Semi-volatile organic substances (SVOCs) including flame retardants and plasticizers may emit from products during their life cycle and pose health risks, such as endocrine disruption and neurodevelopmental effects. Cars, as enclosed spaces with SVOC-emitting materials, can contribute significantly to human exposure, especially for toddlers and professional drivers who may have higher risk. This study assessed exposure to a selection of SVOCs in car cabins for various scenarios, considering car cabin temperatures (25 °C or 60 °C), user physiology (adult/toddler), and time spent in cars (average user/professional driver). Scenarios included cooling dynamics in warm cars, assuming ventilation reduces cabin temperature from 60 °C to 25 °C within 5 min. The highest total exposure dose was estimated for di-(2-propylheptyl) phthalate (DPHP) and tris(1,3-dichloroisopropyl) phosphate (TDCIPP). Inhalation and air-to-skin contact were the dominant exposure routes for compounds like 2,2′-methylenebis (4-methyl-6-tert-butylphenol) (MBMBP), while ingestion of dust was significant for others like TDCIPP. Data collected from the literature on levels of SVOCs in private houses, offices and childcare facilities facilitated estimation of the contribution of cars to the total dose obtained throughout the day. Although toddlers and professional drivers had similar estimated exposure doses, despite differing times spent in cars, a first-tier risk assessment showed no immediate health concerns, with hazard quotients well below 1. However, refined models and further analysis for sensitive groups remain critical for a deeper analysis of risk

Effective chemical hazard labelling systems are essential for safeguarding human health and the environment as a result of widespread chemical use, and machine-learning models can be used to predict hazard labels efficiently and reduce the use of animal tests. This investigation shows the utility of N-grams and other fingerprint featurization procedures for predicting classification, labelling and packaging (CLP). Regulation H-statements, particularly in an ensemble (consensus) setting. Consensus modelling by class or Conformal Prediction median p-values seems to be particularly advantageous in order to obtain both high conformal prediction validity and efficiency as well as good balanced accuracy, sensitivity and specificity. Utilization of the N-grams allows handling of all symbols in SMILES strings including those related to metals and salts that may be important for the compounds to exhibit their experimental determined toxicities. The models developed in this study are efficient tools to access hazard classification H-statements of chemicals, which can be useful for chemical hazard assessment, read-across as well as risk management.

The demands on chemical industry to transform towards safety and sustainability will require multi-disciplinary research and development where experts on chemistry and chemical engineering, toxicology, ecotoxicology, and life cycle assessment collaborate to develop novel production methods, chemicals and materials. Here we summarise the results of the Mistra SafeChem programme which has yielded considerable output in the areas of catalysis/bio-catalysis, hazard screening for humans and ecosystems and life cycle assessment with chemical footprints, both as individual scientific achievements and as part of an integrated approach to assess safety and sustainability of novel chemicals and chemical synthesis processes, in chemical value chains and across collaborations between industry- academia/ industry-industry. The outcomes from the programme are summarised and discussed and experiences from dialogues on the future of safe and sustainable chemistry are presented

Many environmental toxicants can activate estrogen receptor α (ERα), disrupting normal endocrine function. While these activities are predicted across in silico, in vitro, and in vivo models, translating active concentrations between these systems remains challenging. We hypothesized that cellular uptake and the resulting free intracellular toxicant concentration could bridge this gap. Using cell-free (hER) and cell-based (ERα-CALUX cells) estrogen assays, we tested this hypothesis by determination of the free intracellular concentration available for binding to the intracellularly located ERα. Predictive modeling identified three classes of estrogenic chemicals from the ToxCast collection: bisphenols, parabens, and phthalates. Experimental data confirmed potency differences of up to 100-fold between the cell-free and cell-based models. Cellular toxicokinetic (TK) parameters, including cellular uptake and intracellular binding, were determined using computational and experimental methods. Incorporating experimental TK parameters significantly improved the correlation between ERα activities in the cell-free and cellular models (from r = 0.6230, P = 0.0989 without corrections to r = 0.8869, P = 0.0033 after corrections), and bridged the gap between the cell free and cell based assays. Both computational and experimental TK parameters varied widely across chemical classes and compounds. Correcting active concentrations for free intracellular levels enhanced assay correlations, with experimentally derived corrections showing the strongest improvement with r = 0.8869 (compared to the in silico derived corrections with r = 0.811). Our findings highlight the critical role of free intracellular concentration in determining the biological activity of estrogenic toxicants and emphasize its importance in accurately assessing their endocrine-disrupting potential.

This review article delves into the details of the 3R-Refinement principles as a vital framework for ethically sound rodent research laboratory. It highlights the core objective of the refinement protocol, namely, to enhance the well-being of laboratory animals while simultaneously improving the scientific validity of research outcomes. Through an exploration of key components of the refinement principles, the article outlines how these ethics should be implemented at various stages of animal experiments. It emphasizes the significance of enriched housing environments that reduce stress and encourage natural behaviors, non-restraint methods in handling and training, refined dosing and sampling techniques that prioritize animal comfort, the critical role of optimal pain management and the importance of regular animal welfare assessment in maintaining the rodents well-being. Additionally, the advantages of collaboration with animal care and ethics committees are also mentioned. The other half of the article explains the extensive benefits of the 3R-Refinement protocol such as heightened animal welfare, enhanced research quality, reduced variability, and positive feedback from researchers and animal care staff. Furthermore, it addresses avenues for promoting the adoption of the protocol, such as disseminating best practices, conducting training programs, and engaging with regulatory bodies. Overall, this article highlights the significance of 3R-Refinement protocol in aligning scientific advancement with ethical considerations along with shaping a more compassionate and responsible future for animal research.

Amide bond synthesis is ranked as the second most important challenge in key green chemistry research areas identified by the ACS Green Chemistry Institute. While developing more sustainable amide bond forming reactions has been in focus, significantly less attention has been given to human toxicity and environmental aspects of the underlying amine and acid substrates and their corresponding coupled products, a potentially important contribution to the overall sustainability of the amide-bond-forming reactions. Here, we explore biocatalytic amide bond formation from a safer-and-more-sustainable-by-design perspective in which commercially available amines and acids as well as their corresponding amide products were evaluated in silico based on potential human toxicity and environmental fate and exposure. This in silico filtering resulted in a panel of 188 amine and 54 acid building blocks that could be classified as safe, referred to herein as “safechems”. To enable couplings of safechems, we generated a panel of robust and promiscuous ancestral ATP-dependent amide bond synthetases (ABS) using McbA from Marinactinospora thermotolerans SCSIO 00652 as a template. Ancestral ABS enzymes exhibited complementary specificities in the coupling of a representative safechem subset of 17 amines and 16 acids while showing an increased thermostability of up to 20 °C compared to the extant biocatalyst. Finally, the pool of safechems and their corresponding amides were evaluated by USEtox (the UNEP-SETAC toxicity model), analysing not only the intrinsic properties of the compounds but evaluating their complete impact pathway including fate, exposure and effects. The amides were in general predicted as more toxic compared to the starting acids and amines through non-additive effects, emphasising that focusing on the toxicity of the building blocks alone is not sufficient to strive towards low human and ecotoxicity impact. Pursuing a safer and more sustainable by design perspective in the implementation of safechems did not prevent us from generating an array of novel products with potentially potent applications as exemplified here by enzymatic synthesis of substructures that are part of drug candidates for e.g. cancer treatment. 

In the reperfusion era, a new paradigm of treating patients with endovascular treatment (EVT) and neuroprotective drugs is emerging as a promising therapeutic option for patients with acute ischemic stroke (AIS). In this context, ApTOLL, a Toll-like receptor 4 (TLR4) antagonist with proven neuroprotective effect in preclinical models of stroke and a very good pharmacokinetic and safety profile in healthy volunteers, is a promising first-in-class aptamer with the potential to address this huge unmet need. This protocol establishes the clinical trial procedures to conduct a Phase Ib/IIa clinical study (APRIL) to assess ApTOLL tolerability, safety, pharmacokinetics, and biological effect in patients with AIS who are eligible for EVT. This will be a multicenter, double-blind, randomized, placebo-controlled, Phase Ib/IIa clinical study to evaluate the administration of ApTOLL together with EVT in patients with AIS. The study population will be composed of men and non-pregnant women with confirmed AIS with a <6h window from symptoms onset to ApTOLL/placebo administration. The trial is currently being conducted and is divided into two parts: Phase Ib and Phase IIa. In Phase Ib, 32 patients will be allocated to four dose ascending levels to select, based on safety criteria, the best two doses to be administered in the following Phase IIa in which 119 patients will be randomized to three arms of treatment (dose A, dose B, and placebo).

The rise of artificial intelligence (AI) based algorithms has gained a lot of interest in the pharmaceutical development field. Our study demonstrates utilization of traditional machine learning techniques such as random forest (RF), support-vector machine (SVM), extreme gradient boosting (XGBoost), deep neural network (DNN) as well as advanced deep learning techniques like gated recurrent unit-based DNN (GRU-DNN) and graph neural network (GNN), towards predicting human ether-á-go-go related gene (hERG) derived toxicity. Using the largest hERG dataset derived to date, we have utilized 203,853 and 87,366 compounds for training and testing the models, respectively. The results show that GNN, SVM, XGBoost, DNN, RF, and GRU-DNN all performed well, with validation set AUC ROC scores equals 0.96, 0.95, 0.95, 0.94, 0.94 and 0.94, respectively. The GNN was found to be the top performing model based on predictive power and generalizability. The GNN technique is free of any feature engineering steps while having a minimal human intervention. The GNN approach may serve as a basis for comprehensive automation in predictive toxicology. We believe that the models presented here may serve as a promising tool, both for academic institutes as well as pharmaceutical industries, in predicting hERG-liability in new molecular structures. 

An in silico method has been developed that permits the binary differentiation between pure liquids causing serious eye damage or eye irritation, and pure liquids with no need for such classification, according to the UN GHS system. The method is based on the finding that the Hansen Solubility Parameters (HSP) of a liquid are collectively important predictors for eye irritation. Thus, by applying a two-tier approach in which in silico predicted pKa values (firstly) and a trained model based solely on in silico-predicted HSP data (secondly) were used, we have developed, and validated, a fully in silico approach for predicting the outcome of a Draize test (in terms of UN GHS Cat. 1/Cat. 2A/Cat. 2B or UN GHS No Cat.) with high validation set performance (sensitivity = 0.846, specificity = 0.818, balanced accuracy = 0.832) using SMILES only. The method is applicable to pure non-ionic liquids with molecular weight below 500 g/mol, fewer than six hydrogen bond donors (e.g. nitrogen–hydrogen or oxygen–hydrogen bonds) and fewer than eleven hydrogen bond acceptors (e.g. nitrogen or oxygen atoms). Due to its fully in silico characteristics, this method can be applied to pure liquids that are still at the desktop design stage and not yet in production.