Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach

Hung Jin Huang, Yu Hsuan Lee, Chu Lin Chou, Cai Mei Zheng, Hui Wen Chiu

Research output: Contribution to journalArticlepeer-review

Abstract

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure–activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.

Original languageEnglish
Pages (from-to)1876-1884
Number of pages9
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Genetic algorithm
  • Nephrotoxicity
  • QSAR

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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