Search

Background: While physiologically based pharmacokinetic (PBPK) models generally predict pharmacokinetics in pregnant women successfully, the confidence in predicting fetal pharmacokinetics is limited because many parameters affecting placental drug transfer have not been mechanistically accounted for.Objectives: The objectives of this study were to implement different maternal and fetal unbound drug fractions in a PBPK framework; to predict fetal pharmacokinetics of eight drugs in the third trimester; and to quantitatively investigate how alterations in various model parameters affect predicted fetal pharmacokinetics.Methods: The ordinary differential equations of previously developed pregnancy PBPK models for eight drugs (acyclovir, cefuroxime, diazepam, dolutegravir, emtricitabine, metronidazole, ondansetron, and raltegravir) were amended to account for different unbound drug fractions in mother and fetus. Local sensitivity analyses were conducted for various parameters relevant to placental drug transfer, including influx/efflux transfer clearances across the apical and basolateral membrane of the trophoblasts.Results: For the highly-protein bound drugs diazepam, dolutegravir and ondansetron, the lower fraction unbound in the fetus vs. mother affected predicted pharmacokinetics in the umbilical vein by ≥10%. Metronidazole displayed blood flow-limited distribution across the placenta. For all drugs, umbilical vein concentrations were highly sensitive to changes in the apical influx/efflux transfer clearance ratio. Additionally, transfer clearance across the basolateral membrane was a critical parameter for cefuroxime and ondansetron.Conclusion: In healthy pregnancies, differential protein binding characteristics in mother and fetus give rise to minor differences in maternal-fetal drug exposure. Further studies are needed to differentiate passive and active transfer processes across the apical and basolateral trophoblast membrane.

The regulatory framework for considering the fetal effects of new drugs is limited. This is partially due to the fact that pediatric regulations (21 CFR subpart D) do not apply to the fetus, and only US Health and Human Service (HHS) regulations apply to the fetus. The HHS regulation 45 CFR Part 46 Subpart B limits research approvable by an institutional review board to research where the risk to the fetus is minimal unless the research holds out the prospect of a direct benefit to the fetus or the pregnant woman (45 CFR 46.204). Research that does not meet these requirements, but presents an opportunity to understand, prevent, or alleviate a serious problem affecting the health of pregnant women, fetuses, or neonates, may be permitted by the Secretary of the HHS after expert panel consultation and opportunity for public review and comment (45 CFR 46.407). If the product is regulated by the US Food and Drug Administration (FDA), FDA may get involved in the review process. The FDA does however have a Reviewer Guidance on Evaluating the Risks of Drug Exposure in Human Pregnancies from 2005 and this guidance does discuss the intensity of drug exposure. Estimation of that exposure using physiologically based pharmacokinetic (PBPK) modeling has been suggested by some investigators. Given that drug exposure during pregnancy will impact the fetus, a number of new guidances in the last 2 years also address inclusion of pregnant women in clinical drug trials. Therefore, the drug-specific information on fetal pharmacology will increase dramatically in the next decade due to interest in drugs administered in pregnancy and with the assistance of model-informed drug development.

The development of UAV sensors has made it possible to obtain a diverse array of spectral images in a single flight. In this study, high-resolution UAV-derived images of urban areas were employed to create land cover maps, including car-road, sidewalk, and street vegetation. A total of nine orthoimages were produced, and the variables effective in producing UAV-based land cover maps were identified. Based on analyses of the object-based images, 126 variables were derived by computing 14 statistical values for each image. The random forest (RF) classifier was used to evaluate the priority of the 126 variables. This was followed by optimizing the RF through variable reduction and by comparing the initial and optimized RF, the utility of the high-priority variable was evaluated. Computing variable importance, the most influential variables were evaluated in the order of normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), land surface temperature (LST), soil adjusted vegetation index (SAVI), blue, green, red, rededge. Finally, no significant changes between initial and optimized RF in the classification were observed from a series of analyses even though the reduced variables number was applied for the classification.

Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, but there is a limitation that a lot of manpower and budget are consumed to investigate the current situation, so it is necessary to efficiently investigate the OCPs. This study compared the accuracy of various machine learning techniques for the efficient detection and management of outdoor compost piles (OCPs), a non-point pollution source in agricultural areas in South Korea, using unmanned aerial vehicle (UAV) images. RGB, multispectral, and thermal infrared UAV images were taken in August and October 2019. Additionally, vegetation indices (NDVI, NDRE, ENDVI, and GNDVI) and surface temperature were also considered. Four machine learning techniques, including support vector machine (SVM), decision tree (DT), random forest (RF), and k-NN, were implemented, and the machine learning technique with the highest accuracy was identified by adjusting several variables. The accuracy of all machine learning techniques was very high, reaching values of up to 0.96. Particularly, the accuracy of the RF method with the number of estimators set to 10 was highest, reaching 0.989 in August and 0.987 in October. The proposed method allows for the prediction of OCP location and area over large regions, thereby foregoing the need for OCP field measurements. Therefore, our findings provide highly useful data for the improvement of OCP management strategies and water quality.

Water quality deterioration due to outdoor loading of livestock manure requires efficient management of outside manure piles (OMPs). This study was designed to investigate OMPs using unmanned aerial vehicles (UAVs) for efficient management of non-point source pollution in agricultural areas. A UAV was used to acquire image data, and the distribution and cover installation status of OMPs were identified through ortho-images; the volumes of OMP were calculated using digital surface model (DSM). UAV- and terrestrial laser scanning (TLS)-derived DSMs were compared for identifying the accuracy of calculated volumes. The average volume accuracy was 92.45%. From April to October, excluding July, the monthly average volumes of OMPs in the study site ranged from 64.89 m3 to 149.69 m3. Among the 28 OMPs investigated, 18 were located near streams or agricultural waterways. Establishing priority management areas among the OMP sites distributed in a basin is possible using spatial analysis, and it is expected that the application of UAV technology will contribute to the efficient management of OMPs and other non-point source pollutants.

In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.

The accuracy of land surface temperatures (LSTs) acquired by an unmanned aerial vehicle (UAV) was verified by comparison with in-situ LSTs of various land cover materials at the Changwon National University Campus, Changwon City, South Korea. UAV imaging and in-situ measurements were performed on 31 July and 2 August 2019. During the in-situ measurements, LST was measured at 160 points using an infrared thermometer. The linear regression model between the UAV and in-situ measurements exhibited a very high correlation on both days, with R2 values greater than 0.7004. The root mean square error (RMSE), however, was 4.030 °C on 31 July and 5.446 °C on 2 August and it also varied depending on the land cover type. These results may depend on various factors, such as the field of view and performance of the TIR (Thermal infrared radiance) camera, as well as the weather and atmospheric conditions. Accurately diagnosing the thermal characteristics of urban areas based on the spatial elements can be used to accurately analyze the thermal characteristics of urban areas and to make effective policy decisions. Techniques for verifying and improving the accuracy of UAV TIR LST data for various land cover materials are required to enable precise investigation of the thermal characteristics of urban areas.

The aim was to identify microclimate characteristics in relation to ground cover in green areas and the reflectivity of building coating materials. Furthermore, microclimate modeling of temperatures was conducted using ENVI-met, to analyze the effects of improved thermal environments based on increased green areas and increased reflectivity of exterior coatings. The accuracy of ENVI-met was validated through comparisons with field temperature measurements. The RMSE deviation of the predicted and actual field temperature values was 3–6°C; however, the explanatory power was as high as 60%. ENVI-met was performed for commercial and single residential areas that have high densities of artificial cover materials, before and after changes related to development of green areas and to increase in the reflectivity of coating materials. The results indicated that both areas exhibited distinct temperature reductions due to the creation of green spaces. When the reflectivity of the coating material was increased, a temperature increase was observed in all land-use types. Therefore, in order to improve the thermal environment of complex urban areas, it is necessary to improve green-area development and to use high-reflectivity ground and building cover materials, while taking into account the spatial characteristics of land-use types and their surrounding areas.

This study compared Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) surface temperature data with in situ measurements to validate the use of ASTER data for studying heat islands in urban settings with complex spatial characteristics. Eight sites in Changwon, Korea, were selected for analyses. Surface temperature data were extracted from the thermal infrared (TIR) band of ASTER on four dates during the summer and fall of 2012, and corresponding in situ measurements of temperature were also collected. Comparisons showed that ASTER derived temperatures were generally 4.27°C lower than temperatures collected by in situ measurements during the daytime, except on cloudy days. However, ASTER temperatures were higher by 2.23–2.69°C on two dates during the nighttime. Temperature differences between a city park and a paved area were insignificant. Differences between ASTER derived temperatures and onsite measurements are caused by a variety of factors including the application of emissivity values that do not consider the complex spatial characteristics of urban areas. Therefore, to improve the accuracy of surface temperatures extracted from infrared satellite imagery, we propose a revised model whereby temperature data is obtained from ASTER and emissivity values for various land covers are extracted based on in situ measurements.

Familial hypercholesterolemia (FH) is an autosomal dominant genetic disorder of lipoprotein metabolism that leads to an increased risk of developing atherosclerosis and coronary artery disease. Hypercholesterolemia in pediatric patients is typically due to FH. Treatment of pediatric FH is achieved through lifestyle modifications, lipid-modifying pharmacotherapy, and/or apheresis. The primary objective of this review is to describe the characteristics of clinical trials conducted in pediatric patients with FH with data submitted to the US Food and Drug Administration from 2007 to 2020. Of 10 trials with 8 products in pediatric FH submitted to the Food and Drug Administration, 1 product was studied in both the heterozygous and the homozygous phenotypes, 5 were studied for heterozygous hypercholesterolemia only, and 2 were studied for homozygous familial hypercholesterolemia only. Most of the trials included pediatric patients =10 years of age and older. Clinical trial characteristics including the primary efficacy end points between pediatric and adult trials were mostly identical. Many lipid-lowering drugs with novel mechanisms of action have been recently approved or are currently being studied. In summary, the drug treatment of hypercholesterolemia in pediatric patients is expanding beyond the use of statins, and now involves multiple mechanisms of action involving cholesterol metabolism. As younger pediatric patients are diagnosed and treated for heterozygous familial hypercholesterolemia and homozygous familial hypercholesterolemia, optimizing the doses of these agents and safety studies specific to younger pediatric patients will be necessary. [ABSTRACT FROM AUTHOR]