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Sharma N, Sharma M, Sajid Jamal QM, Kamal MA, Akhtar S. Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:19127-19141. [PMID: 31025282 DOI: 10.1007/s11356-019-05152-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 04/10/2019] [Indexed: 06/09/2023]
Abstract
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.
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Affiliation(s)
- Neha Sharma
- Department of Bioengineering, Faculty of Engineering, Integral University, Lucknow, UP, 226026, India
- Advanced Center of Bioengineering and Bioinformatics, Integral Information and Research Centre, Integral University, Lucknow, UP, 226026, India
| | - Mala Sharma
- Advanced Center of Bioengineering and Bioinformatics, Integral Information and Research Centre, Integral University, Lucknow, UP, 226026, India
- Department of Biosciences, Integral University, Lucknow, UP, 226026, India
| | - Qazi M Sajid Jamal
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, King Abdulaziz Rd, Al Bukayriyah, 52741, Al Qassim, Saudi Arabia
| | - Mohammad A Kamal
- King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Enzymoics, 7, Peterlee Place, Hebersham, NSW, 2770, Australia
- Novel Global Community Educational Foundation, Peterlee Place, Hebersham, NSW, 2770, Australia
| | - Salman Akhtar
- Department of Bioengineering, Faculty of Engineering, Integral University, Lucknow, UP, 226026, India.
- Advanced Center of Bioengineering and Bioinformatics, Integral Information and Research Centre, Integral University, Lucknow, UP, 226026, India.
- Novel Global Community Educational Foundation, Peterlee Place, Hebersham, NSW, 2770, Australia.
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Erkimbaev AO, Zitserman VY, Kobzev GA, Kosinov AV. Ontological Concepts and Taxonomies for Nano World. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2019. [DOI: 10.1142/s021964921950014x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
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.
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Affiliation(s)
- Adilbek O. Erkimbaev
- Joint Institute for High Temperatures of the Russian, Academy of Sciences, Izhorskaya St., 13, Bd. 2, Moscow 125412, Russia
| | - Vladimir Yu. Zitserman
- Joint Institute for High Temperatures of the Russian, Academy of Sciences, Izhorskaya St., 13, Bd. 2, Moscow 125412, Russia
| | - Georgii A. Kobzev
- Joint Institute for High Temperatures of the Russian, Academy of Sciences, Izhorskaya St., 13, Bd. 2, Moscow 125412, Russia
| | - Andrey V. Kosinov
- Joint Institute for High Temperatures of the Russian, Academy of Sciences, Izhorskaya St., 13, Bd. 2, Moscow 125412, Russia
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Sneha P, Doss CGP. Molecular Dynamics: New Frontier in Personalized Medicine. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2015; 102:181-224. [PMID: 26827606 DOI: 10.1016/bs.apcsb.2015.09.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.
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Affiliation(s)
- P Sneha
- Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India
| | - C George Priya Doss
- Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India.
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Dieb TM, Yoshioka M, Hara S, Newton MC. Framework for automatic information extraction from research papers on nanocrystal devices. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2015; 6:1872-82. [PMID: 26665057 PMCID: PMC4660922 DOI: 10.3762/bjnano.6.190] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/20/2015] [Indexed: 05/16/2023]
Abstract
To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called " NaDev" (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called "NaDevEx" (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39-73%); however, precision is better (75-97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system.
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Affiliation(s)
- Thaer M Dieb
- Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan
| | - Masaharu Yoshioka
- Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan
| | - Shinjiro Hara
- Research Center for Integrated Quantum Electronics, Hokkaido University, Kita 13, Nishi 8, Sapporo 060-8628, Japan
| | - Marcus C Newton
- Physics & Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
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Lewinski NA, McInnes BT. Using natural language processing techniques to inform research on nanotechnology. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2015; 6. [PMID: 26199848 PMCID: PMC4505089 DOI: 10.3762/bjnano.6.149] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
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.
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Affiliation(s)
- Nastassja A Lewinski
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA
| | - Bridget T McInnes
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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Powers CM, Mills KA, Morris SA, Klaessig F, Gaheen S, Lewinski N, Ogilvie Hendren C. Nanocuration workflows: Establishing best practices for identifying, inputting, and sharing data to inform decisions on nanomaterials. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2015; 6:1860-71. [PMID: 26425437 PMCID: PMC4578434 DOI: 10.3762/bjnano.6.189] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 08/07/2015] [Indexed: 05/20/2023]
Abstract
There is a critical opportunity in the field of nanoscience to compare and integrate information across diverse fields of study through informatics (i.e., nanoinformatics). This paper is one in a series of articles on the data curation process in nanoinformatics (nanocuration). Other articles in this series discuss key aspects of nanocuration (temporal metadata, data completeness, database integration), while the focus of this article is on the nanocuration workflow, or the process of identifying, inputting, and reviewing nanomaterial data in a data repository. In particular, the article discusses: 1) the rationale and importance of a defined workflow in nanocuration, 2) the influence of organizational goals or purpose on the workflow, 3) established workflow practices in other fields, 4) current workflow practices in nanocuration, 5) key challenges for workflows in emerging fields like nanomaterials, 6) examples to make these challenges more tangible, and 7) recommendations to address the identified challenges. Throughout the article, there is an emphasis on illustrating key concepts and current practices in the field. Data on current practices in the field are from a group of stakeholders active in nanocuration. In general, the development of workflows for nanocuration is nascent, with few individuals formally trained in data curation or utilizing available nanocuration resources (e.g., ISA-TAB-Nano). Additional emphasis on the potential benefits of cultivating nanomaterial data via nanocuration processes (e.g., capability to analyze data from across research groups) and providing nanocuration resources (e.g., training) will likely prove crucial for the wider application of nanocuration workflows in the scientific community.
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Affiliation(s)
- Christina M Powers
- National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, NC 27711, USA
- Currently: Office of Transportation and Air Quality, Office of Air Quality, 2000 Traverwood Rd, Ann Arbor, MI 48105, USA
| | - Karmann A Mills
- RTI International, 3040 Cornwallis Rd., Research Triangle Park, NC 27709, USA
| | - Stephanie A Morris
- Office of Cancer Nanotechnology Research, National Cancer Institute/NIH, 31 Center Drive, Bethesda, MD 20892, USA
| | - Fred Klaessig
- Pennsylvania Bio Nano Systems, LLC, 69 Homestead Drive, Doylestown, PA 18901, USA
| | - Sharon Gaheen
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, 8560 Progress Drive, Frederick, MD 21702, USA
| | - Nastassja Lewinski
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, 601 W. Main St., P.O. Box 843028, Richmond, VA 23284, USA
| | - Christine Ogilvie Hendren
- Center for the Environmental Implications of NanoTechnology (CEINT), Duke University, P.O. Box 90287, 121 Hudson Hall, Durham, NC 27708, USA
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de la Iglesia D, García-Remesal M, Anguita A, Muñoz-Mármol M, Kulikowski C, Maojo V. A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov. PLoS One 2014; 9:e110331. [PMID: 25347075 PMCID: PMC4210133 DOI: 10.1371/journal.pone.0110331] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2014] [Accepted: 09/21/2014] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Diana de la Iglesia
- Biomedical Informatics Group, Dept. Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
| | - Miguel García-Remesal
- Biomedical Informatics Group, Dept. Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
| | - Alberto Anguita
- Biomedical Informatics Group, Dept. Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
| | - Miguel Muñoz-Mármol
- Biomedical Informatics Group, Dept. Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
| | - Casimir Kulikowski
- Department of Computer Science, Rutgers – The State University of New Jersey, Piscataway, New Jersey, United States of America
| | - Víctor Maojo
- Biomedical Informatics Group, Dept. Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
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Panneerselvam S, Choi S. Nanoinformatics: emerging databases and available tools. Int J Mol Sci 2014; 15:7158-82. [PMID: 24776761 PMCID: PMC4057665 DOI: 10.3390/ijms15057158] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 03/28/2014] [Accepted: 04/09/2014] [Indexed: 01/20/2023] Open
Abstract
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.
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Affiliation(s)
- Suresh Panneerselvam
- Department of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea.
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea.
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