Affinity of drugs and small biologically active molecules to carbon nanotubes: a pharmacodynamics and nanotoxicity factor?

Nanomaterials in biology and medicine: a new perspective on its toxicity and applications

Nanotechnology offers excellent prospects for application in biology and medicine. It is used for detecting biological molecules, imaging, and as therapeutic agents. Due to nano-size (1-100 nm) and high surface-to-volume ratio, nanomaterials possess highly specific and distinct characteristics in the biological environment. Recently, the use of nanomaterials as sensors, theranostic, and drug delivery agents has become popular. The safety of these materials is being questioned because of their biological toxicity, such as inflammatory responses, cardiotoxicity, cytotoxicity, inhalation problems, etc., which can have a negative impact on the environment. This review paper focuses primarily on the toxicological effects of nanomaterials along with the mechanisms involved in cell interactions and the generation of reactive oxygen species by nanoparticles, which is the fundamental source of nanotoxicity. We also emphasize the greener synthesis of nanomaterials in biomedicine, as it is non-hazardous, feasible, and economical. The review articles shed light on the complexities of nanotoxicology in biosystems and the environment.

Supervised learning model predicts protein adsorption to carbon nanotubes

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Supervised learning model predicts protein adsorption to carbon nanotubes

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Big Data and Artificial Intelligence Modeling for Drug Discovery

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation

Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.

Experimental and computational studies of physicochemical properties influence NSAID-cyclodextrin complexation

The objective of this research was to investigate physicochemical properties of an active pharmaceutical ingredient (API) that influence cyclodextrin complexation through experimental and computational studies. Native β-cyclodextrin (B-CD) and two hydroxypropyl derivatives were first evaluated by conventional phase solubility experiments for their ability to complex four poorly water-soluble nonsteroidal anti-inflammatory drugs (NSAIDs). Differential scanning calorimetry was used to confirm complexation. Secondly, molecular modeling was used to estimate Log P and aqueous solubility (S o) of the NSAIDs. Molecular dynamics simulations (MDS) were used to investigate the thermodynamics and geometry of drug-CD cavity docking. NSAID solubility increased linearly with increasing CD concentration for the two CD derivatives (displaying an AL profile), whereas increases in drug solubility were low and plateaued in the B-CD solutions (type B profile). The calculated Log P and S o of the NSAIDs were in good concordance with experimental values reported in the literature. Side chain substitutions on the B-CD moiety did not significantly influence complexation. Explicitly, complexation and the associated solubility increase were mainly dependent on the chemical structure of the NSAID. MDS indicated that each NSAID-CD complex had a distinct geometry. Moreover, complexing energy had a large, stabilizing, and fairly constant hydrophobic component for a given CD across the NSAIDs, while electrostatic and solvation interaction complex energies were quite variable but smaller in magnitude.

REACH and in silico methods: an attractive opportunity for medicinal chemists

REACH, the most ambitious chemical legislation in the world, provides unprecedented opportunities for medicinal chemists. Companies must report (eco)toxicological information about their chemicals, disseminated to the public domain by the European Chemicals Agency after their registration. The availability of this wealth of new toxicological data, together with the promotion of REACH of in silico methods, appoints medicinal chemists to a leading role in the regulatory hazard assessment process. In fact, Quantitative Structure-Activity Relation ship (QSAR) models and predictive toxicology have been applied in drug design and development for decades. Here, we discuss toxicological endpoints and areas where further development is needed to provide an enlightened appraisal of this attractive new opportunity.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

The effect of protein corona composition on the interaction of carbon nanotubes with human blood platelets

Carbon nanotubes (CNT) are one of the most promising nanomaterials for use in medicine. The blood biocompatibility of CNT is a critical safety issue. In the bloodstream, proteins bind to CNT through non-covalent interactions to form a protein corona, thereby largely defining the biological properties of the CNT. Here, we characterize the interactions of carboxylated-multiwalled carbon nanotubes (CNTCOOH) with common human proteins and investigate the effect of the different protein coronas on the interaction of CNTCOOH with human blood platelets (PLT). Molecular modeling and different photophysical techniques were employed to characterize the binding of albumin (HSA), fibrinogen (FBG), γ-globulins (IgG) and histone H1 (H1) on CNTCOOH. We found that the identity of protein forming the corona greatly affects the outcome of CNTCOOH's interaction with blood PLT. Bare CNTCOOH-induced PLT aggregation and the release of platelet membrane microparticles (PMP). HSA corona attenuated the PLT aggregating activity of CNTCOOH, while FBG caused the agglomeration of CNTCOOH nanomaterial, thereby diminishing the effect of CNTCOOH on PLT. In contrast, the IgG corona caused PLT fragmentation, and the H1 corona induced a strong PLT aggregation, thus potentiating the release of PMP.

Oxidative stress-mediated antibacterial activity of graphene oxide and reduced graphene oxide in Pseudomonas aeruginosa

Background

Graphene holds great promise for potential use in next-generation electronic and photonic devices due to its unique high carrier mobility, good optical transparency, large surface area, and biocompatibility. The aim of this study was to investigate the antibacterial effects of graphene oxide (GO) and reduced graphene oxide (rGO) in Pseudomonas aeruginosa. In this work, we used a novel reducing agent, betamercaptoethanol (BME), for synthesis of graphene to avoid the use of toxic materials. To uncover the impacts of GO and rGO on human health, the antibacterial activity of two types of graphene-based material toward a bacterial model P. aeruginosa was studied and compared.

Methods

The synthesized GO and rGO was characterized by ultraviolet-visible absorption spectroscopy, particle-size analyzer, X-ray diffraction, scanning electron microscopy and Raman spectroscopy. Further, to explain the antimicrobial activity of graphene oxide and reduced graphene oxide, we employed various assays, such as cell growth, cell viability, reactive oxygen species generation, and DNA fragmentation.

Results

Ultraviolet-visible spectra of the samples confirmed the transition of GO into graphene. Dynamic light-scattering analyses showed the average size among the two types of graphene materials. X-ray diffraction data validated the structure of graphene sheets, and high-resolution scanning electron microscopy was employed to investigate the morphologies of prepared graphene. Raman spectroscopy data indicated the removal of oxygen-containing functional groups from the surface of GO and the formation of graphene. The exposure of cells to GO and rGO induced the production of superoxide radical anion and loss of cell viability. Results suggest that the antibacterial activities are contributed to by loss of cell viability, induced oxidative stress, and DNA fragmentation.

Conclusion

The antibacterial activities of GO and rGO against P. aeruginosa were compared. The loss of P. aeruginosa viability increased in a dose- and time-dependent manner. Exposure to GO and rGO induced significant production of superoxide radical anion compared to control. GO and rGO showed dose-dependent antibacterial activity against P. aeruginosa cells through the generation of reactive oxygen species, leading to cell death, which was further confirmed through resulting nuclear fragmentation. The data presented here are novel in that they prove that GO and rGO are effective bactericidal agents against P. aeruginosa, which would be used as a future antibacterial agent.

Cell specific cytotoxicity and uptake of graphene nanoribbons

The synthesis of oxidized graphene nanoribbons (O-GNR) via longitudinal unzipping of carbon nanotubes opens avenues for their further development for a variety of biomedical applications. Evaluation of the cyto- and bio-compatibility is necessary to develop any new material for in vivo biomedical applications. In this study, we report the cytotoxicity screening of O-GNRs water-solubilized with PEG-DSPE (1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)]), using six different assays, in four representative cell lines; Henrietta Lacks cells (HeLa) derived from cervical cancer tissue, National Institute of Health 3T3 mouse fibroblast cells (NIH-3T3), Sloan Kettering breast cancer cells (SKBR3) and Michigan cancer foundation-7 breast cancer cells (MCF7). These cell lines significantly differed in their response to O-GNR-PEG-DSPE formulations; assessed and evaluated using various endpoints (lactate dehydrogenase (LDH) release, cellular metabolism, lysosomal integrity and cell proliferation) for cytotoxicity. In general, all the cells showed a dose-dependent (10-400 μg/ml) and time-dependent (12-48 h) decrease in cell viability. However, the degree of cytotoxicity was significantly lower in MCF7 or SKBR3 cells compared to HeLa cells. These cells were 100% viable upto 48 h, when incubated at 10 μg/ml O-GNR-PEG-DSPE concentration, and showed decrease in cell viability above this concentration with ~78% of cells viable at the highest concentration (400 μg/ml). In contrast, significant cell death (5-25% cell death depending on the time point, and the assay) was observed for HeLa cells even at a low concentration of 10 μg/ml. The decrease in cell viability was steep with increase in concentration with the CD(50) values ≥ 100 μg/ml depending on the assay, and time point. Transmission electron microscopy of the various cells treated with the O-GNR solutions show higher uptake of the O-GNR-PEG-DSPEs into HeLa cells compared to other cell types. Additional analysis indicates that this increased uptake is the dominant cause of the significantly higher toxicity exhibited by HeLa cells. The results suggest that water-solubilized O-GNR-PEG-DSPEs have a heterogenous cell-specific cytotoxicity, and have significantly different cytotoxicity profile compared to graphene nanoparticles prepared by the modified Hummer's method (graphene nanoparticles prepared by oxidation of graphite, and its mechanical exfoliation) or its variations.

The primacy of physicochemical characterization of nanomaterials for reliable toxicity assessment: a review of the zebrafish nanotoxicology model

Engineered nanomaterials (ENMs) have become increasingly prevalent in the past two decades in academic, medical, commercial, and industrial settings. The unique properties imbued with nanoparticles, as the physiochemical properties change from the bulk material to the surface atoms, present unique and often challenging characteristics that larger macromolecules do not possess. While nanoparticle characteristics are indeed exciting for unique chemistries, surface properties, and diverse applications, reports of toxicity and environmental impacts have tempered this enthusiasm and given cause for an exponential increase for concomitant nanotoxicology assessment. Currently, nanotoxicology is a steadily growing with new literature and studies being published more frequently than ever before; however, the literature reveals clear, inconsistent trends in nanotoxicological assessment. At the heart of this issue are several key problems including the lack of validated testing protocols and models, further compounded by inadequate physicochemical characterization of the nanomaterials in question and the seminal feedback loop of chemistry to biology back to chemistry. Zebrafish (Danio rerio) are emerging as a strong nanotoxicity model of choice for ease of use, optical transparency, cost, and high degree of genomic homology to humans. This review attempts to amass all contemporary nanotoxicology studies done with the zebrafish and present as much relevant information on physicochemical characteristics as possible. While this report is primarily a physicochemical summary of nanotoxicity studies, we wish to strongly emphasize that for the proper evolution of nanotoxicology, there must be a strong marriage between the physical and biological sciences. More often than not, nanotoxicology studies are reported by groups dominated by one discipline or the other. Regardless of the starting point, nanotoxicology must be seen as an iterative process between chemistry and biology. It is our sincere hope that the future will introduce a paradigm shift in the approach to nanotoxicology with multidisciplinary groups for data analysis to produce predictive and correlative models for the end goal of rapid preclinical development of new therapeutics into the clinic or insertion into environmental protection.

Cytotoxicity of Single-Walled Carbon Nanotubes with Human Ocular Cells

In this paper, we report the first study on cytotoxicity of single-walled carbon nanotubes (SWCNTs) and theirs derivatives with human ocular cells, such as ARPE-19 cells. In particular, we have systematically investigated the cytotoxicity of SWCNTs, hydroxyl-functionalized SWCNTs (SWCNT-OH), and carboxylic functionalized SWCNTs (SWCNT-COOH) with ARPE-19 cells by examining their influence on the cell morphology, viability, oxidative stress, membrane integrity and apoptosis. To this end, various methods, including optical micrography, CCK-8 assay, LDH assay, SOD assay, TEM and Apoptosis assay, have been used in this study. Our results suggest that SWCNTs could cause an decrease in the cell survival rate, changes in the SOD level, membrane integrity and cell apoptosis, indicating a high toxicity to ARPE-19 cells. However, chemical functionalization of SWCNTs with –OH and –COOH groups was found to significantly improve the biocompatibility of SWCNTs. Among the SWCNTs and their derivatives studied in this work, the SWCNT-COOH exhibits the best biocompatibility to ARPE-19 cells.

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.