Human genetic risk of treatment with antiviral nucleoside analog drugs that induce lethal mutagenesis: The special case of molnupiravir.

This review considers antiviral nucleoside analog drugs, including ribavirin, favipiravir, and molnupiravir, which induce genome error catastrophe in SARS-CoV or SARS-CoV-2 via lethal mutagenesis as a mode of action. In vitro data indicate that molnupiravir may be 100 times more potent as an antiviral agent than ribavirin or favipiravir. Molnupiravir has recently demonstrated efficacy in a phase 3 clinical trial. Because of its anticipated global use, its relative potency, and the reported in vitro "host" cell mutagenicity of its active principle, ß-d-N4-hydroxycytidine, we have reviewed the development of molnupiravir and its genotoxicity safety evaluation, as well as the genotoxicity profiles of three congeners, that is, ribavirin, favipiravir, and 5-(2-chloroethyl)-2'-deoxyuridine. We consider the potential genetic risks of molnupiravir on the basis of all available information and focus on the need for additional human genotoxicity data and follow-up in patients treated with molnupiravir and similar drugs. Such human data are especially relevant for antiviral NAs that have the potential of permanently modifying the genomes of treated patients and/or causing human teratogenicity or embryotoxicity. We conclude that the results of preclinical genotoxicity studies and phase 1 human clinical safety, tolerability, and pharmacokinetics are critical components of drug safety assessments and sentinels of unanticipated adverse health effects. We provide our rationale for performing more thorough genotoxicity testing prior to and within phase 1 clinical trials, including human PIG-A and error corrected next generation sequencing (duplex sequencing) studies in DNA and mitochondrial DNA of patients treated with antiviral NAs that induce genome error catastrophe via lethal mutagenesis.

Approaches for identifying germ cell mutagens: Report of the 2013 IWGT workshop on germ cell assays(☆).

This workshop reviewed the current science to inform and recommend the best evidence-based approaches on the use of germ cell genotoxicity tests. The workshop questions and key outcomes were as follows. (1) Do genotoxicity and mutagenicity assays in somatic cells predict germ cell effects? Limited data suggest that somatic cell tests detect most germ cell mutagens, but there are strong concerns that dictate caution in drawing conclusions. (2) Should germ cell tests be done, and when? If there is evidence that a chemical or its metabolite(s) will not reach target germ cells or gonadal tissue, it is not necessary to conduct germ cell tests, notwithstanding somatic outcomes. However, it was recommended that negative somatic cell mutagens with clear evidence for gonadal exposure and evidence of toxicity in germ cells could be considered for germ cell mutagenicity testing. For somatic mutagens that are known to reach the gonadal compartments and expose germ cells, the chemical could be assumed to be a germ cell mutagen without further testing. Nevertheless, germ cell mutagenicity testing would be needed for quantitative risk assessment. (3) What new assays should be implemented and how? There is an immediate need for research on the application of whole genome sequencing in heritable mutation analysis in humans and animals, and integration of germ cell assays with somatic cell genotoxicity tests. Focus should be on environmental exposures that can cause de novo mutations, particularly newly recognized types of genomic changes. Mutational events, which may occur by exposure of germ cells during embryonic development, should also be investigated. Finally, where there are indications of germ cell toxicity in repeat dose or reproductive toxicology tests, consideration should be given to leveraging those studies to inform of possible germ cell genotoxicity.

Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan.

Furan is a chemical hepatocarcinogen in mice and rats. Its previously postulated cancer mode of action (MOA) is chronic cytotoxicity followed by sustained regenerative proliferation; however, its molecular basis is unknown. To this end, we conducted toxicogenomic analysis of B3C6F1 mouse livers following three week exposures to non-carcinogenic (0, 1, 2mg/kgbw) or carcinogenic (4 and 8mg/kgbw) doses of furan. We saw enrichment for pathways responsible for cytotoxicity: stress-activated protein kinase (SAPK) and death receptor (DR5 and TNF-alpha) signaling, and proliferation: extracellular signal-regulated kinases (ERKs) and TNF-alpha. We also noted the involvement of NF-kappaB and c-Jun in response to furan, which are genes that are known to be required for liver regeneration. Furan metabolism by CYP2E1 produces cis-2-butene-1,4-dial (BDA), which is required for ensuing cytotoxicity and oxidative stress. NRF2 is a master regulator of gene expression during oxidative stress and we suggest that chronic NFR2 activity and chronic inflammation may represent critical transition events between the adaptive (regeneration) and adverse (cancer) outcomes. Another objective of this study was to demonstrate the applicability of toxicogenomics data in quantitative risk assessment. We modeled benchmark doses for our transcriptional data and previously published cancer data, and observed consistency between the two. Margin of exposure values for both transcriptional and cancer endpoints were also similar. In conclusion, using furan as a case study we have demonstrated the value of toxicogenomics data in elucidating dose-dependent MOA transitions and in quantitative risk assessment.

Preparation of archival formalin-fixed paraffin-embedded mouse liver samples for use with the Agilent gene expression microarray platform.

INTRODUCTION: Tissue samples are routinely formalin-fixed and paraffin-embedded (FFPE) for long term preservation. Gene expression analysis of archival FFPE tissues may advance knowledge of the molecular perturbations contributing to disease. However, formalin causes extensive degradation of RNA. METHODS: We compared RNA quality/yield from FFPE samples using six commercial FFPE RNA extraction kits. In addition we compared four DNA microarray protocols for the Agilent 8×60K platform using 16year old FFPE mouse liver samples treated with phenobarbital or vehicle. RESULTS: Despite low quality RNA, archival phenobarbital samples exhibited strong induction of the positive control genes Cyp2b9 and Cyp2b10 by quantitative real-time PCR (qPCR). We tested one- and two-color microarray designs and evaluated the effects of increasing the amount of hybridized cDNA. Canonical gene responders to phenobarbital were measurably induced under each experimental condition. Increasing the amount of labeled cDNA did not improve the overall signal intensity. One-color experiments yielded larger fold changes than two-color and the number of differentially expressed genes varied between protocols. Gene expression changes were validated by qPCR and literature searches. Individual protocols exhibited high rates of false positives; however, pathway analysis revealed that nine of the top ten canonical pathways were consistent across experiments. Genes that were differentially expressed in more than one experiment were more likely to be validated. Thus, we recommend that experiments on FFPE samples be done in duplicate to reduce false positives. DISCUSSION: In this analysis of archival FFPE samples we were able to identify pathways that are consistent with phenobarbital's mechanism of action. Therefore, we conclude that FFPE samples can be used for meaningful microarray gene expression analyses.

Harnessing genomics to identify environmental determinants of heritable disease.

Next-generation sequencing technologies can now be used to directly measure heritable de novo DNA sequence mutations in humans. However, these techniques have not been used to examine environmental factors that induce such mutations and their associated diseases. To address this issue, a working group on environmentally induced germline mutation analysis (ENIGMA) met in October 2011 to propose the necessary foundational studies, which include sequencing of parent-offspring trios from highly exposed human populations, and controlled dose-response experiments in animals. These studies will establish background levels of variability in germline mutation rates and identify environmental agents that influence these rates and heritable disease. Guidance for the types of exposures to examine come from rodent studies that have identified agents such as cancer chemotherapeutic drugs, ionizing radiation, cigarette smoke, and air pollution as germ-cell mutagens. Research is urgently needed to establish the health consequences of parental exposures on subsequent generations.

Genetic toxicology in the 21st century: reflections and future directions.

A symposium at the 40th anniversary of the Environmental Mutagen Society, held from October 24-28, 2009 in St. Louis, MO, surveyed the current status and future directions of genetic toxicology. This article summarizes the presentations and provides a perspective on the future. An abbreviated history is presented, highlighting the current standard battery of genotoxicity assays and persistent challenges. Application of computational toxicology to safety testing within a regulatory setting is discussed as a means for reducing the need for animal testing and human clinical trials, and current approaches and applications of in silico genotoxicity screening approaches across the pharmaceutical industry were surveyed and are reported here. The expanded use of toxicogenomics to illuminate mechanisms and bridge genotoxicity and carcinogenicity, and new public efforts to use high-throughput screening technologies to address lack of toxicity evaluation for the backlog of thousands of industrial chemicals in the environment are detailed. The Tox21 project involves coordinated efforts of four U.S. Government regulatory/research entities to use new and innovative assays to characterize key steps in toxicity pathways, including genotoxic and nongenotoxic mechanisms for carcinogenesis. Progress to date, highlighting preliminary test results from the National Toxicology Program is summarized. Finally, an overview is presented of ToxCast™, a related research program of the U.S. Environmental Protection Agency, using a broad array of high throughput and high content technologies for toxicity profiling of environmental chemicals, and computational toxicology modeling. Progress and challenges, including the pressing need to incorporate metabolic activation capability, are summarized.

Characterizing and predicting carcinogenicity and mode of action using conventional and toxicogenomics methods.

The results of predictive toxicogenomics investigations over the past 6 years reviewed in this report have shed new light on the potential of molecular expression analysis to more properly classify both genotoxic and nongenotoxic carcinogens and to predict the carcinogenicity of untested chemicals. Predictive toxicogenomics uses global molecular expression data resulting from genomic perturbation (e.g., transcription or gene expression profiles) to predict a toxicological outcome, such as carcinogenicity. The classification of carcinogens has become an essential and highly debatable component of cancer risk assessment largely because of the default assumptions that drive regulatory decision-making regarding the presumed linearity of the dose-response curve for genotoxic carcinogens. Nongenotoxic mechanisms of carcinogenesis complicate the well-established relationship between genotoxicity and carcinogenicity and challenge the interpretation of the results of rodent carcinogenicity studies in terms of their relevance to humans. Although the number of presumed nongenotoxic rodent carcinogens has dramatically increased over the past two decades, the fact remains that more than 90% of the known human carcinogens are detected in conventional short-term tests for genotoxicity and induce tumors at multiple sites in rodents. In toxicogenomics studies, a strong DNA damage response at the gene expression level suggests direct DNA modification whereas increased expression of genes involved in cell cycle progression is more characteristic of the indirect-acting agents such as those that induce oxidative stress. Metabolism genes are prominently represented among gene expression profiles that discriminate nongenotoxic modes of action (e.g., cytotoxicity and regenerative proliferation, xenobiotic receptor agonists, peroxisome proliferator-activated receptors, or hormonal-mediated processes). The evidence accumulated to date suggests that gene expression profiles reflect underlying modes or mechanisms of action, such that they will be useful in the prediction of chemical carcinogenicity, especially in conjunction with conventional short-term tests for gene mutation, chromosomal aberration and aneuploidy.

Improving prediction of chemical carcinogenicity by considering multiple mechanisms and applying toxicogenomic approaches.

While scientific knowledge of the potential health significance of chemical exposures has grown, experimental methods for predicting the carcinogenicity of environmental agents have not been substantially updated in the last two decades. Current methodologies focus first on identifying genotoxicants under the premise that agents capable of directly damaging DNA are most likely to be carcinogenic to humans. Emphasis on the distinction between genotoxic and non-genotoxic carcinogens is also motivated by assumed implications for the dose-response curve; it is purported that genotoxicants would lack a threshold in the low dose region, in contrast to non-genotoxic agents. However, for the vast majority of carcinogens, little if any empirical data exist to clarify the nature of the cancer dose-response relationship at low doses in the exposed human population. Recent advances in scientific understanding of cancer biology-and increased appreciation of the multiple impacts of carcinogens on this disease process-support the view that environmental chemicals can act through multiple toxicity pathways, modes and/or mechanisms of action to induce cancer and other adverse health outcomes. Moreover, the relationship between dose and a particular outcome in an individual could take multiple forms depending on genetic background, target tissue, internal dose and other factors besides mechanisms or modes of action; inter-individual variability and susceptibility in response are, in turn, key determinants of the population dose-response curve. New bioanalytical approaches (e.g., transcriptomics, proteomics, and metabolomics) applied in human, animal and in vitro studies could better characterize a wider array of hazard traits and improve the ability to predict the potential carcinogenicity of chemicals.

Chemical effects in biological systems (CEBS) object model for toxicology data, SysTox-OM: design and application.

MOTIVATION: The CEBS data repository is being developed to promote a systems biology approach to understand the biological effects of environmental stressors. CEBS will house data from multiple gene expression platforms (transcriptomics), protein expression and protein-protein interaction (proteomics), and changes in low molecular weight metabolite levels (metabolomics) aligned by their detailed toxicological context. The system will accommodate extensive complex querying in a user-friendly manner. CEBS will store toxicological contexts including the study design details, treatment protocols, animal characteristics and conventional toxicological endpoints such as histopathology findings and clinical chemistry measures. All of these data types can be integrated in a seamless fashion to enable data query and analysis in a biologically meaningful manner. RESULTS: An object model, the SysBio-OM (Xirasagar et al., 2004) has been designed to facilitate the integration of microarray gene expression, proteomics and metabolomics data in the CEBS database system. We now report SysTox-OM as an open source systems toxicology model designed to integrate toxicological context into gene expression experiments. The SysTox-OM model is comprehensive and leverages other open source efforts, namely, the Standard for Exchange of Nonclinical Data (http://www.cdisc.org/models/send/v2/index.html) which is a data standard for capturing toxicological information for animal studies and Clinical Data Interchange Standards Consortium (http://www.cdisc.org/models/sdtm/index.html) that serves as a standard for the exchange of clinical data. Such standardization increases the accuracy of data mining, interpretation and exchange. The open source SysTox-OM model, which can be implemented on various software platforms, is presented here. AVAILABILITY: A universal modeling language (UML) depiction of the entire SysTox-OM is available at http://cebs.niehs.nih.gov and the Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. Currently, the public toxicological data in CEBS can be queried via a web application based on the SysTox-OM at http://cebs.niehs.nih.gov CONTACT: xirasagars@saic.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Toxicogenomics and systems toxicology: aims and prospects.

Toxicogenomics combines transcript, protein and metabolite profiling with conventional toxicology to investigate the interaction between genes and environmental stress in disease causation. The patterns of altered molecular expression that are caused by specific exposures or disease outcomes have revealed how several toxicants act and cause disease. Despite these success stories, the field faces noteworthy challenges in discriminating the molecular basis of toxicity. We argue that toxicology is gradually evolving into a systems toxicology that will eventually allow us to describe all the toxicological interactions that occur within a living system under stress and use our knowledge of toxicogenomic responses in one species to predict the modes-of-action of similar agents in other species.

CEBS object model for systems biology data, SysBio-OM.

MOTIVATION: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). RESULTS: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the MicroArray Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. AVAILABILITY: A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

Database development in toxicogenomics: issues and efforts.

The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

The impact of new technologies on human population studies.

Human population studies involve clinical or epidemiological observations that associate environmental exposures with health endpoints and disease. Clearly, these are the most sought after data to support assessments of human health risk from environmental exposures. However, the foundations of many health risk assessments rest on experimental studies in rodents performed at high doses that elicit adverse outcomes, such as organ toxicity or tumors. Using the results of human studies and animal data, risk assessors define the levels of environmental exposures that may lead to disease in a portion of the population. These decisions on potential health risks are frequently based on the use of default assumptions that reflect limitations in our scientific knowledge. An important immediate goal of toxicogenomics, including proteomics and metabonomics, is to offer the possibility of making decisions affecting public health and public based on detailed toxicity, mechanistic, and exposure data in which many of the uncertainties have been eliminated. Ultimately, these global technologies will dramatically impact the practice of public health and risk assessment as applied to environmental health protection. The impact is already being felt in the practice of toxicology where animal experimentation using highly controlled dose-time parameters is possible. It is also being seen in human population studies where understanding human genetic variation and genomic reactions to specific environmental exposures is enhancing our ability to uncover the causes of variations in human response to environmental exposures. These new disciplines hold the promise of reducing the costs and time lines associated with animal and human studies designed to assess both the toxicity of environmental pollutants and efficacy of therapeutic drugs. However, as with any new science, experience must be gained before the promise can be fulfilled. Given the numbers and diversity of drugs, chemicals and environmental agents; the various species in which they are studied and the time and dose factors that are critical to the induction of beneficial and adverse effects, it is only through the development of a profound knowledge base that toxicology and environmental health can rapidly advance. The National Institute of Environmental Health Sciences (NIEHS), National Center for Toxicogenomics and its university-based Toxicogenomics Research Consortium (TRC), and resource contracts, are engaged in the development, application and standardization of the science upon which to the build such a knowledge base on Chemical Effects in Biological Systems (CEBS). In addition, the NIEHS Environmental Genome Project (EGP) is working to systematically identify and characterize common sequence polymorphisms in many genes with suspected roles in determining chemical sensitivity. The rationale of the EGP is that certain genes have a greater than average influence over human susceptibility to environmental agents. If we identify and characterize the polymorphism in those genes, we will increase our understanding of human disease susceptibility. This knowledge can be used to protect susceptible individuals from disease and to reduce adverse exposure and environmentally induced disease.

Toxicogenomic approach for assessing toxicant-related disease.

The problems of identifying environmental factors involved in the etiology of human disease and performing safety and risk assessments of drugs and chemicals have long been formidable issues. Three principal components for predicting potential human health risks are: (1) the diverse structure and properties of thousands of chemicals and other stressors in the environment; (2) the time and dose parameters that define the relationship between exposure and disease; and (3) the genetic diversity of organisms used as surrogates to determine adverse chemical effects. The global techniques evolving from successful genomics efforts are providing new exciting tools with which to address these intractable problems of environmental health and toxicology. In order to exploit the scientific opportunities, the National Institute of Environmental Health Sciences has created the National Center for Toxicogenomics (NCT). The primary mission of the NCT is to use gene expression technology, proteomics and metabolite profiling to create a reference knowledge base that will allow scientists to understand mechanisms of toxicity and to be able to predict the potential toxicity of new chemical entities and drugs. A principal scientific objective underpinning the use of microarray analysis of chemical exposures is to demonstrate the utility of signature profiling of the action of drugs or chemicals and to utilize microarray methodologies to determine biomarkers of exposure and potential adverse effects. The initial approach of the NCT is to utilize proof-of-principle experiments in an effort to "phenotypically anchor" the altered patterns of gene expression to conventional parameters of toxicity and to define dose and time relationships in which the expression of such signature genes may precede the development of overt toxicity. The microarray approach is used in conjunction with proteomic techniques to identify specific proteins that may serve as signature biomarkers. The longer-range goal of these efforts is to develop a reference relational database of chemical effects in biological systems (CEBS) that can be used to define common mechanisms of toxicity, chemical and drug actions, to define cellular pathways of response, injury and, ultimately, disease. In order to implement this strategy, the NCT has created a consortium of research organizations and private sector companies to actively collaborative in populating the database with high quality primary data. The evolution of discrete databases to a knowledge base of toxicogenomics will be accomplished through establishing relational interfaces with other sources of information on the structure and activity of chemicals such as that of the National Toxicology Program (NTP) and with databases annotating gene identity, sequence, and function.

A survey of EPA/OPP and open literature on selected pesticide chemicals. III. Mutagenicity and carcinogenicity of benomyl and carbendazim.

The known aneuploidogens, benomyl and its metabolite, carbendazim (methyl 2-benzimidazole carbamate (MBC)), were selected for the third in a series of ongoing projects with selected pesticides. Mutagenicity and carcinogenicity data submitted to the US Environmental Protection Agency's (US EPA's) Office of Pesticide Programs (OPP) as part of the registration process are examined along with data from the open literature. Mutagenicity and carcinogenicity profiles are developed to provide a complete overview and to determine whether an association can be made between benomyl- and MBC-induced mouse liver tumors and aneuploidy. Since aneuploidogens are considered to indirectly affect DNA, the framework adopted by the Agency for evaluating any mode of action (MOA) for carcinogenesis is applied to the benomyl/MBC data. Both agents displayed consistent, positive results for aneuploidy induction but mostly negative results for gene mutations. Non-linear dose responses were seen both in vitro and in vivo for aneuploidy endpoints. No evidence was found suggesting that an alternative MOA other than aneuploidy may be operative. The data show that by 14 days of benomyl treatment, events associated with liver toxicity appear to set in motion the sequence of actions that leads to neoplasms. Genetic changes (as indicated by spindle impairment leading to missegregation of chromosomes, micronucleus induction and subsequent aneuploidy in bone marrow cells) can commence within 1-24h after dosing, well within the time frame for early key events. Critical steps associated with frank tumor formation in the mouse liver include hepatotoxicity, increased liver weights, cell proliferation, hypertrophy, and other steps involving hepatocellular alteration and eventual progression to neoplasms. The analysis, however, reveals weaknesses in the data base for both agents (i.e. no studies on mouse tubulin binding, no in vivo assays of aneuploidy on the target tissue (liver), and no clear data on cell proliferation relative to dose response and time dependency). The deficiencies in defining the MOA for benomyl/MBC introduce uncertainties into the analysis; consequently, benomyl/MBC induction of aneuploidy cannot be definitively linked to mouse liver carcinogenicity at this time.

Quality review procedures necessary for rodent pathology databases and toxicogenomic studies: the National Toxicology Program experience.

Accuracy of the pathology data is crucial since rodent studies often provide critical data used for setting human chemical exposure standards. Diagnoses represent a judgment on the expected biological behavior of a lesion and peer review can improve diagnostic accuracy and consistency. With the conduct of 500 2-year rodent studies, the National Toxicology Program (NTP) has refined its process for comprehensive review of the pathology data and diagnoses. We have found that careful judgment can improve and simplify the review, whereas simply applying a set review procedure may not assure study quality. The use of reviewing pathologists and pathology peer review groups is a very effective procedure to increase study quality with minimal time and cost. New genomic technology to assess differential gene expression is being used to predict morphological phenotypes such as necrosis, hyperplasia, and neoplasia. The challenge for pathologists is to provide uniform pathology phenotypes that can be correlated with the gene expression changes. The lessons learned in assuring data quality in standard rodent studies also applies to the emerging field of toxicogenomics.