New Horizons in Toxicity Prediction.
Lhasa Limited Symposium Event in Collaboration with
the University of Cambridge - February 2009
A Report by Wendy A. Warr, wendy@warr.com, http://www.warr.com
Introduction
David Hawkins, Lhasa Limited; Robert Glen, University of Cambridge
Toxicology is a multidisciplinary science that examines the adverse
effects of chemicals on organisms. It is a rapidly developing area with
many new scientists entering the field. There is a shift from primarily
in vivo animal studies to in vitro assays, in vivo assays with lower
organisms, and computational modeling for toxicity assessments.1
The
conference explored various current approaches to toxicity prediction,
covering and comparing the tools and methods available today, uses by
regulators, industry and academia, and a look at emerging areas and
technologies.
Current
Approaches for Toxicity Prediction
Pharmaceutical perspective - Edwin Matthews, FDA
The
goal of
the Food and Drug Administration (FDA) Center for Drug Evaluation and
Research (CDER) ComTox program is to be able to predict accurately
chemical toxicities with in silico software for all toxicological and
clinical effect endpoints of interest to the U.S. FDA. A benefit would
be substantially to reduce, replace and refine the need for animal
toxicological testing in establishing the safety of chemical
substances. The Informatics and Computational Safety Analysis Staff
(ICSAS), part of CDER's Office of Pharmaceutical Science, is
facilitating an orderly transition to a new in silico testing paradigm.
This is being articulated in parallel to the current Organization for
Economic Cooperation and Development (OECD) and European Union QSAR
efforts, but there are substantial strategic differences in these
approaches, e.g., ICSAS employs commercial software products (which are
excluded from the EU effort); freeware is only used for special
applications. There is a commitment to global QSAR and expert systems.
Commitment to global QSAR means that new models must be added all the
time.
Three software platforms are already validated and two additional
platforms are being validated. The programs are Derek for Windows and
Meteor; Leadscope FDA Model Applier and Predictive Data Miner;
MCASE/MC4PC and META; Prous BioEpisteme and Integrity; and QSARIS
(formerly MDL-QSAR, now from Scimatics).2
Insilicofirst (founding
members Lhasa, Leadscope, MCASE and Molecular Networks)3
is a
collaborative endeavor working to develop a computational prediction
system to support the environmental safety assessment of chemicals.
The FDA has several reasons for using more than one QSAR software
program. None of the programs has all the necessary functionalities,
and none has 100% coverage, sensitivity, and specificity. All of the
programs are complementary and can be used for consensus prediction
strategies. Moreover, FDA cannot endorse a single (Q)SAR program.
Components of the multiple platform strategy are predicted or
experimental value; bioavailability; structural analogues; coverage
(i.e., domain of applicability); metabolites; weight of evidence
predictions (combining predictions from multiple QSAR programs); and
mechanism of action.
Matthews listed some unmet needs in QSARs and expert systems. These
include integrated fragment and descriptor paradigms and 3D
descriptors; QSARs based upon pure active ingredient (PAI) and
metabolites; QSARs for drug-drug interaction, for animal organ
toxicities, and for regulatory dose concentration endpoints (e.g.,
lowest observed effect level (LOEL) and no observed effect level
(NOEL)); and expert system rules for toxicities of substances such as
biologicals which cannot be predicted by QSAR. Other unmet needs are
databases of pharmaceutical off-target activities, of pharmaceutical
Investigational New Drug (IND), of confidential business information
and of regulatory dose concentration endpoints; integration of FDA and
Environmental Protection Agency (EPA) archival data; and advanced
linguistic software to extract data.
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