Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Chemical Characterization, Bioactivity and Toxicity of European Flora Plant Extracts in Search for Potential Natural Origin Preservatives

Consumer demand for natural and healthier products has led to an increasing interest in the bioactive and therapeutic properties of plant extracts. In this study, we evaluated the phenolic compounds profile, bioactivities, and toxicities of plant extracts from eight European flora species, including Calendula officinalis L., Calluna vulgaris (L.) Hull, Hippophae rhamnoides L., Juglans regia L., Mentha cervina L., Rubus idaeus L., Sambucus nigra L., and Vitis vinifera L. The aim was to identify potential preservatives of natural origin. Phenolic compounds were identified by HPLC-DAD-ESI-MS. Caffeic acid derivatives, ellagitannins, flavonols, and flavones were the major phenolic compounds identified. The total phenolic content varied from 16.0 ± 0.2 (V. vinifera) to 123 ± 2 mg/g (H. rhamnoides) of dry extract. All extracts showed antioxidant potential and exhibited activity against some of the microorganisms tested. S. nigra showed the highest activity in the inhibition of oxidative hemolysis (OxHLIA) assay and H. rhamnoides, notably, had the lowest IC50 values in TBARS and DPPH assays, as well as the lowest minimum inhibitory concentration (MIC) values. Regarding in vitro cytotoxicity, in tumor and non-tumor cell lines, although some extracts revealed toxicity against normal cells, it was found that the samples C. vulgaris, V. vinifera and R. idaeus might be used against tumor cells since the active concentration is much lower than the one causing toxicity. In vivo acute toxicity tests using Artemia franciscana suggest low toxicity for most extracts, with LC50 > 400 mg/L. These results showed the potential of the studied extracts as natural preservatives, given their richness in compounds with bioactive properties, highlight their potential value to the production chain.

QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis

Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis), avermectins are outside the applicability domain of these models. The influence of the molecular structures of various organic compounds on the acute toxicity towards T. pyriformis was studied using the OCHEM web platform (https://ochem.eu). A data set of 1792 toxicants was used to create models. The QSAR (Quantitative Structure-Activity Relationship) models were developed using the molecular descriptors Dragon, ISIDA, CDK, PyDescriptor, alvaDesc, and SIRMS and machine learning methods, such as Least Squares Support Vector Machine and Transformer Convolutional Neural Network. The HYBOT descriptors and Random Forest were used for a comparative QSAR investigation. Since the best predictive ability was demonstrated by the Transformer Convolutional Neural Network model, it was used to predict the toxicity of individual avermectins towards T. pyriformis. During a structural interpretation of the developed QSAR model, we determined the significant molecular transformations that increase and decrease the acute toxicity of organic compounds.

Simplex representation of molecular structure as universal QSAR/QSPR tool

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.

The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds

Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.