Using surrogate passive sampler devices and predictive machine learning algorithms to replace invertebrate use in micropollutant bioconcentration test

使用替代被动采样器设备和预测机器学习算法替代微污染物生物浓度测试中的无脊椎动物使用

• 批准号: 2125200
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2125200
• 关键词: Using; surrogate; passive; sampler; devices

Passive sampler devices (PSDs) are often used to monitor longer term occurrence of micropollutants, and mainly hydrophobic organic chemicals (HOCs), in the environment. PSDs are generally composed of a polymeric sorbent and collect solutes by passive diffusion. Recently, PSDs have been used as potential surrogates for fish/invertebrates in bioconcentration studies for HOCs (logKow 4-6)1. More recently, PSDs have been tailored for polar organic chemicals (POCs). For example, pharmaceuticals and personal care products (PPCPs) have a logKow of -1 to 4 and have multiple ionisation states. This makes modelling for PPCPs significantly more challenging. Laboratory based risk assessment for 7,000 pharmaceuticals (excluding their metabolites and transformation products) is impractical and highly costly. There exists an exciting opportunity in this project to develop and apply predictive approaches to prioritise laboratory testing or remove the need to use model organisms entirely. In 2016, we were the first to successfully model and predict POC uptake rate constants (Rs) onto PSDs1. We have also recently used machine learning by itself to predict PPCP bioconcentration in G. pulex with some limited success in a previously BBSRC funded CASE studentship (BB/K501177/1). Therefore, it is now timely for us to extend this knowledge to 3R-type predictive approaches for additional POC bioconcentration studies now incorporating PSDs to improve the machine learning approach and to mimic the actual bioavailability more realistically. This also represents an excellent way to prioritise risk assessment for selected emerging POCs to biota for which no knowledge or standard reference materials exist.

被动采样器(PSD)通常被用来监测环境中微污染物的长期存在，主要是疏水性有机物(HOCs)。PSD通常由聚合物吸附剂组成，通过被动扩散收集溶质。最近，PSD已被用作鱼类/无脊椎动物的潜在替代品，用于HOCs的生物浓缩研究(logKow 4-6)1。最近，PSD已被用于极性有机化学品(POCs)。例如，药品和个人护理产品(PPCP)的logKow为-1到4，并具有多种电离状态。这使得购买力平价的建模更具挑战性。对7,000种药物(不包括其代谢物和转化产品)进行基于实验室的风险评估是不切实际的，而且成本高昂。在这个项目中有一个令人兴奋的机会，可以开发和应用预测性方法来优先进行实验室测试，或者完全消除使用模型生物体的需要。2016年，我们率先成功地对PSD 1上的POC摄取速率常数(Rs)进行了建模和预测。我们最近也使用机器学习本身来预测PPCP在G.Pulex中的生物浓度，在以前BBSRC资助的案例学生中取得了一些有限的成功(BB/K501177/1)。因此，现在正是我们将这一知识扩展到3R型预测方法以进行额外的POC生物浓度研究的时候了，该方法现已纳入PSD，以改进机器学习方法并更真实地模拟实际生物利用度。这也代表了一种极好的方法，可以将选定的新兴POC的风险评估优先考虑到没有相关知识或标准参考材料的生物群。

项目成果

Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts.

• DOI: 10.1039/d0ay02013c
• 发表时间: 2021-01
• 期刊: Analytical methods : advancing methods and applications
• 作者: Alexandra K Richardson;Marcus Chadha;Helena Rapp-Wright;G. Mills;G. Fones;A. Gravell;S. Stürzenbaum;D. Cowan;D. J. Neep;L. Barron