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
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.