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

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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.