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Development of Nano-QSARs as Predictive Tools for Nanomaterials’ Cytotoxicity

Bigdeli, Arafeh | 2016

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 48257 (03)
  4. University: Sharif University of Technology
  5. Department: Chemistry
  6. Advisor(s): Hormozi Nezhad, Mohammad Reza
  7. Abstract:
  8. The increasing role of nanotechnology in our every-day-life, has aroused global concern regarding their hazardous potential, resulting in a demand for parallel risk assessment. Quatitative structure-activity relationships enable researches to use unique properties of nanoparticles as predictors for their toxicity or any other biological response. Extracting rational correlations between physicochemical properties of nanoparticles and their biological response, not only reveals the way that nanoparticles behave upon entering into biological media, but also leads to the design of safer and efficient nanoparticles for various applications of interest. This PhD dissertation presents QSAR tools for understanding and predicting the biological behavior of nanoparticles. In this regard, two main goals have been followed: presenting nano-specific descriptors and then further developing predictive and descriptive nano-QSAR models. For the first aim, due to the important role of surface-related properties of nanoparticles in their biological response, descriptors demonstrating accurate information about nanoparticles surface, structure and morphology were introduced. These nanodescriptors were extracted from transmission electron microscopy images using image processing methods and included: size, surface area, aspect ratio, shape, corner count, aggregation state and curvature. The represented descriptors can provide informative data required for a thorough computational approach on nanomaterials. These descriptors can also be considered in modeling procedures in order to screen more parameters and consequently, be used to further understand the mechanisms which govern the biological behavior of nanoparticles. The third chapter of the dissertation covers this topic. Moving towards the second aim, we have utilized the extracted image nanodescriptors in order to develop quantitative structure-activity relationships for a set of gold nanoparticles with different size and surface coatings. In addition to the image nanodescriptors, experimental information of the gold nanoparticles were gathered to establish a partial leaset regression. The resulting nano-QSAR model could surve important results in both predictive and descriptive frameworks. In addition to predicting the exocytosis value of gold nanoparticles from macrophages and determing the important factors involved, the PLS model was able to identify the importance priority of the selected variables in the model. This quantitative comparison among the variables allows deep understanding of the interactions of nanoparticles with biological species. This study is thoroughly discussed in chapter four. In the last part of this dissertation, a different approach has been used for introducing nanodescriptors. Since nanoparticles gain a new identity upon entering the biological media by forming a shell of proteins adsorbed on their surface (known as protein corona), in this work we tried to take into account the effect of this protein corona formation on the biological behavior of nanoparticles. Thus, a set of new descriptors, named protein corona fingerprints were presented to reveal the effect of protein corona formation. The physicochemical properties of 17 liposomes together with the protein corona fingerprints were used to predict multiple biological responses. The cytotoxicity and cellular uptake of PC3 and HeLa cells was quantitativley correlated to the synthetic identity, biological identity and protein corona of the liposomes. The use of several cellular responses herein highlighted the fact that different descriptors seem to be relevant to diverse biological processes. The developed QSAR models provided important information for further understanding the interactions of liposomes with living cells
  9. Keywords:
  10. Nanoparticles ; Descriptor ; Morphology ; Biological Behavior ; Cytoxicity ; Quantitative Structure-Activity Relationship (QSAR)Model ; Corona Protein

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