Quantitative Nanostructure−Activity Relationship Modeling

Abstract

Evaluation of biological effects, both desired and undesired, caused by Manufactured NanoParticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as Quantitative Structure – Activity Relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach Quantitative Nanostructure-Activity Relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (PNAS, 2008, 105, pp 7387–7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol., 2005, 23, pp 1418–1423). We have generated QNAR models using machine learning approaches such as Support Vector Machine (SVM)-based classification and k Nearest Neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and R2 of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials towards better and safer products.

(Q)SAR modelling of nanomaterial toxicity: A critical review

“…In another study, two different experimental nano-toxicity datasets were employed to derive a mathematical relationship between the toxicity of NPs and their physicochemical properties (Fourches, et al, 2010). The advantage of the data used in this study was the concurrent testing of ENMs under the same circumstances.…”

“…The dataset used by (Fourches, et al, 2010) was also employed here with minor changes. The difference was that new descriptors encoding the presence or absence of some particular features, such as coating, were added and used as descriptors by .…”

“…Despite all the challenges and open questions, there are some pioneering studies investigating the use of (Q)SAR models to predict the toxicity of ENMs Fourches, et al, 2010; R. R. Liu, Zhang, et al, 2013;T.…”

Current situation on the availability of nanostructure–biological activity data

“…Despite these limitations, there is a growing literature on the use of (Q)SARlike models in nanotoxicology studies. There are a great number of reviews [3,5,10,[12][13][14] and research articles [15][16][17][18][19][20][21][22][23][24][25][26][27][28] devoted to the investigations of in silico modelling of ENM toxicity in peer-reviewed scientific journals.…”

“…This dataset has been used by seven different research groups [17,19,21,22  Characterization: In this study, the authors did not provide any characterization data (i.e. they attempted to develop 3D-(Q)SARs based on field contributions which are measured in silico).…”