PMID- 37506640
OWN - NLM
STAT- MEDLINE
DCOM- 20230831
LR  - 20230831
IS  - 1873-3336 (Electronic)
IS  - 0304-3894 (Linking)
VI  - 459
DP  - 2023 Oct 5
TI  - "Data fusion" quantitative read-across structure-activity-activity relationships 
      (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic 
      mixtures toward three bacterial species.
PG  - 132129
LID - S0304-3894(23)01412-7 [pii]
LID - 10.1016/j.jhazmat.2023.132129 [doi]
AB  - Antibiotics are often found in the environment as pollutants. They are usually 
      found as mixtures in the environment and may produce toxicity against different 
      ecological species due to joint exposure in the sub-optimal range. Sometimes the 
      degradation products of parent chemicals also interact with it and cause mixture 
      toxicity. In this study, we have developed three different mixture-Quantitative 
      Structure-Activity Relationship (mixture-QSAR) models for three different 
      bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The 
      toxicity data were collected from a previous experimental report in the 
      literature, which comprised binary and ternary mixtures of sulfonamides (SAs), 
      sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored 
      the interspecies modeling to find inter-correlation among the toxicity of these 
      studied organisms and have developed quantitative structure activity-activity 
      relationship (QSAAR) models by employing the "data fusion" quantitative 
      read-across structure-activity-activity relationship (q-RASAAR) and partial least 
      squares (PLS) regression algorithms. All the models are strictly validated using 
      both internal and external validation tests as suggested in the OECD guidelines. 
      Three different mixing rules have been used in this study for descriptor 
      computations to incorporate the additive and interaction effects among the 
      mixture components. To the best of our knowledge, this is the first report of 
      interspecies mixture toxicity models which can predict the cellular toxicity of 
      binary and ternary mixtures against any of the three above-mentioned organisms.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Chatterjee, Mainak
AU  - Chatterjee M
AD  - Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical 
      Technology, Jadavpur University, Kolkata 700032, India.
FAU - Roy, Kunal
AU  - Roy K
AD  - Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical 
      Technology, Jadavpur University, Kolkata 700032, India. Electronic address: 
      kunalroy_in@yahoo.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230723
PL  - Netherlands
TA  - J Hazard Mater
JT  - Journal of hazardous materials
JID - 9422688
RN  - 0 (Anti-Bacterial Agents)
RN  - 21240MF57M (Sulfanilamide)
RN  - 0 (Sulfonamides)
SB  - IM
MH  - *Anti-Bacterial Agents/toxicity/chemistry
MH  - Sulfanilamide
MH  - *Sulfonamides/toxicity/chemistry
MH  - Quantitative Structure-Activity Relationship
OTO - NOTNLM
OT  - Antibiotics
OT  - Mixture toxicity
OT  - Q-RASAAR
OT  - QSAAR
OT  - QSAR
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/07/29 06:41
MHDA- 2023/08/31 06:42
CRDT- 2023/07/28 18:10
PHST- 2023/05/10 00:00 [received]
PHST- 2023/06/28 00:00 [revised]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/08/31 06:42 [medline]
PHST- 2023/07/29 06:41 [pubmed]
PHST- 2023/07/28 18:10 [entrez]
AID - S0304-3894(23)01412-7 [pii]
AID - 10.1016/j.jhazmat.2023.132129 [doi]
PST - ppublish
SO  - J Hazard Mater. 2023 Oct 5;459:132129. doi: 10.1016/j.jhazmat.2023.132129. Epub 
      2023 Jul 23.