Quantitative Nanostructure-activity Relationship Research Articles

Predicting the Physicochemical Properties and Biological Activities of Monolayer-Protected Gold Nanoparticles Using Simulation-Derived Descriptors.

Gold nanoparticles are versatile materials for biological applications because their properties can be modulated by assembling ligands on their surface to form monolayers. However, the physicochemical properties and behaviors of monolayer-protected nanoparticles in biological environments are difficult to anticipate because they emerge from the interplay of ligand-ligand and ligand-solvent interactions that cannot be readily inferred from ligand chemical structure alone. In this work, we demonstrate that quantitative nanostructure-activity relationship (QNAR) models can employ descriptors calculated from molecular dynamics simulations to predict nanoparticle properties and cellular uptake. We performed atomistic molecular dynamics simulations of 154 monolayer-protected gold nanoparticles and calculated a small library of simulation-derived descriptors that capture nanoparticle structural and chemical properties in aqueous solution. We then parametrized QNAR models using interpretable regression algorithms to predict experimental measurements of nanoparticle octanol-water partition coefficients, zeta potentials, and cellular uptake obtained from a curated database. These models reveal that simulation-derived descriptors can accurately predict experimental trends and provide physical insight into what descriptors are most important for obtaining desired nanoparticle properties or behaviors in biological environments. Finally, we demonstrate model generalizability by predicting cell uptake trends for 12 nanoparticles not included in the original data set. These results demonstrate that QNAR models parametrized with simulation-derived descriptors are accurate, generalizable computational tools that could be used to guide the design of monolayer-protected gold nanoparticles for biological applications without laborious trial-and-error experimentation.

New Relevant Descriptor of Linear QNAR Models for Toxicity Assessment of Silver Nanoparticles.

The use of silver nanoparticles (NPs) in medical, industrial and agricultural fields is becoming more widespread every year. This leads to an increasing number of experimental toxicological and microbiological studies of silver NPs aimed at establishing the risk–benefit ratio for their application. The following key parameters affecting the biological activity of silver dispersions are traditionally taken into consideration: mean diameter of NPs, surface potential of NPs and equilibrium concentration of Ag+. These characteristics are mainly predetermined by the chemical nature of the capping agent used for stabilization. However, the extent to which they influence the biological activity and the toxicity of silver NPs varies greatly. In this work, dispersions of silver NPs stabilized with a wide array of substances of different chemical nature were used for quantitative evaluation of whether the various measurable properties of silver NPs fit as descriptors of linear QNAR (quantitative nanostructure–activity relationship) models for silver NP toxicity evaluation with respect to a model eukaryotic microorganism—Saccharomyces cerevisiae yeast cells. It was shown that among the factors that determine silver NP toxicity, the charge of particles, their colloidal stability and the ability to generate Ag+ ions carry more importance than the descriptors related to the particle size. A significant synergistic effect between the ζ-potential and the colloidal stability of silver NPs on their toxicity was also discovered. Following this, a new descriptor has been proposed for the integral characterization of the silver dispersion colloidal stability. According to the obtained data, it can be considered applicable for building QNAR models of higher efficacy. The validity testing of the proposed model for theoretical prediction of silver NP toxicity using a wide range of living organisms has shown that this new descriptor correlates with toxicity much better compared to most traditionally used descriptors. Consequently, it seems promising in terms of being used not only in situations involving the rather narrow array of the objects tested, but also for the construction of silver NP toxicity models with respect to other living organisms.

Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Health risks induced by PM2.5 have become one of the major concerns among living populations, especially in regions facing serious pollution such as China and India. Furthermore, the composition of PM2.5 is complex and it also varies with time and locations. To facilitate our understanding of PM2.5-induced toxicity, a predictive modeling framework was developed in the present study. The core of this study was 1) to construct a virtual carbon nanoparticle library based on the experimental data to simulate the PM2.5 structures; 2) to quantify the nanoparticle structures by novel nanodescriptors; and 3) to perform computational modeling for critical toxicity endpoints. The virtual carbon nanoparticle library was developed to represent the nanostructures of 20 carbon nanoparticles, which were synthesized to simulate PM2.5 structures and tested for potential health risks. Based on the calculated nanodescriptors from virtual carbon nanoparticles, quantitative nanostructure-activity relationship (QNAR) models were developed to predict cytotoxicity and four different inflammatory responses induced by model PM2.5. The high predictability (R2>0.65 for leave-one-out validations) of the resulted consensus models indicated that this approach could be a universal tool to predict and analyze the potential toxicity of model PM2.5, ultimately understanding and evaluating the ambient PM2.5-induced toxicity.

Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties

The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials’ data is resulting from in vitro comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure–activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.

The effect of surface charge and pH on the physiological behaviour of cobalt, copper, manganese, antimony, zinc and titanium oxide nanoparticles in vitro

The precise knowledge on various interactions of metal nanoparticles (NP) in a living organism is scarce. It is expected that metals can bind to nucleic acids, peptides and proteins (e.g. enzymes), and modify the functioning of vital cellular compartments after entering the organism. The predictive factors for quantitative nanostructure-activity relationship (QNAR) analysis could enhance efficient and harmless usage of nanoparticles (NPs) in the industry as well in the medicine. The studies value the composition of the NP corona determined by time, temperature and source of protein which has been found to implicate the physiological behaviour of NPs. One has largely been ignored: the NPs specific isoelectric point (IEP) and pH at the state of measurement. Herein, this study investigates the effect of pH and surface charge of six metal oxide (MeOx) NPs on time dependency of cytotoxicity. Several aspects of the characterization of ultrafine particles in the actual test system which is the most relevant for the interpretation of the toxicological data are referred: (i) the difference of pH in the room temperature and in the incubation conditions (ii) the difference of dispersions in MilliQ and complete cell media; (iii) the need to exemplify also the pH and isoelectric point when the hydrodynamic size is measured; (iv) the importance of time due to the time-dependent equilibration and changes of NPs corona. The surface charge determines the formation of corona and could be modified by pH. MeOx NPs without fully charge equilibrated corona might play the main role of MeOx NPs entering into the cell and consequently the time dependent manifestation of the cellular effect.

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

The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.

Inhibition of thrombin by functionalized C60 nanoparticles revealed via in vitro assays and in silico studies

The studies on the human toxicity of nanoparticles (NPs) are far behind the rapid development of engineered functionalized NPs. Fullerene has been widely used as drug carrier skeleton due to its reported low risk. However, different from other kinds of NPs, fullerene-based NPs (C60 NPs) have been found to have an anticoagulation effect, although the potential target is still unknown. In the study, both experimental and computational methods were adopted to gain mechanistic insight into the modulation of thrombin activity by nine kinds of C60 NPs with diverse surface chemistry properties. In vitro enzyme activity assays showed that all tested surface-modified C60 NPs exhibited thrombin inhibition ability. Kinetic studies coupled with competitive testing using 3 known inhibitors indicated that six of the C60 NPs, of greater hydrophobicity and hydrogen bond (HB) donor acidity or acceptor basicity, acted as competitive inhibitors of thrombin by directly interacting with the active site of thrombin. A simple quantitative nanostructure-activity relationship model relating the surface substituent properties to the inhibition potential was then established for the six competitive inhibitors. Molecular docking analysis revealed that the intermolecular HB interactions were important for the specific binding of C60 NPs to the active site canyon, while the additional stability provided by the surface groups through van der Waals interaction also play a key role in the thrombin binding affinity of the NPs. Our results suggest that thrombin is a possible target of the surface-functionalized C60 NPs relevant to their anticoagulation effect.

Competitive Inhibition Mechanism of Acetylcholinesterase without Catalytic Active Site Interaction: Study on Functionalized C60 Nanoparticles via in Vitro and in Silico Assays.

Acetylcholinesterase (AChE) activity regulation by chemical agents or, potentially, nanomaterials is important for both toxicology and pharmacology. Competitive inhibition via direct catalytic active sites (CAS) binding or noncompetitive inhibition through interference with substrate and product entering and exiting has been recognized previously as an AChE-inhibition mechanism for bespoke nanomaterials. The competitive inhibition by peripheral anionic site (PAS) interaction without CAS binding remains unexplored. Here, we proposed and verified the occurrence of a presumed competitive inhibition of AChE without CAS binding for hydrophobically functionalized C60 nanoparticles (NPs) by employing both experimental and computational methods. The kinetic inhibition analysis distinguished six competitive inhibitors, probably targeting the PAS, from the pristine and hydrophilically modified C60 NPs. A simple quantitative nanostructure-activity relationship (QNAR) model relating the pocket accessible length of substituent to inhibition capacity was then established to reveal how the geometry of the surface group decides the NP difference in AChE inhibition. Molecular docking identified the PAS as the potential binding site interacting with the NPs via a T-shaped plug-in mode. Specifically, the fullerene core covered the enzyme gorge as a lid through π-π stacking with Tyr72 and Trp286 in the PAS, while the hydrophobic ligands on the fullerene surface inserted into the AChE active site to provide further stability for the complexes. The modeling predicted that inhibition would be severely compromised by Tyr72 and Trp286 deletions, and the subsequent site-directed mutagenesis experiments proved this prediction. Our results demonstrate AChE competitive inhibition of NPs without CAS participation to gain further understanding of both the neurotoxicity and the curative effect of NPs.

A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells

Nanotechnology has led to the development of new nanomaterials with unique properties and a wide variety of applications. In the present study, we focused on the cellular uptake of a group of nanoparticles with a single metal core by pancreatic cancer cells, which has been studied by Yap et al. (Rsc Advances, 2012, 2 (2):8489–8496) using classification models. In this work, the development of a further Quantitative Nanostructure–Activity Relationship (QNAR) model was performed by linear multiple linear regression (MLR) and nonlinear artificial neural network (ANN) techniques to accurately predict the cellular uptake values of these compounds by dividing them into three groups. Judging from the attained statistical results, our derived QNAR models have an acceptable overall accuracy and robustness, as well as good predictivity on the external data sets. Moreover, the results of this study provide some insights on how engineered nanomaterial features influence cellular responses and thereby outline possible approaches for developing and applying predictive computational models for biological responses caused by exposure to nanomaterials.

Review of Current and Emerging Approaches for Quantitative Nanostructure-Activity Relationship Modeling

Quantitative structure-activity/property relationships (QSAR/QSPR) approaches that have been applied with success in a number of studies are currently used as indispensable tools in the computational analysis of nanomaterials. Evolution of nano-QSAR methodology to the ranks of novel field of knowledge has resulted in the development of new so-called “nano-descriptors” and extension of the statistical approaches domain. This brief review focuses on the critical analysis of advantages and disadvantages of existing methods of nanoparticles' representation and their analysis in framework of structure-activity relationships. It summarizes recent QSAR/QSPR studies on inorganic nanomaterials. Here the authors describe practices for the QSAR modeling of inorganic nanoparticles, existing datasets, and discuss applicable descriptors and future perspectives of this field. About 50 different (Q)SAR/SPR models for inorganic nanomaterials have been developed during the past 5 years. An analysis of these peer reviewed publications shows that the most popular property of nanoparticles modeled via QSAR is their toxicity towards different bacteria, cell lines, and microorganisms. It has been clearly shown how nano-QSAR can contribute to the elucidation of toxicity mechanisms and different physical properties of inorganic nanomaterials.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms.

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.

Quantitative Nanostructure–Activity Relationship modelling of nanoparticles

In recent years, the interest of the pharmaceutical industry to explore the use of nanoparticles for disease treatment or drug delivery has increased the need to evaluate their therapeutic efficacy or toxicity. However, evaluation of such properties using experimental means is time-consuming and costly. Thus, researchers are investigating the potential of using Quantitative Nanostructure–Activity Relationship (QNAR) models to predict the properties of nanoparticles prior to their synthesis. In this study, we developed a reliable, user-friendly and freely-accessible QNAR model to predict the cellular uptake of 105 nanoparticles with a single metal core by pancreatic cancer cells. Four modelling methods, namely Naive Bayes, Logistic Regression, k nearest neighbour and support vector machine, were used to develop candidate models. A final consensus model was then developed using the top 5 candidate models. Validation of the final consensus model was done using a rigorous process by repeating the entire model development process five times using different combinations of training and validation sets. The final consensus model had a sensitivity of 86.7 to 98.2% and specificity of 67.3 to 76.6%. The majority of the wrong predictions were due to nanoparticles which had OC–O–CO bonding. Descriptors that were included in the final consensus models were mainly related to lipophilicity and hydrogen bonding. With the recent advances in QNAR methodology and its encouraging prediction toward virtual nanoparticles, the full potential of QNAR modelling should be exploited in the future to provide critical support to experimental studies over the design of nanomaterials.

Exploring Quantitative Nanostructure-Activity Relationships (QNAR) Modeling as a Tool for Predicting Biological Effects of Manufactured Nanoparticles

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.

Quantitative Nanostructure−Activity Relationship Modeling

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 (Proc. Natl. Acad. Sci. 2008, 105, 7387-7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol. 2005, 23, 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 having an R(2) 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 toward better and safer products.