George H. Searfoss

Development and evaluation of a genomic signature for the prediction and mechanistic assessment of nongenotoxic hepatocarcinogens in the rat.

Evaluating the risk of chemical carcinogenesis has long been a challenge owing to the protracted nature of the pathology and the limited translatability of animal models. Although numerous short-term in vitro and in vivo assays have been developed, they have failed to reliably predict the carcinogenicity of nongenotoxic compounds. Extending upon previous microarray work (Fielden, M. R., Nie, A., McMillian, M., Elangbam, C. S., Trela, B. A., Yang, Y., Dunn, R. T., II, Dragan, Y., Fransson-Stehen, R., Bogdanffy, M., et al. (2008). Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci. 103, 28-34), we have developed and extensively evaluated a quantitative PCR-based signature to predict the potential for nongenotoxic compounds to induce liver tumors in the rat as a first step in the safety assessment of potential nongenotoxic carcinogens. The training set was derived from liver RNA from rats treated with 72 compounds and used to develop a 22-gene signature on the TaqMan array platform, providing an economical and standardized assay protocol. Independent testing on over 900 diverse samples (66 compounds) confirmed the interlaboratory precision of the assay and its ability to predict known nongenotoxic hepatocarcinogens (NGHCs). When tested under different experimental designs, strains, time points, dose setting criteria, and other preanalytical processes, the signature sensitivity and specificity was estimated to be 67% (95% confidence interval [CI] = 38-88%) and 59% (95% CI = 44-72%), respectively, with an area under the receiver operating characteristic curve of 0.65 (95% CI = 0.46-0.83%). Compounds were best classified using expression data from short-term repeat dose studies; however, the prognostic expression changes appeared to be preserved after longer term treatment. Exploratory evaluations also revealed that different modes of action for nongenotoxic and genotoxic compounds can be discriminated based on the expression of specific genes. These results support a potential early preclinical testing paradigm to catalyze broader understanding of putative NGHCs.

Hepatic Gene Expression Changes in Mice Associated with Prolonged Sublethal Microcystin Exposure

Microcystin-LR (MCLR) is an acute hepatotoxicant and suspected carcinogen. Previous chronic studies have individually described hepatic morphologic changes, or alterations in the cytoskeleton, cell signaling or redox pathways. The objective of this study was to characterize chronic effects of MCLR in wild-type mice utilizing gene array analysis, morphology, and plasma chemistries. MCLR was given daily for up to 28 days. RNA from the 28-day study was hybridized onto mouse genechip arrays. RNA from 4 hours, 24 hours, 4 days, 1 day, and 28 days for selected genes was processed for quantitative-PCR. Increases in plasma hepatic enzyme activities and decreases in total protein, albumin and glucose concentrations were identified in MCLR-treated groups at 14 and 28 days. Histologically, marked hepatokaryomegaly was identified in the 14-day MCLR group with the addition of giant cells at 28 days. Major gene transcript changes were identified in the actin organization, cell cycle, apoptotic, cellular redox, cell signaling, albumin metabolism, and glucose homeostasis pathways, and the organic anion transport polypeptide system. Using toxicogenomics, we have identified key molecular pathways involved in chronic sublethal MCLR exposure in wild-type mice, genes participating in those critical pathways and related them to cellular and morphologic alterations seen in this and other studies.

The Role of Transcriptome Analysis in Pre-Clinical Toxicology

A major benefit of the genomics revolution in biomedical research has been the establishment of transcriptome analysis as an enabling technology in the drug development process. Nowhere in the realm of drug development has the expectation of the impact of transcriptome analysis been greater than in the area of pre-clinical toxicology. Transcriptome analysis, along with other new high-content data generating technologies, has the potential to radically improve the drug safety assessment process by allowing drug development teams to identify potential toxicity liabilities earlier, and thus proceed only with those molecules that have both efficacy at the target and a low potential for toxicity in the human population. In this review we will briefly describe the major ways in which transcriptome analysis is being applied in the pre-clinical safety assessment process, focusing primarily on four areas where transcriptome analysis has already begun to have impact. These include using transcriptome analysis to: 1) understand mechanisms of toxicity: 2) predict toxicity: 3) develop in vivo and in vitro surrogate models and screens; and, 4) develop toxicity biomarkers. We will close by briefly addressing future trends and needs in the application of transcriptome analysis to drug safety assessment.

Chronic microcystin exposure induces hepatocyte proliferation with increased expression of mitotic and cyclin-associated genes in P53-deficient mice.

Homozygous p53 deficient knockout mice were used to assess the role of p53 in tumor promotion by the protein phosphatase inhibitor and hepatic tumor promoter microcystin-LR (MCLR). More than 50% of human cancers bear mutations in the p53 gene, and in particular, p53 tumor suppressor gene mutations have been shown to play a major role in hepatocarcinogenesis. Trp53 homozygous (inactivated p53) and age-matched wild-type control mice were assigned to vehicle or MCLR-treated groups. MCLR or saline was administered daily for up to 28 days. RNA from the 28-day study was hybridized onto Mouse Genome GeneChip arrays. Selected RNA from 28 days and earlier time points was also processed for quantitative polymerase chain reaction (PCR). Livers from the 28-day, Trp53-deficient, MCLR group displayed greater hyperplastic and dysplastic changes morphologically and increases in Ki-67 and phosphohistone H3 (mitotic marker) immunoreactivity. Gene-expression analysis revealed significant increases in expression of cell-cycle regulation and cellular proliferation genes in the MCLR-treated, p53-deficient mutant mice compared to controls. These data suggest that regulation of the cell cycle by p53 is important in preventing the proliferative response associated with chronic, sublethal microcystin exposure, and therefore, conclude that p53 plays an important role in MCLR-induced tumor promotion.

Kainic Acid-induced F-344 Rat model of Mesial Temporal Lobe Epilepsy: Gene Expression and Canonical Pathways

Mesial temporal lobe epilepsy (MTLE) is a severe neurological condition of unknown pathogenesis for which several animal models have been developed. To obtain a better understanding of the underlying molecular mechanisms and identify potential biomarkers of lesion progression, we used a rat kainic acid (KA) treatment model of MTLE coupled with global gene expression analysis to examine temporal (four hours, days 3, 14, or 28) gene regulation relative to hippocampal histopathological changes. The authors recommend reviewing the companion histopathology paper (Sharma et al. 2008) to get a better understanding of the work presented here. Analysis of filtered gene expression data using Ingenuity Pathways Analysis (Ingenuity Systems, http://www.ingenuity.com) revealed that a number of genes pertaining to neuronal plasticity (RhoA, Rac1, Cdc42, BDNF, and Trk), neurodegeneration (Caspase3, Calpain 1, Bax, a Cytochrome c, and Smac/Diablo), and inflammation/immune-response pathways (TNF-α, CCL2, Cox2) were modulated in a temporal fashion after KA treatment. Expression changes for selected genes known to have a role in neuronal plasticity were subsequently validated by quantitative polymerase chain reaction (qPCR). Notably, canonical pathway analysis revealed that a number of genes within the axon guidance signaling canonical pathway were up-regulated from Days 3 to 28, which correlated with aberrant mossy fiber (MF) sprouting observed histologically beginning at Day 6. Importantly, analysis of the gene expression data also identified potential biomarkers for monitoring neurodegeneration (Cox2) and neuronal/synaptic plasticity (Kalrn).

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

Discover Toxicology: An Early Safety Assessment Approach

Early safety assessment efforts from target identification to lead development have undergone rapid growth and evolution over the last 10 years. In this chapter, we will discuss the current development trends driving the need for early safety assessment practices. We will discuss the key areas of focus which include target-related, off-target-related, and chemical property-related toxicities. We will offer an overview of the various scientific approaches being utilized in each of these focus areas along with an organizational framework that has proven effective in de-risking the early portfolio. We will conclude with some perspectives on application within the project team setting and traps associated with data over interpretation.