Chemical Space Covered by Applicability Domains of Quantitative Structure-Property Relationships and Semiempirical Relationships in Chemical Assessments.

A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure-property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol-air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.

Universal Langmuir and Fractal Analysis of High-Resolution Adsorption Isotherms of Argon and Nitrogen on Macroporous Silica.

High-resolution isotherms of argon and nitrogen adsorption on macroporous silica have been simulated with universal Langmuir and fractal models. A four-parameter, fractal universal Langmuir equation is a good fit to the data at low pressures. Standard Gibbs energy changes calculated from equilibrium adsorption coefficients show a series of broad peaks that indicate adsorbate structural transformations as a function of pressure and coverage. The Freundlich equation or mean fractal model is also a good fit to isotherms at low pressures. Pressure-varying fractals are accurate fits to the data. Fractal exponents provide information on adsorbate coverage and surface access. Broad peaks in pressure-varying exponents are indicators of adsorbate structure. From adsorptive gas amounts, mean and pressure-varying fractal exponents provide details of adsorbate fractal dimensions and surface roughness. Both Ar and N2 adsorption cause increases in mean surface roughness when compared with pure silica. Surface roughness fluctuations from pressure-dependent adsorptive gas fractal dimensions are associated with adsorbate structure. At one trough, the surface is smooth and is linked to close-packed Ar or N2. For Ar adsorption at 87 K, this structure is a complete monolayer (1.00(4)), while for Ar (77 K), 1.15(4) layers and for N2 (87 K), 2.02(10) layers. The universal Langmuir specific area of the silica is 10.1(4) m2 g-1. Pressure- and coverage-dependent adsorbate structures range from filling defects and holes on the surface to cluster formation to adsorbed Ar or N2 evenly distributed or packed across the surface. The Ar (87 K) isotherm is most sensitive to adsorbate structural transformations.

Pressure-varying Langmuir parameters and stepped nitrogen adsorption on alumina and silica.

Insights into surface structures and thermodynamics are provided for nitrogen adsorption on two nonporous alumina adsorbents and two macroporous silica adsorbents by modelling high-resolution data using the simple Langmuir isotherm equation combined with pressure-varying flexible least squares. The fitted parameters, maximum adsorption capacity and standard Gibbs energy change for each adsorbent show multiple steps that are assumed to be indicative of transitions to different complete monolayer and multilayer structures. Pressure-varying N2 cross-sectional areas for three of the adsorbents are calculated by assuming that one of the steps is the Brunauer-Emmett-Teller monolayer with molecular area 16.2 Å2. The silica with added octyldimethylsilyl groups has pressure-varying parameter profiles that differ from the other adsorbents and here the N2 cross-sectional area is assumed to be 21.3 Å2 to ensure consistency with the literature surface area. Seven monolayers and multilayers are identified across the four adsorbents, and corresponding molecular areas compare favourably with reported values. At low pressures, adsorption occurs at the strongest sites, and is localised and dependent on surface heterogeneity and topography. Up to five complete, two-dimensional lattice structures are apparent in the mid-pressure ranges. At high pressures, multilayers and liquefaction points are observed and are independent of surface composition and heterogeneity.

Which Molecular Features Affect the Intrinsic Hepatic Clearance Rate of Ionizable Organic Chemicals in Fish?

Greater knowledge of biotransformation rates for ionizable organic compounds (IOCs) in fish is required to properly assess the bioaccumulation potential of many environmentally relevant contaminants. In this study, we measured in vitro hepatic clearance rates for 50 IOCs using a pooled batch of liver S9 fractions isolated from rainbow trout (Oncorhynchus mykiss). The IOCs included four types of strongly ionized acids (carboxylates, phenolates, sulfonates, and sulfates), three types of strongly ionized bases (primary, secondary, tertiary amines), and a pair of quaternary ammonium compounds (QACs). Included in this test set were several surfactants and a series of beta-blockers. For linear alkyl chain IOC analogues, biotransformation enzymes appeared to act directly on the charged terminal group, with the highest clearance rates for tertiary amines and sulfates and no clearance of QACs. Clearance rates for C12-IOCs were higher than those for C8-IOC analogues. Several analogue series with multiple alkyl chains, branched alkyl chains, aromatic rings, and nonaromatic rings were evaluated. The likelihood of multiple reaction pathways made it difficult to relate all differences in clearance to specific molecular features the tested IOCs. Future analysis of primary metabolites in the S9 assay is recommended to further elucidate biotransformation pathways for IOCs in fish.

Cow's Milk Allergy Prescribing Is Influenced by Regional and National Guidance.

OBJECTIVES: Cow's milk allergy (CMA) is the most common food allergy in children with many clinical manifestations, leading to misdiagnoses such as gastro-oesophageal reflux, infantile colic, and lactose intolerance with inappropriate prescribing. We aimed to determine the impact of infant feeding guidelines on CMA prescribing in UK primary care using a simple and inexpensive training package. METHODS: Prospectively collected data of infant feeding prescriptions in Northern Ireland from June 2012 to March 2014 were analysed with the intervention period between November 2012 and March 2013. A comparison was made between hypoallergenic formulae, appropriate for CMA, versus alternative prescriptions including antiregurgitation and colic products, lactose-free and partially hydrolysed milks, or infant Gaviscon. RESULTS: Comparing pre- and postintervention period, the total quantity of hypoallergenic formulae increased by 63.2% and alternative prescriptions decreased by 44.6% (P < 0.001). The total amount of all prescribed products decreased by 41.0% (P < 0.001). During the study period, the proportion of recommended CMA treatment increased from 3.4% before training to 9.8% in the short- and long-term follow-up (P < 0.001). The overall increase was £33,508 per year or £95.5 per general practitioner's surgery. CONCLUSIONS: We present the first study evaluating the impact of infant feeding guidelines on CMA prescribing in UK primary care. Practical advice and teaching of health professionals allowed for effective implementation of regional and national guidelines, with a significant impact on prescription patterns. This study shows promising results for prospective research on a national scale, including socioeconomical impact and cost-effectiveness.

Microbial profiling of combat wound infection through detection microarray and next-generation sequencing.

Combat wound healing and resolution are highly affected by the resident microbial flora. We therefore sought to achieve comprehensive detection of microbial populations in wounds using novel genomic technologies and bioinformatics analyses. We employed a microarray capable of detecting all sequenced pathogens for interrogation of 124 wound samples from extremity injuries in combat-injured U.S. service members. A subset of samples was also processed via next-generation sequencing and metagenomic analysis. Array analysis detected microbial targets in 51% of all wound samples, with Acinetobacter baumannii being the most frequently detected species. Multiple Pseudomonas species were also detected in tissue biopsy specimens. Detection of the Acinetobacter plasmid pRAY correlated significantly with wound failure, while detection of enteric-associated bacteria was associated significantly with successful healing. Whole-genome sequencing revealed broad microbial biodiversity between samples. The total wound bioburden did not associate significantly with wound outcome, although temporal shifts were observed over the course of treatment. Given that standard microbiological methods do not detect the full range of microbes in each wound, these data emphasize the importance of supplementation with molecular techniques for thorough characterization of wound-associated microbes. Future application of genomic protocols for assessing microbial content could allow application of specialized care through early and rapid identification and management of critical patterns in wound bioburden.

Proteomic sample preparation for blast wound characterization.

BACKGROUND: Blast wounds often involve diverse tissue types and require substantial time and treatment for appropriate healing. Some of these subsequent wounds become colonized with bacteria requiring a better understanding of how the host responds to these bacteria and what proteomic factors contribute wound healing outcome. In addition, using reliable and effective proteomic sample preparation procedures can lead to novel biomarkers for improved diagnosis and therapy. RESULTS: To address this need, suitable sample preparation for 2-D DIGE proteomic characterization of wound effluent and serum samples from combat-wounded patients was investigated. Initial evaluation of crude effluent and serum proved the necessity of high abundant protein depletion. Subsequently, both samples were successfully depleted using Agilent Multiple Affinity Removal system and showed greatly improved 2-D spot maps, comprising 1,800 and 1,200 protein spots, respectively. CONCLUSION: High abundant protein removal was necessary for both wound effluent and serum. This is the first study to show a successful method for high abundant protein depletion from wound effluent which is compatible with downstream 2-D DIGE analysis. This development allows for improved biomarker discovery in wound effluent and serum samples.

Estimating screening-level organic chemical half-lives in humans.

Relatively few measured data are available for the thousands of chemicals requiring hazard and risk assessment. The whole body, total elimination half-life (HLT) and the whole body, primary biotransformation half-life (HLB) are key parameters determining the extent of bioaccumulation, biological concentration, and risk from chemical exposure. A one-compartment pharmacokinetic (1-CoPK) mass balance model was developed to estimate organic chemical HLB from measured HLT data in mammals. Approximately 1900 HLs for human adults were collected and reviewed and the 1-CoPK model was parametrized for an adult human to calculate HLB from HLT. Measured renal clearance and whole body total clearance data for 306 chemicals were used to calculate empirical HLB,emp. The HLB,emp values and other measured data were used to corroborate the 1-CoPK HLB model calculations. HLs span approximately 7.5 orders of magnitude from 0.05 h for nitroglycerin to 2 × 10(6) h for 2,3,4,5,2',3',5',6'-octachlorobiphenyl with a median of 7.6 h. The automated Iterative Fragment Selection (IFS) method was applied to develop and evaluate various quantitative structure-activity relationships (QSARs) to predict HLT and HLB from chemical structure and two novel QSARs are detailed. The HLT and HLB QSARs show similar statistical performance; that is, r(2) = 0.89, r(2-ext) = 0.72 and 0.73 for training and external validation sets, respectively, and root-mean-square errors for the validation data sets are 0.70 and 0.75, respectively.

Using model-based screening to help discover unknown environmental contaminants.

Of the tens of thousands of chemicals in use, only a small fraction have been analyzed in environmental samples. To effectively identify environmental contaminants, methods to prioritize chemicals for analytical method development are required. We used a high-throughput model of chemical emissions, fate, and bioaccumulation to identify chemicals likely to have high concentrations in specific environmental media, and we prioritized these for target analysis. This model-based screening was applied to 215 organosilicon chemicals culled from industrial chemical production statistics. The model-based screening prioritized several recognized organosilicon contaminants and generated hypotheses leading to the selection of three chemicals that have not previously been identified as potential environmental contaminants for target analysis. Trace analytical methods were developed, and the chemicals were analyzed in air, sewage sludge, and sediment. All three substances were found to be environmental contaminants. Phenyl-tris(trimethylsiloxy)silane was present in all samples analyzed, with concentrations of ∼50 pg m(-3) in Stockholm air and ∼0.5 ng g(-1) dw in sediment from the Stockholm archipelago. Tris(trifluoropropyl)trimethyl-cyclotrisiloxane and tetrakis(trifluoropropyl)tetramethyl-cyclotetrasiloxane were found in sediments from Lake Mjøsa at ∼1 ng g(-1) dw. The discovery of three novel environmental contaminants shows that models can be useful for prioritizing chemicals for exploratory assessment.

Osteogenic gene expression correlates with development of heterotopic ossification in war wounds.

BACKGROUND: Heterotopic ossification (HO) is a frequent complication of modern wartime extremity injuries. The biological mechanisms responsible for the development of HO in traumatic wounds remain elusive. QUESTION/PURPOSES: The aims of our study were to (1) characterize the expression profile of osteogenesis-related gene transcripts in traumatic war wounds in which HO developed; and (2) determine whether expression at the mRNA level correlated with functional protein expression and HO formation. METHODS: Biopsy specimens from 54 high-energy penetrating extremity wounds obtained at the initial and final surgical débridements were evaluated. The levels of selected osteogenic-related gene transcripts from RNA extracts were assessed by quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) analysis. As a result of its key role in osteogenesis, the concentration of BMP-2 in the effluent of 29 wounds also was determined. RESULTS: The transcripts of 13 genes (ALPL [p = 0.006], BMP-2 [p < 0.001], BMP-3 [p = 0.06], COL2A1 [p < 0.001], COLL10A1 [p < 0.001], COL11A1 [p = 0.006], COMP [p = 0.02], CSF2 [p = 0.003], CSF3 [p = 0.012], MMP8 [p < 0.001], MMP9 [p = 0.014], SMAD1 [p = 0.024], and VEGFA [p = 0.017]) were upregulated greater than twofold in wounds in which HO developed compared with wounds in which it did not develop. Gene transcript expression of BMP-2 also correlated directly with functional protein expression in the wounds that formed HO (p = 0.029). CONCLUSIONS: Important differences exist in the osteogenic gene expression profile of wounds in which HO developed compared with wounds in which it did not develop. The upregulation of multiple osteogenesis-related gene transcripts indicates the presence of a proosteogenic environment necessary for ectopic bone formation in traumatic wounds. CLINICAL RELEVANCE: Understanding the osteogenic environment associated with war wounds may allow for the development of novel therapeutic strategies for HO.

Identifying uncertainty in physical-chemical property estimation with IFSQSAR.

This study describes the development and evaluation of six new models for predicting physical-chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol-water (KOW), octanol-air (KOA), and air-water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure-Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for "novel chemicals" in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7-1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.

Modeling sorption of environmental organic chemicals from water to soils.

Reliable estimation of chemical sorption from water to solid phases is an essential prerequisite for reasonable assessments of chemical hazards and risks. However, current fate and exposure models mostly rely on algorithms that lack the capability to quantify chemical sorption resulting from interactions with multiple soil constituents, including amorphous organic matter, carbonaceous organic matter, and mineral matter. Here, we introduce a novel, generic approach that explicitly combines the gravimetric composition of various solid constituents and poly-parameter linear free energy relationships to calculate the solid-water sorption coefficient (Kd) for non-ionizable or predominantly neutral organic chemicals with diverse properties in a neutral environment. Our approach demonstrates an overall statistical uncertainty of approximately 0.9 log units associated with predictions for different types of soil. By applying this approach to estimate the sorption of 70 diverse chemicals from water to two types of soils, we uncover that different chemicals predominantly exhibit sorption onto different soil constituents. Moreover, we provide mechanistic insights into the limitation of relying solely on organic carbon normalized sorption coefficient (KOC) in chemical hazard assessment, as the measured KOC can vary significantly across different soil types, and therefore, a universal cut-off threshold may not be appropriate. This research highlights the importance of considering chemical properties and multiple solid constituents in sorption modeling and offers a valuable theoretical approach for improved chemical hazard and exposure assessments.

Wound outcome in combat injuries is associated with a unique set of protein biomarkers.

BACKGROUND: The ability to forecast whether a wound will heal after closure without further debridement(s), would provide substantial benefits to patients with severe extremity trauma. METHODS: Wound effluent is a readily available material which can be collected without disturbing healthy tissue. For analysis of potential host response biomarkers, forty four serial combat wound effluent samples from 19 patients with either healing or failing traumatic- and other combat-related wounds were examined by 2-D DIGE. Spot map patterns were correlated to eventual wound outcome (healed or wound failure) and analyzed using DeCyder 7.0 and differential proteins identified via LC-MS/MS. RESULTS: This approach identified 52 protein spots that were differentially expressed and thus represent candidate biomarkers for this clinical application. Many of these proteins are intimately involved in inflammatory and immune responses. Furthermore, discriminate analysis further refined the 52 differential protein spots to a smaller subset of which successfully differentiate between wounds that will heal and those that will fail and require further surgical intervention with greater than 83% accuracy. CONCLUSION: These results suggest candidates for a panel of protein biomarkers that may aid traumatic wound care prognosis and treatment. We recommend that this strategy be refined, and then externally validated, in future studies of traumatic wounds.

General model for estimating partition coefficients to organisms and their tissues using the biological compositions and polyparameter linear free energy relationships.

Equilibrium partition coefficients of organic chemicals from water to an organism or its tissues are typically estimated by using the total lipid content in combination with the octanol-water partition coefficient (K(ow)). This estimation method can cause systematic errors if (1) different lipid types have different sorptive capacities, (2) nonlipid components such as proteins have a significant contribution, and/or (3) K(ow) is not a suitable descriptor. As an alternative, this study proposes a more general model that uses detailed organism and tissue compositions (i.e., contents of storage lipid, membrane lipid, albumin, other proteins, and water) and polyparameter linear free energy relationships (PP-LFERs). The values calculated by the established PP-LFER-composition-based model agree well with experimental in vitro partition coefficients and in vivo steady-state concentration ratios from the literature with a root mean squared error of 0.32-0.53 log units, without any additional fitting. This model estimates a high contribution of the protein fraction to the overall tissue sorptive capacity in lean tissues (e.g., muscle), in particular for H-bond donor polar compounds. Direct model comparison revealed that the simple lipid-octanol model still calculates many tissue-water partition coefficients within 1 log unit of those calculated by the PP-LFER-composition-based model. Thus, the lipid-octanol model can be used as an order-of-magnitude approximation, for example, for multimedia fate modeling, but may not be suitable for more accurate predictions. Storage lipid-rich phases (e.g., adipose, milk) are prone to particularly large systematic errors. The new model provides useful implications for validity of lipid-normalization of concentrations in organisms, interpretation of biomonitoring results, and assessment of toxicity.

Exploring the role of shelf sediments in the Arctic Ocean in determining the Arctic contamination potential of neutral organic contaminants.

The main objective of this study was to model the contribution of shelf sediments in the Arctic Ocean to the total mass of neutral organic contaminants accumulated in the Arctic environment using a standardized emission scenario for sets of hypothetical chemicals and realistic emission estimates (1930-2100) for polychlorinated biphenyl congener 153 (PCB-153). Shelf sediments in the Arctic Ocean are shown to be important reservoirs for neutral organic chemicals across a wide range of partitioning properties, increasing the total mass in the surface compartments of the Arctic environment by up to 3.5-fold compared to simulations excluding this compartment. The relative change in total mass for hydrophobic organic chemicals with log air-water partition coefficients ≥0 was greater than for chemicals with properties similar to typical POPs. The long-term simulation of PCB-153 generated modeled concentrations in shelf sediments in reasonable agreement with available monitoring data and illustrate that the relative importance of shelf sediments in the Arctic Ocean for influencing surface ocean concentrations (and therefore exposure via the pelagic food web) is most pronounced once primary emissions are exhausted and secondary sources dominate. Additional monitoring and modeling work to better characterize the role of shelf sediments for contaminant fate is recommended.

Effect of wind on the chemical uptake kinetics of a passive air sampler.

Passive air samplers (PASs) operate in different types of environment under various wind conditions, which may affect sampling rates and thus introduce uncertainty to PAS-derived air concentrations. To quantify the effect of wind speed and angle on the uptake in cylindrical PASs using XAD-resin as the sampling medium, we measured the uptake kinetics of polychlorinated biphenyls (PCBs) in XAD and of water in silica-gel, both under quasi wind-still condition and with lab-generated wind blowing toward the PASs at various speeds and angles. Passive sampling rates (PSRs) of PCBs under laboratory generated windy conditions were approximately 3-4 times higher than under wind-still indoor conditions. The rate of water uptake by silica-gel increased with wind speed, following a logarithmic function so that PSRs are more strongly influenced at lower wind speed. PSRs of both PCBs and water varied little with wind angle, which is consistent with computational fluid dynamic simulations showing that different angles of wind incidence cause only minor variations of air velocities within the cylindrical sampler housing. Because modifications of the design of the cylindrical PAS were not successful in eliminating the wind speed dependence of PSRs at low wind levels, indoor and outdoor deployments require different sets of PSRs. The effect of wind speed and angle on the PSRs of the cylindrical PAS are much smaller than what has been reported for the double-bowl polyurethane foam PAS. PSRs of the cylindrical XAD-PAS therefore tend to vary much less between sampling sites exposed to different wind conditions.

Sequential extraction of labile and recalcitrant fractions of soil organic matter: A case study focusing on antimony (Sb) in humic acids, fulvic acids and humin fractions of long-term aged contaminated soils.

Soil organic matter (SOM) plays a key role in environmental chemistry of macro and micro nutrients as well as heavy metal (loids). In this research, a modified sequential extraction scheme was used to isolate labile and recalcitrant SOM from organic rich soils after 18 months of ageing with antimony. Humic substances were extracted with a mixture of 0.5 M sodium hydroxide +0.1 M sodium pyrophosphate solution from soils. Then soils deprived of humic substances were sequentially subjected to extraction with glycerol, citric acid, pre-treated with acid and extracted with boiling alkali mixture. The humic acids (HA) and fulvic acids (FA) of isolated SOM fractions were separated and HAs been characterized using FTIR, 1H NMR, and UV-VIS. Acid-alkali treatment of the most recalcitrant SOM fraction (A1-ROM) led to the extraction of sparingly soluble, highly aromatic compound with considerable amounts of N (44% of the extractable N), possibly due to the breakdown of bounds between aromatic rings and amine functional groups. Nevertheless, the highest content of C and TOC was associated with the glycerol extractable SOM. Substantial amounts of Fe and Al were extracted with glycerol, resulting in a dramatic rise of Sb in SOM extracts. The largest increase (60%) in Sb concentrations was observed after the removal of Fe with citric acid. The humic substances (HS) were responsible for 63% of extractable Sb, whereas even after exhaustive alkali extractions 22% of the total Sb remained in the residual humin fraction. Within the HS fraction, 95% of antimony was associated with the low molecular weight FAs. Antimony concentrations in organic fractions correlated significantly with TOC and N contents, possibly due to the role of amine functional groups in Sb complexation. The results of this research highlight the importance of Fe-Al-SOM bridging and humin fraction in sequestration of Sb in recalcitrant SOM pools.

Hazard vs. exposure: Does it make a difference in identifying chemicals with persistence and mobility concerns?

Persistent and mobile (PM) chemicals are considered emerging threats to the environment and drinking water because they can be transported over long distances, penetrate natural and artificial barriers, and resist removal by traditional water treatment procedures. Current chemical regulatory frameworks raise concerns over PM chemicals due to their potential to cause high human exposure through drinking water contamination. However, the criteria used to screen and identify these chemicals often rely on hazard properties related to stability and sorption, such as biodegradation half-lives and organic-carbon-normalized sorption coefficients as respective measures of P and M. Here, we conduct a model-based assessment to examine the consistency between hazard-based and exposure-based approaches in assessing PM chemicals, by evaluating whether chemicals identified as highly P and M are consistently associated with high drinking water exposure potential (DWEP). We discover that chemicals with the top DWEPs tend to be PM chemicals, but the reverse is not always true, because DWEPs are also impacted by volatilization for air-distributed chemicals and advective particle-bound transport for particle-bound chemicals. Our findings suggest that the hazard metrics are better suited for de-prioritizing, as opposed to prioritizing, chemicals that are unlikely to result in significant human exposure through drinking water, as unfavorable values of hazard metrics are a necessary but not sufficient condition for a high DWEP. We also find that distinct mechanisms determine the DWEP in different sources of drinking water: Sorption and stability are more influential on the DWEP of chemicals in groundwater and surface water, respectively, whereas both sorption and stability equally impact water undergoing riverbank filtration. Future studies should focus on optimizing the identification of persistent and mobile chemicals to ensure that exposure potential is taken into consideration.

Bayesian modeling of pretransplant variables accurately predicts kidney graft survival.

INTRODUCTION: Machine learning can enable the development of predictive models that incorporate multiple variables for a systems approach to organ allocation. We explored the principle of Bayesian Belief Network (BBN) to determine whether a predictive model of graft survival can be derived using pretransplant variables. Our hypothesis was that pretransplant donor and recipient variables, when considered together as a network, add incremental value to the classification of graft survival. METHODS: We performed a retrospective analysis of 5,144 randomly selected patients (age ≥18, deceased donor kidney only, first-time recipients) from the United States Renal Data System database between 2000 and 2001. Using this dataset, we developed a machine-learned BBN that functions as a pretransplant organ-matching tool. RESULTS: A network of 48 clinical variables was constructed and externally validated using an additional 2,204 patients of matching demographic characteristics. This model was able to predict graft failure within the first year or within 3 years (sensitivity 40%; specificity 80%; area under the curve, AUC, 0.63). Recipient BMI, gender, race, and donor age were amongst the pretransplant variables with strongest association to outcome. A 10-fold internal cross-validation showed similar results for 1-year (sensitivity 24%; specificity 80%; AUC 0.59) and 3-year (sensitivity 31%; specificity 80%; AUC 0.60) graft failure. CONCLUSION: We found recipient BMI, gender, race, and donor age to be influential predictors of outcome, while wait time and human leukocyte antigen matching were much less associated with outcome. BBN enabled us to examine variables from a large database to develop a robust predictive model.

Iterative fragment selection: a group contribution approach to predicting fish biotransformation half-lives.

There are regulatory needs to evaluate thousands of chemicals for potential hazard and risk with limited available information. An automated method is presented for developing and evaluating Quantitative Structure-Activity Relationships (QSARs) for a range of chemical properties that can be applied for screening level chemical assessments. The method is an integrated algorithm for descriptor generation, data set splitting, cross validation, and model selection. Resulting QSARs are two-dimensional (2D) fragment-based group contribution models. The QSAR development and evaluation method does not require previous expert knowledge for selecting 2D fragments associated with the chemical property of interest. The method includes information on the domain of applicability (structural similarity to the training set) and estimates of the uncertainty in the QSAR predictions. As a demonstration, the method is applied to generate novel QSARs for fish primary biotransformation half-lives (HL(N)). Results from the new HL(N) QSARs are compared to another 2D fragment-based HL(N) QSAR developed with expert judgment, and the predictive powers of the models are found to be similar. The relative merits and limitations of each method are investigated and the new QSAR is found to make comparable predictions with significantly fewer fragments. A coefficient of determination (R(2)) of 0.789 and a root mean squared error (RMSE) of 0.526 were obtained for the training data set and an R(2) of 0.748 and an RMSE of 0.584 were obtained for the validation data set, along with a concordance correlation coefficient (CCC) of 0.857 showing good predictive power.