Modelling of ready biodegradability based on combined public and industrial data sources

Abstract

The European Registration, Evaluation, Authorization and Restriction of Chemical Substances Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB), considering in silico prediction as valid alternative to experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues. In this work, we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (Balance Accuracy (BA) = 0.74-0.79) and data coverage (83-91%). The Generative Topographic Mapping approach identified several chemotypes and structural motifs unique to the industrial dataset, highlighting for which chemical classes currently available models may have less reliable predictions. Finally, public and industrial data were merged into global dataset containing 3146 compounds. This is the biggest dataset reported in the literature so far, covering some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has larger applicability domain than the existing ones.

Visualization and Analysis of the REACH‐chemical Space with Generative Topographic Mapping

“…Biodegradability is determined by a multistep procedure. This assessment usually starts with a very stringent first‐tier assay, providing a binary classification whether the substance rapidly degrades in the environment, called “ready biodegradability” [4] …”

“…Experimental data was collected from multiple publicly available databases and scientific literature [2–4] . Among them, the main source was the database of the European Chemical Agency (ECHA), [14] which comprises the REACH‐registered substances.…”

“…In the past years, several models predicting REACH‐relevant endpoints were obtained with the help of various machine‐learning methods like multiple linear regression, support vector machine or neural networks [2–5] . However, some of them suffered from absence of technical documentation complying with the REACH requirements, [6] for instance concerning the model's Applicability Domain (AD) or its validation procedure [3,7] .…”

“…However, some of them suffered from absence of technical documentation complying with the REACH requirements, [6] for instance concerning the model's Applicability Domain (AD) or its validation procedure [3,7] . In our recent studies, [2,4] we found that for some endpoints currently‐existing tools have disappointing performances when applied to compounds coming from an industrial context, due to their restricted AD.…”

“…Here, a set of curated data for 11 endpoints prepared in our previous studies [2–4] (“Global dataset”) has been used to train the GTM model and to delineate the REACH chemical space. The following 11 endpoints were considered: bioconcentration factor, ready biodegradability, environmental persistence in sediment, soil and water media, acute aquatic toxicity to algae, daphnia and fish, rat acute toxicity, androgen and estrogen receptor binding potential.…”