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.

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.

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.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis.

Drug development involves the thorough assessment of the candidate's safety and efficacy. toxicology (IST) methods can contribute to the assessment, complementing and 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.

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.

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.

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 that they are reproducible.

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.

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 (APD). However, preparing such matrices can be computationally intensive for the method developers, limiting the range of simulated conditions.

Explicit meshfree 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 (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.

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.

New approach methods (NAMs) 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.

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 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.

Pharmacological inactivation of the prion protein by targeting a folding intermediate.

Recent computational advancements in the simulation of biochemical processes allow investigating the mechanisms involved in protein regulation with realistic physics-based models, at an atomistic level of resolution. These techniques allowed us to design a drug discovery approach, named Pharmacological Protein Inactivation by Folding Intermediate Targeting (PPI-FIT), based on the rationale of negatively regulating protein levels by targeting folding intermediates. Here, PPI-FIT was tested for the first time on the cellular prion protein (PrP), a cell surface glycoprotein playing a key role in fatal and transmissible neurodegenerative pathologies known as prion diseases.

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods.

Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes.

Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g.

Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays.

Many toxicological test methods, including assays of cell viability and function, require an evaluation of concentration-response data. This often involves curve fitting, and the resulting mathematical functions are then used to determine the concentration at which a certain deviation from the control value occurs (e.g.

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project.

Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to "similar" (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing.

Corrigendum to A high-throughput approach to identify specific neurotoxicants / developmental toxicants in human neuronal cell function assays.

In this manuscript, which appeared in ALTEX 35 , 235-253 ( doi:10.14573/altex.1712182 ), the Acknowledgements should read: This work was supported by the Land BW, the Doerenkamp-Zbinden Foundation, the DFG (RTG1331, KoRS-CB), the BMBF (NeuriTox), and it has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No.