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• Improved model developed to evaluate a metamaterial-based new cool roof performance. • The model incorporates the spectrum selective approach for radiative sky cooling. • Experiments of the metamaterial-based cool roof are conducted on reduced-size building. • The metamaterial-based cool roof can significantly reduce roof-induced cooling load. A new cool roof with potential to generate significant energy savings in buildings has been developed from a metamaterial film named as RadiCold. Considering that the RadiCold film has unique optical and thermal characteristics and the current Roof Thermal Transfer Value model neglecting the effect of roof thermal mass that may lead to overestimating the cooling load from roofs, this work developed an improved Roof Thermal Transfer Value model and validated the model for both RadiCold cool roof and traditional roofing structures. Data from the reduced-size model building experiments showed that the improved Roof Thermal Transfer Value model can accurately describe the heat gains or losses via the roofs. Under real-world weather conditions in the United States (Tucson AZ, Los Angeles, CA, and Orlando FL), the improved Roof Thermal Transfer Value model has been applied to three types of roof exterior finishing: shingle, Thermoplastic Polyolefin (a cool roof material) and RadiCold. In a typical meteorology year, the modeling results show that the shingle and Thermoplastic Polyolefin roof transfer 78.9–294.1 kWh/(m2·yr) and 8.5–128.2 kWh/(m2·yr) of heat into the building space, respectively, but the RadiCold cool roof dissipates 137.6–268.7 kWh/(m2·yr) of heat from the building space to outdoor environment (e.g., sky). The cooling load reduction from utilizing RadiCold cool roof results in cooling electricity savings of 113.0–143.9 kWh/(m2·yr) compared to the shingle roof and 88.0–92.4 kWh/(m2·yr) compared to the Thermoplastic Polyolefin roof for the three analyzed locations with an assumed air conditioning system's coefficient of performance of 3.0. [ABSTRACT FROM AUTHOR]

Background: Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrack™, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrack™ has been widely adopted by the research community and used for pharmacogenomics data review in the FDA's Voluntary Genomics Data Submission program. Results: ArrayTrack™ has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrack™'s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDAECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of Escherichia coli and Shigella. The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from Salmonella isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA). Conclusion: The application of ArrayTrack™ to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrack™ is free to use and available to public, private, and academic researchers at http://www.fda.gov/ArrayTrack. [ABSTRACT FROM AUTHOR]

Background: Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990's by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing. Results: The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemlDplus, ChemACX, Chem Finder, and NCI DTP. Conclusion: The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at http://edkb.fda.gov/webstart/edkb/index.html. Disclaimer: The views presented in this article do not necessarily reflect those of the US Food and Drug Administration. [ABSTRACT FROM AUTHOR]

Abstract: Quality control of a microarray experiment has become an important issue for both research and regulation. External RNA controls (ERCs), which can be either added to the total RNA level (tERCs) or introduced right before hybridization (cERCs), are designed and recommended by commercial microarray platforms for assessment of performance of a microarray experiment. However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources. The US Food and Drug Administration (FDA)-led community-wide Microarray Quality Control (MAQC) study generates a large amount of microarray data with implementation of ERCs across several commercial microarray platforms. The utility of ERCs in quality control by assessing the ERCs’ concentration–response behavior was investigated in the MAQC study. In this work, an ERC-based correlation analysis was conducted to assess the quality of a microarray experiment. We found that the pairwise correlations of tERCs are sample independent, indicating that the array data obtained from different biological samples can be treated as technical replicates in analysis of tERCs. Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs. The proposed approach is sensitive to identifying outlying assays and is not dependent on the choice of normalization method. [Copyright &y& Elsevier]

The fluctuation of streamflow in snowmelt-dominated watersheds may be an indicator of climate change. However, the relationship between the start of growing season (SOS) and the streamflow in snowmelt-dominated watersheds is not clear. In this study, we update the Coupled Hydro-Ecological Simulation System (CHESS) model by incorporating the Growing Season Index (GSI) module to estimate the start of the growing season. The updated CHESS model is then used to calculate the streamflow in the Cleve Creek, Incline Creek and Twin River watersheds located in Nevada in the United States from 1981 to 2017. This updated CHESS can be applied in any regions that are suitable for deciduous vegetation. The streamflow in the static and dynamic scheme in the three watersheds have been simulated between 1981 and 2017 with the NS of 0.52 and 0.80 in the Cleve Creek, 0.46 and 0.75 in the Incline Creek, and 0.42 and 0.70 in the Twin River watersheds, respectively. The results illustrate that the SOS have come around 3–5 weeks earlier during the last 37 years. The results illustrate a high correlation between the temperature and the timing of the SOS. Early SOS leads to a substantial increase in total annual transpiration. An increase in annual transpiration can reduce aquifer recharge and increase cumulative growing season soil moisture deficit. Comparing to the streamflow without vegetation, the streamflow with vegetation is smaller due to transpiration. As the SOS comes earlier, the peaks of the streamflow with vegetation also come earlier. If the shifts in SOS continue, the effects on annual rates of transpiration can be significant, which may reduce the risk of flooding during snowmelt. On the other hand, earlier SOS may cause soil moisture to decline during summer, which would increase the drought stress in trees and the risk of wildfires and insect infestation. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury (DILI) is a leading cause of drugs failing during clinical trials and being withdrawn from the market. Comparative analysis of drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a drug is a challenge with no existing consensus methods. We proposed a systematic classification scheme using FDA-approved drug labeling to assess the DILI potential of drugs, which yielded a benchmark dataset with 287 drugs representing a wide range of therapeutic categories and daily dosage amounts. The method is transparent and reproducible with a potential to serve as a common practice to study the DILI of marketed drugs for supporting drug discovery and biomarker development. [ABSTRACT FROM AUTHOR]

Introduction: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents., Objective: Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes., Results: Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs., Conclusion: The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health., Competing Interests: Declarations Funding No funding was received in relation to the preparation of this manuscript. This work was completed by FDA staff and a medical resident on rotation with FDA as part of their regular duties. Conflict of interest The authors declare that they have no conflicts of interest. Ethics approval Not applicable, because this article does not contain any studies on human or animal subjects. Consent to participate Not applicable. Consent to publish Not applicable. Data availability For the validation study, the labeling data is publicly available at: https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm. Code availability The source code of the LabelComp tool is available at https://github.com/seldas/labelComp/. Author contributions All authors contributed to the conception and design of the study. LW, JX, and HF developed the LabelComp tool. EHZ and EP assisted in the development of the tool. GAN, EHZ, OD, JHL, DM, EP, and RDV performed the validation study. All authors analyzed the data. GAN and LW wrote the first draft of the manuscript. All authors critically revised the manuscript and read and approved the final manuscript. Disclaimer The views presented in this article do not necessarily reflect those of the U.S. Food and Drug Administration. Any mention of commercial products is for clarification and is not intended as an endorsement., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Life's Essential 8 (LE8) is the American Heart Association (AHA)'s recently updated assessment of cardiovascular health (CVH). Metabolic syndrome (MetS) is one of the most common chronic noncommunicable diseases associated with CVH impairment and an increased risk of mortality. However, the association of LE8 with all-cause and disease-specific mortality in the MetS population remains unknown. We aimed to explore these associations in a national prospective cohort study from NHANES 2005-2018., Methods: The LE8 was calculated according to the assessment criteria proposed by the AHA, which includes health behavior and health factor domains. LE8 scores were categorized as low CVH (0-49), moderate CVH (50-79), and high CVH (80-100). MetS was assessed according to NCEP-ATP III criteria, and mortality data were obtained through prospective linkage to the National Death Index database., Results: 7839 participants with MetS were included and only 3.5% were in high CVH. In the fully adjusted models, LE8 was negatively associated with both all-cause and cardiovascular disease (CVD) mortality (hazard ratios [HR] and 95% confidence intervals [CI] of 0.978 (0.971,0.984) and 0.972 (0.961,0.984), respectively, both p < 0.0001). Both moderate/high CVH were associated with significantly lower mortality compared to low CVH (both p for trend <0.0001). Health behaviors had a more dominant effect compared to health factors. All-cause and CVD mortality gradually decreased with increasing ideal LE8 metrics. LE8 was not significantly associated with cancer mortality. LE8 and health behaviors were linearly associated with all-cause and CVD mortality, whereas health factors were nonlinearly associated (plateaued after ≥50). Education and chronic kidney disease influenced the association of LE8 with all-cause and CVD mortality, respectively., Conclusions: LE8 scores were negatively associated with all-cause and CVD mortality in the MetS population, while health behaviors had a dominant role. Adherence to higher CVH contributes to the prevention of excessive all-cause and CVD mortality in the MetS population., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety., (Published by Elsevier Ltd.)

The US drug labeling document contains essential information on drug efficacy and safety, making it a crucial regulatory resource for Food and Drug Administration (FDA) drug reviewers. Due to its extensive volume and the presence of free-text, conventional text mining analysis have encountered challenges in processing these data. Recent advances in artificial intelligence (AI) for natural language processing (NLP) have provided an unprecedented opportunity to identify key information from drug labeling, thereby enhancing safety reviews and support for regulatory decisions. We developed RxBERT, a Bidirectional Encoder Representations from Transformers (BERT) model pretrained on FDA human prescription drug labeling documents for an enhanced application of drug labeling documents in both research and drug review. RxBERT was derived from BioBERT with further training on human prescription drug labeling documents. RxBERT was demonstrated in several tasks using regulatory datasets, including those involved in the National Institutes of Technology Text Analysis Challenge Dataset (NIST TAC dataset), the FDA Adverse Drug Event Evaluation Dataset (ADE Eval dataset), and the classification of texts from submission packages into labeling sections (US Drug Labeling dataset). For all these tasks, RxBERT reached 86.5 F 1-scores in both TAC and ADE Eval classification, respectively, and prediction accuracy of 87% for the US Drug Labeling dataset. Overall, RxBERT was shown to be as competitive or have better performance compared to other NLP approaches such as BERT, BioBERT, etc. In summary, we developed RxBERT, a transformer-based model specific for drug labeling that outperformed the original BERT model. RxBERT has the potential to be used to assist research scientists and FDA reviewers to better process and utilize drug labeling information toward the advancement of drug effectiveness and safety for public health. This proof-of-concept study also demonstrated a potential pathway to customized large language models (LLMs) tailored to the sensitive regulatory documents for internal application., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: RR is co-founder and co-director of ApconiX, an integrated toxicology and ion channel company that provides expert advice on non-clinical aspects of drug discovery and drug development to academia, industry, and not-for-profit organizations., (Copyright © 2023. Published by Elsevier Inc.)

Pharmacogenomics (PGx), studying the relationship between drug response and genetic makeup of an individual, is accelerating advances in precision medicine. The FDA includes PGx information in the labeling of approved drugs to better inform on their safety and effectiveness. We herein present a summary of PGx information found in 261 prescription drug labeling documents by querying the publicly available FDALabel database. A total of 362 drug-biomarker pairs (DBPs) were identified. We profiled DBPs using frequency of the biomarkers and their therapeutic classes. Four categories of applications (indication, safety, dosing and information) were discussed according to information in labeling. This analysis facilitates better understanding, utilization and translation of PGx information in drug labeling among researchers, healthcare professionals and the public., (Published by Elsevier Ltd.)

Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administration-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR model's performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the model's applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compound's risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

ArrayTrack is a Food and Drug Administration (FDA) bioinformatics tool that has been widely adopted by the research community for genomics studies. It provides an integrated environment for microarray data management, analysis and interpretation. Most of its functionality for statistical, pathway and gene ontology analysis can also be applied independently to data generated by other molecular technologies. ArrayTrack has been undergoing active development and enhancement since its inception in 2001. This review summarises its key functionalities, with emphasis on the most recent extensions in support of the evolving needs of FDA's research programmes. ArrayTrack has added capability to manage, analyse and interpret proteomics and metabolomics data after quantification of peptides and metabolites abundance, respectively. Annotation information about single nucleotide polymorphisms and quantitative trait loci has been integrated to support genetics-related studies. Other extensions have been added to manage and analyse genomics data related to bacterial food-borne pathogens.

Mutations of the human SLC26A4/PDS gene constitute the most common cause of syndromic and nonsyndromic hearing loss. Definition of the SLC26A4 mutation spectrum among different populations with sensorineural hearing loss is important for development of optimal genetic screening services for congenital hearing impairment. We screened for SLC26A4 mutations among Chinese and U.S. subjects with hearing loss, using denaturing HPLC (DHPLC) and direct DNA sequencing. Fifty-two of 55 Chinese subjects with deafness accompanied by enlargement of the vestibular aqueduct (EVA) exhibited at least one mutant SLC26A4 allele, whereas SLC26A4 mutations were found in only 2 of 116 deaf Chinese patients without EVA. The spectrum of SLC26A4 mutations differed among Chinese and U.S. subjects and included 10 previously unreported SLC26A4 variants: 4 in the Chinese population (p.E303Q, p.X329, p.X467, p.X573) and 6 in the U.S. population (p.V250A, p.D266N, p.F354S, p.D697A, p.K715N, p.E737D). Among the seven novel in-frame missense mutations, five encoded SLC26A4 proteins with substantially reduced Cl(-)/anion exchange activity as expressed and measured in Xenopus oocytes, but four of these were sufficiently active to allow study of anion selectivity. The only mutant polypeptide exhibiting complete loss of anion exchange function, p.E303Q, was expressed at or near the oocyte surface at near-wild-type levels. Two variants, p.F354S and p.E737D, displayed selective reduction in relative rate of Cl(-)/HCO(3)(-) exchange compared with similarly measured rates of Cl(-)/Cl(-) and Cl(-)/I(-) exchange. Our data show that mutation analysis of the SLC26A4 gene is of high diagnostic yield among subjects with deafness and bilateral EVA in both China and the U.S. However, the pathogenicity of monoallelic SLC26A4 gene variants in patients with hearing loss remains unclear in many instances.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack, that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and non-clinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration, and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data, and the analysis of results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ) and the prospective user can also request a local installation version by contacting the authors.

Over a few short years, microarray gene expression profiling has permeated most areas of biomedical research. Microarrays are now poised to enter the more demanding realm of clinical applications. The prospect of using microarray data to derive biomarkers of disease or toxicity, predict prognosis, or select treatments raises the validity and reliability bar substantially higher. The potential future payoffs are huge in terms of faster approval of more efficacious and safer medical interventions, and a more personalized implementation of them. Arriving at the future sooner rather than later is the motivation for the FDA-led MicroArray Quality Control (MAQC) project. The widespread collaboration aims to assess achievable technical performance of microarrays and capabilities and limitations of methods for microarray data analysis.

Background: Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular components, facilitating interpretation of data from high-throughput genomics and proteomics technologies. The most effective use of GO information is achieved when its rich and hierarchical complexity is retained and the information is distilled to the biological functions that are most germane to the phenomenon being investigated., Results: Here we present a FDA GO tool named Gene Ontology for Functional Analysis (GOFFA). GOFFA first ranks GO terms in the order of prevalence for a list of selected genes or proteins, and then it allows the user to interactively select GO terms according to their significance and specific biological complexity within the hierarchical structure. GOFFA provides five interactive functions (Tree view, Terms View, Genes View, GO Path and GO TreePrune) to analyze the GO data. Among the five functions, GO Path and GO TreePrune are unique. The GO Path simultaneously displays the ranks that order GOFFA Tree Paths based on statistical analysis. The GO TreePrune provides a visual display of a reduced GO term set based on a user's statistical cut-offs. Therefore, the GOFFA visual display can provide an intuitive depiction of the most likely relevant biological functions., Conclusion: With GOFFA, the user can dynamically interact with the GO data to interpret gene expression results in the context of biological plausibility, which can lead to new discoveries or identify new hypotheses., Availability: GOFFA is available through ArrayTrack softwarehttp://edkb.fda.gov/webstart/arraytrack/.

External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms.

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.

FDA reviewers need a means to rapidly predict organ-specific carcinogenicity to aid in evaluating new chemicals submitted for approval. This research addressed the building of a database to use in developing a predictive model for such an application based on structure-activity relationships (SAR). The Internet availability of the Carcinogenic Potency Database (CPDB) provided a solid foundation on which to base such a model. The addition of molecular structures to the CPDB provided the extra ingredient necessary for SAR analyses. However, the CPDB had to be compressed from a multirecord to a single record per chemical database; multiple records representing each gender, species, route of administration, and organ-specific toxicity had to be summarized into a single record for each study. Multiple studies on a single chemical had to be further reduced based on a hierarchical scheme. Structural cleanup involved removal of all chemicals that would impede the accurate generation of SAR type descriptors from commercial software programs; that is, inorganic chemicals, mixtures, and organometallics were removed. Counterions such as Na, K, sulfates, hydrates, and salts were also removed for structural consistency. Structural modification sometimes resulted in duplicate records that also had to be reduced to a single record based on the hierarchical scheme. The modified database containing 999 chemicals was evaluated for liver-specific carcinogenicity using a variety of analysis techniques. These preliminary analyses all yielded approximately the same results with an overall predictability of about 63%, which was comprised of a sensitivity of about 30% and a specificity of about 77%., (Copyright Taylor & Francis Inc.)

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional (Italic)in vitro(/Italic) and (Italic)in vivo(/Italic) toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, ArrayTrack, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.