Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Kabiruddin Khan; Vinay Kumar; Erika Colombo; Anna Lombardo; Emilio Benfenati; Kunal Roy

doi:10.1016/j.envint.2022.107625

Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Environ Int. 2022 Dec:170:107625. doi: 10.1016/j.envint.2022.107625. Epub 2022 Nov 11.

Authors

Kabiruddin Khan¹, Vinay Kumar², Erika Colombo³, Anna Lombardo³, Emilio Benfenati⁴, Kunal Roy⁵

Affiliations

¹ Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India; QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.
² Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India.
³ Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, via Mario Negri 2, 20156 Milano, Italy.
⁴ Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, via Mario Negri 2, 20156 Milano, Italy. Electronic address: emilio.benfenati@marionegri.it.
⁵ Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India. Electronic address: kunal.roy@jadavpuruniversity.in.

PMID: 36375281
DOI: 10.1016/j.envint.2022.107625

Abstract

Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure-property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the "intelligent consensus" algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.

Keywords: BCF; DrugBank; ECOSAR; Ecotoxicity; OCHEM; Pharmaceuticals; QSPR; SiRMS; Validation.

MeSH terms

Ecosystem*