A comprehensive evaluation of spontaneous pelvic organ prolapse in rhesus macaques as an ideal model for the study of human pelvic organ prolapse.

Pelvic organ prolapse (POP) seriously affects a woman's quality of life, and the treatment complications are severe. Although new surgical treatments are being developed, the host tissue responses and safety need to be evaluated in preclinical trials. However, there is a lack of suitable animal models, as most quadrupeds exhibit different structural and pathological changes. In this study, 72 elderly rhesus macaques (Macaca mulatta) were physically examined, and the incidence of spontaneous POP was similar to that in humans. The vaginal wall from five control monkeys and four monkeys with POP were selected for further analysis. Verhoeff-van Gieson staining showed that elastin content decreased significantly in monkeys with POP compared with control samples. Immunohistological staining revealed that the smooth muscle bundles in monkey POP appeared disorganized, and the number of large muscle bundles decreased significantly. The collagen I/III ratio in monkey POP also significantly decreased, as revealed by Sirius Red staining. These histological and biochemical changes in monkeys with POP were similar to those in humans with POP. Moreover, we generated a single-cell transcriptomic atlas of the prolapsed monkey vagina. Cross-species analysis between humans and monkeys revealed a comparable cellular composition. Notably, a differential gene expression analysis determined that dysregulation of the extracellular matrix and an immune disorder were the conserved molecular mechanisms. The interplay between fibroblasts and macrophages contributed to human and monkey POP. Overall, this study represents a comprehensive evaluation of spontaneous POP in rhesus macaques and demonstrates that monkeys are a suitable animal model for POP research.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data.

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52-0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Evaluation of the herbicide glyphosate, (aminomethyl)phosphonic acid, and glyphosate-based formulations for genotoxic activity using in vitro assays.

Glyphosate, the most heavily used herbicide world-wide, is applied to plants in complex formulations that promote absorption. The National Toxicology Program reported in 1992 that glyphosate, administered to rats and mice at doses up to 50,000 ppm in feed for 13 weeks, showed little evidence of toxicity, and no induction of micronuclei was observed in the mice in this study. Subsequently, mechanistic studies of glyphosate and glyphosate-based formulations (GBFs) that have focused on DNA damage and oxidative stress suggest that glyphosate may have genotoxic potential. However, few of these studies directly compared glyphosate to GBFs, or effects among GBFs. To address these data gaps, we tested glyphosate, glyphosate isopropylamine (IPA), and (aminomethyl)phosphonic acid (AMPA, a microbial metabolite of glyphosate), 9 high-use agricultural GBFs, 4 residential-use GBFs, and additional herbicides (metolachlor, mesotrione, and diquat dibromide) present in some of the GBFs in bacterial mutagenicity tests, and in human TK6 cells using a micronucleus assay and a multiplexed DNA damage assay. Our results showed no genotoxicity or notable cytotoxicity for glyphosate or AMPA at concentrations up to 10 mM, while all GBFs and herbicides other than glyphosate were cytotoxic, and some showed genotoxic activity. An in vitro to in vivo extrapolation of results for glyphosate suggests that it is of low toxicological concern for humans. In conclusion, these results demonstrate a lack of genotoxicity for glyphosate, consistent with observations in the NTP in vivo study, and suggest that toxicity associated with GBFs may be related to other components of these formulations.

A Pragmatic Framework for the Application of New Approach Methodologies in One Health Toxicological Risk Assessment.

Globally, industries and regulatory authorities are faced with an urgent need to assess the potential adverse effects of chemicals more efficiently by embracing new approach methodologies (NAMs). NAMs include cell and tissue methods (in vitro), structure-based/toxicokinetic models (in silico), methods that assess toxicant interactions with biological macromolecules (in chemico), and alternative models. Increasing knowledge on chemical toxicokinetics (what the body does with chemicals) and toxicodynamics (what the chemicals do with the body) obtained from in silico and in vitro systems continues to provide opportunities for modernizing chemical risk assessments. However, directly leveraging in vitro and in silico data for derivation of human health-based reference values has not received regulatory acceptance due to uncertainties in extrapolating NAM results to human populations, including metabolism, complex biological pathways, multiple exposures, interindividual susceptibility and vulnerable populations. The objective of this article is to provide a standardized pragmatic framework that applies integrated approaches with a focus on quantitative in vitro to in vivo extrapolation (QIVIVE) to extrapolate in vitro cellular exposures to human equivalent doses from which human reference values can be derived. The proposed framework intends to systematically account for the complexities in extrapolation and data interpretation to support sound human health safety decisions in diverse industrial sectors (food systems, cosmetics, industrial chemicals, pharmaceuticals etc.). Case studies of chemical entities, using new and existing data, are presented to demonstrate the utility of the proposed framework while highlighting potential sources of human population bias and uncertainty, and the importance of Good Method and Reporting Practices.

Interface Modulation of Metal Sulfide Anodes for Long-Cycle-Life Sodium-Ion Batteries.

Although studies of transition metal sulfides (TMS) as anode materials for sodium-ion batteries are extensively reported, the short cycle life is still a thorny problem that impedes their practical application. In this work, a new capacity fading mechanism of the TMS electrodes is demonstrated; that is, the parasitic reaction between electrolyte anions (i.e., ClO4 - ) and metal sulfides yields non-conductive and unstable solid-electrolyte interphase (SEI) and meanwhile, corrosively turns metal sulfides into less-active oxides. This knowledge guides the development of an electrochemical strategy to manipulate the anion decomposition and construct a stable interface that prevents extensive parasitic reactions. It is shown that introducing sodium nitrate to the electrolyte radically changes the Na+ solvation structure by populating nitrate ions in the first solvation sheath, generating a stable and conductive SEI layer containing both Na3 N and NaF. The optimized interface enables an iron sulfide anode to stably cycle for over 2000 cycles with negligible capacity loss, and a similar enhancement in cycle performance is demonstrated on a number of other metal sulfides. This work discloses metal sulfides' cycling failure mechanism from a unique perspective and highlights the critical importance of manipulating the interface chemistry in sodium-ion batteries.

Surface-redox sodium-ion storage in anatase titanium oxide.

Sodium-ion storage technologies are promising candidates for large-scale grid systems due to the abundance and low cost of sodium. However, compared to well-understood lithium-ion storage mechanisms, sodium-ion storage remains relatively unexplored. Herein, we systematically determine the sodium-ion storage properties of anatase titanium dioxide (TiO2(A)). During the initial sodiation process, a thin surface layer (~3 to 5 nm) of crystalline TiO2(A) becomes amorphous but still undergoes Ti4+/Ti3+ redox reactions. A model explaining the role of the amorphous layer and the dependence of the specific capacity on the size of TiO2(A) nanoparticles is proposed. Amorphous nanoparticles of ~10 nm seem to be optimum in terms of achieving high specific capacity, on the order of 200 mAh g-1, at high charge/discharge rates. Kinetic studies of TiO2(A) nanoparticles indicate that sodium-ion storage is due to a surface-redox mechanism that is not dependent on nanoparticle size in contrast to the lithiation of TiO2(A) which is a diffusion-limited intercalation process. The surface-redox properties of TiO2(A) result in excellent rate capability, cycling stability and low overpotentials. Moreover, tailoring the surface-redox mechanism enables thick electrodes of TiO2(A) to retain high rate properties, and represents a promising direction for high-power sodium-ion storage.

Application of an Accessible Interface for Pharmacokinetic Modeling and In Vitro to In Vivo Extrapolation.

Regulatory toxicology testing has traditionally relied on in vivo methods to inform decision-making. However, scientific, practical, and ethical considerations have led to an increased interest in the use of in vitro and in silico methods to fill data gaps. While in vitro experiments have the advantage of rapid application across large chemical sets, interpretation of data coming from these non-animal methods can be challenging due to the mechanistic nature of many assays. In vitro to in vivo extrapolation (IVIVE) has emerged as a computational tool to help facilitate this task. Specifically, IVIVE uses physiologically based pharmacokinetic (PBPK) models to estimate tissue-level chemical concentrations based on various dosing parameters. This approach is used to estimate the administered dose needed to achieve in vitro bioactivity concentrations within the body. IVIVE results can be useful to inform on metrics such as margin of exposure or to prioritize potential chemicals of concern, but the PBPK models used in this approach have extensive data requirements. Thus, access to input parameters, as well as the technical requirements of applying and interpreting models, has limited the use of IVIVE as a routine part of in vitro testing. As interest in using non-animal methods for regulatory and research contexts continues to grow, our perspective is that access to computational support tools for PBPK modeling and IVIVE will be essential for facilitating broader application and acceptance of these techniques, as well as for encouraging the most scientifically sound interpretation of in vitro results. We highlight recent developments in two open-access computational support tools for PBPK modeling and IVIVE accessible via the Integrated Chemical Environment (https://ice.ntp.niehs.nih.gov/), demonstrate the types of insights these tools can provide, and discuss how these analyses may inform in vitro-based decision making.

Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues.

BACKGROUND: The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOXqP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOXqP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues. METHODS: VPA and a series of structural analogues were tested with the devTOXqP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses. RESULTS: The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data. CONCLUSIONS: This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOXqP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

An Ultrahigh-Power Mesocarbon Microbeads|Na+ -Diglyme|Na3 V2 (PO4 )3 Sodium-Ion Battery.

Sodium-ion batteries (SIBs) show practical applications in large-scale energy storage systems. But, their power density is limited by the sluggish Na+ diffusion into the cathode and anode materials. Herein, the authors demonstrate a prototype of ultrahigh power SIB, consisting of the high-rate Na3 V2 (PO4 )3 (NVP) cathode, graphite-type mesocarbon microbeads (MCMB) anode, and Na+ -diglyme electrolyte. It is found that the overpotential of the NVP cathode obeys the Ohmic rule. Thus, the as-synthesized NVP@C@carbon nanotubes (CNTs) cathode with the high conductive CNTs networks displays high electronic conductivity, reducing the overpotential and charge transfer resistances and leading to the remarkable rate capability over 1000C. For the MCMB anode, the initial [Na-diglyme]+ co-intercalation step is pseudocapacitive dominated, and then the expanded graphite's layers ensure the subsequent fast ions diffusion. The rapid (de)intercalation kinetics in between the cathode and anode are well-matched. Thus, the assembled MCMB|1 m NaPF6 in diglyme|NVP@C@CNTs full-cell SIB delivers the energy density of 88 Wh kg-1 at the high power density of ≈10 kW kg-1 . Even at the ultrahigh power density of 23 kW kg-1 , an energy density of 58 Wh kg-1 is obtained. The encouraging results of the full cell will promote the development of high-power SIB for large-scale applications in the future.

U.S. Federal Agency interests and key considerations for new approach methodologies for nanomaterials.

Engineered nanomaterials (ENMs) come in a wide array of shapes, sizes, surface coatings, and compositions, and often possess novel or enhanced properties compared to larger sized particles of the same elemental composition. To ensure the safe commercialization of products containing ENMs, it is important to thoroughly understand their potential risks. Given that ENMs can be created in an almost infinite number of variations, it is not feasible to conduct in vivo testing on each type of ENM. Instead, new approach methodologies (NAMs) such as in vitro or in chemico test methods may be needed, given their capacity for higher throughput testing, lower cost, and ability to provide information on toxicological mechanisms. However, the different behaviors of ENMs compared to dissolved chemicals may challenge safety testing of ENMs using NAMs. In this study, member agencies within the Interagency Coordinating Committee on the Validation of Alternative Methods were queried about what types of ENMs are of agency interest and whether there is agency-specific guidance for ENM toxicity testing. To support the ability of NAMs to provide robust results in ENM testing, two key issues in the usage of NAMs, namely dosimetry and interference/bias controls, are thoroughly discussed.

Exploring in vitro to in vivo extrapolation for exposure and health impacts of e-cigarette flavor mixtures.

In vitro to in vivo extrapolation (IVIVE) leverages in vitro biological activities to predict corresponding in vivo exposures, therefore potentially reducing the need for animal safety testing that are traditionally performed to support the hazard and risk assessment. Interpretation of IVIVE predictions are affected by various factors including the model type, exposure route and kinetic assumptions for the test article, and choice of in vitro assay(s) that are relevant to clinical outcomes. Exposure scenarios are further complicated for mixtures where the in vitro activity may stem from one or more components in the mixture. In this study, we used electronic cigarette (EC) aerosols, a complex mixture, to explore impacts of these factors on the use of IVIVE in hazard identification, using open-source pharmacokinetic models of varying complexity and publicly available data. Results suggest in vitro assay selection has a greater impact on exposure estimates than modeling approaches. Using cytotoxicity assays, high exposure estimates (>1000 EC cartridges (pods) or > 700 mL EC liquid per day) would be needed to obtain the in vivo plasma levels that are corresponding to in vitro assay data, suggesting acute toxicity would be unlikely in typical usage scenarios. When mechanistic (Tox21) assays were used, the exposure estimates were much lower for the low end, but the range of exposure estimate became wider across modeling approaches. These proof-of-concept results highlight challenges and complexities in IVIVE for mixtures.

Integrating endocrine-related health effects into comparative human toxicity characterization.

Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.

Evaluation of Inhalation Exposures and Potential Health Impacts of Ingredient Mixtures Using in vitro to in vivo Extrapolation.

In vitro methods offer opportunities to provide mechanistic insight into bioactivity as well as human-relevant toxicological assessments compared to animal testing. One of the challenges for this task is putting in vitro bioactivity data in an in vivo exposure context, for which in vitro to in vivo extrapolation (IVIVE) translates in vitro bioactivity to clinically relevant exposure metrics using reverse dosimetry. This study applies an IVIVE approach to the toxicity assessment of ingredients and their mixtures in e-cigarette (EC) aerosols as a case study. Reported in vitro cytotoxicity data of EC aerosols, as well as in vitro high-throughput screening (HTS) data for individual ingredients in EC liquids (e-liquids) are used. Open-source physiologically based pharmacokinetic (PBPK) models are used to calculate the plasma concentrations of individual ingredients, followed by reverse dosimetry to estimate the human equivalent administered doses (EADs) needed to obtain these plasma concentrations for the total e-liquids. Three approaches (single actor approach, additive effect approach, and outcome-oriented ingredient integration approach) are used to predict EADs of e-liquids considering differential contributions to the bioactivity from the ingredients (humectant carriers [propylene glycol and glycerol], flavors, benzoic acid, and nicotine). The results identified critical factors for the EAD estimation, including the ingredients of the mixture considered to be bioactive, in vitro assay selection, and the data integration approach for mixtures. Further, we introduced the outcome-oriented ingredient integration approach to consider e-liquid ingredients that may lead to a common toxicity outcome (e.g., cytotoxicity), facilitating a quantitative evaluation of in vitro toxicity data in support of human risk assessment.

Ultrastable Sodium-Sulfur Batteries without Polysulfides Formation Using Slit Ultramicropore Carbon Carrier.

The formation of the soluble polysulfides (Na2S n , 4 ≤ n ≤ 8) causes poor cycling performance for room temperature sodium-sulfur (RT Na-S) batteries. Moreover, the formation of insoluble polysulfides (Na2S n , 2 ≤ n < 4) can slow down the reaction kinetics and terminate the discharge reaction before it reaches the final product. In this work, coffee residue derived activated ultramicroporous coffee carbon (ACC) material loading with small sulfur molecules (S2-4) as cathode material for RT Na-S batteries is reported. The first principle calculations indicate the space confinement of the slit ultramicropores can effectively suppress the formation of polysulfides (Na2S n , 2 ≤ n ≤ 8). Combining with in situ UV/vis spectroscopy measurements, one-step reaction RT Na-S batteries with Na2S as the only and final discharge product without polysulfides formation are demonstrated. As a result, the ultramicroporous carbon loaded with 40 wt% sulfur delivers a high reversible specific capacity of 1492 mAh g-1 at 0.1 C (1 C = 1675 mA g-1). When cycled at 1 C rate, the carbon-sulfur composite electrode exhibits almost no capacity fading after 2000 cycles with 100% coulombic efficiency, revealing excellent cycling stability and reversibility. The superb cycling stability and rate performance demonstrate ultramicropore confinement can be an effective strategy to develop high performance cathode for RT Na-S batteries.

An integrated chemical environment with tools for chemical safety testing.

Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.

Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.

BACKGROUND: To effectively incorporate in vitro data into regulatory use, confidence must be established in the quantitative extrapolation of in vitro activity to relevant end points in animals or humans. OBJECTIVE: Our goal was to evaluate and optimize in vitro to in vivo extrapolation (IVIVE) approaches using in vitro estrogen receptor (ER) activity to predict estrogenic effects measured in rodent uterotrophic studies. METHODS: We evaluated three pharmacokinetic (PK) models with varying complexities to extrapolate in vitro to in vivo dosimetry for a group of 29 ER agonists, using data from validated in vitro [U.S. Environmental Protection Agency (U.S. EPA) ToxCast™ ER model] and in vivo (uterotrophic) methods. In vitro activity values were adjusted using mass-balance equations to estimate intracellular exposure via an enrichment factor (EF), and steady-state model calculations were adjusted using fraction of unbound chemical in the plasma ([Formula: see text]) to approximate bioavailability. Accuracy of each model-adjustment combination was assessed by comparing model predictions with lowest effect levels (LELs) from guideline uterotrophic studies. RESULTS: We found little difference in model predictive performance based on complexity or route-specific modifications. Simple adjustments, applied to account for in vitro intracellular exposure (EF) or chemical bioavailability ([Formula: see text]), resulted in significant improvements in the predictive performance of all models. CONCLUSION: Computational IVIVE approaches accurately estimate chemical exposure levels that elicit positive responses in the rodent uterotrophic bioassay. The simplest model had the best overall performance for predicting both oral (PPK_EF) and injection (PPK_[Formula: see text]) LELs from guideline uterotrophic studies, is freely available, and can be parameterized entirely using freely available in silico tools. https://doi.org/10.1289/EHP1655.

In vitro to in vivo extrapolation for high throughput prioritization and decision making.

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

Differential modulation of FXR activity by chlorophacinone and ivermectin analogs.

Chemicals that alter normal function of farnesoid X receptor (FXR) have been shown to affect the homeostasis of bile acids, glucose, and lipids. Several structural classes of environmental chemicals and drugs that modulated FXR transactivation were previously identified by quantitative high-throughput screening (qHTS) of the Tox21 10K chemical collection. In the present study, we validated the FXR antagonist activity of selected structural classes, including avermectin anthelmintics, dihydropyridine calcium channel blockers, 1,3-indandione rodenticides, and pyrethroid pesticides, using in vitro assay and quantitative structural-activity relationship (QSAR) analysis approaches. (Z)-Guggulsterone, chlorophacinone, ivermectin, and their analogs were profiled for their ability to alter CDCA-mediated FXR binding using a panel of 154 coregulator motifs and to induce or inhibit transactivation and coactivator recruitment activities of constitutive androstane receptor (CAR), liver X receptor alpha (LXRα), or pregnane X receptor (PXR). Our results showed that chlorophacinone and ivermectin had distinct modes of action (MOA) in modulating FXR-coregulator interactions and compound selectivity against the four aforementioned functionally-relevant nuclear receptors. These findings collectively provide mechanistic insights regarding compound activities against FXR and possible explanations for in vivo toxicological observations of chlorophacinone, ivermectin, and their analogs.

The Use of Signal-Transduction and Metabolic Pathways to Predict Human Disease Targets from Electric and Magnetic Fields Using in vitro Data in Human Cell Lines.

Using in vitro data in human cell lines, several research groups have investigated changes in gene expression in cellular systems following exposure to extremely low frequency (ELF) and radiofrequency (RF) electromagnetic fields (EMF). For ELF EMF, we obtained five studies with complete microarray data and three studies with only lists of significantly altered genes. Likewise, for RF EMF, we obtained 13 complete microarray datasets and 5 limited datasets. Plausible linkages between exposure to ELF and RF EMF and human diseases were identified using a three-step process: (a) linking genes associated with classes of human diseases to molecular pathways, (b) linking pathways to ELF and RF EMF microarray data, and (c) identifying associations between human disease and EMF exposures where the pathways are significantly similar. A total of 60 pathways were associated with human diseases, mostly focused on basic cellular functions like JAK-STAT signaling or metabolic functions like xenobiotic metabolism by cytochrome P450 enzymes. ELF EMF datasets were sporadically linked to human diseases, but no clear pattern emerged. Individual datasets showed some linkage to cancer, chemical dependency, metabolic disorders, and neurological disorders. RF EMF datasets were not strongly linked to any disorders but strongly linked to changes in several pathways. Based on these analyses, the most promising area for further research would be to focus on EMF and neurological function and disorders.