DATE CHANGE: nanoHUB could be intermittently unavailable on 05/06 from 8:00 am – 1:00 pm (EST) for scheduled maintenance. All tool sessions will expire on 05/06 at 8:00 am (EST).
Find information on common issues.
Ask questions and find answers from other users.
Suggest a new site feature or improvement.
Check on status of your tickets.
BME 695L Lecture 10: Nanodelivery of Therapeutic Genes and Molecular Biosensor Feedback Control Systems
26 Oct 2011 | Online Presentations | Contributor(s): James Leary
See references below for related reading.
10.1 Introduction and overview
10.1.1 Some of the advantages of therapeutic genes
10.1.2 Some of the advantages of molecular biosensor feedback control systems
10.1.3 Why a nanodelivery approach is appropriate
10.2 The therapeutic gene approach
10.2.1 What constitutes a "therapeutic gene" ?
10.2.2 Transient versus stable expression modes
10.3 Molecular feedback control systems
10.3.1 Drug delivery has traditionally not used feedback controls
10.3.2 Why feedback control might be a very good idea!
10.3.3 Positive or negative feedback?
10.4 Molecular Biosensors as a component of a nanomedicine feedback control system
10.4.1 What is a molecular biosensor?
10.4.2 How a molecular biosensor functions as a therapeutic gene switch
10.5 Building integrated molecular biosensor/gene delivery systems –some examples
10.5.1 Example of a ribozyme/antivirus system
10.5.2 Example of an ARE biosensor/DNA repair system
10.5.3 Example of a feedback-controlled system for treatment of retinopathies
BME 695L Lecture 11: Assessing Nanotoxicity at the Single Cell Level
27 Oct 2011 | Online Presentations | Contributor(s): James Leary
11.1 Need for single cell measures of nanotoxicity
11.1.1 There is more than one way for a cell to die...
11.1.2 "Necrosis" vs. "Apoptosis"
11.1.3 There are other forms of "toxicity"
11.1.4 Some other challenges in measuring toxicity of nanomaterials
11.2 Necrosis vs. Apoptosis mechanisms
11.2.1 Necrosis is unplanned "cell injury"
11.2.2 Apoptosis is planned "programmed cell death"
11.2.3 Why it is important to distinguish between necrosis and apoptosis?
11.3 Single-cell assays for necrosis and apoptosis
11.3.1 Dye exclusion assays for necrosis
11.3.2 TUNEL assays for late apoptosis
11.3.3 Annexin V assays for early apoptosis
11.3.4 COMET assays for DNA damage and repair
11.3.5 Light scatter assays
11.3.6 Dihydroethidium assays for oxidative stress
11.4 Nanotoxicity in vivo – some additional challenges
11.4.1 Single-cell nanotoxicity, plus biodistribution measuring challenges….
11.4.2 Accumulations and agglomerations of nanoparticles can change toxicity locally to
tissues and organs
11.4.3 Filtration issues of nanoparticles – size matters – toxicity to kidney, liver and lung
11.4.4 Functional sensitivity of heart and brain to nanotoxicity largely unknown
BME 695L Lecture 12: Assessing Drug Efficacy and Nanotoxicity at the Single Cell Level
22 Nov 2011 | Online Presentations | Contributor(s): James Leary
12.1 Introduction to measures of efficacy for nanomedicine
12.1.1 for evaluation purposes, does structure/size reveal function?
12.1.2 nanomedical treatment at the single cell level requires evaluation at the single cell level
12.1.3 the difficulty of anything but simple functional assays (e.g. phosphorylated “functional” proteins)
12.1.4 the need for assays which at least show correlation to functional activity
12.2 Quantitative single cell measurements of one or more proteins per cell by flow and image/confocal cytometry
12.2.1 cell surface measures of protein expression on live, single cells
12.2.2 high-throughput flow cytometric screening of bioactive compounds
12.2.3 challenges of measuring protein expression inside fixed, single cells
12.2.4 when location is important 2D or 3D imaging is required to get spatial location of proteins inside cells (“locational proteomics” at the single-cell level)
12.3 Quantitative multiparameter phospho-specific flow/image cytometry as a single-cell,structural-functional measurement
12.3.1 attempts to measure "functional proteins" by detecting phosphorylation
12.3.2 example of phospho-specific, multiparameter flow cytometry
12.3.3 example of measuring single cell gene silencing by phospho-specific flow cytometry
12.4 Quantitative measures of gene expression – the promises and the realities
12.4.1 is gene expression at the single cell level really possible?
12.4.2 is it even useful to measure a single gene's changes?
12.4.3 gene arrays of purified cell subpopulations
12.4.4 RNA amplification techniques to attempt to perform single cell gene arrays
BME 695L Lecture 13: Designing Nanomedical Systems (NMS) for In-vivo Use
13.1 Bringing in-vivo considerations into NMS design
13.1.1 the in-vitro to ex-vivo to in-vivo paradigm
18.104.22.168 In-vitro - importance of choosing suitable cell lines
22.214.171.124 adding the complexity of in-vivo background while keeping the simplicity of in-vitro
126.96.36.199 all the complexity of ex-vivo plus the “active” components of a real animal
13.1.2 In-vivo systems are open, “active” systems with multiple layers of complexity
188.8.131.52 In-vitro and ex-vivo are mostly “closed” systems, but not absolutely
184.108.40.206 What is an “open” system?
220.127.116.11 Attempts to isolate open systems
13.1.3 Layers of complexity of in-vivo systems
18.104.22.168 Human cells in nude mice – a mixture of in-vitro and in-vivo
22.214.171.124 “Model” small animal systems
126.96.36.199 better model larger animal systems
13.2 Circulation time and biodistribution
13.2.1 factors affecting circulation time
188.8.131.52 "stealth layer" coating
184.108.40.206 zeta potential in-vivo in varying environments
220.127.116.11 filtration and excretion
13.2.2 where do the NMS go in-vivo?
18.104.22.168 checking the obvious organs (liver, spleen, kidney, blood…)
22.214.171.124 finding NMS in tissues and organs
126.96.36.199.2 within dissected tissue sections
188.8.131.52.3 in blood (ex-vivo versus in-vivo flow cytometry)
184.108.40.206.4 what is excreted?
13.2.3 Circulation time and dose optimization
220.127.116.11 measure drug concentration over time
18.104.22.168 is there an optimal drug dose?
13.4 In-vivo targeting and mistargeting
13.4.1 mode of administration (intravenous, oral, intra-tumor…)
13.4.2 how can we assess targeting in-vivo? (MRI, fluorescence, …)
13.4.3 a rare-cell targeting problem
13.4.4 consequences of mistargeting
13.4.5 balancing dosing, therapeutic efficacy, and consequences of mistargeting
13.5 Evaluating therapeutic efficacy in-vivo
13.5.1 advantages of non-invasive measurements
13.5.2 measures of tumor load/shrinkage (tumor size, weight,..)
13.5.3 other measures of disease effects
22.214.171.124 direct measurement of restoration of lost or compromised functions
126.96.36.199 indirect measures of disease effects (e.g. behavior, weight gain/loss, .)
13.5.4 Some examples of in-vivo work with NMS
13.6.1 Choosing an appropriate animal model and getting it approved takes time!
13.6.2 Animal experiments are expensive and time-consuming
13.6.3 Performing in-vivo measurements of drug delivery and therapeutic efficacy are more challenging and expensive than in-vitro or ex-vivo work!
13.6.4 But ultimately you must show that the NMS works in-vivo
BME 695L Lecture 14: Designing and Testing Integrated Nanomedical Systems
14.1 Introduction to integrated designs
14.1.1 “Total design” but there is some order in the design process
14.1.2 A brief outline of the total design process
14.2 Choose autonomous or non-autonomous design
14.2.1 If autonomous, will there be error-checking to correct mistargeting?
14.2.2 If autonomous, does the NMS perform all of the multi-step process sufficiently to accomplish the objective?
14.2.3 If non-autonomous, what form of external modulation of the in-vivo nanomedical system will be used?
14.2.4 If non-autonomous, are the external interactions able to adequately control the actions of the NMS?
14.2.5 Evaluate reaction of NMS to external intervention
14.2.6 Compare actions of NMS with and without external intervention.
14.2.7 How do the actions of the NMS scale (linear? nonlinear? resonance? ) with the size or extent of the external intervention?
14.3 Choose core material, size and shape
14.3.1 How will the core be used for diagnosis? Therapeutics?
14.3.2 Does this dictate the core material? Size?
14.3.3 Does shape alter circulation time or target cell penetration?
14.3.4 Evaluate size and shape of nanosized core by transmission (TEM) or scanning electron microscopy (SEM), or by atomic force microscopy (AFM)
14.3.5 Evaluate size of complete NMS (other parts may not be electron dense) by dynamic light scattering (DLS)
14.3.6 Evaluate materials present at each layer of construction by x-ray photoelectron spectroscopy (XPS)
14.4 Design NMS targeting and evaluate its effectiveness
14.4.1 Choose cell surface biomarker on diseased cell. Is it unique or just elevated in expression (e.g. folate receptors)
14.4.2 Choose targeting molecule type (antibody, peptide, aptamer…)
14.4.3 Use flow or image cytometry to evaluate correctness of targeting to diseased cell using that biomarker system
14.4.4 How much mis-targeting is anticipated? What are the consequences of mistargeting?
14.4.5 Determine degree of mistargeting and consider the costs of misclassification (e.g. how many normal cells are mis-targeted for each diseased cell successfully targeted)
14.4.6 Based on the costs of misclassification, reconsider additional or alternative diseased cell biomarkers?
14.4.7 Evaluate intracellular targeting by TEM if NMS is not fluorescent)
14.4.8 Evaluate intracellular targeting by 3D confocal fluorescence microscopy (if NMS is fluorescent)
14.4.9 Evaluate intracellular targeting by 2D fluorescence microscopy if confocal microcopy is unavailable
14.5 Choose zeta potential
14.5.1 Determine required zeta potential for outer/inner layers
14.5.2 Determine pH of encountered microenvironments
14.5.3 Determine ionic strength of encountered microenvironments
14.5.4 Evaluate suitability of zeta potential
14.5.5 If signs of agglomeration, modify zeta potential of NMS.
14.5.6 Are the NMS sticking to any surfaces or cell types?
14.5.7 Are the NMS being rapidly filtered by the kidneys in-vivo?
14.6 Choose stealth molecule
14.6.1 Determine required time of circulation
14.6.2 Circulation time will determine dose needed
14.6.3 Evaluate effectiveness of stealth molecule
188.8.131.52 Do the NMS show signs of protein deposition in-vitro or in-vivo?
184.108.40.206 Are the circulation times of the NMS adequate to sufficiently target the diseased cells in-vivo?
14.7 Choose type and intracellular target of therapy
14.7.1 Eliminate or fix the diseased cells?
14.7.2 If choice is elimination, choose appropriate therapeutic molecule that will accomplish this action
14.7.3 If choice is to fix the diseased cells, what therapeutic molecule can accomplish this action and how will it be controlled?
14.7.4 Choose molecular measure of effectiveness of therapy (induced apoptosis, restoration of normal phenotype, …)
14.7.5 Use single cell analysis by flow cytometry to measure that molecular measure, if cells are in suspension.
14.7.6 Use scanning image cytometry to measure that molecular measure, if cells are attached
14.8 A few final words on design of integrated nanomedical systems
14.8.1 We are still in the early days of designing nanomedical systems. Some of the necessary feedback we need for better designs awaits early clinical trials on human patients and volunteers
14.8.2 We do not understand some of the processes well enough to fully control their design. Still it is important to know what is important even if can not yet control it!
Top 5 shown | See more results