (qHTS) assays provide
an opportunity to meet these goals, holding the potential for wide chemical
coverage, reduced cost of testing on a per-substance basis and minimal use of animals.
Moreover, the ability of a substance to induce a toxicological response is better
understood by analyzing the response profile over a broad dose range than by
evaluating effects at one or a few doses. Pharmaceutical applications of qHTS generally
seek to minimize false positives in activity assessment and often rely on heuristic
algorithms to make activity calls. In contrast, multiple questions must be asked
to determine the toxicological relevance of a chemical and statistical testing should
be used to minimize false negatives. Here, we developed a multiple-stage decision
tree statistical model and applied it to qHTS data from nine cell-based nuclear receptor
agonist assays (AR, ER, FXR, GR, PPARα, PPARλ, RXR,TRβ, VDR).
Data were fit to a four-parameter Hill equation and an overall F-test comparing the
best fit to the Hill equation and a horizontal line (no response) was calculated for
each chemical. Substances with a robust dose-response were identified in the first
stage. In the second stage, compounds not detected as active in the first stage were
evaluated for a maximal response at the lowest dose by comparing the distribution
of measured responses to a control value. Chemicals with a weak dose-response were
identified in the third stage, and the final stage separated substances exhibiting a cytotoxic
response at the lowest dose from inactive compounds. Our model identified
more active compounds than a previously utilized heuristic approach.
483 TESTING MULTIPLE AVAILABLE QSARS FOR
REPRODUCTIVE TOXICITY AND CARCINOGENICITY
TO DRINKING WATER CONTAMINANTS.
M. Heringa 1 , A. Roncaglioni 2 , R. Benigni 3 and A. Worth 4 . 1 KWR Watercycle
Research Institute, Nieuwegein, Netherlands, 2 Istituto di Ricerche Farmacologiche
“Mario Negri”(IRFMN), Milan, Italy, 3 Istituto Superiore di Sanita (ISS), Rome,
Italy and 4 European Commission - Joint Research Centre (JRC), Ispra, Italy.
Within the REACH legislation in Europe, Quantitative or Qualitative Structure
Activity Relationship- (QSAR-) toxicity predictions are an important alternative to
animal testing. Also in the hazard identification of detected drinking water contaminants
for which no experimental toxicity data can be found, QSARs are helpful.
However, proper use of QSARs is not always straightforward, as there are many
models available, each with its own specifications. To illustrate what a risk-assessor
runs into when using QSARs, toxicity predictions were sought for 10 selected case
compounds for the endpoints mutagenicity, carcinogenicity, and reproductive toxicity
(here: developmental toxicity and estrogenicity). The models applied were:
CASETOX, TOPKAT, Derek for Windows, PharmaAlgorithms, the OSIRIS
IRFMN RBA model, HazardExpert, Toxtree, and lazar. Only two of these models
could be judged as “scientifically valid”, a requirement posed by REACH, which is
based on the five OECD principles. The other models were used as supportive information.
For mutagenicity, for eight of the ten compounds all models (seven)
agreed in their predictions, for carcinogenicity this was the case for one compound
only, for developmental toxicity for three compounds and for estrogenicity for nine
compounds. The application of multiple QSAR models has made apparent that it
is unclear how conflicting predictions of multiple models should be evaluated. This
indicates a strategy on the use of multiple QSAR models is necessary, for which
some ideas are discussed.
484 CONSENSUS MULTIPLE-POTENCY QSAR MODELING
FOR PREDICTION OF RODENT CARCINOGENICITY.
E. J. Matthews 2 and K. P. Cross 1 . 1 Leadscope, Inc., Columbus, OH and 2 U.S.
FDA/CFSAN/OFAS, College Park, MD. Sponsor: R. Tice.
The performance of quantitative structure-activity relationship (QSAR) models depends
extensively upon the quality of the data used to construct the models, and
the methods employed to optimize model predictive performance. For rodent carcinogenicity
models, the nature, location, prevalence, and extensiveness of tumors
SOT 2011 ANNUAL MEETING 103