Literature DB >> 21275889

Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles.

Denis Fourches1, Dongqiuye Pu, Alexander Tropsha.   

Abstract

Evaluation of desired and undesired, biological effects of Manufactured NanoParticles (MNPs) is of critical importance for the future of nanotechnology. Experimental studies, especially toxicological, are time-consuming and costly, calling for the development of efficient computational tools capable of predicting biological events caused by MNPs from their structure and physical chemical properties. This mini-review assesses the potential of modern cheminformatics methods such as Quantitative Structure - Activity Relationship modeling to develop statistically significant and externally predictive models that can accurately forecast biological effects of MNPs from the knowledge of their physical, chemical, and geometrical properties. We discuss major approaches for model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and organic decorations, for which experimentally measured properties can be used as particle's descriptors, and (ii) those involving MNPs possessing the same core (e.g., carbon nanotubes), but different surface-modifying organic molecules, for which computational descriptors can be calculated for a single representative of the decorative molecule. We illustrate those concepts with three case studies for which we successfully built and validated predictive models. In summary, this mini-review demonstrates that, analogous to conventional applications of QSAR modeling for the analysis of datasets of bioactive organic molecules, its application to modeling MNPs that we term Quantitative Nanostructure Activity Relationship (QNAR) modeling can be useful for (i) predicting activity profiles of novel MNPs solely from their representative descriptors and (ii) designing and manufacturing safer nanomaterials with desired properties.

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Year:  2011        PMID: 21275889     DOI: 10.2174/138620711794728743

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  16 in total

Review 1.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

2.  The dose makes the poison.

Authors: 
Journal:  Nat Nanotechnol       Date:  2011-06-06       Impact factor: 39.213

3.  Physicochemical signatures of nanoparticle-dependent complement activation.

Authors:  Dennis G Thomas; Satish Chikkagoudar; Alejandro Heredia-Langer; Mark F Tardiff; Zhixiang Xu; Dennis E Hourcade; Christine T N Pham; Gregory M Lanza; Kilian Q Weinberger; Nathan A Baker
Journal:  Comput Sci Discov       Date:  2014-03-21

Review 4.  Chemical basis of interactions between engineered nanoparticles and biological systems.

Authors:  Qingxin Mu; Guibin Jiang; Lingxin Chen; Hongyu Zhou; Denis Fourches; Alexander Tropsha; Bing Yan
Journal:  Chem Rev       Date:  2014-06-13       Impact factor: 60.622

Review 5.  Toward a systematic exploration of nano-bio interactions.

Authors:  Xue Bai; Fang Liu; Yin Liu; Cong Li; Shenqing Wang; Hongyu Zhou; Wenyi Wang; Hao Zhu; David A Winkler; Bing Yan
Journal:  Toxicol Appl Pharmacol       Date:  2017-03-24       Impact factor: 4.219

6.  Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.

Authors:  Wenyi Wang; Alexander Sedykh; Hainan Sun; Linlin Zhao; Daniel P Russo; Hongyu Zhou; Bing Yan; Hao Zhu
Journal:  ACS Nano       Date:  2017-11-22       Impact factor: 15.881

7.  Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS.

Authors:  Yasuyuki Zushi
Journal:  Anal Chem       Date:  2022-06-14       Impact factor: 8.008

8.  Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles.

Authors:  Denis Fourches; Dongqiuye Pu; Liwen Li; Hongyu Zhou; Qingxin Mu; Gaoxing Su; Bing Yan; Alexander Tropsha
Journal:  Nanotoxicology       Date:  2015-11-02       Impact factor: 5.913

Review 9.  Reproducibility, sharing and progress in nanomaterial databases.

Authors:  Alexander Tropsha; Karmann C Mills; Anthony J Hickey
Journal:  Nat Nanotechnol       Date:  2017-12-06       Impact factor: 40.523

Review 10.  The biomechanisms of metal and metal-oxide nanoparticles' interactions with cells.

Authors:  Sondra S Teske; Corrella S Detweiler
Journal:  Int J Environ Res Public Health       Date:  2015-01-22       Impact factor: 3.390

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