The increasing use of pesticides has raised global concerns about their ecological safety, especially in aquatic systems. Fosetyl-aluminium (fosetyl-Al), a systemic fungicide, acts by inhibiting fungal growth and stimulating plant defense. In this study, the Ecological Structure–Activity Relationship (ECOSAR) model was applied to predict its acute and chronic toxicity towards aquatic and terrestrial organisms. Results consistently indicated “no effects at saturation,” suggesting minimal acute risk under realistic exposure. These findings, corroborated by experimental data, highlight fosetyl-Al as an environmentally low-risk fungicide and demonstrate the value of QSAR-based in silico approaches in preliminary ecological risk assessment.

Cardiovascular diseases (CVDs) remain a leading cause of mortality in modern society, with factors such as sedentary lifestyles, unhealthy diets, and obesity contributing to their increasing prevalence. The widespread use of Statins for lipid-lowering therapy in both primary and secondary cardiovascular prevention is anticipated to rise in response to this trend. Given the rapid escalation in the prevalence of Statin usage, it is imperative to understand their toxicological effects on public health. While previous studies have explored various pharmacological effects of statins, comprehensive investigations into their genotoxic and Mutagenic potential are lacking. In this study, we conducted a comprehensive In silico evaluation of Statins using four different toxicological assessment programs, focusing on various genotoxicity, carcinogenicity, Mutagenicity, and Micronucleus formation endpoints. By comparing program outputs with experimental data, we assessed the reliability of In silico Toxicity predictions and discussed the consistency among different platforms. Our findings suggest discrepancies among the predictions of different programs, highlighting the importance of integrating multiple sources of data and methodologies in Toxicity evaluations. Despite inconsistencies, integrating in silico predictions with future in vitro and in vivo studies can contribute to a better understanding of the toxicological properties of statins and ensure their safe usage. This study underscores the necessity of careful evaluation and utilization of multiple data sources in decision-making regarding the toxicological profile of statins. Ultimately, leveraging in silico methods to guide future comprehensive toxicological studies will enhance our understanding of Statins' safety profiles and contribute to public health research.

Bu çalışmada, Benalaxyl-M maddesinin bal arıları (Apis mellifera) üzerindeki akut toksik etkileri, makine öğrenimi tabanlı bir QSAR (Kantitatif Yapı-Aktivite İlişkisi) aracı olan BeeToxAI kullanılarak in silico yöntemlerle değerlendirilmiştir. Analiz sonuçları, Benalaxyl-M'nin bal arıları için akut oral ve kontakt toksisite açısından toksik olmadığını (%83-86 güvenilirlik) göstermiştir. Uygulanabilirlik Alanı analizi, tahminlerin güvenilir olduğunu doğrularken, katkı haritaları moleküldeki toksisiteye katkı yapan fonksiyonel grupları belirlemede kullanılmıştır. Elde edilen bulgular, Benalaxyl-M'nin bal arıları üzerindeki düşük akut riskini ortaya koysa da, sonuçların deneysel çalışmalarla desteklenmesi önerilmektedir. Bu çalışma, pestisitlerin ekotoksikolojik risk değerlendirmelerinde yapay zeka tabanlı araçların potansiyelini vurgulamaktadır.