Workshop Highlights Relevant to Nano WG High Quality Data Sets - There is a critical need for high quality data sets as input to predictive models. This includes the need for a reference data set that can be used to validate predictive models. Standard Operating Procedures for Nanotechnology Data Coordination - Develop standard operating procedures supporting nanotechnology data management and analysis (similar to the TCGA data submission guidelines) to inform predictive modeling. For example, procedures for data management and analysis can be developed for the top nanotechnology including specifying the format of the assay data files (raw, processed, etc.), providing assay specific ISA-TAB-Nano examples, and drafting a workflow diagram of the analytical pipeline (for each data type and across data types). Nanomaterial MetaAnalysis - There was an interest in starting with a few specific nanomaterials and assimilating all available data for these nanomaterials from the different disciplines (biological, environmental, material science, manufacturing) to identify any commonalities/differences, and to inform predictive modeling tools. NNI Initiatives - The NNI discussed their initiative to develop a nanotechnology knowledge infrastructure with four primary thrusts: 1) Build a diverse collaborative community 2) Foster an agile modeling network for multidisciplinary intellectual collaboration, 3) Build a sustainable nanotechnology cyber-toolbox, and 4) Create a robust digital nanotechnology data and information infrastructure. caNanoLab was referenced as a resource and NIH was referenced in an expected agency contributions table to participate in all four thrusts. Materials Genome Initiative - The Materials Genome Initiative can benefit from the nanotechnology community driven approach and can leverage existing nanotechnology standards.

Session 2: Use of NanoInformatics for Predictive Modeling Nathan Baker, Yoram Cohen, Sharon Gaheen, and Mark Tuominen

Example Biomedical Use Case for Predictive Modeling in Nanotechnology What are the effects of nanomaterial physical- chemical properties (size, shape, surface, etc.) on biodistribution? Can we create a model to predict the biodistribution of a nanomaterial?

Physical-Chemical Characterizations Size – Size, PDI Shape – Type, Aspect Ratio, Min/Max Dimension Surface – Charge, Surface Area, Zeta Potential, isHydropobhic Physical State – Type (Fluid-gas, Solid Crystal, etc.) Purity – % Purity Molecular Weight Solubility – Solvent, isSoluble, Critical Concentration Relaxivity – R1, R2, T1, T2 Stability Durability

Biodistribution Data Animal Information Treatment Information Pharmacokinetics – LADME Properties (AUC, Clearance, Concentration, Elimination Rate, Half Life, Protein Binding, Volume of Distribution) Toxicology – Pathology – Clinical Chemistry – Organ/Body Weight – Hematology – Developmental Toxicology – Clinical Observation

Outputs from Predictive Toxicology and Mechanistic Models Expected Output = Effectiveness of desired outcome / minimization of undesirable outcome Outcomes – Was the appropriate cell targeted? – Was therapeutic efficacy achieved? – Were there toxic side effects? – What was the mode of action (bioavailability and indirect effects)? Effects of nanomaterial features on outcomes – Translate changes in structure to biological outcomes – Identify conditional behaviors (correlative vs. causative)

Applying Existing Models to Nanotechnology Use Cases (1 of 2) Model Needs and Challenges – Need models that characterize nanomaterials within the plasma – Need models that understand nanomaterial metabolism – Need models that understand biodistribution and cellular uptake – Need to effectively model nanomaterial transport – Need to model the polyvalency of nanomaterials – Need to validate and verify models with high quality data sets Traditional Pharmacokinetics Models – Lack modeling the complexities of the nanomaterial and biodistribution – Captures exit/entrance of materials into compartments but lacks capturing material movement – Does not account for material degredation Fluid Dynamics Models – Assist in modeling where the nanomaterials go – Need to model mechanical toxicity inherent in nanomaterials and not traditionally in small molecules

Applying Existing Models to Nanotechnology Use Cases (2 of 2) In Vitro Models – Need to address problems in how to test a nanomaterial in a 2D cell culture effectively (in vitro) to inform and predict in vivo outcomes – May be opportunities for public-private partnerships for in vitro/in vivo validation and correlation to identify trends Animal Models – Need to effectively model physiological conditions and bioavailability in different species

Nanomaterial Representation to Parameterize Models Typically work backwards from the scientific end point to determine the model parameters needed (e.g. Find the pathology of the nanomaterial and then characterize the nanomaterial) Need to parameterize nanomaterial composition, physico-chemical characterizations, in vitro characterizations (blood contact, immuno tox), in vivo characterizations (PK/Tox) – Need to capture stability and durability of the nanomaterial – Capturing the inherent and derived nanomaterial surface is important – The bulk nanomaterial and the surface is needed to draw correlations – Need to model how the material forms – The challenge is measuring and “doing it right” Need to understand that models are: – Animal and species dependent – Route of exposure dependent – The transport of the nanomaterial changes the model Need consistent nomenclature to describe the nanomaterial representation

NanoInformatics as a Model for the Materials Genome Initiative Nanoinformatics has been community driven with strong stakeholder presence – It is important to involve that stakeholders as early as possible Standards and informatics approaches have been and are continuing to be developed in nanoinformatics that can be used and expanded upon – Standards: NPO, ISA-TAB-Nano – Informatics Approaches: Nanotechnology resources, nanoHUB for model sharing Providing motivation for participation (data sharing) is important – Leverage a “solutions oriented approach” that focuses on scientific endpoints – Start with small pieces that can be applied to all systems – Demonstrate a scientific ROI on application of models Provide guidelines and inceptives to enforce standards utilization – Consider requiring safety principles for nanomaterials (e.g. EU REACH program) – Drive publications to require nanomaterial information based on a set of guidelines – Require use of standard naming conventions (e.g. IUPAC) for nanomaterials

Session Output: Key Points 1.Strong need for capturing complexity most accurately 2.Validation/verification is key to any model and requires reliable, reproducible, and robust data sets 3.The scientific endpoint of the model is the primary target 4.End user interaction and involvement is critical