California dreaming: Why environmental justice is integral to the success of climate change policy

In the realm of climate policy, issues of environmental justice (EJ) are often treated as second-order affairs compared to overarching sustainability goals. We argue that EJ is in fact critical to successfully addressing our national and global climate challenges; indeed, centering equity amplifies the voices of the diverse constituencies most impacted by climate change and that are needed to build successful coalitions that shape and advance climate change policy. We illustrate this perspective by highlighting the experience of California and the contentious processes by which EJ became integrated into the state's climate action efforts. We examine the achievements and shortcomings of California's commitment to climate justice and discuss how lessons from the Golden State are influencing the evolution of current federal climate change policy.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project

BACKGROUND

Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored.

METHODS

PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software.

DISCUSSION

Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

Climate Change, Migration, and Health Disparities at and Beyond the US-Mexico Border

This JAMA Insights in the Climate Change and Health series discusses the increase in extreme weather events caused by climate change and how these events bring about increased migration due to effects on water availability, food access, and rates of endemic diseases.

Usage of model combination in computational toxicology

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells

Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

Making in silico predictive models for toxicology FAIR

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

BACKGROUND AND OBJECTIVE

In silico prediction of drug-induced ventricular arrhythmia often requires computationally intensive simulations, making its application tedious and non-interactive. This inconvenience can be mitigated using matrices of precomputed simulation results, allowing instantaneous computation of biomarkers such as action potential duration at 90% of the repolarisation (APD₉₀). However, preparing such matrices can be computationally intensive for the method developers, limiting the range of simulated conditions. In this work, we aim to optimise the generation of these matrices so that they can be obtained with less effort and for a broader range of input values.

METHODS

Machine learning methods were applied, building models trained with only a small fraction of the originally simulated results. The predictive performances of the models were assessed by comparing their predicted values with the actual simulation results, using percentual mean absolute error and mean relative error, as well as the percentage of data with a relative error below 5%.

RESULTS

Our method obtained highly accurate estimations of the original values, leading to a nearly one hundred-fold decrease in computation time. This method also allows precomputing more complex matrices, describing the effect of more ion channels on the APD_90. The best results were obtained by applying Support Vector Machine models, which yielded errors below 1% in most cases. This approach was further validated by predicting the APD₉₀ of a set of 12 CiPA compounds and exporting the optimal settings for predicting APD₉₀ using a different set of ion channels, always with satisfactory results.

CONCLUSIONS

The proposed method effectively reduces the computational effort required to generate matrices of precomputed electrophysiological simulation values. The same approach can be applied in other fields where computationally costly simulations are applied repeatedly using slightly different input values.

Family Matters: Modeling Naturalization Propensities in the United States

Despite the benefits of gaining citizenship, many eligible immigrants in the United States are not naturalizing. In this article, we examine factors that lead to naturalization in the United States, finding that immigrants’ pathways to citizenship are simultaneously shaped by individual characteristics, place-based attributes, and family dynamics. Of notable significance, and largely omitted from previous empirical work on naturalization, we find that having a naturalized spouse prior to one’s own naturalization is associated with a higher probability of naturalization, whereas being married to an undocumented immigrant reduces the probability of naturalization. Similarly, having a naturalized adult in the family other than a spouse improves the odds of naturalization, but having an undocumented family member other than a spouse reduces the odds. These findings suggest that while eligible immigrants with naturalized family members are more likely to improve their access to naturalization through pooled resources and increased information sharing, eligible immigrants with undocumented family members are more likely to avoid the naturalization process entirely, likely due to chilling effects from immigrant enforcement and policies that target close ties with liminal legality. These findings suggest that immigrants’ access to citizenship could be improved by (1) reaching immigrants who are the first in their families to naturalize and (2) improving the context of reception for undocumented immigrants and mixed-status families. More broadly, while individual factors play a role in naturalization, complex contextual factors, including place and family, shape immigrants’ pathways to citizenship and provide opportunities for new policies to promote immigrant integration.

Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry

The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company-academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.

Explicit meshfree u - p w solution of the dynamic Biot formulation at large strain

In this paper, an efficient and robust methodology to simulate saturated soils subjected to low-medium frequency dynamic loadings under large deformation regime is presented. The coupling between solid and fluid phases is solved through the dynamic reduced formulation u - p w (solid displacement - pore water pressure) of the Biot's equations. The additional novelty lies in the employment of an explicit two-steps Newmark predictor-corrector time integration scheme that enables accurate solutions of related geomechanical problems at large strain without the usually high computational cost associated with the implicit counterparts. Shape functions based on the elegant Local Maximum Entropy approach, through the Optimal Transportation Meshfree framework, are considered to solve numerically different dynamic problems in fluid saturated porous media.

New approach methods supporting read-across: Two neurotoxicity AOP-based IATA case studies

Read-across approaches are considered key in moving away from in vivo animal testing towards addressing data-gaps using new approach methods (NAMs). Ample successful examples are still required to substantiate this strategy. Here we present and discuss the learnings from two OECD IATA endorsed read-across case studies. They involve two classes of pesticides -rotenoids and strobilurins- each having a defined mode-of-action that is assessed for its neurological hazard by means of an AOP-based testing strategy coupled to toxicokinetic simulations of human tissue concentrations. The endpoint in question is potential mitochondrial respiratory chain mediated neurotoxicity, specifically through inhibition of complex I or III. An AOP linking inhibition of mitochondrial respiratory chain complex I to the degeneration of dopaminergic neurons formed the basis for both cases, but was deployed in two different regulatory contexts. The two cases also exemplify several different read-across concepts: analogue versus category approach, consolidated versus putative AOP, positive versus negative prediction (i.e., neurotoxicity versus low potential for neurotoxicity), and structural versus biological similarity. We applied a range of NAMs to explore the toxicodynamic properties of the compounds, e.g., in silico docking as well as in vitro assays and readouts -including transcriptomics- in various cell systems, all anchored to the relevant AOPs. Interestingly, although some of the data addressing certain elements of the read-across were associated with high uncertainty, their impact on the overall read-across conclusion remained limited. Coupled to the elaborate regulatory review that the two cases underwent, we propose some generic learnings of AOP-based testing strategies supporting read-across.

Flame: an open source framework for model development, hosting, and usage in production environments

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Guidelines for FAIR sharing of preclinical safety and off-target pharmacology data

Pre-competitive data sharing can offer the pharmaceutical industry significant benefits in terms of reducing the time and costs involved in getting a new drug to market through more informed testing strategies and knowledge gained by pooling data. If sufficient data is shared and can be co-analyzed, then it can also offer the potential for reduced animal usage and improvements in the in silico prediction of toxicological effects. Data sharing benefits can be further enhanced by applying the FAIR Guiding Principles, reducing time spent curating, transforming and aggregating datasets and allowing more time for data mining and analysis. We hope to facilitate data sharing by other organizations and initiatives by describing lessons learned as part of the Enhancing TRANslational SAFEty Assessment through Integrative Knowledge Management (eTRANSAFE) project, an Innovative Medicines Initiative (IMI) partnership which aims to integrate publicly available data sources with proprietary preclinical and clinical data donated by pharmaceutical organizations. Methods to foster trust and overcome non-technical barriers to data sharing such as legal and IPR (intellectual property rights) are described, including the security requirements that pharmaceutical organizations generally expect to be met. We share the consensus achieved among pharmaceutical partners on decision criteria to be included in internal clearance pro­cedures used to decide if data can be shared. We also report on the consensus achieved on specific data fields to be excluded from sharing for sensitive preclinical safety and pharmacology data that could otherwise not be shared.

Tissue-Sparing Outcome of Mohs Micrographic Surgery in Squamous Cell Carcinomas

BACKGROUND

Micrographic Mohs Surgery (MMS) offers the highest cure rates and healthy skin tissue sparing effect compared with standard excision.

OBJECTIVE

To quantify the tissue-sparing properties of MMS in squamous cell carcinoma (SCC) in comparison with standard excision (SE).

METHODS

A bidirectional, descriptive study, including 94 cases of SCC, was performed, on patients with histologic diagnosis of SCC (in situ, well differentiated, moderately differentiated, and undifferentiated), that where operated with MMS between 2013 and 2018 at Hospital de Clínicas Dr. Manuel Quintela in Montevideo, Uruguay. Tumor size and defect area after MMS were measured in 2 perpendicular directions. The suspected defect area was calculated with standard excision using a 4-mm margin for low risk lesions and a 10-mm margin for high risk lesions. The primary outcome of this study was the size of the defect area post MMS compared with the calculated defect area with standard excision.

RESULTS

The median tumor size was 1,41mm², and the median defect size after MMS was 4,12mm². The median defect size calculated for standard surgical excision was 8,36mm².

LIMITATIONS

We do not use all National Comprehensive Cancer Network (NCCN) criteria. We define low and high risk lesions just taking into account anatomical location, size, histopathology and whether it was a primary or recurrent tumor.

CONCLUSION

Our results show that MMS has a tissue-sparing effect of at least 52% compared to SE.

In vitro and in vivo antimicrobial activity of sodium colistimethate and amikacin-loaded nanostructured lipid carriers (NLC)

Sodium colistimethate (SCM) and amikacin (AMK) are among the few antibiotics effective against resistant P. aeruginosa, K. pneumoniae and A. baumannii; however, their toxicity severely limits their use. Enclosing antibiotics into nanostructured lipid carriers (NLC) might decrease drug toxicity and improve antibiotic disposition. In this work, SCM or AMK was loaded into different NLC formulations, through high pressure homogenization, and their in vitro and in vivo effectiveness was analyzed. The encapsulation process did not reduce drug effectiveness since in vitro SCM-NLC and AMK-NLC drug activity was equal to that of the free drugs. As cryoprotectant, trehalose showed better properties than dextran. Instead, positive chitosan coating was discarded due to its limited cost-efficiency. Finally, the in vivo study in acute pneumonia model revealed that intraperitoneal administration was superior to the intramuscular route and confirmed that (-) SCM-NLC with trehalose, was the most suitable formulation against an extensively drug-resistant A. baumannii strain.

0088 Gravity Dreams Following a Virtual Reality Flight Simulation

Introduction

Flying is a prevalent but infrequent experience in dreams. Despite a broad interest in such unique dream experiences, there is still no experimental procedure for reliably inducing them. Our study aimed 1) to induce flying dreams in the laboratory using virtual reality (VR), 2) to examine phenomenological correlates of flying dreams, such as lucidity and emotions and 3) to investigate the dynamics of dreamed gravity imagery in relation to participant state and trait factors.

Methods

A total of 137 healthy participants (24.01±4.03 y.o.; 85 F; 52 M) took part in a custom-built immersive VR task in which they learn how to ‘fly’ as precisely and quickly as possible, engaging vestibular, motor and visuo-spatial systems. Dreams were collected a) from home dream journals for 5 days before and 10 days after the laboratory VR task and b) after a 90-min morning nap in laboratory. Dream reports were scored by 2 independent judges for flying and other gravity-related imagery. Linear mixed models statistics were used to compare dreams from this cohort with a separate control cohort (N=52) that followed a similar protocol in the same lab but did not undertake a virtual flying task.

Results

The VR task successfully increased the likelihood of experiencing flying in dreams from both the laboratory nap (7.1%) and the following night (10.6%) compared to baseline (1.3%) and the control cohort on those days (Lab: 2.4%; following night: 0%). In contrast, the occurrence of other gravity imagery showed no differences. Flying dreams were altered qualitatively, exhibiting higher levels of lucid-control and emotional intensity after VR exposure. Moreover, various factors such as sex, prior dream experiences and sensory immersion in VR differentially modulated flying dream induction.

Conclusion

Our findings provide both quantitative and qualitative insights into flying dreams that may facilitate understanding of these typical dream experiences and future developments in dream flight-induction technologies.

Support

Natural Sciences and Engineering Research Council of Canada