The impact of geologic variability on capacity and cost estimates for storing CO 2 in deep-saline aquifers

While numerous studies find that deep-saline sandstone aquifers in the United States could store many decades worth of the nation's current annual CO 2 emissions, the likely cost of this storage (i.e. the cost of storage only and not capture and transport costs) has been harder to constrain. We use publicly available data of key reservoir properties to produce geo-referenced rasters of estimated storage capacity and cost for regions within 15 deep-saline sandstone aquifers in the United States. The rasters reveal the reservoir quality of these aquifers to be so variable that the cost estimates for storage span three orders of magnitude and average>$100/tonne CO 2. However, when the cost and corresponding capacity estimates in the rasters are assembled into a marginal abatement cost curve (MACC), we find that ~75% of the estimated storage capacity could be available for<$2/tonne. Furthermore, ~80% of the total estimated storage capacity in the rasters is concentrated within just two of the aquifers-the Frio Formation along the Texas Gulf Coast, and the Mt. Simon Formation in the Michigan Basin, which together make up only ~20% of the areas analyzed. While our assessment is not comprehensive, the results suggest there should be an abundance of low-cost storage for CO 2 in deep-saline aquifers, but a majority of this storage is likely to be concentrated within specific regions of a smaller number of these aquifers. © 2011 Elsevier B.V.

Economics of new oncology drug development.

PURPOSE: Review existing studies and provide new results on the development, regulatory, and market aspects of new oncology drug development. METHODS: We utilized data from the US Food and Drug Administration (FDA), company surveys, and publicly available commercial business intelligence databases on new oncology drugs approved in the United States and on investigational oncology drugs to estimate average development and regulatory approval times, clinical approval success rates, first-in-class status, and global market diffusion. RESULTS: We found that approved new oncology drugs to have a disproportionately high share of FDA priority review ratings, of orphan drug designations at approval, and of drugs that were granted inclusion in at least one of the FDA's expedited access programs. US regulatory approval times were shorter, on average, for oncology drugs (0.5 years), but US clinical development times were longer on average (1.5 years). Clinical approval success rates were similar for oncology and other drugs, but proportionately more of the oncology failures reached expensive late-stage clinical testing before being abandoned. In relation to other drugs, new oncology drug approvals were more often first-in-class and diffused more widely across important international markets. CONCLUSION: The market success of oncology drugs has induced a substantial amount of investment in oncology drug development in the last decade or so. However, given the great need for further progress, the extent to which efforts to develop new oncology drugs will grow depends on future public-sector investment in basic research, developments in translational medicine, and regulatory reforms that advance drug-development science.

Functional annotation signatures of disease susceptibility loci improve SNP association analysis.

BACKGROUND: Genetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan. RESULTS: We developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations. CONCLUSIONS: We show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Genomics research: world survey of public funding.

BACKGROUND: Over the past two decades, genomics has evolved as a scientific research discipline. Genomics research was fueled initially by government and nonprofit funding sources, later augmented by private research and development (R&D) funding. Citizens and taxpayers of many countries have funded much of the research, and have expectations about access to the resulting information and knowledge. While access to knowledge gained from all publicly funded research is desired, access is especially important for fields that have broad social impact and stimulate public dialogue. Genomics is one such field, where public concerns are raised for reasons such as health care and insurance implications, as well as personal and ancestral identification. Thus, genomics has grown rapidly as a field, and attracts considerable interest. RESULTS: One way to study the growth of a field of research is to examine its funding. This study focuses on public funding of genomics research, identifying and collecting data from major government and nonprofit organizations around the world, and updating previous estimates of world genomics research funding, including information about geographical origins. We initially identified 89 publicly funded organizations; we requested information about each organization's funding of genomics research. Of these organizations, 48 responded and 34 reported genomics research expenditures (of those that responded but did not supply information, some did not fund such research, others could not quantify it). The figures reported here include all the largest funders and we estimate that we have accounted for most of the genomics research funding from government and nonprofit sources. CONCLUSION: Aggregate spending on genomics research from 34 funding sources averaged around $2.9 billion in 2003-2006. The United States spent more than any other country on genomics research, corresponding to 35% of the overall worldwide public funding (compared to 49% US share of public health research funding for all purposes). When adjusted to genomics funding intensity, however, the United States dropped below Ireland, the United Kingdom, and Canada, as measured both by genomics research expenditure per capita and per Gross Domestic Product.

Muscle Logic: New Knowledge Resource for Anatomy Enables Comprehensive Searches of the Literature on the Feeding Muscles of Mammals.

BACKGROUND: In recent years large bibliographic databases have made much of the published literature of biology available for searches. However, the capabilities of the search engines integrated into these databases for text-based bibliographic searches are limited. To enable searches that deliver the results expected by comparative anatomists, an underlying logical structure known as an ontology is required. DEVELOPMENT AND TESTING OF THE ONTOLOGY: Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repository for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). We compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. RESULTS AND SIGNIFICANCE: Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities.