Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory

Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature

INTRODUCTION

Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations.

AREAS COVERED

The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process.

EXPERT OPINION

Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data

BACKGROUND

The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles.

RESULTS

Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R² and Q²-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity.

CONCLUSIONS

The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.

Molecular dynamics insight of interaction between the functionalized-carbon nanotube and cancerous cell membrane in doxorubicin delivery

BACKGROUND AND OBJECTIVE

Doxorubicin (DOX) is a known anticancer drug which is widely used in cancer therapy. Carbon nanotubes (CNTs) are among the most promising platforms for smart drug delivery applications. However, due to the toxicity and their low sulubility their application is limited and their functionalization with wide range of biomolecules are suggested. Therefore, the functionalized carbon nanotubes (f-CNT) with carboxyl (CNT-COO) and folic acid (CNT-COO-FA) were investigated as drug-carrier.

METHODS

Molecular dynamics (MD) simulation along with the Density Functional Theory (DFT) methods are being used to study the drug loading process on functionalized carbon nanotubes.

RESULTS

The results indicate that doxorubicin molecules interact more with CNT-COO-FA than CNT-COO. The embedded dipalmitoylphosphatidylcholine (DPPC) lipid bilayer with a folate receptor was considered a cancerous cell's representative model. Then the drug release from the f-CNTs near the lipid bilayer was simulated. The results showed that CNT-COO-FA with a pH and ligand-sensitive mechanism strongly interacts with cancerous cells, which led to higher drug release, in agreement with the experimental results. The conformational changes of the lipid bilayer and folate receptor during drug release were evaluated. The analysis showed that drug release from CNT-COO-FA has significantly changed lipid bilayer and receptor conformations. The obtained results were interpreted and justified by considering the molecular mechanisms which control the drug delivery in the studied systems.

CONCLUSIONS

Based on the obtained results, CNT-COO-FA has a better performance during the drug release compared to CNT-COO in delivering doxorubicin. Both pH and ligand sensitive mechanisms are found to be responsible for higher drug delivery efficiency of CNT-COO-FA. In contrast, CNT-COO can only enhance drug delivery efficiently with a pH-sensitive mechanism.

Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology

Worldwide nanotechnology development and application have fueled many scientific advances, but technophilic expectations and technophobic demands must be counterbalanced in parallel. Some of the burning issues today are the following: (1) Where is nano today? (2) How good are the communication and investment networks between academia/research and governments? (3) Is there any spotlight application for nanotechnology? Nanomedicine is a particular arm of nanotechnology within the healthcare landscape, focused on diagnosis, treatment, and monitoring of emerging (such as coronavirus disease 2019, COVID-19) and contemporary (including diabetes, cardiovascular diseases, neurodegenerative disorders, and cancer) diseases. However, it may only represent the bright side of the coin. In fact, in the recent past, the concept of nanotoxicology has emerged to address the dark shadows of nanomedicine. The nanomedicine field requires more nanotoxicological studies to identify undesirable effects and guarantee safety. Here, we provide an overall perspective on nanomedicine and nanotoxicology as central pieces of the giant puzzle of nanotechnology. First, the impact of nanotechnology on education and research is highlighted, followed by market trends and scientific output tendencies. In the next section, the nanomedicine and nanotoxicology dilemma is addressed through the interplay of in silico, in vitro, and in vivo models with the support of omics and microfluidic approaches. Lastly, a reflection on the regulatory issues and clinical trials is provided. Finally, some conclusions and future perspectives are proposed for a clearer and safer translation of nanomedicines from the bench to the bedside.

SAFETY PROFILE AND PREVENTION OF COGNITIVE DEFICIT IN ALZHEIMER’S DISEASE MODEL OF GRAPHENE FAMILY NANOMATERIALS, TUCUMA OIL (Astrocaryum vulgare) AND ITS SYNERGISMS

Alzheimer's disease is a worldwide health issue, and there are currently no treatments that can stop this disease. Oxidized graphene derivatives have gained prominence in use in biological systems due to their excellent physical-chemical characteristics, biocompatibility and ability to overcome the blood-brain barrier. Other substances highlighted are those of natural origin from the Amazon biome, such as tucuma, a fruit whose oil has been widely studied in therapeutic applications. Thus, the aim of this study was to investigate the action of graphene oxide, reduced graphene oxide and tucuma oil, isolated and combined, as an alternative for treatment of Alzheimer's disease through studies in silico, in vitro, in vivo and ex vivo. Computational simulation via docking was used to verify the affinity of the substances with the proteins β-amyloid and acetylcholinesterase, in which the reduced graphene oxide was the one that showed the most favorable interaction. The results of the ab initio simulation showed that the synergism between the nanostructures and the oil occurs through physical adsorption. The experimental results revealed that the substances and their combinations were nontoxic, both at the cellular and systemic level. In general, all treatments had positive results against induced memory deficit, but reduced graphene oxide was the most prominent, as it was able to protect against memory damage in all behavioral tests performed, with anticholinesterase activity and antioxidant effect. In conclusion, the reduced graphene oxide is, among the treatments studied, the one with great therapeutic potential to be investigated in the treatment of this disease.

Single-walled silicon nanotube as an exceptional candidate to eliminate SARS-CoV-2: a theoretical study

In this work, computational chemistry methods were used to study a silicon nanotube (Si₁₉₂H₁₆) as possible virucidal activity against SARS-CoV-2. This virus is responsible for the COVID-19 disease. DFT calculations showed that the structural parameters of the Si₁₉₂H₁₆ nanotube are in agreement with the theoretical/experimental parameters reported in the literature. The low energy gap value (0.29 eV) shows that this nanotube is a semiconductor and exhibits high reactivity. For nanomaterials to be used as virucides, they need to have high reactivity and high inhibition constant values. Therefore, the adsorption of ³O₂ and H₂O on the surface of Si₁₉₂H₁₆ (Si₁₉₂H₁₆@O₂-H₂O) was performed. In this process, the formation and activation energies were -51.63 and 16.62 kcal/mol, respectively. Molecular docking calculations showed that the Si₁₉₂H₁₆ and Si₁₉₂H₁₆@O₂H-OH nanotubes bind favorably on the receptor-binding domain of the SARS-CoV-2 spike protein with binding energy of -11.83 (Ki = 2.13 nM) and -11.13 (Ki = 6.99 nM) kcal/mol, respectively. Overall, the results obtained herein indicate that the Si₁₉₂H₁₆ nanotube is a potential candidate to be used against COVID-19 from reactivity process and/or steric impediment in the S-protein.Communicated by Ramaswamy H. Sarma.

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors.

New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

Engineered nanomaterials: From their properties and applications, to their toxicity towards marine bivalves in a changing environment

As a consequence of their unique characteristics, the use of Engineered Nanomaterials (ENMs) is rapidly increasing in industrial, agricultural products, as well as in environmental technology. However, this fast expansion and use make likely their release into the environment with particular concerns for the aquatic ecosystems, which tend to be the ultimate sink for this type of contaminants. Considering the settling behaviour of particulates, benthic organisms are more likely to be exposed to these compounds. In this way, the present review aims to summarise the most recent data available from the literature on ENMs behaviour and fate in aquatic ecosystems, focusing on their ecotoxicological impacts towards marine and estuarine bivalves. The selection of ENMs presented here was based on the OECD's Working Party on Manufactured Nanomaterials (WPMN), which involves the safety testing and risk assessment of ENMs. Physical-chemical characteristics and properties, applications, environmental relevant concentrations and behaviour in aquatic environment, as well as their toxic impacts towards marine bivalves are discussed. Moreover, it is also identified the impacts derived from the simultaneous exposure of marine organisms to ENMs and climate changes as an ecologically relevant scenario.

Interactions of nanomaterials with ion channels and related mechanisms

The pharmacological potential of nanotechnology, especially in drug delivery and bioengineering, has developed rapidly in recent decades. Ion channels, which are easily targeted by external agents, such as nanomaterials (NMs) and synthetic drugs, due to their unique structures, have attracted increasing attention in the fields of nanotechnology and pharmacology for the treatment of ion channel-related diseases. NMs have significant effects on ion channels, and these effects are manifested in many ways, including changes in ion currents, kinetic characteristics and channel distribution. Subsequently, intracellular ion homeostasis, signalling pathways, and intracellular ion stores are affected, leading to the initiation of a range of biological processes. However, the effect of the interactions of NMs with ion channels is an interesting topic that remains obscure. In this review, we have summarized the recent research progress on the direct and indirect interactions between NMs and ion channels and discussed the related molecular mechanisms, which are crucial to the further development of ion channel-related nanotechnological applications.

A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective

In this study, photocatalytic properties and in vitro cytotoxicity of 29 TiO2-based multi-component nanomaterials (i.e., hybrids of more than two composition types of nanoparticles) were evaluated using a combination of the experimental testing and supervised machine learning modeling. TiO2-based multi-component nanomaterials with metal clusters of silver, and their mixtures with gold, palladium, and platinum were successfully synthesized. Two activities, photocatalytic activity and cytotoxicity, were studied. A novel cheminformatic approach was developed and applied for the computational representation of the photocatalytic activity and cytotoxicity effect. In this approach, features of investigated TiO2-based hybrid nanomaterials were reflected by a series of novel additive descriptors for hybrid and hybrid nanostructures (denoted as "hybrid nanosctructure descriptors"). These descriptors are based on quantum chemical calculations and the Smoluchowski equation. The obtained experimental data and calculated hybrid-nanostructure descriptors were used to develop novel predictive Quantitative Structure-Activity Relationship computational models (called "nano-QSARmix"). The proposed modeling approach is an initial step in the understanding of the relationships between physicochemical properties of hybrid nanoparticles, their toxicity, and photochemical activity under UV-vis irradiation. Acquired knowledge supports the safe-by-design approaches relevant to the development of efficient hybrid nanomaterials with reduced hazardous effects.

Toxicity evaluation of carboxylated carbon nanotubes to the reef-forming tubeworm Ficopomatus enigmaticus (Fauvel, 1923)

In recent years, oxidative stress has been recognized as one of the most common effects of nanoparticles in different organisms. Ficopomatus enigmaticus (Fauvel, 1923), a member of a large family of serpulidae polychates, is an important encrusting organism in a diverse set of marine habitats, from harbours to coral reefs. This species has been previously studied for ecotoxicological purposes, despite the lack of reported studies on this species biochemical response after exposure to different pollutants. For these reasons, and for the first time, a set of biomarkers related to oxidative status were assessed in polychaetes after 28 days of exposure. Furthermore, polychaetes metabolic performance and potential neurotoxicity were investigated. Results clearly demonstrated induced toxicity in the filter-feeder polychaetes after exposure to nanoparticles. Indeed, CNTs altered the biochemical and physiological status of F. enigmaticus, both in terms of energy reserves (reduction of protein and glycogen contents), oxidative status (expressed as damage in cell function such as protein carbonyl content and lipid peroxidation) and activation of antioxidant enzymes defences (Glutathione reductase, Catalase, Glutathione peroxidase and Glutathione S-transferases activities). The present study showed for the first time that this species can be used as a model organism for nanoparticle toxicology.

Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework

The European market for pesticides is currently legislated through the well-developed Regulation (EC) No. 1107/2009. This regulation promotes the competitiveness of European agriculture, recognizing the necessity of safe pesticides for human and animal health and the environment to protect crops against pests, diseases and weeds. In this sense, nanotechnology can provide a tremendous opportunity to achieve a more rational use of pesticides. However, the lack of information regarding nanopesticides and their fate and behavior in the environment and their effects on human and animal health is inhibiting rapid nanopesticide incorporation into European Union agriculture. This review analyzes the recent state of knowledge on nanopesticide risk assessment, highlighting the challenges that need to be overcame to accelerate the arrival of these new tools for plant protection to European agricultural professionals. Novel nano-Quantitative Structure-Activity/Structure-Property Relationship (nano-QSAR/QSPR) tools for risk assessment are analyzed, including modeling methods and validation procedures towards the potential of these computational instruments to meet the current requirements for authorization of nanoformulations. Future trends on these issues, of pressing importance within the context of the current European pesticide legislative framework, are also discussed. Standard protocols to make high-quality and well-described datasets for the series of related but differently sized nanoparticles/nanopesticides are required.