Peter Lord

Predictive Toxicogenomics Approaches Reveal Underlying Molecular Mechanisms of Nongenotoxic Carcinogenicity

Toxicogenomics technology defines toxicity gene expression signatures for early predictions and hypotheses generation for mechanistic studies, which are important approaches for evaluating toxicity of drug candidate compounds. A large gene expression database built using cDNA microarrays and liver samples treated with over one hundred paradigm compounds was mined to determine gene expression signatures for nongenotoxic carcinogens (NGTCs). Data were obtained from male rats treated for 24 h. Training/testing sets of 24 NGTCs and 28 noncarcinogens were used to select genes. A semiexhaustive, nonredundant gene selection algorithm yielded six genes (nuclear transport factor 2, NUTF2; progesterone receptor membrane component 1, Pgrmc1; liver uridine diphosphate glucuronyltransferase, phenobarbital‐inducible form, UDPGTr2; metallothionein 1A, MT1A; suppressor of lin‐12 homolog, Sel1h; and methionine adenosyltransferase 1, alpha, Mat1a), which identified NGTCs with 88.5% prediction accuracy estimated by cross‐validation. This six genes signature set also predicted NGTCs with 84% accuracy when samples were hybridized to commercially available CodeLink oligo‐based microarrays. To unveil molecular mechanisms of nongenotoxic carcinogenesis, 125 differentially expressed genes (P < 0.01) were selected by Students t‐test. These genes appear biologically relevant, of 71 well‐annotated genes from these 125 genes, 62 were overrepresented in five biochemical pathway networks (most linked to cancer), and all of these networks were linked by one gene, c‐myc. Gene expression profiling at early time points accurately predicts NGTC potential of compounds, and the same data can be mined effectively for other toxicity signatures. Predictive genes confirm prior work and suggest pathways critical for early stages of carcinogenesis.

Interlaboratory Evaluation of Genomic Signatures for Predicting Carcinogenicity in the Rat

The Critical Path Institute recently established the Predictive Safety Testing Consortium, a collaboration between several companies and the U.S. Food and Drug Administration, aimed at evaluating and qualifying biomarkers for a variety of toxicological endpoints. The Carcinogenicity Working Group of the Predictive Safety Testing Consortium has concentrated on sharing data to test the predictivity of two published hepatic gene expression signatures, including the signature by Fielden et al. (2007, Toxicol. Sci. 99, 90-100) for predicting nongenotoxic hepatocarcinogens, and the signature by Nie et al. (2006, Mol. Carcinog. 45, 914-933) for predicting nongenotoxic carcinogens. Although not a rigorous prospective validation exercise, the consortium approach created an opportunity to perform a meta-analysis to evaluate microarray data from short-term rat studies on over 150 compounds. Despite significant differences in study designs and microarray platforms between laboratories, the signatures proved to be relatively robust and more accurate than expected by chance. The accuracy of the Fielden et al. signature was between 63 and 69%, whereas the accuracy of the Nie et al. signature was between 55 and 64%. As expected, the predictivity was reduced relative to internal validation estimates reported under identical test conditions. Although the signatures were not deemed suitable for use in regulatory decision making, they were deemed worthwhile in the early assessment of drugs to aid decision making in drug development. These results have prompted additional efforts to rederive and evaluate a QPCR-based signature using these samples. When combined with a standardized test procedure and prospective interlaboratory validation, the accuracy and potential utility in preclinical applications can be ascertained.

Release of (and lessons learned from mining) a pioneering large toxicogenomics database

AIM We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. MATERIALS & METHODS The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. RESULTS The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. CONCLUSION Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

The Evolution of Gene Expression Studies in Drug Safety Assessment

Since the identification in the 1950s of deoxyribonucleic acid as the building block of life, the impact of molecular biology has been far-reaching. Understanding the processes of how DNA is replicated, transcribed into RNA and then translated into protein products has not only provided a fundamental knowledge of life but has also spawned a plethora of applications. Molecular biology has been high profile and widespread in research into the biology of disease and in drug discovery. It has additionally found application in understanding the adverse effects, or toxicity, of candidate drugs and how they interfere with biochemical and biological processes. In recent times the biggest impact of molecular biology in toxicology has been through the study of differential gene expression, largely as a result of the advent of genomics. This review seeks to describe how toxicogenomics strategies have been implemented and integrated into nonclinical studies of drug safety.