Measuring Environmental Performance for Oil and Gas Development

This study documents environmental performance for firms that extract oil and gas using hydraulic fracturing (fracking) technologies. Environmental performance is measured using public regulatory data for unconventional oil and gas development in the State of Pennsylvania. Using the performance measure, I show considerable variation in environmental performance for companies in the study. Of particular interest is that ‘independent’ oil and gas companies – smaller companies that specialize in exploration and production – performed better on average than the ‘majors’ – the large, international oil companies. The data and methods presented in the study should prove useful to researchers and stakeholders who find the lack of transparency in regulatory systems and environmental reporting a stumbling block to furthering research on environmental performance for firms that employ hydraulic fracturing

Mauritian Cynomolgus Macaques Share Two Exceptionally Common Major Histocompatibility Complex Class I Alleles That Restrict Simian Immunodeficiency Virus-Specific CD8+ T Cells▿ †

Vaccines that elicit CD8+ T-cell responses are routinely tested for immunogenicity in nonhuman primates before advancement to clinical trials. Unfortunately, the magnitude and specificity of vaccine-elicited T-cell responses are variable in currently utilized nonhuman primate populations, owing to heterogeneity in major histocompatibility (MHC) class I genetics. We recently showed that Mauritian cynomolgus macaques (MCM) have unusually simple MHC genetics, with three common haplotypes encoding a shared pair of MHC class IA alleles, Mafa-A*25 and Mafa-A*29. Based on haplotype frequency, we hypothesized that CD8+ T-cell responses restricted by these MHC class I alleles would be detected in nearly all MCM. We examine here the frequency and functionality of these two alleles, showing that 88% of MCM express Mafa-A*25 and Mafa-A*29 and that animals carrying these alleles mount three newly defined simian immunodeficiency virus-specific CD8+ T-cell responses. The epitopes recognized by each of these responses accumulated substitutions consistent with immunologic escape, suggesting these responses exert antiviral selective pressure. The demonstration that Mafa-A*25 and Mafa-A*29 restrict CD8+ T-cell responses that are shared among nearly all MCM indicates that these animals are an advantageous nonhuman primate model for comparing the immunogenicity of vaccines that elicit CD8+ T-cell responses

NetMHCpan, a method for MHC class I binding prediction beyond humans.

Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method's ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan

Prediction of Exposure–Response Relationships to Support First-in-Human Study Design

In drug development, phase 1 first-in-human studies represent a major milestone as the drug moves from preclinical discovery to clinical development activities. The safety of human subjects is paramount to the conduct of these studies and regulatory considerations guide activities. Forces of evolution on the pharmaceutical industry are re-shaping the first-in-human dose selection strategy. Namely, high attrition rates in part due to lack of efficacy have led to the re-organization of research and development organizations around the umbrella of translational research. Translational research strives to bring basic research advances into the clinic and support the reverse transfer of information to enhance compound selection strategies. Pharmacokinetic/pharmacodynamic (PK/PD) modeling holds a unique position in translational research by attempting to integrate diverse sets of information. PK/PD modeling has demonstrated utility in dose selection and trial design for later stages of drug development and is now being employed with greater prevalence in the translational research setting to manage risk (i.e., oncology and inflammation/immunology). Moving from empirical Emax models to more mechanistic representations of the biological system, a higher fidelity of human predictions is expected. Strategies that have proven useful for PK predictions are being applied to PK/PD predictions. This review article examines examples of the application of PK/PD modeling in establishing target concentrations for supporting first-in-human study design