Predicting the ecotoxicity of endocrine disruptive chemicals: Multitasking in silico approaches towards global models

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Halder, AK (Author) Other; Moura, AS (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Science of the Total Environment

Vol. 889

ISSN: 0048-9697

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-GCC

DOI: 10.1016/j.scitotenv.2023.164337

Abstract (EN): Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2,301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87% in both training and prediction sets. However, maximum external predictivity was achieved when a new multitasking consensus modeling approach was applied to these models. Additionally, the developed linear model provided means to investigate the determining factors for eliciting higher ecotoxicity by the EDCs towards different biological species, identifying several factors such as solvation, molecular mass and surface area as well as the number of specific molecular fragments (e.g.: aromatic hydroxy and aliphatic aldehyde). The resource to non-commercial open-access tools to develop the models is a useful step towards library screening to speed up regulatory decision on discovery of safe alternatives to reduce the hazards of EDCs.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11