Perturbational profiling of nanomaterial biologic activity

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Mechanistic Understanding of Protein Corona Formation around Nanoparticles: Old Puzzles and New Insights

Although a wide variety of nanoparticles (NPs) have been engineered for use as disease markers or drug delivery agents, the number of nanomedicines in clinical use has hitherto remained small. A key obstacle in nanomedicine development is the lack of a deep mechanistic understanding of NP interactions in the bio-environment. Here, the focus is on the biomolecular adsorption layer (protein corona), which quickly enshrouds a pristine NP exposed to a biofluid and modifies the way the NP interacts with the bio-environment. After a brief introduction of NPs for nanomedicine, proteins, and their mutual interactions, research aimed at addressing fundamental properties of the protein corona, specifically its mono-/multilayer structure, reversibility and irreversibility, time dependence, as well as its role in NP agglomeration, is critically reviewed. It becomes quite evident that the knowledge of the protein corona is still fragmented, and conflicting results on fundamental issues call for further mechanistic studies. The article concludes with a discussion of future research directions that should be taken to advance the understanding of the protein corona around NPs. This knowledge will provide NP developers with the predictive power to account for these interactions in the design of efficacious nanomedicines.

Colloidal stability classification of TiO2 nanoparticles in artificial and in natural waters by cluster analysis and a global stability index: Influence of standard and natural colloidal particles

In the field of exposure-driven risk assessment of engineered nanoparticles (NPs), the highly complex interactions of NPs with natural components in surface waters are considered key factors to understand their fate and behavior in the environment. However, since experimental approaches aiming at imitating environmentally relevant conditions include many parameters and lead to a high number of outcomes, statistical tools can be extremely useful to support the results' interpretation. In this context, a multimethod approach was applied to investigate the colloidal behavior of TiO₂ NPs in both artificial waters and natural brackish water (from the Venice lagoon, Italy), in the presence of standard kaolinite and natural organic matter (NOM), or of the fine fraction of natural colloidal particles (NCPs) from the lagoon sediment. In detail, the experimental data obtained, i.e. hydrodynamic size, surface charge and sedimentation velocity values, were i) statistically treated by hierarchical clustering and ii) merged into a global stability index (I_G). The hierarchical clustering allowed to group the dispersions into three colloidal stability classes, where the main discriminant was the medium composition (i.e. ionic strength and presence of NOM), while the I_G allowed to establish a colloidal stability ranking of the dispersions within each class. Moreover, the comparison among the different dispersions suggested that kaolinite could be considered as a suitable surrogate for NCPs, to estimate the colloidal behavior and environmental fate of TiO₂ NPs in natural aqueous media.

Biomarkers of nanomaterials hazard from multi-layer data

There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.

NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO₂-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO₂-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

Review of aquatic toxicity of pharmaceuticals and personal care products to algae

Pharmaceuticals and Personal Care Products (PPCPs) have been frequently detected in the environment around the world. Algae play a significant role in aquatic ecosystem, thus the influence on algae may affect the life of higher trophic organisms. This review provides a state-of-the-art overview of current research on the toxicity of PPCPs to algae. Nanoparticles, contained in personal care products, also have been considered as the ingredients of PPCPs. PPCPs could cause unexpected effects on algae and their communities. Chlorophyta and diatoms are more accessible and sensitive to PPCPs. Multiple algal endpoints should be considered to provide a complete evaluation on PPCPs toxicity. The toxicity of organic ingredients in PPCPs could be predicted through quantitative structure-activity relationship model, whereas the toxicity of nanoparticles could be predicted with limitations. Light irradiation can change the toxicity through affecting algae and PPCPs. pH and natural organic matter can affect the toxicity through changing the existence of PPCPs. For joint and tertiary toxicity, experiments could be conducted to reveal the toxic mechanism. For multiple compound mixture toxicity, concentration addition and independent addition models are preferred. However, there has no empirical models to study nanoparticle-contained mixture toxicity. Algae-based remediation is an emerging technology to prevent the release of PPCPs from water treatment plants. Although many individual algal species are identified for removing a few compounds from PPCPs, algal-bacterial photobioreactor is a preferable alternative, with higher chances for industrial applications.

NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features

Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data and learn quantitative feature-toxicity relationships to yield predictive models. In this work, we adopted a principled approach to this problem by formulating a novel feature space based on intrinsic and extrinsic physicochemical properties, including periodic table properties but exclusive of in vitro characteristics such as cell line, cell type, and assay method. An optimal hypothesis space was developed by applying variance inflation analysis to the correlation structure of the features. Consequent to a stratified train-test split, the training dataset was balanced for the toxic outcomes and a mapping was then achieved from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models, namely, logistic regression, random forest, support vector machines, and neural networks. Evaluation on an unseen test set yielded >96% balanced accuracy for the random forest, and neural network with one-hidden-layer models. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and an embedded applicability check. Interpretability investigations of the models identified the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the best of the above models. NanoTox is the first open-source nanotoxicology pipeline, freely available under the GNU General Public License (https://github.com/NanoTox).

Induced pluripotent stem cell-derived vascular networks to screen nano-bio interactions

The vascular bioactivity/safety of nanomaterials is typically evaluated by animal testing, which is of low throughput and does not account for biological differences between animals and humans such as ageing, metabolism and disease profiles. The development of personalized human in vitro platforms to evaluate the interaction of nanomaterials with the vascular system would be important for both therapeutic and regenerative medicine. A library of 30 nanoparticle (NP) formulations, in use in imaging, antimicrobial and pharmaceutical applications, was evaluated in a reporter zebrafish model of vasculogenesis and then tested in personalized humanized models composed of human-induced pluripotent stem cell (hiPSC)-derived endothelial cells (ECs) with "young" and "aged" phenotypes in 3 vascular network formats: 2D (in polystyrene dish), 3D (in Matrigel) and in a blood vessel on a chip. As a proof of concept, vascular toxicity was used as the main readout. The results show that the toxicity profile of NPs to hiPSC-ECs was dependent on the "age" of the endothelial cells and vascular network format. hiPSC-ECs were less susceptible to the cytotoxicity effect of NPs when cultured in flow than in static conditions, the protective effect being mediated, at least in part, by glycocalyx. Overall, the results presented here highlight the relevance of in vitro hiPSC-derived vascular systems to screen vascular nanomaterial interactions.

Electrodynamic assisted self-assembled fibrous hydrogel microcapsules: a novel 3D in vitro platform for assessment of nanoparticle toxicity

Nanoparticle (NP) toxicity assessment is a critical step in assessing the health impacts of NP exposure to both consumers and occupational workers. In vitro assessment models comprising cells cultured in a two-dimensional tissue culture plate (2D-TCP) are an efficient and cost-effective choice for estimating the safety risks of NPs. However, in vitro culture of cells in 2D-TCPs distorts cell–integrin and cell–cell interactions and is not able to replicate an in vivo phenotype. Three-dimensional (3D) in vitro platforms provide a unique alternative to bridge the gap between traditional 2D in vitro and in vivo models. In this study, novel microcapsules of alginate hydrogel incorporated with natural polymeric nanofibers (chitin nanofibrils) and synthetic polymeric nanofibers poly(lactide-co-glycolide) are designed as a 3D in vitro platform. This study demonstrates for the first time that electrodynamic assisted self-assembled fibrous 3D hydrogel (3D-SAF hydrogel) microcapsules with a size in the range of 300–500 μm in diameter with a Young's modulus of 12.7–42 kPa can be obtained by varying the amount of nanofibers in the hydrogel precursor solutions. The 3D-SAF microcapsules were found to mimic the in vivo cellular microenvironment for cells to grow, as evaluated using A549 cells. Higher cellular spreading and prolonged proliferation of A549 cells were observed in 3D-SAF microcapsules compared to control microcapsules without the nanofibers. The 3D-SAF microcapsule integrated well plate was used to assess the toxicity of model NPs, e.g., Al₂O₃ and ZnO. The toxicity levels of the model NPs were found to be dependent on the chemistry of the NPs and their physical agglomeration in the test media. Our results demonstrate that 3D-SAF microcapsules with an in vivo mimicking microenvironment can be developed as a physiologically relevant platform for high-throughput toxicity screening of NPs or pharmaceutical drugs.

Electrohydrodynamic-assisted fabrication of novel nano-net-nanofibrous 3D-SAF hydrogel microcapsules leads to them having tunable mechanical and cell adhesive properties that are applicable to diverse biomedical fields.

Use of size-dependent electron configuration fingerprint to develop general prediction models for nanomaterials

Due to the lack of nano descriptors that can appropriately represent the wide chemical space of engineered nanomaterials (ENMs), applicability domain of nano-quantitative structure-activity relationship models are limited to certain types of ENMs, such as metal oxides, metals, carbon-based nanomaterials, or quantum dots. In this study, a size-dependent electron configuration fingerprint (SDEC FP) was introduced to estimate the quantity of electrons based on the core, doping, and coating materials of ENMs in different sizes. SDEC FP was used in prediction model development and nanostructure similarity analysis on datasets including metal and carbon-based nanomaterials with and without surface modifications. Cytotoxicity and zeta potential prediction models developed with SDEC FP achieved good prediction accuracies on test set. Nanostructure similarity analysis was performed through principal component analysis which showed that structural similarity between ENMs measured by SDEC FP was highly correlated with their properties.

The Current Landscape of Novel Formulations and the Role of Mathematical Modeling in Their Development

Drug delivery is an integral part of the drug development process, influencing safety and efficacy of active pharmaceutical ingredients. The application of nanotechnology has enabled the discovery of novel formulations for numerous therapeutic purposes across multiple disease areas. However, evaluation of novel formulations in clinical scenarios is slow and hampered due to various ethical and logistical barriers. Computational models have the ability to integrate existing domain knowledge and mathematical correlations, to rationalize the feasibility of using novel formulations for safely enhancing drug delivery, identifying suitable candidates, and reducing the burden on preclinical and clinical studies. In this review, types of novel formulations and their application through several routes of administration and the use of modeling approaches that can find application in different stages of the novel formulation development process are discussed.

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

Despite considerable efforts, the properties that drive the cytotoxicity of engineered nanomaterials (ENMs) remain poorly understood. Here, the authors inverstigate a panel of 31 ENMs with different core chemistries and a variety of surface modifications using conventional in vitro assays coupled with omics-based approaches. Cytotoxicity screening and multiplex-based cytokine profiling reveals a good concordance between primary human monocyte-derived macrophages and the human monocyte-like cell line THP-1. Proteomics analysis following a low-dose exposure of cells suggests a nonspecific stress response to ENMs, while microarray-based profiling reveals significant changes in gene expression as a function of both surface modification and core chemistry. Pathway analysis highlights that the ENMs with cationic surfaces that are shown to elicit cytotoxicity downregulated DNA replication and cell cycle responses, while inflammatory responses are upregulated. These findings are validated using cell-based assays. Notably, certain small, PEGylated ENMs are found to be noncytotoxic yet they induce transcriptional responses reminiscent of viruses. In sum, using a multiparametric approach, it is shown that surface chemistry is a key determinant of cellular responses to ENMs. The data also reveal that cytotoxicity, determined by conventional in vitro assays, does not necessarily correlate with transcriptional effects of ENMs.

Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties

Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

Measuring the Stability of Supramolecular Complexes in the Proximity of Single-Walled Carbon Nanotubes

The decoration of SWNTs with supramolecular motifs is a common strategy for their subsequent noncovalent functionalization. However, due to the lack of a standard methodology, there are no quantitative measurements showing the extent to which the supramolecular equilibria are affected by one of the host-guest couple being anchored to the SWNT. Here, we use a method we initially developed to quantify association of small organic molecules to the walls of SWNTs to compare association constants of two host-guest systems, a Hamilton receptor-cyanuric acid derivative and a crown ether-ammonium couple, in solution and when the host is covalently attached to the SWNTs. Our data show that association does occur, but the stability of the complexes is significantly affected, as reflected in a sizable reduction in their association constant, when compared to solution.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Reimagining What We Measure in Atherosclerosis-a "Phenotype Stack"

The term phenotype is so commonly used that we often assume that we each mean the same thing. The general definition, the set of observable characteristics of an individual resulting from the interaction of their genotype with the environment, is often left to the eye of the beholder. Whether applied to the multiple levels of biological phenomena or the intact human being, our ability to characterize, classify, and analyze phenotype has been limited by measurement deficits, computing limitations, and a culture that avoids the generalizable. With the advent of modern technology, there is the potential for a revolution in phenotyping, which incorporates old and new in structured ways to dramatically advance basic understanding of biology and behavior and to lead to major improvements in clinical care and public health. This revolution in how we think about phenotypes will require a radical change in the scale at which biomedicine operates with significant changes in the unit of action, which will have far-reaching implications for how care, translation, and discovery are implemented.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Resolution Limit of Taylor Dispersion: An Exact Theoretical Study

Taylor dispersion is a microfluidic analytical technique with a high dynamic range and therefore is suited well to measuring the hydrodynamic radius of small molecules, proteins, supramolecular complexes, macromolecules, nanoparticles and their self-assembly. Here we calculate an unaddressed yet fundamental property: the limit of resolution, which is defined as the smallest change in the hydrodynamic radius that Taylor dispersion can resolve accurately and precisely. Using concepts of probability theory and inferential statistics, we present a comprehensive theoretical approach, addressing uniform and polydisperise particle systems, which involve either model-based or numerical analyses. We find a straightforward scaling relationship in which the resolution limit is linearly proportional to the optical-extinction-weighted average hydrodynamic radius of the particle systems.

Nanomaterials and nanocomposite applications in veterinary medicine

Nowadays, nanotechnology has made huge, significant advancements in biotechnology and biomedicine related to human and animal science, including increasing health safety, production, and the elevation of national income. There are various fields of nanomaterial applications in veterinary medicine such as efficient diagnostic and therapeutic tools, drug delivery, animal nutrition, breeding and reproduction, and valuable additives. Additional benefits include the detection of pathogens, protein, biological molecules, antimicrobial agents, feeding additives, nutrient delivery, and reproductive aids. There are many nanomaterials and nanocomposites that can be used in nanomedicine such as metal nanoparticles, liposomes, carbon nanotubes, and quantum dots. In the near future, nanotechnology research will have the ability to produce novel tools for improving animal health and production. Therefore, this chapter was undertaken to spotlight novel methods created by nanotechnology for application in the improvement of animal health and production. In addition, the toxicity of nanomaterials is fully discussed to avoid the suspected health hazards of toxicity for animal health safety.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

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.

Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This will require substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the current environment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies will enable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissue or liquid biopsy, has "co-clinical" modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labels has directly contributed to the paucity of etiologic and mechanistic understanding by directly reducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal 'models' and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animal models causes a failure of translation and is one form of 'phenotype gap.' In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.

Multiple linear regression applied to predicting droplet size of complex perfluorocarbon nanoemulsions for biomedical applications

Multiple linear regression (MLR) modeling as a novel methodological advancement for design, development, and optimization of perfluorocarbon nanoemulsions (PFC NEs) is presented. The goal of the presented work is to develop MLR methods applicable to design, development, and optimization of PFC NEs in broad range of biomedical uses. Depending on the intended use of PFC NEs as either therapeutics or diagnostics, NE composition differs in respect to specific applications (e.g. magnetic resonance imaging, drug delivery, etc). PFC NE composition can significantly impact on PFC NE droplet size which impacts the NE performance and quality. We demonstrated earlier that microfluidization combined with sonication produces stable emulsions with high level of reproducibility. The goal of the presented work was to establish correlation between droplet size and composition in complex PFC-in-oil-in-water NEs while manufacturing process parameters are kept constant. Under these conditions, we demonstrate that MLR model can predict droplet size based on formulation variables such as amount and type of PFC oil and hydrocarbon oil. To the best of our knowledge, this is the first report where PFC NE composition was directly related to its colloidal properties and MLR used to predict colloidal properties from composition variables.

Genomics and immunity of the Mediterranean mussel Mytilus galloprovincialis in a changing environment

The Mediterranean mussel (Mytilus galloprovincialis) is a marine invasive species cultured all over the world. Mussels are an appreciated resource in local aquaculture enterprises because of their robust production and resilience that translates into a reliable economic value. So far, no massive mortalities have been reported in natural or cultured populations of this species. In the last years, the knowledge about its immune system has greatly improved but there are still many questions to be answered. One of them is why mussels, with their high filtering activity, are able to be exposed to a high number of potential pathogens without getting infected and without developing an elevated inflammatory response. The sequencing of the mussel genome has revealed a very complex organization with high heterozygosity, abundance of repetitive sequences and extreme intraspecific sequence diversity among individuals, mainly in immune related genes. Among those genes, antimicrobial peptides are the most expressed gene families in mussels, highly polymorphic and with antimicrobial effect against molluscs pathogens, but also against pathogens of lower vertebrates and humans. The combination of a complex genome with the adaptation of mussel immune system to a changing environment could explain this high variability, not only in immune-related genes, but also in the functional response among individuals sampled in the same location and date.

Biocompatibility of Amine-Functionalized Silica Nanoparticles: The Role of Surface Coverage

Here, amorphous silica nanoparticles (NPs), one of the most abundant nanomaterials, are used as an example to illustrate the utmost importance of surface coverage by functional groups which critically determines biocompatibility. Silica NPs are functionalized with increasing amounts of amino groups, and the number of surface exposed groups is quantified and characterized by detailed NMR and fluorescamine binding studies. Subsequent biocompatibility studies in the absence of serum demonstrate that, irrespective of surface modification, both plain and amine-modified silica NPs trigger cell death in RAW 264.7 macrophages. The in vitro results can be confirmed in vivo and are predictive for the inflammatory potential in murine lungs. In the presence of serum proteins, on the other hand, a replacement of only 10% of surface-active silanol groups by amines is sufficient to suppress cytotoxicity, emphasizing the relevance of exposure conditions. Mechanistic investigations identify a key role of lysosomal injury for cytotoxicity only in the presence, but not in the absence, of serum proteins. In conclusion, this work shows the critical need to rigorously characterize the surface coverage of NPs by their constituent functional groups, as well as the impact of serum, to reliably establish quantitative nanostructure activity relationships and develop safe nanomaterials.

Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape

Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, which is one of the barriers to their broader use and acceptance, especially for regulatory purposes. Quantitative structure-activity relationships (QSARs) are in silico models based on the assumption that the activity of a substance is related to its chemical structure. These models can be used to provide information on (eco)toxicological effects in hazard assessment. In an environmental risk assessment, environmental exposure models can be used to estimate the predicted environmental concentration (PEC). In addition, physiologically based kinetic (PBK) models can be used in various ways to support a human health risk assessment. In this paper, we first propose model reporting templates for systematically and transparently describing models that could potentially be used to support regulatory risk assessments of MNs, for example under the REACH regulation. The model reporting templates include (a) the adaptation of the QSAR Model Reporting Format (QMRF) to report models for MNs, and (b) the development of a model reporting template for PBK and environmental exposure models applicable to MNs. Second, we show the usefulness of these templates to report different models, resulting in an overview of the landscape of available computational models for MNs.

Comparison of conventional and advanced in vitro models in the toxicity testing of nanoparticles

Humans are exposed to a wide variety of nanoparticles (NPs) present in the environment, in consumer, health and medical products, and in food. Conventional cytotoxicity testing compared to animal testing is less expensive, faster and avoids ethical problems at the expense of a lower predictive value. New cellular models and exposure conditions have been developed to overcome the limitations of conventional cell culture and obtain more predictive data. The use of three-dimensional culture, co-culture and inclusion of mechanical stimulation can provide physiologically more relevant culture conditions. These systems are particularly relevant for oral, respiratory and intravenous exposure to NPs and it may be assumed that physiologically relevant application of the NPs can improve the predictive value of in vitro testing. Various groups have used advanced culture and exposure systems, but few direct comparisons between data from conventional cultures and from advanced systems exist. In silico models may present another option to predict human health risk by NPs without using animal studies. In the absence of validation, the question whether these alternative models provide more predictive data than conventional testing remains elusive.

Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials

The present study is based on the application of a multivariate statistical analysis approach for the selection of optimal descriptors of nanomaterials with the objective of robust qualitative modeling of their toxicity. A novel data mining protocol has been developed for the selection of an optimal subset of descriptors of nanomaterials by using the well-known multivariate method principal component analysis (PCA). The selected subsets of descriptors were validated for qualitative modeling of the toxicity of nanomaterials in the PC space. The analysis and validation of the proposed schemes were based on five decisive nanomaterial toxicity data sets available in the published literature. Optimal descriptors were selected on the basis of the maximum loading criteria and using a threshold value of cumulative variance ≤90% on PC directions. A maximum inter-class separation(B) and the minimum intra-classes separation(A) were obtained for toxic vs. nontoxic nanomaterials in the PC space with the selected subsets of optimal descriptors compared to their other combinations for each of the datasets.

Peptide-based nanoprobes for molecular imaging and disease diagnostics

Pathological changes in a diseased site are often accompanied by abnormal activities of various biomolecules in and around the involved cells. Identifying the location and expression levels of these biomolecules could enable early-stage diagnosis of the related disease, the design of an appropriate treatment strategy, and the accurate assessment of the treatment outcomes. Over the past two decades, a great diversity of peptide-based nanoprobes (PBNs) have been developed, aiming to improve the in vitro and in vivo performances of water-soluble molecular probes through engineering of their primary chemical structures as well as the physicochemical properties of their resultant assemblies. In this review, we introduce strategies and approaches adopted for the identification of functional peptides in the context of molecular imaging and disease diagnostics, and then focus our discussion on the design and construction of PBNs capable of navigating through physiological barriers for targeted delivery and improved specificity and sensitivity in recognizing target biomolecules. We highlight the biological and structural roles that low-molecular-weight peptides play in PBN design and provide our perspectives on the future development of PBNs for clinical translation.

Evaluation of antibacterial activity and toxic metal removal of chemically synthesized magnetic iron oxide titanium coated nanoparticles and application in bacterial treatment

Co-precipitation method was used for preparation of two types of iron oxide nanoparticles coated by titanium dioxide according to divalent salts used. The average size of iron oxide nanoparticles coated by titanium dioxide measured by particle size analyzer, ranged approximately between 20 nm and 100 nm with mean particle size of 60 nm. Characterization of the prepared nanoparticles was done by X-ray diffraction analysis and scanning electron microscope indicating the sole existence of inverse cubic spinel phase of magnetic iron oxide nanoparticles. Further, the antibacterial activity of two prepared iron oxide nanoparticles was evaluated against four pathogenic bacteria where both preparations showed promising antibacterial activities against Gram positive and Gram negative strains which offers a potential application in pharmaceutical and biomedical industries. The antibacterial activity showed high reduction percent after 30 min by 150 μg mL^-1 of nanoparticles prepared. Also, high reduction percent was achieved for removal of iron and manganese ions from polluted water and good effect on decreasing chemical oxygen demand and biochemical oxygen demand concentrations with decreased percent of total nitrogen concentration.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach

Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.

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.

Nanomaterial libraries and model organisms for rapid high-content analysis of nanosafety

Safety analysis of engineered nanomaterials (ENMs) presents a formidable challenge regarding environmental health and safety, due to their complicated and diverse physicochemical properties. Although large amounts of data have been published regarding the potential hazards of these materials, we still lack a comprehensive strategy for their safety assessment, which generates a huge workload in decision-making. Thus, an integrated approach is urgently required by government, industry, academia and all others who deal with the safe implementation of nanomaterials on their way to the marketplace. The rapid emergence and sheer number of new nanomaterials with novel properties demands rapid and high-content screening (HCS), which could be performed on multiple materials to assess their safety and generate large data sets for integrated decision-making. With this approach, we have to consider reducing and replacing the commonly used rodent models, which are expensive, time-consuming, and not amenable to high-throughput screening and analysis. In this review, we present a ‘Library Integration Approach’ for high-content safety analysis relevant to the ENMs. We propose the integration of compositional and property-based ENM libraries for HCS of cells and biologically relevant organisms to be screened for mechanistic biomarkers that can be used to generate data for HCS and decision analysis. This systematic approach integrates the use of material and biological libraries, automated HCS and high-content data analysis to provide predictions about the environmental impact of large numbers of ENMs in various categories. This integrated approach also allows the safer design of ENMs, which is relevant to the implementation of nanotechnology solutions in the pharmaceutical industry.

A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials

As listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.

Toward a systematic exploration of nano-bio interactions

Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

Protein corona: a new approach for nanomedicine design

After administration of nanoparticle (NP) into biological fluids, an NP-protein complex is formed, which represents the "true identity" of NP in our body. Hence, protein-NP interaction should be carefully investigated to predict and control the fate of NPs or drug-loaded NPs, including systemic circulation, biodistribution, and bioavailability. In this review, we mainly focus on the formation of protein corona and its potential applications in pharmaceutical sciences such as prediction modeling based on NP-adsorbed proteins, usage of active proteins for modifying NP to achieve toxicity reduction, circulation time enhancement, and targeting effect. Validated correlative models for NP biological responses mainly based on protein corona fingerprints of NPs are more highly accurate than the models solely set up from NP properties. Based on these models, effectiveness as well as the toxicity of NPs can be predicted without in vivo tests, while novel cell receptors could be identified from prominent proteins which play important key roles in the models. The ungoverned protein adsorption onto NPs may have generally negative effects such as rapid clearance from the bloodstream, hindrance of targeting capacity, and induction of toxicity. In contrast, controlling protein adsorption by modifying NPs with diverse functional proteins or tailoring appropriate NPs which favor selective endogenous peptides and proteins will bring promising therapeutic benefits in drug delivery and targeted cancer treatment.

Biological Surface Adsorption Index of Nanomaterials: Modelling Surface Interactions of Nanomaterials with Biomolecules

Quantitative analysis of the interactions between nanomaterials and their surrounding environment is crucial for safety evaluation in the application of nanotechnology as well as its development and standardization. In this chapter, we demonstrate the importance of the adsorption of surrounding molecules onto the surface of nanomaterials by forming biocorona and thus impact the bio-identity and fate of those materials. We illustrate the key factors including various physical forces in determining the interaction happening at bio-nano interfaces. We further discuss the mathematical endeavors in explaining and predicting the adsorption phenomena, and propose a new statistics-based surface adsorption model, the Biological Surface Adsorption Index (BSAI), to quantitatively analyze the interaction profile of surface adsorption of a large group of small organic molecules onto nanomaterials with varying surface physicochemical properties, first employing five descriptors representing the surface energy profile of the nanomaterials, then further incorporating traditional semi-empirical adsorption models to address concentration effects of solutes. These Advancements in surface adsorption modelling showed a promising development in the application of quantitative predictive models in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.