Relationship of binding-site occupancy, transthyretin stabilisation and disease modification in patients with tafamidis-treated transthyretin amyloid cardiomyopathy

Tafamidis inhibits progression of transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) by binding TTR tetramer and inhibiting dissociation to monomers capable of denaturation and deposition in cardiac tissue. While the phase 3 ATTR-ACT trial demonstrated the efficacy of tafamidis, the degree to which the approved dose captures the full potential of the mechanism has yet to be assessed. We developed a model of dynamic TTR concentrations in plasma to relate TTR occupancy by tafamidis to TTR stabilisation. We then developed population pharmacokinetic–pharmacodynamic models to characterise the relationship between stabilisation and measures of disease progression. Modelling individual patient data of tafamidis exposure and increased plasma TTR confirmed that single-site binding provides complete tetramer stabilisation in vivo. The approved dose was estimated to reduce unbound TTR tetramer by 92%, and was associated with 53%, 56% and 49% decreases in the rate of change in NT-proBNP, KCCQ-OS, and six-minute walk test disease progression measures, respectively. Simulating complete TTR stabilisation predicted slightly greater reductions of 58%, 61% and 54%, respectively. These findings support the value of TTR stabilisation as a clinically beneficial treatment option in ATTR-CM and the ability of tafamidis to realise nearly the full therapeutic benefit of this mechanism. NCT01994889</p

A Physiologically Based <i>in Silico</i> Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule’s physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure–activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs

A Physiologically Based <i>in Silico</i> Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule’s physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure–activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs

Topographic insights in the Frome-Callabonna system and the elevation of a newly surveyed highstand shoreline

Lakes Frome, Callabonna, Blanche and Gregory are playa lakes on the eastern and northern sides of the Flinders Ranges, South Australia. Between 2007 and 2019 we surveyed key topographic features of the lakes, including shorelines, lake floors and the alluvial sills that separate the lakes with differential GPS (DGPS). We combine these observations with the analysis of a hybrid DEM that blends data from multiple sources. The lowest elevation of the Frome-Callabonna system based on the hybrid DEM is −8.33 m Australian Height Datum (AHD) at Lake Callabonna but −5.42 m AHD based on DGPS field data. Both values are considerably lower than previous estimates based on spot heights and contours. The DGPS data for Lake Callabonna support the Shuttle Radar Terrestrial Mission (SRTM) estimates of lake-floor elevations but with a mean difference of 1.7 m in elevation. There are larger differences in elevation between the hybrid DEM and the DGPS data for the floor of Lake Frome (mean difference of 4.25 m). We also report on a newly topographically surveyed high shoreline at Lake Callabonna between 20.1 and 20.8 m AHD, the highest to date

A Physiologically Based <i>in Silico</i> Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule’s physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure–activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs

A Physiologically Based <i>in Silico</i> Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule’s physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure–activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs

Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements

Landslides are major geological hazards and frequently occur in mountainous areas with steep slopes, often causing significant loss. Interferometric Synthetic Aperture Radar (InSAR) has been widely used in landslide measurement over the last three decades. However, InSAR only can measure one-dimensional displacements (i.e. those in the radar’s line of sight (LOS) direction), resulting in the uncertainty between LOS displacement and the real slope displacement. In this paper, based on ascending and descending data from Sentinel-1 satellite, a wide-area potential landslide early identification was carried out using SBAS-InSAR in the whole of Mao County, a mountainous area in western Sichuan (China), with a total of 41 potential landslides successfully detected. Based on the quantitative analysis, the results show that the InSAR LOS measurement values are slope aspect and gradient-dependent. Finally, we innovatively derived a LOS displacement sensitivity map of InSAR in landslide measurement, revealing the relationship between LOS displacement, real displacements on slopes with arbitrary aspects and gradients, and SAR geometric distortion. This is a generalized finding useful for any slopes. It provides theoretical support to acquire and understand the real slope displacement from InSAR landslide measurement, which is vital to assist in correctly interpreting LOS displacement and carrying out subsequent quantitative geological engineering analysis

Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements

Landslides are major geological hazards and frequently occur in mountainous areas with steep slopes, often causing significant loss. Interferometric Synthetic Aperture Radar (InSAR) has been widely used in landslide measurement over the last three decades. However, InSAR only can measure one-dimensional displacements (i.e. those in the radar’s line of sight (LOS) direction), resulting in the uncertainty between LOS displacement and the real slope displacement. In this paper, based on ascending and descending data from Sentinel-1 satellite, a wide-area potential landslide early identification was carried out using SBAS-InSAR in the whole of Mao County, a mountainous area in western Sichuan (China), with a total of 41 potential landslides successfully detected. Based on the quantitative analysis, the results show that the InSAR LOS measurement values are slope aspect and gradient-dependent. Finally, we innovatively derived a LOS displacement sensitivity map of InSAR in landslide measurement, revealing the relationship between LOS displacement, real displacements on slopes with arbitrary aspects and gradients, and SAR geometric distortion. This is a generalized finding useful for any slopes. It provides theoretical support to acquire and understand the real slope displacement from InSAR landslide measurement, which is vital to assist in correctly interpreting LOS displacement and carrying out subsequent quantitative geological engineering analysis

Biochemical and Structural Characterization of the Human TL1A Ectodomain^,

TNF-like 1A (TL1A) is a newly described member of the TNF superfamily that is directly implicated in the pathogenesis of autoimmune diseases, including inflammatory bowel disease, atherosclerosis, and rheumatoid arthritis. We report the crystal structure of the human TL1A extracellular domain at a resolution of 2.5 Å, which reveals a jelly-roll fold typical of the TNF superfamily. This structural information, in combination with complementary mutagenesis and biochemical characterization, provides insights into the binding interface and the specificity of the interactions between TL1A and the DcR3 and DR3 receptors. These studies suggest that the mode of interaction between TL1A and DcR3 differs from other characterized TNF ligand/receptor complexes. In addition, we have generated functional TL1A mutants with altered disulfide bonding capability that exhibit enhanced solution properties, which will facilitate the production of materials for future cell-based and whole animal studies. In summary, these studies provide insights into the structure and function of TL1A and provide the basis for the rational manipulation of its interactions with cognate receptors

Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.</p