QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

Abstract

The bioconcentration factor (BCF), a key parameter required by the REACH regulation, estimates the tendency for a xenobiotic to concentrate inside living organisms. In silico methods can be valid alternatives to costly data measurements. However, in the industrial context, these theoretical approaches may fail to predict BCF with reasonable accuracy. We analyzed whether models built on public data only have adequate performances when challenged to predict industrial compounds. A new set of 1129 compounds has been collected by merging publicly available datasets. Generative Topographic Mapping was employed to compare this chemical space with a set of new compounds issued from the industry. Some new chemotypes absent in the training set (such as siloxanes) have been detected. A new BCF model has been built using ISIDA (In SIlico design and Data Analysis) fragment descriptors, support vector regression and random forest machinelearning methods. It has been externally validated on: (i) collected data from the literature and (ii) industrial data. The latter also served as benchmark for the freely available tools VEGA, EPISuite, TEST, OPERA. New model performs (RMSE of 0.58 log BCF units) comparably to existing ones but benefits of an extended applicability, covering the industrial set chemical space (78% data coverage).

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

“…All out-of-AD decisions (based on the fragment control) are not considered for the voting (step 2). If the percentage of the votes for a given class (B/nB) was between 40 and 60%, the decision was rejected since close to random (step 3); otherwise, the consensus prediction is given, together with its reliability (step 4) [34]. The data coverage is calculated as a ratio of the compounds accepted at steps 1 to 3 and total number in the dataset.…”

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

“…BCF estimates the tendency for a xenobiotic to concentrate inside living organisms. It is defined as the process of concentration of the chemical from the water phase through non‐dietary routes, such as absorption from respiratory surfaces (e. g. lungs/gills) or skin [2] …”

“…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.…”