持久性、流动性和毒性药物(PMT)的计算机预测:巴西圣保罗大都会区的案例研究

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-02-01 DOI:10.1016/j.comtox.2022.100254
Vinicius Roveri , Luciana Lopes Guimarães
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引用次数: 4

摘要

基于定量构效关系((Q)SAR)模型的计算建模(计算机)方法是评估药物化合物潜在的“持久性、移动性和毒性”(PMT)的有力工具。此外,欧盟REACH法规建议使用(Q)SAR模型。在此背景下,本研究的目的是首次根据REACH指南,通过五个(Q)SAR更新模型,估计圣保罗大都市区(一个拥有2100万巴西人的大城市)115种最畅销药物的PMT潜力,即:OPERA QSAR;VEGA QSAR (Version 1.1.5);EPI套件(4.11版);西非经委会(2.0版);和QSAR工具箱(版本4.5)。本研究优先考虑来自OPERA和VEGA的计算机预测,因为这两个qsar都可以生成可靠的预测,即它们具有有关适用领域的详细信息。计算机预测考虑了十个终点:(i)分子量(MW);(ii)“全面拆除STP”:污水处理厂;(iii)辛醇-水分配系数(KOW);预测的现成生物降解性;(v)土壤有机吸附系数;“短期和长期生态评价”;(七)“致癌性”;(八)“诱变”;(ix)“雌激素受体结合”;(x)“克莱默决策树”。主要结果表明:(a)这115种药物涵盖了广泛的所谓小分子(范围从100到600 MW);(b)在STP中,76种药物的预测去除率低于10%;(c)此外,101种化学物质具有低(Log KOW <2.5)或中等吸附电位(2.5<log kw <4.0);(d)最终,36个ppcp在证据权重评估后被认为是“持久的”。在成瘾性方面,这36种持久性化学物质中有17种在水中被归类为“非常流动”(log KOC <3)。最后,在17种ppcp中,只有三种,即环丙贝特、氟康唑和甲氧氯普胺,表现出一种或多种毒性特征(见第vi - x项)。这些结果将有可能提醒人们,在这个巴西大都市的水源附近随意处置这些ppcp会产生潜在风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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In silico prediction of persistent, mobile, and toxic pharmaceuticals (PMT): A case study in São Paulo Metropolitan Region, Brazil

Computational modelling (in silico) methods based on quantitative structure-activity relationship ((Q)SAR) models, are powerful tools for the assessment of the potential “persistency, mobility, and toxicity” (PMT) of pharmaceuticals compounds. Moreover, the use of (Q)SAR models, is recommended by European Union’s REACH Regulation. In this context, the aims of this research were estimating, for the first time and based by REACH guidelines, the PMT potentials of 115 most sold pharmaceuticals in São Paulo Metropolitan Region (a megacity with 21 million of Brazilian), through five (Q)SAR updated models, namely: the OPERA QSAR; the VEGA QSAR (Version 1.1.5); the EPI Suite (Version 4.11); the ECOSAR (Version, 2.0); and the QSAR Toolbox (Version 4.5). This study prioritized the in-silico predictions from the OPERA and the VEGA, because both QSARs can generate reliable predictions, i.e., they have detailed information about the applicability domains. In silico predictions were performed considering ten endpoints: (i) Molecular weight (MW); (ii) “STP total removal”: Sewage Treatment Plant; (iii) Octanol-water partition coefficient (KOW); (iv) Predicted ready biodegradability; (v) Soil organic adsorption coefficient (KOC); (vi) “Short-term and long-term ecological assessments”; (vii) “Carcinogenicity”; (viii) “Mutagenicity”; (ix) “Estrogen receptor binding”; (x) “Cramer decision tree”. The main results showed that: (a) These 115 pharmaceuticals cover a wide range of so-called small molecules (range from 100 to 600 MW); (b) In STP, a predicted removal lower than 10 % was found for 76 pharmaceuticals; (c) Additionally, 101 chemicals has low (Log KOW <2.5), or medium sorption potential (2.5< log KOW <4.0); (d) Ultimately, 36 PPCPs were considered “persistent” after a weight-of-evidence assessment. In addiction, 17 among these 36 persistent chemicals, were classified as “very mobile” in water (log KOC <3). Finally, only three among 17 PPCPs, namely ciprofibrate, fluconazole and metoclopramide, exhibited one or more toxic characteristics (described in items vi – x). These results it will be possible to alert about the potential risks arising from the indiscriminate disposal of these PPCPs along the water sources of this Brazilian mega metropolis.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
期刊最新文献
Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review From model performance to decision support – The rise of computational toxicology in chemical safety assessments Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment The OECD (Q)SAR Assessment Framework: A tool for increasing regulatory uptake of computational approaches A developmental and reproductive toxicity adverse outcome pathway network to support safety assessments
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