Next Article in Journal
Exploring the Degradation of Ibuprofen by Bacillus thuringiensis B1(2015b): The New Pathway and Factors Affecting Degradation
Previous Article in Journal
Design, Synthesis and Antifungal Activity of Psoralen Derivatives
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Molecules 2017, 22(10), 1671; doi:10.3390/molecules22101671

Predictive QSAR Models for the Toxicity of Disinfection Byproducts

1,2,3
,
1
,
1
,
2,3,* , 1,2,3
and
1,2,3
1
College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
2
Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China
3
Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Received: 26 September 2017 / Revised: 30 September 2017 / Accepted: 1 October 2017 / Published: 9 October 2017
(This article belongs to the Section Molecular Diversity)
View Full-Text   |   Download PDF [875 KB, uploaded 9 October 2017]   |  

Abstract

Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2) > 0.7, explained variance in leave-one-out prediction (Q2LOO) and in leave-many-out prediction (Q2LMO) > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3) > 0.7, and concordance correlation coefficient (CCC) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs. View Full-Text
Keywords: disinfection byproduct; QSAR; validation; toxicity; drinking water disinfection byproduct; QSAR; validation; toxicity; drinking water
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Qin, L.; Zhang, X.; Chen, Y.; Mo, L.; Zeng, H.; Liang, Y. Predictive QSAR Models for the Toxicity of Disinfection Byproducts. Molecules 2017, 22, 1671.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]

Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top