Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models

Water Research, Journal Year: 2024, Volume and Issue: 256, P. 121643 - 121643

Published: April 18, 2024

Language: Английский

---

Novel Quantitative Structure–Activity Relationship Tox21 Techniques for Combined Toxicity Prediction

Published: Jan. 14, 2025

Humans and animals are exposed to mixtures of various environmental pollution; however, there is limited toxicity data for chemical mixtures, the traditional methodologies evaluating effects including concentration addition (CA) independent action (IA) models have been increasingly challenged replaced. The computational approaches quantitative structure–activity/property/toxicity relationship (QSAR/QSPR/QSTR) already proven efficient alternatives assessing mixtures. In this chapter, QSAR predicting endocrine-disrupting activities acute toxicities, as well based on machine-learning method, biomolecular interaction networks, toxicokinetic–toxicodynamic studies, high-throughput transcriptomics approach, geospatial modeling approach reviewed. prediction needs be integrated a comprehensive systems-level analysis identify their effect by integrating bioactivity bioactivity, targets pathways, gene expression, protein interactions, localized exposure data, which will help provide solid foundation analyses.

Language: Английский

---

Combined Molecular Toxicity Mechanism of Emerging Pollutant Mixtures

Published: Jan. 14, 2025

Pharmaceuticals and personal care products (PPCPs) compounds are vital components of daily life modern health care. Over the past 20–25 years, a substantial amount work has been done to elucidate occurrence, bioaccumulation, fate, risks PPCPs in environment. The ubiquity most environment their potential deleterious effects on ecological human have engaged community scientists government regulators. Nontarget organisms continuously exposed multiple PPCP compounds, evidence for underestimated toxicity from such mixtures is mounting. Yet, increasing research around world still focuses overwhelmingly molecular mechanism single PPCP, scientific about combined limited date. This chapter provides an overview "state-of-the-art" literature data mechanisms PPCPs, with special emphasis mixture scenarios.

Language: Английский

---

Prediction and mechanism of combined toxicity of surfactants and antibiotics in aquatic environment based on in silico method

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: unknown, P. 137390 - 137390

Published: Jan. 1, 2025

Language: Английский

---

Fluorescence sensor array based on covalent organic frameworks and QSAR study for identification of organic pesticides

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 112986 - 112986

Published: Feb. 1, 2025

Language: Английский

---

Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121089 - 121089

Published: Feb. 1, 2025

Language: Английский

---

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 489, P. 137650 - 137650

Published: Feb. 22, 2025

Language: Английский

---

Predictive Tox-21 Methods for Assessing Emerging Pollutants in the Marine Environment

Published: Jan. 1, 2025

Language: Английский

---

Predicting the Time-Dependent Toxicities of Binary Mixtures of Five Antibiotics to Vibrio qinghaiensis sp.-Q67 Based on the QSAR Model

Environment & Health, Journal Year: 2024, Volume and Issue: 2(7), P. 465 - 473

Published: April 17, 2024

Antibiotics may be exposed in a mixed state natural environments. The toxicity of antibiotic mixtures exhibits time-dependent characteristics, and data on the is also relatively lacking. In this study, toxicities 45 binary composed five antibiotics were investigated against Vibrio qinghaiensis sp.-Q67 (Q67) at multiple exposure times (4, 6, 8, 10, 12 h). Quantitative structure–activity relationship (QSAR) models developed for predicting mixtures. results showed that best QSAR presented coefficient determination (R2) (0.818–0.913) explained variance prediction leave-one-out (Q2LOO) (0.781–0.894) predictive ability (Q2F1, Q2F2, Q2F3 > 0.682, concordance correlation 0.859). R2 values outperformed (0.628–0.810) conventional concentration addition (0.654–0.792) independent action models. Furthermore, higher Q2LOO 4 h compared to other times. Specifically, model 30% effective (EC30) had 0.902 0.883, while 50% (EC50) 0.913 0.894. CATS2D_04_DP descriptor was found most dominant negatively correlated factor influencing Q67 nine over reduction number DP pharmacophore point pairs with topological distance represented molecules primary cause rise Q67.

Language: Английский

---

Quality control of naringenin-carbamazepine drug-drug cocrystal: Quantitative analytical method construction of ATR-FTIR and Raman combined with chemometrics

Microchemical Journal, Journal Year: 2024, Volume and Issue: 202, P. 110774 - 110774

Published: May 15, 2024

Language: Английский

---