Development of in silico models for the prediction of toxicity incorporating ADME information

Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-à-go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible

Development of Sstreamflow Projections Under Changing Climate Conditions Over Colorado River Basin Headwaters

The current drought over the Colorado River Basin has raised concerns that the US Department of the Interior, Bureau of Reclamation (Reclamation) may impose water shortages over the lower portion of the basin for the first time in history. The guidelines that determine levels of shortage are affected by relatively short-term (3 to 7 month) forecasts determined by the Colorado Basin River Forecast Center (CBRFC) using the National Weather Service (NWS) River Forecasting System (RFS) hydrologic model. While these forecasts by the CBRFC are useful, water managers within the basin are interested in long-term projections of streamflow, particularly under changing climate conditions. In this study, a bias-corrected, statistically downscaled dataset of projected climate is used to force the NWS RFS utilized by the CBRFC to derive projections of streamflow over the Green, Gunnison, and San Juan River headwater basins located within the Colorado River Basin. This study evaluates the impact of changing climate to evapotranspiration rates and contributes to a better understanding of how hydrologic processes change under varying climate conditions. The impact to evapotranspiration rates is taken into consideration and incorporated into the development of streamflow projections over Colorado River headwater basins in this study. Additionally, the NWS RFS is modified to account for impacts to evapotranspiration due to changing temperature over the basin. Adjusting evapotranspiration demands resulted in a 6% to 13% average decrease in runoff over the Gunnison River Basin when compared to static evapotranspiration rates. Streamflow projections derived using projections of future climate and the NWS RFS provided by the CBRFC resulted in decreased runoff in 2 of the 3 basins considered. Over the Gunnison and San Juan River basins, a 10% to 15% average decrease in basin runoff is projected through the year 2099. However, over the Green River basin, a 5% to 8% increase in basin runoff is projected through 2099. Evidence of nonstationary behavior is apparent over the Gunnison and San Juan River basins

Development of Streamflow Projections Under Changing Climate Conditions Over Colorado River Basin Headwaters

The current drought over the Colorado River Basin has raised concerns that the US Department of the Interior, Bureau of Reclamation (Reclamation) may impose water shortages over the lower portion of the basin for the first time in history. The guidelines that determine levels of shortage are affected by forecasts determined by the Colorado Basin River Forecast Center (CBRFC). While these forecasts by the CBRFC are useful, water managers within the basin are interested in long-term projections of streamflow, particularly under changing climate conditions. In this study, a bias-corrected, statistically downscaled dataset of projected climate is used to force a hydrologic model utilized by the CBRFC to derive projections of streamflow over the Green, Gunnison, and San Juan River headwater basins located within the Colorado River Basin. This study evaluates the impact of changing climate to evapotranspiration rates. The impact to evapotranspiration rates is taken into consideration and incorporated into the development of streamflow projections over Colorado River headwater basins in this study. Additionally, the CBRFC hydrologic model is modified to account for impacts to evapotranspiration due to changing temperature over the basin. Adjusting evapotranspiration demands over the Gunnison resulted in a 6% to 13% average decrease in runoff over the Gunnison River Basin when compared to static evapotranspiration rates. Streamflow projections derived using projections of future climate and the CBRFC’s hydrologic model resulted in decreased runoff in 2 of the 3 basins considered. Over the Gunnison and San Juan River basins, a 10% to 15% average decrease in basin runoff is projected through the year 2099. However, over the Green River basin, a 5% to 8% increase in basin runoff is projected through 2099. Evidence of nonstationary behavior is apparent over the Gunnison and San Juan River basins

Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action.

Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure-activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems

A comparative DFT study of electronic properties of 2H-, 4H- and 6H-SiC(0001) and SiC(000-1) clean surfaces: Significance of the surface Stark effect

Electric field, uniform within the slab, emerging due to Fermi level pinning at its both sides is analyzed using DFT simulations of the SiC surface slabs of different thickness. It is shown that for thicker slab the field is nonuniform and this fact is related to the surface state charge. Using the electron density and potential profiles it is proved that for high precision simulations it is necessary to take into account enough number of the Si-C layers. We show that using 12 diatomic layers leads to satisfactory results. It is also demonstrated that the change of the opposite side slab termination, both by different type of atoms or by their location, can be used to adjust electric field within the slab, creating a tool for simulation of surface properties, depending on the doping in the bulk of semiconductor. Using these simulations it was found that, depending on the electric field, the energy of the surface states changes in a different way than energy of the bulk states. This criterion can be used to distinguish Shockley and Tamm surface states. The electronic properties, i.e. energy and type of surface states of the three clean surfaces: 2H-, 4H-, 6H-SiC(0001), and SiC($000 \bar{1}$) are analyzed and compared using field dependent DFT simulations.Comment: 18 pages, 10 figures, 4 table

Using Multi-indices Approach to Quantify Mangrove Changes Over the Western Arabian Gulf along Saudi Arabia Coast

Mangroves habitat present an important resource for large coastal communities benefiting from activities such as fisheries, forest products and clean water as well as protection against coastal erosion and climate related extreme events. Yet they are increasingly threatened by natural pressure and anthropogenic activities. We observed an inaccurate distribution of mangroves over the Western Arabian Gulf (WAG) which is a vital habitat and resource for the local ecosystem, according to the United Stated Geological Survey (USGS) mangrove database through spectral analysis. Change detection analysis is conducted on mangrove forests along the Saudi Arabian coast of the WAG for the years 2000, 2010 and 2018 using Landsat 7 & 8 data. Three supervised classification methodologies are employed for mangrove mapping, including Supported Vector Machine (SVM), Decision Tree (DT), referred to as Classification and Regression Trees (CART) and Random Forest (RF). CART’s accuracy was recorded to be \u3e95% while other classifiers were \u3e90%. The CART supervised learning classifier, mapping mangroves’ distribution and biomass using Google Earth Engine (GEE) online platform, indicates an overall increase in the northern Tarut Bay and Tarut Island, by 0.21 km2 from 2000 to 2010 and by 1.4 km2 from 2010 to 2018. The increase might be due to mitigation strategies such as mangrove breeding and plantation. It can be challenging to detect changes in certain regions due to the inadequate resolution of Landsat where submerged mangroves can be confused with salt marshes and macro algae. We employed a new method to identify and analyze submerged mangrove forests distribution via a submerged mangrove recognition index (SMRI) and Normalized Difference Vegetation Index (NDVI) in Abu Ali Island. Our results show the robustness of SMRI as an effective indicator to detect submerged mangroves in both high and medium spatial resolution satellite images. NDVI values differentiated submerged mangroves from tidal flats between Landsat 7 & 8 as well as during conditions of low and high tides. High resolution WorldView-2 image showed agreement of mangroves distribution with the SMRI and NDVI results

Transmission Distortion of MCT1 rs1049434 among Polish Elite Athletes

Background: To date, nearly 300 genetic markers were linked to endurance and power/strength traits. The current study aimed to compare genotype distributions and allele frequencies of the common polymorphisms: MCT1 rs1049434, NRF2 rs12594956, MYBPC3 rs1052373 and HFE rs1799945 in Polish elite athletes versus nonathletes. Methods: The study involved 101 male elite Polish athletes and 41 healthy individuals from the Polish population as a control group. SNP data were extracted from whole-genome sequencing (WGS) performed using the following parameters: paired reads of 150 bps, at least 90 Gb of data per sample with 300 M reads and 30x mean coverage. Results: All the analyzed polymorphisms conformed to Hardy-Weinberg equilibrium (HWE) in athletes and the control group, except the MCT1 rs1049434, where allele T was over-represented in the elite trainers' group. No significant between-group differences were found for analyzed polymorphisms. Conclusions: The MCT1 rs1049434 transmission distortion might be characteristic of Polish athletes and the effect of strict inclusion criteria. This result and the lack of statistically significant changes in the frequency of other polymorphisms between the groups might result from the small group size

An Assessment of Atmospheric and Meteorological Factors Regulating Red Sea Phytoplankton Growth

This study considers the various factors that regulate nutrients supply in the Red Sea. Multi-sensor observation and reanalysis datasets are used to examine the relationships among dust deposition, sea surface temperature (SST), and wind speed, as they may contribute to anomalous phytoplankton blooms, through time-series and correlation analyses. A positive correlation was found at 0–3 months lag between chlorophyll-a (Chl-a) anomalies and dust anomalies over the Red Sea regions. Dust deposition process was further examined with dust aerosols’ vertical distribution using satellite lidar data. Conversely, a negative correlation was found at 0–3 months lag between SST anomalies and Chl-a that was particularly strong in the southern Red Sea during summertime. The negative relationship between SST and phytoplankton is also evident in the continuously low levels of Chl-a during 2015 to 2016, which were the warmest years in the region on record. The overall positive correlation between wind speed and Chl-a relate to the nutritious water supply from the Gulf of Aden to the southern Red Sea and the vertical mixing encountered in the northern part. Ocean Color Climate Change Initiative (OC-CCI) dataset experience some temporal inconsistencies due to the inclusion of different datasets. We addressed those issues in our analysis with a valid interpretation of these complex relationships

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models--the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)--produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models\u27 responses to various climate factors. Here, we use the models\u27 eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models\u27 connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts

Ultrasonic Technique for Measuring the Mean Flow Velocity behind a Throttle: A Metrological Analysis

This article discusses an ultrasonic technique for monitoring liquid flow. The mean velocity of the flow behind an obstruction in the throttle type was measured using an ultrasonic flow meter. Furthermore, the equations used to analyze the velocity distribution in a distorted turbulent flow are presented. The measurement findings were compared with those of separate velocity distribution models to select the best equation for characterizing the measured flow. Measurements were taken for two distinct throttle settings by varying the distance from the throttle and angle of the flowmeter head position. The measurements were performed to determine the order of measurement errors if the ultrasonic flowmeter was installed behind the obstruction, without retaining the appropriate distances. The results of , experiments conducted behind a throttle allowed us to conclude that the distance between the measurement location and the barrier affected the accuracy of the acquired results