Tools and approaches for data deposition into nanomaterial databases
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This document summarizes tools and approaches for depositing nanomaterial data into databases. It discusses the need for organized nanomaterial data and describes objectives to develop an interactive notebook called NanoBook for capturing characterization data. It aims to enhance the Nanomaterial Registry ontology and implement computational tools for quantitative structure-property relationship modeling to guide experimental design of novel nanomaterials. Challenges include developing descriptors for diverse nanoparticle structures and conducting systematic studies. The proposed solution is an open science data repository to map, import, export and analyze nanomaterial documents and data.
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Tools and approaches for data deposition into nanomaterial databases
- 1. Tools and approaches for data deposition into nanomaterial databases Valery Tkachenko2, Richard Zakharov2, Alexander Kabanov3, Karmann Mills4, Tony Hickey4, Alexander Tropsha1 ACS Spring 2017 San Francisco, April 1-6th 2017 1. Univ of North Carolina, Chapel Hill, NC, United States. 2. Science Data Software, Rockville, MD, United States. 3. School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States. 4. RTI, Chapel Hill, NC, United States.
- 2. The newly-appointed President-Elect of the Royal Society of Chemistry today forecast the impact of advances in modelling and computational informatics on chemistry The growing appreciation of molecular modeling and informatics
- 3. Nanomaterial Community Need Data Isolated data sets from individual groups and researchers Information Curated, organized data for distinguishing gaps and trends in information Knowledge Identification of relationships between properties and behavior Wisdom Capability to predict endpoints of new materials based on the knowledge of old materials Accelerate This Progression
- 4. Predictive data models & toolsExperimental Design Data Analysis and Modeling Structured Data Repository Data collection, curation, integration, and structuring (ontology) Literature data Electronic Databases: Processing Experimental Data Disease Experimental Validation Effect Decision support Karmann Mills and Anthony Hickey RTI International, RTP, NC 27709 and Alex Tropsha Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599
- 6. Objective 1. Develop NanoBook, an Interactive NoteBook for capturing and sharing data on nanomaterial characterization Nanomaterial Registry: Interactive Data Infrastructure for Promoting Progress in Nanoscience Nanomaterial Registry
- 7. Objective 2. Enhance content and data organization of the NR based on nanomaterial ontology Nanomaterial Registry: provide a set of core components that can be adapted broadly to support scientific registries An NPO representation of selected concepts related to the structure, composition, and properties of nanoparticles.
- 8. Objective 3. Develop and implement computational tools for Quantitative Nanostructure-Property Relationships (QNPR) modeling of structured nanomaterials data to guide the experimental design of novel nanomaterials with the desired properties and safety profiles Nanomaterial Registry: provide a set of core components that can be adapted broadly to support scientific registries - Building of models using machine learning methods (NN, SVM, etc.) - Validation of models according to numerous statistical procedures and their applicability domains. Fourches D, Pu D, Tropsha A. Comb Chem High Throughput Screen. 2011 Mar 1;14(3):217-25] Thousands of molecular descriptors are available for organic compounds constitutional, topological, structural, quantum mechanics based, fragmental, steric, pharmacophoric, geometrical, thermodynamical conformational, etc.
- 9. Challenges of Quantitative Modeling of Nanoparticles NP structures are very diverse a real challenge to develop quantitative parameters (descriptors) of MNPs. Systematic physico-chemical, geometrical, structural and biological studies of large groups of NPs are nearly absent. Computational modeling of nanoparticles is only beginning to emerge; best if done in collaboration with experimental scientists. S. Stern and S. McNeil, Toxicological Sciences, 101(1), 4-21, 2008.
- 10. Controlled Vocabulary 10 Zhang, J Coll Int Sci, 2(15) 2009 www.nano-lab.com www.nano-lab.com Tomalia, J Nanopart Res (2009) 11 Wang, Materials Today 2004 • ISO • NCI Thesaurus • EPA • OECD, etc.
- 11. MINIMAL INFORMATION ABOUT NANOMATERIALS 2,031 Records 45% 75% In vitro Endpoints In vivo Biological Assays Physical/Chemical Analysis 50% Surface Charge 50% Surface Chemistry 80% Size 25% Aggregation/ Agglomeration State 10% Surface Reactivity 2,031 91% 7% Media Characterization Soil General Study Details Water Environmental Assays 59 819 15% Stability 20% Solubility 45% Purity 100% Composition 40% Surface Area 60% Shape 55% Size Distribution 0.12% Exposure SummaryEcological Exposure Summary Endpoints 2% Air Test Subject Characterization General Study Details
- 12. Fourches, Muratov, Tropsha. Nat Chem Biol. 2015,11(8):535. How the problem is being solved now
- 13. Solution
- 14. Open Science Data Repository
- 15. Open Science Data Repository (OSDR)
- 16. OSDR - mapping and conversion
- 17. OSDR - import
- 18. OSDR - export
- 19. OSDR - documents
- 21. Roadmap
- 23. We live in a hyperconnected World
- 26. Thank you Email: info@scidatasoft.com Slides: http://www.slideshare.net/valerytkachenko16
Notas do Editor
- NP is defined as structure whose size is smaller than 100 nanometer at least at one dimension. If the particle has two dimension are within nanosize, it is called quantum wire, like he nanotube. if a particle has three dimension are within nanosize, it is called quantum dot. The NP market increased rapidly in the past year. They have broad application due to their extradinary property. Some NP are mechanically strong and resist tear and wear than their bulky counterpart. They are used as filler in many places, e.g. tire, tennis racket . Like the fullerene Some NP exibits special optical properties, they are used as imaging agent to mark biomaterial. like all kinds of quantum dots. The NP in this study is belong to this category. Some NP is good at passing signal. They are used as biosensor like the nanotube. NP are attractive for cancer drug delivery. For the traditional small molecule drug, if the molecule can get into the blood and not being cleared quickly, the small molecule drug can reach anywhere in the body. This can cause unwanted side effect. NP carrier are much larger than the small molecule. It is only available to certain organ or system depends on its size. Thus it can be used to target cancer drug to only to target organ to reduce drug toxicity. Particle with different size will end up at different organ or system in vivo. Particles larger than 7 micrometer will be filtered at the finest lung capillary. This sized particle could be used to deliver drug to lung. Particles between 2 to 7 micro meter mainly be captured by the RES system. The RES system is part of the immune system, consists of the phagocytic cells like magrophage. This size particle can be used to deliverer drug to immune system. Particle ranged between 50 and 200 nm are confined in the blood system and has the largest distribution volume. Particle at this size has long half life and has more opportunity to be delivered to tissue that are more available to large molecules. Particle that are small will be cleared from the blood through renal filtration.