Bioassay Data Research Articles

Molecular surveillance of insecticide resistance in Phlebotomus argentipes targeted by indoor residual spraying for visceral leishmaniasis elimination in India.

Molecular surveillance of resistance is an increasingly important part of vector borne disease control programmes that utilise insecticides. The visceral leishmaniasis (VL) elimination programme in India uses indoor residual spraying (IRS) with the pyrethroid, alpha-cypermethrin to control Phlebotomus argentipes the vector of Leishmania donovani, the causative agent of VL. Prior long-term use of DDT may have selected for knockdown resistance (kdr) mutants (1014F and S) at the shared DDT and pyrethroid target site, which are common in India and can also cause pyrethroid cross-resistance. We monitored the frequency of these marker mutations over five years from 2017-2021 in sentinel sites in eight districts of north-eastern India covered by IRS. Frequencies varied markedly among the districts, though finer scale variation, among villages within districts, was limited. A pronounced and highly significant increase in resistance-associated genotypes occurred between 2017 and 2018, but with relative stability thereafter, and some reversion toward more susceptible genotypes in 2021. Analyses linked IRS with mutant frequencies suggesting an advantage to more resistant genotypes, especially when pyrethroid was under-sprayed in IRS. However, this advantage did not translate into sustained allele frequency changes over the study period, potentially because of a relatively greater net advantage under field conditions for a wild-type/mutant genotype than projected from laboratory studies and/or high costs of the most resistant genotype. Further work is required to improve calibration of each 1014 genotype with resistance, preferably using operationally relevant measures. The lack of change in resistance mechanism over the span of the study period, coupled with available bioassay data suggesting susceptibility, suggests that resistance has yet to emerge despite intensive IRS. Nevertheless, the advantage of resistance-associated genotypes with IRS and under spraying, suggest that measures to continue monitoring and improvement of spray quality are vital, and consideration of future alternatives to pyrethroids for IRS would be advisable.

Safety Assessment of Recycled Plastics from Post-Consumer Waste with a Combination of a Miniaturized Ames Test and Chromatographic Analysis

European circular economy goals require the use of recycled polymers in sensitive applications such as food packaging. As plastic recyclates can contain unknown post-consumer substances, the European Food Safety Authority evaluates recycling processes using a worst-case assumption: all contaminants are DNA-reactive mutagens/carcinogens with extremely low safety thresholds. The current data are insufficient to estimate whether this assumption is justified. To provide scientific evidence on the presence of DNA-reactive mutagens in recycled plastics, 119 input and output samples from plastic recycling were tested with a miniaturized Ames test. DNA-reactive mutagens were not detected in recycled polyethylene terephthalate, which is already approved for food contact. However, other types of recycled plastics (polyethylene, polypropylene and polystyrene), which are currently unauthorized for food contact, showed DNA-reactive, mutagenic effects in a total of 51 samples. The DNA-reactive substances that are responsible for the detected mutagenic activity could not be identified by comparison of the bioassay data with analytical results from a chromatographical screening. The data from the Ames test analysis of different independent batches and a comparison of input and output material indicate that the DNA-reactive contaminants are not randomly introduced through the misuse of recycled packaging by consumers, but are systematically formed during the recycling process from precursors in the input. This publication highlights the need to identify the source for this critical contaminant to enable the future use of polyethylene, polypropylene and polystyrene in sensitive applications.

Modified human respiratory tract model to describe the retention of plutonium in scar tissues.

The Human Respiratory Tract Model described in Publication 130 of the International Commission on Radiological Protection provides some mechanisms to account for retention of material that can be subject to little to no mechanical transport or absorption into the blood. One of these mechanisms is 'binding', which refers to a process by which a fraction ('bound fraction') of the dissolved material chemically binds to the tissue of the airway wall. The value of the bound fraction can have a significant impact on the radiation doses imparted to different parts of the respiratory tract. To properly evaluate-and quantify-bound fraction for an element, one would need information on long-term retention of the element in individual compartments of the respiratory tract. Such data on regional retention of plutonium in the respiratory tract of four workers-who had inhaled materials with solubility ranging from soluble nitrate to very insoluble high-fired oxides-were obtained at the United States Transuranium and Uranium Registries. An assumption of bound fraction alone was found to be inconsistent with this dataset and also with a review of the literature. Several studies show evidence of retention of a large amount of Pu activity in the scar tissues of humans and experimental animals, and accordingly, a model structure with scar-tissue compartments was proposed. The transfer rates to these compartments were determined using Markov Chain Monte Carlo analysis of the bioassay and post-mortem data, considering the uncertainties associated with deposition, dissolution and particle clearance parameters. The models predicted that a significant amount-between 20 and 100% for the cases analyzed-of plutonium retained in the respiratory tract was sequestered in the scar tissues. Unlike chemically-bound Pu that irradiates sensitive epithelial cells, Pu in scar tissues may not be dosimetrically significant because the scar tissues absorb most, if not all, of the energy from alpha emissions.

Graph Attention Network based mapping of knowledge relations between chemical spaces of Nuclear factor kappa B and Centella asiatica

The confounding nature of the innate immunity target Nuclear Factor kappa B (NF-κB) and its interaction with Centella asiatica (CA) molecules necessitate the intervention of advanced technologies, such as deep learning methods. The integration of chemical space concepts with deep learning technologies is a new way of knowledge mapping used to explore drug-target interactions, especially in molecular libraries derived from traditional medicine based molecular sources. The current constraint of virtual screening for mechanistic target hunting is the use of a binary classification model that includes active and inactive molecules from in vitro experiments to explore drug-target interaction. This study aims to explore the regulatory nature of the molecules from the inhibition and activation of the NF-κB bioassay data set and map this information for a knowledge-based analysis against the molecules of CA, a low-growing tropical plant. This finding has led to a new direction in the field, transitioning from the conventional active-inactive framework to a more comprehensive active-inactive-regulatory model. This approach can be thoroughly explored by leveraging a graph-based deep learning system. The study presents an innovative approach using a Graph Attention Network (GAT) to rank CA molecules in chemical space based on their similarity with NF-κB bioassay molecules, enabling the efficient analysis of complex relationships between molecules and their regulatory function. Graph Attention Network (GAT) overcomes the limitations of traditional deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in handling non-Euclidean graph data and allows for a more precise understanding of similarity ranking by utilizing molecular graphs and attention behavior. By measuring similarity and arranging a matrix of similarity ranking based on GAT, deep neural ranking-based algorithms confirmed the regulatory behaviour of an innate immunity target NF-κB with the support of underlying inverse mapping in the surjective chemical spaces of NF-κB bioassays and CA molecular spaces. Overall, the study introduces new techniques for exploring the regulatory behaviour of complex targets like NF-κB. We then used t-SNE for clustering in chemical space and scaffold hunting for scaffold property analysis and identified nine CA molecules that exhibit regulatory behavior of NF-κB target and are recommended for further investigation.

Dose reconstruction for plutonium-239 intakes at the Rocky Flats Plant

Purpose The Rocky Flats (RF) Plant was a weapons manufacturing facility that operated from the early 1950s to 1989. Its primary missions were the production of plutonium (Pu) pits for thermonuclear weapons and the processing of retired weapons for Pu recovery. The purpose of this study was to estimate radiation doses to a cohort of 4499 RF workers from an intake of 239Pu, the primary plutonium isotope handled at the site. Materials and methods The latest biokinetic models of the International Commission on Radiological Protection, or site-specific variations of those models, were used to estimate 239Pu intakes for each worker based on model fits to bioassay data often coupled with lung measurements. Results Urinary excretion and lung retention data for most 239Pu intakes could be fit reasonably well by a mixture of Pu dioxide and moderately soluble material. For some workers, better fits were obtained by application of other absorption types including Type S, 239Pu nitrate, or pure 239Pu dioxide, or by assuming intake via a wound. The lungs typically received the highest tissue doses, with fifty-year committed equivalent doses in the range of 0.5–1 Sv for 275 workers, 1–5 Sv for 115 workers, and greater than 5 Sv for 12 workers. Conclusions RF was a unique site regarding a large number of lung measurements available for determining the appropriate absorption types for inhaled material. This provided higher confidence in reconstructed 239Pu doses than is generally gained from urinary data alone.

Precision and Accuracy of Field Versus Laboratory Bioassay Insecticide Efficacy for the Control of Immature Bemisia tabaci.

Ecotoxicological studies often result in reports on the limitation and sometime failures of biological assay data to predict field response to similar treatments. Nevertheless, it is widely accepted that controlled bioassays can better quantify the specific mortality response of a target pest species to a specific toxin. To quantify the relationship between whitefly bioassay and field response data, we evaluated a controlled laboratory bioassay and a concurrent cucurbit field trial method to assess insecticide efficacy for controlling the sweetpotato whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae). This was based on oviposition and nymphal development. We specifically tested the assumptions that a maximum dose bioassay would more precisely measure insecticide efficacy as compared with a comparable field spray test evaluation, and the response would be equal between the bioassay and the field as a measure of control accuracy for both adult oviposition and development of nymphal stages. To make a direct comparison, we tested the same whitefly population subsamples from 352 plots in eight cucurbit field experiments in Georgia, USA, in 2021 and 2022. The bioassays provide significantly precision for estimating proportional whitefly response. As expected, treatment-specific nonequivalence in immature whitefly counts between the bioassay and field, i.e., a lack of accuracy, only occurred with insecticides that were not highly toxic to all growth stages of whiteflies.

О значении хемосистематической корреляции при изучении аллелопатического потенциала аборигенных и чужеродных видов трав

This abstract presents the results of screening for allelopathic activity of three pairs “aboriginal-exotic” of comparison species of herbs, including invasive species, systematically related at the level of the genus. It was shown that a 1% leaf extract of Solidago virgaurea and S. canadensis, Pulmonaria obscura and P. saccharata, Veronica serpyllifolia and V. filiformis had a similar or equal inhibitory effect on the germination and growth of test-plant roots in biotests owing to its chemosystematic correlation. Extracts of Solidago and Pulmonaria species slowed down the rate of germination and root growth by 1.5–2 times lower than the control. In the Salidago sp. experiment options the dynamics of the processes developed according to the type of stress-reaction. The absolute rate of root growth at the exponential stage differed twice in the Solidago sp. options: 16.8±0.43 and 8.99±0.23 mm/day in native and exotic specie, respectively. The total germination was close to or equal to the control level. In a comparison pair Pulmonaria sp. germination decreased by 58.66 and 43.5%. V. filiformis extract inhibited germination by 24.4% more than the native species. Extracts 0.05% extracts of Solidago sp. had no effect, P. saccharata and V. filiformis extracts caused the effects of hormesis. P. saccharate option produced an – acceleration of the absolute and relative root growth rate of the test-plants by two times, exceeding the total length of the roots by half compared to the control and the native species. In the V. filiformis option, the rates of germination and root growth was inhibited by 24.7 and 50.2% of the values of the paired species, respectively. The comparative method increases the level of analysis of bioassay data, allows for a deeper study of the specifics of test-plants reactions, and allows the evaluation of the modes of action of allelochemics.

Design, synthesis and biological activity of novel triketone herbicides containing natural product fragments

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) belongs to the non-heme Fe2+ − containing enzyme family and is an important enzyme in tyrosine decomposition. HPPD is crucial to the discovery of novel bleaching herbicides. To develop novel HPPD inhibitor herbicides containing the β-triketone motif, a series of 4-hydroxyl-3-(substituted aryl)-pyran-2-one derivatives were designed using the active fragment splicing method. The title compounds were synthesized and characterized through infrared spectroscopy (IR), 1H nuclear magnetic resonance (1H NMR), 13C nuclear magnetic resonance (13C NMR), and high-resolution mass spectrometry (HRMS). The X-ray diffraction method determined the single crystal structure of I-17. Preliminary bioassay data revealed that several novel compounds, especially I-12 and II-3, showed excellent herbicidal activity against broadleaf and monocotyledonous weeds at a dose of 150 g ai/ha. The results of crop selectivity and carotenoids determination indicated that compound I-12 is more suitable for wheat and cotton fields than mesotrione. Additionally, compound II-3 is safer for soybeans and peanuts than mesotrione. The inhibitory activity of Arabidopsis thaliana HPPD (AtHPPD) verified that compound II-3 showed the most activity with an IC50 value of 0.248 μM, which was superior to that of mesotrione (0.283 μM) in vitro. The binding mode of compound II-3 and AtHPPD was confirmed through molecular docking and molecular dynamics simulations. This study provides insights into the future development of natural and efficient herbicides.

Abstract 5341: Data mining of PubChem bioassay data reveals OXPHOS inhibitors with anti-cancer activity

Abstract High Grade Serous Ovarian Cancer (HGSOC) is a fatal disease that is typically detected at an advanced stage when there are limited modalities for effective treatment. Treatment efficacy against cancer is dependent on tumor metabolism and its adaptation for most therapies. Warburg effect is an important adaptation found in tumors where cells primarily catabolize glucose even in presence of sufficient oxygen and nutrients. Nevertheless, recent evidence suggests cells in tumor microenvironments remain dependent on mitochondrial ATP production via oxidative phosphorylation (OXPHOS). OXPHOS also represents a major metabolic pathway in cancer stem cells and tumor cells of relapse patients. Molecular signatures of glycolysis and OXPHOS are both detected in tumors. This points to the OXPHOS pathway as a potential therapeutic target in cancer. Here we apply a data mining approach to identify OXPHOS inhibitors to evaluate as candidates for therapies against HGSOC. High-Throughput Screening (HTS) on mechanistic targets like OXPHOS are cost prohibitive for academic laboratories. As an alternative, we deployed a data mining pipeline to find active chemical classes among 8415 available OXPHOS-related bioassays in the PubChem data repository. Active compound clusters frequently contain α,β-unsaturated carbonyl groups, which are also observed in both endogenous quinone electron carriers and atovaquone–a potent inhibitor of OXPHOS. Out of 312041 unique compounds tested in the assays, we selected 6 representative molecules from each of the 6 largest active chemical clusters to test for OXPHOS inhibition, ROS production, and reduced cancer cell proliferation. We assayed cell viability (MTT) to check for proliferation of human (OVCAR3, OVCAR5, OVISE) and mouse (ID8) ovarian cancer cells against 6 compounds, Oryzalin, Allylyestrenol, Esbiothrin, Lacidipine, Coumatetralyl, and CID929419 (MLS000713029), 72 h post treatment. ID8 and OVCAR5 significantly decreased cell proliferation after Lacidipine and Oryzalin treatment. Significant increase in intracellular ROS, a frequent consequence of OXPHOS inhibition, was observed in all cell lines after Lacidipine treatment. Elevated ROS was also observed in ID8 and OVISE cells after Esbiothrin treatment. To confirm OXPHOS inhibition as the mechanism of action, we performed Mito Stress Test on the Seahorse Analyzer and measured Oxygen Consumption Rate. Treatment with Oryzalin, Allylyestrenol, Esbiothrin, and Lacidipine significantly decreased basal respiration rate and ATP production in ID8 and OVCAR3 cells. This suggests that 4 of the 6 compound classes prioritized by the data mining pipeline inhibits OXPHOS in ovarian cancer cells. Thus, our pipeline provides an efficient alternative to HTS for identifying OXPHOS inhibitors. Next, we plan to test the in-vivo efficacy of these drugs against ovarian tumors in a mouse model and investigate their molecular mechanisms. Citation Format: Sejal T. Sharma, Liping Feng, Nicha Boonpattrawong, Lisa M. Barroilhet, Manish Patankar, Spencer S. Ericksen. Data mining of PubChem bioassay data reveals OXPHOS inhibitors with anti-cancer activity. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5341.

Deciphering the origin of total estrogenic activity of complex mixtures.

Identifying compounds with endocrine properties in food is getting increasingly important. Current chemical analysis methodology is mainly focused on the identification of known substances without bringing insight for biological activity. Recently, the application of bioassays has been promoted for their potential to detect unknown bioactive substances and to provide information on possible interactions between molecules. From the toxicological perspective, measuring endocrine activity cannot inform on endocrine disruption and/or health risks without sufficient knowledge on the nature of the responsible factors. The present study addresses a promising approach using High Performance Thin-Layer Chromatography (HPTLC) coupled to bioassays were analyzed using the Liquid Chromatography Mass-Spectrometry (LC-MS). The estrogen receptor activation was assessed using the transcription activation Estrogen Receptor Alpha Chemical Activated LUciferase gene eXpression assay (ERα- CALUX) and the HPTLC coupled to the Estrogen Screen Yeast assay (p-YES). Seven isoflavones were identified in the soy isolates. Estrogen receptor activation was assessed for both, the identified isoflavones and the soy isolates with ERα-CALUX test. Correlation between the soy isolates extracts and the identified isoflavones was shown. Moreover, p-YES revealed the presence of an estrogenic bioactive zone. Analysis of the bioactive zone through LCHRMS highlighted signals corresponding to several isoflavones already detected in the isolates as well as two additional ones. For all detected isoflavones, an estrogenic activity dose-response was established in both bioassays. Finally, genistein, daidzein, and naringenin were found as the most active substances. A concordance analysis integrating the analytical and bioassay data indicated that genistein and daidzein were the drivers of the estrogenic activity of these soy protein isolates. Altogether, these data suggest that the integration of HPTLC-bioassay together with chemical analysis is a powerful approach to characterize the endocrine activity of complex mixtures.

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data.

Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., "assay repositioning". The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.

Insecticide resistance management of Bemisia tabaci (Hemiptera: Aleyrodidae) in Australian cotton - pyriproxyfen, spirotetramat and buprofezin.

Bemisia tabaci is a globally significant agricultural pest including in Australia, where it exhibits resistance to numerous insecticides. With a recent label change, buprofezin (group 16), is now used for whitefly management in Australia. This study investigated resistance to pyriproxyfen (group 7C), spirotetramat (group 23) and buprofezin using bioassays and available molecular markers. Bioassay and selection testing of B.tabaci populations detected resistance to pyriproxyfen with resistance ratios ranging from 4.1 to 56. Resistance to spirotetramat was detected using bioassay, selection testing and sequencing techniques. In populations collected from cotton, the A2083V mutation was detected in three populations of 85 tested, at frequencies ≤4.1%, whereas in limited surveillance of populations from an intensive horticultural region the frequency was ≥75.8%. The baseline susceptibility of B.tabaci to buprofezin was determined from populations tested from 2019 to 2020, in which LC50 values ranged from 0.61 to 10.75 mg L-1 . From the bioassay data, a discriminating dose of 200 mg L-1 was developed. Recent surveillance of 16 populations detected no evidence of resistance with 100% mortality recorded at doses ≤32 mg L-1 . A cross-resistance study found no conclusive evidence of resistance to buprofezin in populations with high resistance to pyriproxyfen or spirotetramat. In Australian cotton, B.tabaci pest management is challenged by ongoing resistance to pyriproxyfen, while resistance to spirotetramat is an emerging issue. The addition of buprofezin provides a new mode-of-action for whitefly pest management, which will strengthen the existing insecticide resistance management strategy. © 2023 Commonwealth of Australia. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Prediction of zebrafish embryonic developmental toxicity by integrating omics with adverse outcome pathway

New approach methodologies (NAMs), especially omics-based high-throughput bioassays have been developed rapidly, providing rich mechanistic information such as molecular initiation events (MIEs) and (sub)cellular key events (KEs) in adverse outcome pathways (AOPs). However, how to apply the knowledge of MIEs/KEs to predict adverse outcomes (AOs) induced by chemicals represents a new challenge for computational toxicology. Here, an integrated method named ScoreAOP was developed and evaluated to predict chemicals’ developmental toxicity for zebrafish embryos by integrating four related AOPs and dose-dependent reduced zebrafish transcriptome (RZT). The rules of ScoreAOP included 1) sensitivity of responsive KEs demonstrated by point of departure of KEs (PODKE), 2) evidence reliability and 3) distance between KEs and AOs. Moreover, eleven chemicals with different modes of action (MoAs) were tested to evaluate ScoreAOP. Results showed that eight of the eleven chemicals caused developmental toxicity at tested concentration in apical tests. All the tested chemicals’ developmental defects were predicted using ScoreAOP, whereas eight out of the eleven chemicals predicted by ScoreMIE which was developed to score MIEs disturbed by chemicals based on in vitro bioassays data. Finally, in terms of mechanism explanation, ScoreAOP clustered chemicals with different MoAs while ScoreMIE failed, and ScoreAOP revealed the activation of aryl hydrocarbon receptor (AhR) plays a significant role in dysfunction of cardiovascular system, resulting in zebrafish developmental defects and mortality. In conclusion, ScoreAOP represents a promising approach to apply mechanism information obtained from omics to predict AOs induced by chemicals.

Optimal efficacy in mosquito-host interaction inhibition by 35-60 kHz animal sounds for malaria control

Female Anopheles gambiae sensu stricto, the malaria vector, continues to develop resistance to synthetic insecticides thus threatening its control. Studies on mosquitoe's phonotaxis showed that the electronic mosquito repellent (EMR) (40-55 kHz) sound, Odorrana tormota (35-60 kHz) sound and winded-EMR sound yielded 68.99%, 45.88% and 60.70% repellency respectively, supporting feasibility of non-pollutant sounds in mosquito control. Rudimental studies with O. tormota sounds didn’t observe protocols for mosquito rearing standard operation procedures (SOPs) and World Health Organisation (WHO) guidelines for efficacy testing of spatial repellents. Also, phonotactic inhibition by natural sounds of Delphinapterus leucas and male A. gambiae s.s had not been studied. This research, therefore, determined spatial activity index (SAI) and protection index (PI) of mated female A. gambiae s.s evoked by 35-60 kHz sounds of O. tormota, D. leucas, and male A. gambiae s.s in adherence to mosquito rearing SOPs and WHO guidelines. Mosquitoes were reared in Kenya Medical Research Institute (KEMRI) laboratories. Sounds of a hundred 3-5-day-old male A. gambiae s.s were recorded using Avisoft recorder whereas the sounds of O. tormota and D. leucas were acquired. Abnormal mosquito behavioural responses were observed during the bioassays. The bioassay data was collected and analyzed using statistical softwares. Mean SAI evoked by O. tormota, male A. gambiae s.s, and D. leucas were 0.142, 0.318, and 0.206 respectively, indicating negative phonotaxis. The PI elicited by O. tormota was 80.06% exceeding male A. gambiae s.s and D. leucas by 1.65% and 8.24% respectively. The constant frequency modulated sound of O. tormota yielded mean bandwidth (mean entire; 13.95 kHz), maximum frequency (mean entire; 41.61 kHz), maximum energy (8.02 Pa2s), entropy (2.87 bits), delta power (24.10 dB), average power (55.60 dB), maximum power frequency (38.09 kHz) and maximum power (75.90 dB).

Bridging the lab to field divide: Advancing oil spill biological effects models requires revisiting aquatic toxicity testing

Oil fate and exposure modeling addresses the complexities of oil composition, weathering, partitioning in the environment, and the distributions and behaviors of aquatic biota to estimate exposure histories, i.e., oil component concentrations and environmental conditions experienced over time. Several approaches with increasing levels of complexity (i.e., aquatic toxicity model tiers, corresponding to varying purposes and applications) have been and continue to be developed to predict adverse effects resulting from these exposures. At Tiers 1 and 2, toxicity-based screening thresholds for assumed representative oil component compositions are used to inform spill response and risk evaluations, requiring limited toxicity data, analytical oil characterizations, and computer resources. Concentration-response relationships are employed in Tier 3 to quantify effects of assumed oil component mixture compositions. Oil spill modeling capabilities presently allow predictions of spatial and temporal compositional changes during exposure, which support mixture-based modeling frameworks. Such approaches rely on summed effects of components using toxic units to enable more realistic analyses (Tier 4). This review provides guidance for toxicological studies to inform the development of, provide input to, and validate Tier 4 aquatic toxicity models for assessing oil spill effects on aquatic biota.Evaluation of organisms’ exposure histories using a toxic unit model reflects the current state-of the-science and provides an improved approach for quantifying effects of oil constituents on aquatic organisms. Since the mixture compositions in toxicity tests are not representative of field exposures, modelers rely on studies using single compounds to build toxicity models accounting for the additive effects of dynamic mixture exposures that occur after spills. Single compound toxicity data are needed to quantify the influence of exposure duration and modifying environmental factors (e.g., temperature, light) on observed effects for advancing use of this framework. Well-characterized whole oil bioassay data should be used to validate and refine these models.

Characterising the intensity of insecticide resistance: A novel framework for analysis of intensity bioassay data

Insecticide resistance is a growing problem that risks harming the progress made by vector control tools in reducing the malaria burden globally. New methods for quantifying the extent of resistance in wild populations are urgently needed to guide deployment of interventions to improve disease control. Intensity bioassays measure mosquito mortality at a range of insecticide doses and characterise phenotypic resistance in regions where resistance is already detected. These data are increasingly being collected but tend to exhibit high measurement error and there is a lack of formal guidelines on how they should be analysed or compared. This paper introduces a novel Bayesian framework for analysing intensity bioassay data, which uses a flexible statistical model able to capture a wide variety of relationships between mortality and insecticide dose. By accounting for background mortality of mosquitoes, our approach minimises the impact of this source of measurement noise resulting in more precise quantification of resistance. It outputs a range of metrics for describing the intensity and variability in resistance within the sample and quantifies the level of measurement error in the assay. The functionality is illustrated with data from laboratory-reared mosquitoes to show how the lethal dose varies within and between different strains. The framework can also be used to formally test hypotheses by explicitly considering the high heterogeneity seen in these types of data in field samples. Here we show that the intensity of resistance (as measured by the median lethal dose (LC50) of insecticide) increases over 7 years in mosquitoes from one village in Burkina Faso but remains constant in another. This work showcases the benefits of statistically rigorous analysis of insecticide bioassay data and highlights the additional information available from this and other dose-response data.

Status of insecticide resistance in the dengue vector Aedes aegypti in India: A review.

The study aims to present an overview of the extent of insecticide resistance in the dengue vector Aedes aegypti in India. Published data on insecticide resistance in this species were systematically searched through online databases like PubMed, Google, and Google Scholar. From each study, data were extracted and analyzed to understand the spatial and temporal patterns. Particular emphasis was given to the commonly used insecticides for mosquito control. Forty-three studies met the inclusion criteria, of which 13 had adult bioassay data, 13 larval bioassay data and 17 contained both. Data demonstrated high resistance to DDT and resistance is also widespread against carbamates. There is mounting evidence of increased tolerance to pyrethroids and organophosphorus compounds, viz permethrin, deltamethrin, lambda-cyhalothrin, malathion, and temephos. The emergence of resistance to all the insecticide classes further justifies the need for annual resistance monitoring and to maintain a nationwide database that can provide a reference for formulating effective control strategies.

Chelation Model Validation: Modeling of a Plutonium-238 Inhalation Incident Treated with DTPA at Los Alamos National Laboratory.

Accidental inhalation of plutonium at the workplace is a non-negligible risk, even when rigorous safety standards are in place. The intake and retention of plutonium in the human body may be a source of concern. Thus, if there is a suspicion of a significant intake of plutonium, medical countermeasures such as chelation treatment may be administered to the worker. The present work aimed to interpret the bioassay data of a worker involved in an inhalation incident due to a glovebox breach at Los Alamos National Laboratory's plutonium facility. The worker was treated with intravenous injections of calcium salts of diethylenetriaminepentaacetic acid (DTPA) in an attempt to reduce the amount of plutonium from the body and therefore reduce the internal radiation dose. It is well known in the internal dosimetry field that the administration of chelation treatment poses additional challenges to the dose assessment. Hence, a recently developed chelation model was used for the modeling of the bioassay data. The objectives of this work are to describe the incident, model the chelation-affected and non-affected bioassay data, estimate the plutonium intake, and assess the internal radiation dose.

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data.

The development of toxicity classification models using the ToxCast database has been extensively studied. Machine learning approaches are effective in identifying the bioactivity of untested chemicals. However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. Thirty models established by combining five molecular fingerprints (i.e., Morgan, MACCS, RDKit, Pattern, and Layered) and six algorithms [i.e., gradient boosting tree, random forest (RF), multi-layered perceptron, k-nearest neighbor, logistic regression, and naive Bayes] were trained using the selected assay data set. Of the 30 trained models, MACCS-RF showed the best performance and thus was selected for analyses of the effects of CI and DS. Results showed that recall and F1 were significantly lower when training with the CI assays than with the DS assays. In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. This study also emphasized the importance of using appropriate evaluation metrics and tuning hyperparameters in model development.

Dose Assessment Following a 238 Pu Inhalation Incident at Los Alamos National Laboratory.

A glovebox breach at the plutonium facility at Los Alamos National Laboratory potentially exposed 15 individuals to 238 Pu aerosols. One of the individuals (P0) received two 1-g intravenous DTPA treatments, one on the day of the intake and another the following day. Several urine samples were collected from the individuals involved in the incident. Particle size analysis on the PPE and solubility analysis of the particles on a filter sample were conducted in vitro. The applicability of the results from the in vitro studies for dose assessment was questionable because of the effect of the cloth mask the workers were wearing for COVID-related protection. Based on several considerations, including the effect of cloth masks on the "effective" particle size inhaled and the analysis of fecal-to-urine ratio, the default Type M 1 μm AMAD model was used to estimate intakes and doses. Using the urinary excretion data collected after 100 d post last chelation treatment, the committed effective dose, E(50), for P0 was calculated to be 5.2 mSv. For all others, the bioassay data were consistent with no intakes or very small intakes [corresponding to E(50) less than 0.1 mSv].