Nanoinformatics: a new area of research in nanomedicine

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management

BACKGROUND

Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients.

OBJECTIVE

The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour.

CONCLUSION

This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the "cords" of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

Development and analysis of machine-learning guided flash nanoprecipitation (FNP) for continuous chitosan nanoparticles production

Chitosan-based nanoparticles (CNPs) are widely used in drug delivery, cosmetics formulation and food applications. To accelerate the manufacturing of CNPs, the present study develops a workflow to prepare CNPs in continues model. Based on machine learning, the workflow precisely predicts size and polymer dispersity index (PDI) value of CNPs, which impacts on the colloidal stability and applications. Multi-inlet vortex mixer (MIVM) device was fabricated by 3D printing as the reactor. Peristaltic pump was applied to deliver the reaction streams into the MIVM device and produce CNPs by flash nanoprecipitation (FNP) in continuous way. The developed MIVM device produces CNPs in controlled manner at higher output which is promising for upscale applications. Twelve machine learning algorithms were employed to investigate the potential relationship between the reaction independent variables and hydrodynamic characteristics of CNPs. Random Forest, Decision Tree, Extra Tree and Bagging algorithms performed better than other algorithms with the average prediction accuracy around 90 %. The current study demonstrated that supervised machine learning guided FNP using the developed MIVM device is an effective strategy for accurate and intelligent production of CNPs and other similar nanoparticles.

Merging data curation and machine learning to improve nanomedicines

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Re-imagining Priorities for Chemistry: A Central Science for "Freedom from Fear and Want"

Human security, defined as "freedom from want and fear and freedom to live in dignity", provides an overarching concept to address threats to human security dimensions such as health, food, economics, the environment and sustainable development, while placing the individual at the centre of attention. Chemistry is central to addressing these challenges, but surprisingly its role and contributions to human security have hitherto not been explicitly set out. This article situates chemistry in the human security framework, highlighting areas where chemistry knowledge, methods and products are vital. It underscores three complementary facets: 1) chemistry contributes to many dimensions of human security, but needs to do much more in the light of oncoming global challenges; 2) the human security framing illuminates areas where chemistry itself needs to adapt to contribute better, by intensification of current approaches and/or by building or strengthening chemistry tools, skills and competencies; and 3) repositioning as central to human security affords chemistry a powerful opportunity to refresh itself as a science for the benefit of society-and it will need to engage more directly and dynamically at the interface of science, society and policy in order to do so.

The NanoInformatics Knowledge Commons: Capturing spatial and temporal nanomaterial transformations in diverse systems

The empirical necessity for integrating informatics throughout the experimental process has become a focal point of the nano-community as we work in parallel to converge efforts for making nano-data reproducible and accessible. The NanoInformatics Knowledge Commons (NIKC) Database was designed to capture the complex relationship between nanomaterials and their environments over time in the concept of an 'Instance'. Our Instance Organizational Structure (IOS) was built to record metadata on nanomaterial transformations in an organizational structure permitting readily accessible data for broader scientific inquiry. By transforming published and on-going data into the IOS we are able to tell the full transformational journey of a nanomaterial within its experimental life cycle. The IOS structure has prepared curated data to be fully analyzed to uncover relationships between observable phenomenon and medium or nanomaterial characteristics. Essential to building the NIKC database and associated applications was incorporating the researcher's needs into every level of development. We started by centering the research question, the query, and the necessary data needed to support the question and query. The process used to create nanoinformatic tools informs usability and analytical capability. In this paper we present the NIKC database, our developmental process, and its curated contents. We also present the Collaboration Tool which was built to foster building new collaboration teams. Through these efforts we aim to: 1) elucidate the general principles that determine nanomaterial behavior in the environment; 2) identify metadata necessary to predict exposure potential and bio-uptake; and 3) identify key characterization assays that predict outcomes of interest.

Targeted drug delivery strategies for precision medicines

Progress in the field of precision medicine has changed the landscape of cancer therapy. Precision medicine is propelled by technologies that enable molecular profiling, genomic analysis, and optimized drug design to tailor treatments for individual patients. Although precision medicines have resulted in some clinical successes, the use of many potential therapeutics has been hindered by pharmacological issues, including toxicities and drug resistance. Drug delivery materials and approaches have now advanced to a point where they can enable the modulation of a drug's pharmacological parameters without compromising the desired effect on molecular targets. Specifically, they can modulate a drug's pharmacokinetics, stability, absorption, and exposure to tumours and healthy tissues, and facilitate the administration of synergistic drug combinations. This Review highlights recent progress in precision therapeutics and drug delivery, and identifies opportunities for strategies to improve the therapeutic index of cancer drugs, and consequently, clinical outcomes.

Relevant Physicochemical Methods to Functionalize, Purify, and Characterize Surface-Decorated Lipid-Based Nanocarriers

Surface functionalization of lipid-based nanocarriers (LBNCs) with targeting ligands has attracted huge interest in the field of nanomedicines for their ability to overcome some physiological barriers and their potential to deliver an active molecule to a specific target without causing damage to healthy tissues. The principal objective of this review is to summarize the present knowledge on LBNC decoration used for biomedical applications, with an emphasis on the ligands used, the functionalization approaches, and the purification methods after ligand corona formation. The most potent experimental techniques for the LBNC surface characterization are described. The potential of promising methods such as nuclear magnetic resonance spectroscopy and isothermal titration calorimetry to characterize ligand surface corona is also outlined.

Nanotechnology-Based Histone Deacetylase Inhibitors for Cancer Therapy

Histone deacetylase inhibitors (HDACi) have been approved and achieved success in hematologic malignancies. But its application in solid tumors still confronts big challenges and is hampered by low treatment efficacy. Nanotechnology has been widely applied in cancer therapy, and nanomedicine could improve drug stability, prolong the circulation half-life, and increase intratumoral drug accumulation. Therefore, nanomedicine is a promising strategy to enhance HDACi therapy efficacy. The review provides a summary of the advances of HDACi nanomedicines with a focus on the design principles of the targeting delivery systems for HDACi.

Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment

Empowering role of nanoinformatics in design and elucidation of nanoparticles for effective cancer treatment has made this field a fascinating area for researchers, inspiring them to enhance up the quality and efficacy of existing anticancer medicines. Theoretical and computational modeling is being seen as a forefront solution for problems related to surface chemistry, optimized geometry, or other properties in nanoparticle designing and drug delivery. The current review aims to acquaint with the insight story of the incubation of in silico tools and techniques in nanotechnology to develop better anticancer nanomedicines. The review also recapitulates the assets and liabilities of this field and present an outline of existing inventiveness and endeavors of nanoinformatics. We propose how nanoinformatics could hasten up the advancements in anticancer nanomedicines through use of computational tools, nanoparticles repositories & various modeling and simulation methods.

Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

Modern nanotechnology research has generated numerous experimental data for various nanomaterials. However, the few nanomaterial databases available are not suitable for modeling studies due to the way they are curated. Here, we report the construction of a large nanomaterial database containing annotated nanostructures suited for modeling research. The database, which is publicly available through http://www.pubvinas.com/, contains 705 unique nanomaterials covering 11 material types. Each nanomaterial has up to six physicochemical properties and/or bioactivities, resulting in more than ten endpoints in the database. All the nanostructures are annotated and transformed into protein data bank files, which are downloadable by researchers worldwide. Furthermore, the nanostructure annotation procedure generates 2142 nanodescriptors for all nanomaterials for machine learning purposes, which are also available through the portal. This database provides a public resource for data-driven nanoinformatics modeling research aimed at rational nanomaterial design and other areas of modern computational nanotechnology.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

A Safe-by-Design Strategy towards Safer Nanomaterials in Nanomedicines

The marriage of nanotechnology and medicine offers new opportunities to fight against human diseases. Benefiting from their unique optical, thermal, magnetic, or redox properties, a wide range of nanomaterials have shown potential in applications such as diagnosis, drug delivery, or tissue repair and regeneration. Despite the considerable success achieved over the past decades, the newly emerging nanomedicines still suffer from an incomplete understanding of their safety risks, and of the relationships between their physicochemical characteristics and safety profiles. Herein, the most important categories of nanomaterials with clinical potential and their toxicological mechanisms are summarized, and then, based on this available information, an overview of the principles in developing safe-by-design nanomaterials for medical applications and of the recent progress in this field is provided. These principles may serve as a starting point to guide the development of more effective safe-by-design strategies and to help identify the major knowledge and skill gaps.

Ontological Concepts and Taxonomies for Nano World

The purpose of the paper is to provide a detailed overview of the methods of indexing and categorizing data generated to solve problems in a complex and multifaceted field of knowledge related to the application of nanotechnology. Analysis of the capabilities and restrictions of various categorization methods are applied to the issues of the subject field, starting with simple classification schemes and up to high level ontologies. The content of integrating methods and approaches developed in many natural sciences is considered: life science, chemistry, material science, etc. The main restriction of the currently applicable ontologies and vocabularies has been identified — a primary focus on the tasks of bio- and medical informatics. It is shown that the way to overcome them includes the adoption of a new system for describing nanomaterials proposed in the CODATA-VAMAS international project. The overview shows how the extreme broadness and continuous evolution of the subject field are reflected in the means of data categorization. It is shown that the most developed of them can serve as a basis for building a knowledge base. The prospective tasks of nanoinformatics are stated required to be solved to cover fundamentally unlimited classes of materials, their properties and fields of application.

Gold Nanoparticle Toxicity in Mice and Rats: Species Differences

Nanotoxicity studies are greatly needed to advance nanomedical technologies into clinical practice. We assessed the toxic effects of a single intravenous exposure to commercially available gold nanoparticles (GNPs) in mice and rats. Fifteen-nm GNPs were purchased and independently characterized. Animals were exposed to either 1,000 mg GNPs/kg body weight (GNP group) or phosphate-buffered saline. Subsets of animals were euthanized and samples collected at 1, 7, 14, 21, and 28 days postexposure. Independent characterization demonstrated that the physicochemical properties of the purchased GNPs were in good agreement with the information provided by the supplier. Mice exposed to GNPs developed granulomas in the liver and transiently increased serum levels of the pro-inflammatory cytokine interleukin-18. No such alterations were found in rats. While there was no fatality in mice post-GNP exposure, a number of the rats died within hours of GNP administration. Differences in GNP biodistribution and excretion were also detected between the two species, with rats having a higher relative accumulation of GNPs in spleen and greater fecal excretion. In conclusion, GNPs have the ability to incite a robust macrophage response in mice, and there are important species-specific differences in their biodistribution, excretion, and potential for toxicity.

Quantitative self-assembly prediction yields targeted nanomedicines

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Molecular dynamics simulations and applications in computational toxicology and nanotoxicology

Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.

A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles

This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design.

Nanomaterials-Tools, Technology and Methodology of Nanotechnology Based Biomedical Systems for Diagnostics and Therapy

Nanomedicine helps to fight diseases at the cellular and molecular level by utilizing unique properties of quasi-atomic particles at a size scale ranging from 1 to 100 nm. Nanoparticles are used in therapeutic and diagnostic approaches, referred to as theranostics. The aim of this review is to illustrate the application of general principles of nanotechnology to select examples of life sciences, molecular medicine and bio-assays. Critical aspects relating to those examples are discussed.

Using natural language processing techniques to inform research on nanotechnology

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND

Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results.

METHODS

We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset.

RESULTS AND CONCLUSIONS

The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Association rule mining of cellular responses induced by metal and metal oxide nanoparticles

Relationships among fourteen different biological responses (including ten signaling pathway activities and four cytotoxicity effects) of murine macrophage (RAW264.7) and bronchial epithelial (BEAS-2B) cells exposed to six metal and metal oxide nanoparticles (NPs) were analyzed using both statistical and data mining approaches. Both the pathway activities and cytotoxicity effects were assessed using high-throughput screening (HTS) over an exposure period of up to 24 h and concentration range of 0.39-200 mg L(-1). HTS data were processed by outlier removal, normalization, and hit-identification (for significantly regulated cellular responses) to arrive at reliable multiparametric bioactivity profiles for the NPs. Association rule mining was then applied to the bioactivity profiles followed by a pruning process to remove redundant rules. The non-redundant association rules indicated that "significant regulation" of one or more cellular responses implies regulation of other (associated) cellular response types. Pairwise correlation analysis (via Pearson's χ(2) test) and self-organizing map clustering of the different cellular response types indicated consistency with the identified non-redundant association rules. Furthermore, in order to explore the potential use of association rules as a tool for data-driven hypothesis generation, specific pathway activity experiments were carried out for ZnO NPs. The experimental results confirmed the association rule identified for the p53 pathway and mitochondrial superoxide levels (via MitoSox reagent) and further revealed that blocking of the transcriptional activity of p53 lowered the MitoSox signal. The present approach of using association rule mining for data-driven hypothesis generation has important implications for streamlining multi-parameter HTS assays, improving the understanding of NP toxicity mechanisms, and selection of endpoints for the development of nanomaterial structure-activity relationships.

Computational nanomedicine: modeling of nanoparticle-mediated hyperthermal cancer therapy

Nanoparticle-mediated hyperthermia for cancer therapy is a growing area of cancer nanomedicine because of the potential for localized and targeted destruction of cancer cells. Localized hyperthermal effects are dependent on many factors, including nanoparticle size and shape, excitation wavelength and power, and tissue properties. Computational modeling is an important tool for investigating and optimizing these parameters. In this review, we focus on computational modeling of magnetic and gold nanoparticle-mediated hyperthermia, followed by a discussion of new opportunities and challenges.

Nanoinformatics: emerging databases and available tools

Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research.

Different effects of the immunomodulatory drug GMDP immobilized onto aminopropyl modified and unmodified mesoporous silica nanoparticles upon peritoneal macrophages of women with endometriosis

The aim of the present work was to compare in vitro the possibility of application of unmodified silica nanoparticles (UMNPs) and modified by aminopropyl groups silica nanoparticles (AMNPs) for topical delivery of immunomodulatory drug GMDP to the peritoneal macrophages of women with endometriosis. The absence of cytotoxic effect and high cellular uptake was demonstrated for both types of silica nanoparticles. The immobilization of GMDP on the UMNPs led to the suppression of the stimulatory effect of GMDP on the membrane expression of scavenger receptors SR-AI and SR-B, mRNAs expression of NOD2 and RAGE, and synthesis of proteolytic enzyme MMP-9 and its inhibitor TIMP-1. GMDP, immobilized onto AMNPs, enhanced the initially reduced membrane expression of SRs and increased NOD2, RAGE, and MMP-9 mRNAs expression by macrophages. Simultaneously high level of mRNAs expression of factors, preventing undesirable hyperactivation of peritoneal macrophages (SOCS1 and TIMP-1), was observed in macrophages incubated in the presence of GMDP, immobilized onto AMNPs. The effect of AMNPs immobilized GMDP in some cases exceeded the effect of free GMDP. Thus, among the studied types of silica nanoparticles, AMNPs are the most suitable nanoparticles for topical delivery of GMDP to the peritoneal macrophages.

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts.

Nanoinformatics for biomedicine: emerging approaches and applications

This special issue on nanoinformatics for biomedicine is a collection of recent papers from the 2012 IEEE Workshop on Nanoinformatics for Biomedicine (NanoInfo 2012) and other work in the area. These papers illustrate different aspects of nanoinformatics to support biomedical research and to advance knowledge on nanomaterial–biological interactions. The topics covered include data curation, data standards, data mining and predictive modeling, machine learning, and translational research. The objectives of this special issue are multifold: (1) to bring together and showcase some of the latest research results in the field; (2) to introduce some useful repositories, systems, and analysis tools; and (3) to stimulate more research activities in the field.

Influence of nanoparticles of platinum on chicken embryo development and brain morphology

Platinum nanoparticles (NP-Pt) are noble metal nanoparticles with unique physiochemical properties that have recently elicited much interest in medical research. However, we still know little about their toxicity and influence on general health. We investigated effects of NP-Pt on the growth and development of the chicken embryo model with emphasis on brain tissue micro- and ultrastructure. The embryos were administered solutions of NP-Pt injected in ovo at concentrations from 1 to 20 μg/ml. The results demonstrate that NP-Pt did not affect the growth and development of the embryos; however, they induced apoptosis and decreased the number of proliferating cells in the brain tissue. These preliminary results indicate that properties of NP-Pt might be utilized in brain cancer therapy, but potential toxic side effects must be elucidated in extensive follow-up research.