PMID- 35498319
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230916
IS  - 2046-2069 (Electronic)
IS  - 2046-2069 (Linking)
VI  - 10
IP  - 9
DP  - 2020 Jan 29
TI  - QSAR modeling of the toxicity classification of superparamagnetic iron oxide 
      nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study 
      from data curation to model development.
PG  - 5385-5391
LID - 10.1039/c9ra09475j [doi]
AB  - The use of in silico approaches for the prediction of biomedical properties of 
      nano-biomaterials (NBMs) can play a significant role in guiding and reducing 
      wetlab experiments. Computational methods, such as data mining and machine 
      learning techniques, can increase the efficiency and reduce the time and cost 
      required for hazard and risk assesment and for designing new safer NBMs. A major 
      obstacle in developing accurate and well-validated in silico models such as Nano 
      Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the 
      volume of data published in the literature is increasing, the data are fragmented 
      in many different publications and are not sufficiently curated for modelling 
      purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their 
      structures, making data collection and curation and QSAR model development more 
      challenging compared to traditional small molecules. The aim of this study was to 
      construct and fully validate a Nano-QSAR model for the prediction of 
      toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), 
      focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents 
      for non-invasive stem cell labelling and tracking. To achieve this goal, we first 
      performed an extensive search through the literature for collecting and curating 
      relevant data and we developed a dataset containing both physicochemical and 
      toxicological properties of SPIONs. The data were analysed next, using Automated 
      machine learning (Auto-ML) approaches for optimising the development and 
      validation of nanotoxicity classification QSAR models of SPIONs. Further analysis 
      of relative attribute importances revealed that physicochemical properties such 
      as the size and the magnetic core are the dominant attributes correlated to the 
      toxicity of SPIONs. Our results suggest that as more systematic information from 
      NBM experimental tests becomes available, computational tools could play an 
      important role in supporting the safety-by-design (SbD) concept in regenerative 
      medicine and disease therapeutics.
CI  - This journal is (c) The Royal Society of Chemistry.
FAU - Kotzabasaki, Marianna I
AU  - Kotzabasaki MI
AUID- ORCID: 0000-0001-9134-0366
AD  - School of Chemical Engineering, National Technical University of Athens 9 Heroon 
      Polytechneiou Street, Zografou Campus 15780 Athens Greece 
      mariannako@chemeng.ntua.gr jasonsoti1@gmail.com hsarimv@central.ntua.gr 
      +302107723138 +302107723236 +306936396688 +302107723237.
FAU - Sotiropoulos, Iason
AU  - Sotiropoulos I
AUID- ORCID: 0000-0001-6157-1593
AD  - School of Chemical Engineering, National Technical University of Athens 9 Heroon 
      Polytechneiou Street, Zografou Campus 15780 Athens Greece 
      mariannako@chemeng.ntua.gr jasonsoti1@gmail.com hsarimv@central.ntua.gr 
      +302107723138 +302107723236 +306936396688 +302107723237.
FAU - Sarimveis, Haralambos
AU  - Sarimveis H
AUID- ORCID: 0000-0002-8607-9965
AD  - School of Chemical Engineering, National Technical University of Athens 9 Heroon 
      Polytechneiou Street, Zografou Campus 15780 Athens Greece 
      mariannako@chemeng.ntua.gr jasonsoti1@gmail.com hsarimv@central.ntua.gr 
      +302107723138 +302107723236 +306936396688 +302107723237.
LA  - eng
PT  - Journal Article
DEP - 20200203
PL  - England
TA  - RSC Adv
JT  - RSC advances
JID - 101581657
PMC - PMC9049038
COIS- There are no conflicts to declare.
EDAT- 2020/02/03 00:00
MHDA- 2020/02/03 00:01
PMCR- 2020/02/03
CRDT- 2022/05/02 07:09
PHST- 2019/11/13 00:00 [received]
PHST- 2020/01/21 00:00 [accepted]
PHST- 2022/05/02 07:09 [entrez]
PHST- 2020/02/03 00:00 [pubmed]
PHST- 2020/02/03 00:01 [medline]
PHST- 2020/02/03 00:00 [pmc-release]
AID - c9ra09475j [pii]
AID - 10.1039/c9ra09475j [doi]
PST - epublish
SO  - RSC Adv. 2020 Feb 3;10(9):5385-5391. doi: 10.1039/c9ra09475j. eCollection 2020 
      Jan 29.