Using human genetics to improve safety assessment of therapeutics.

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Modernizing Human Cancer Risk Assessment of Therapeutics.

Cancer risk assessment of therapeutics is plagued by poor translatability of rodent models of carcinogenesis. In order to overcome this fundamental limitation, new approaches are needed that enable us to evaluate cancer risk directly in humans and human-based cellular models. Our enhanced understanding of the mechanisms of carcinogenesis and the influence of human genome sequence variation on cancer risk motivates us to re-evaluate how we assess the carcinogenic risk of therapeutics. This review will highlight new opportunities for applying this knowledge to the development of a battery of human-based in vitro models and biomarkers for assessing cancer risk of novel therapeutics.

Quantitative histological assessment of xenobiotic-induced liver enzyme induction and pituitary-thyroid axis stimulation in rats using whole-slide automated image analysis.

Preclinical evaluation of a new compound, RO2910, identified a hypertrophic response in liver, thyroid gland, and pituitary gland (pars distalis). We aimed to develop and validate automated image analysis methods to quantify and refine the interpretation of semi-quantitative histology. Wistar-Han rats were administered RO2910 for 14 days. Liver, thyroid, and pituitary gland tissues were processed for routine histology and immunolabeled with anti-thyroid stimulating hormone (TSH) antibody (pituitary) and anti-topoisomerase II antibody (thyroid). Glass slides were scanned, image analysis methods were developed and applied to whole-slide images, and numerical results were compared with histopathology, circulating hormone levels, and liver enzyme mRNA expression for validation. Quantitative analysis of slides had strong individual correlation with semi-quantitative histological evaluation of all tissues studied. Hepatocellular hypertrophy quantification also correlated strongly with liver enzyme mRNA expression. In the pars distalis, measurement of TSH weak-staining areas correlated with both hypertrophy scores and circulating TSH levels. Whole-slide image analysis enabled automated quantification of semi-quantitative histopathology findings and a more refined interpretation of these data. The analysis also enabled a direct correlation with non-histological parameters using straightforward statistical analysis to provide a more refined dose- and sex-response relationship and integration among affected parameters. These findings demonstrate the utility of our image analysis to support preclinical safety evaluations.

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.

A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals.

There are currently no accurate and well-validated short-term tests to identify nongenotoxic hepatic tumorigens, thus necessitating an expensive 2-year rodent bioassay before a risk assessment can begin. Using hepatic gene expression data from rats treated for 5 days with one of 100 structurally and mechanistically diverse nongenotoxic hepatocarcinogens and nonhepatocarcinogens, a novel multigenebiomarker (i.e., signature) was derived to predict the likelihood of nongenotoxic chemicals to induce liver tumors in longer term studies. Independent validation of the signature on 47 test chemicals indicates an assay sensitivity and specificity of 86% and 81%, respectively. Alternate short-term in vivo pathological and genomic biomarkers were evaluated in parallel for comparison, including liver weight, hepatocellular hypertrophy, hepatic necrosis, serum alanine aminotransferase activity, induction of cytochrome P450 genes, and repression of Tsc-22 or alpha2-macroglobulin messenger RNA. In contrast to these biomarkers, the gene expression-based signature was more accurate. Unlike existing tests, an understanding of potential modes of action for hepatic tumorigenicity can be derived by comparison of the signature profile of test chemicals to hepatic tumorigens of known mechanism, including regenerative proliferation, proliferation associated with xenobiotic receptor activation, peroxisome proliferation, and steroid hormone-mediated mechanisms. This signature is not only more accurate than current methods, but also facilitates the identification of mode of action to aid in the early assessment of human cancer risk.

Iconix Biosciences, Inc.

Iconix Biosciences has developed leading products and services that apply novel and proprietary genomic technologies to profile candidate drug compounds in early discovery through to preclinical development, leading to a better understanding of candidate drugs in a faster, more cost-effective manner. The toxicology community is embracing this approach to increase the accuracy, sensitivity and speed of toxicity testing. Changing this paradigm will significantly impact the failure rate of late-stage preclinical compounds and provide a compelling return on investment. Through strategic growth and research, the company has identified the factors and is creating the environment that will lead to a 'tipping point' in the pharmaceutical and biotechnology industries, such that toxicogenomics becomes a standard practice in the drug discovery and development process.

The state-of-the-art in predictive toxicogenomics.

Predictive toxicogenomics, ie, the acquisition of advanced knowledge of the safety profile of a compound using genomic biomarkers, is a technology that provides much optimism for improving early drug discovery decisions. Toxicogenomics creates an opportunity to shift attrition to earlier stages in drug development to a point where course-corrective action can be taken with relatively lower financial costs, thus improving the efficiency of the drug development process. This review will survey the current state-of-the-art in toxicogenomics for predicting toxicity, both in vivo and in vitro, with emphasis on the use of classification algorithms and the importance of toxicogenomic databases for biomarker discovery and validation.

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.