We are grateful to the editor and to the anonymous referees for suggestions that&#160;improved&#160;the article.

<ol>
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J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+lipid+nanoparticle+formulations+for+mRNA+delivery+in+vivo+with+fractional+factorial+and+definitive+screening+designs.">Optimization of lipid nanoparticle formulations for mRNA delivery in vivo with fractional factorial and definitive screening designs.</a> Nano Letters. 15, 7300-7306 (2015).</li><li>Jones, B., Nachtsheim, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+class+of+three-level+designs+for+definitive+screening+in+the+presence+of+second-order+effects.">A class of three-level designs for definitive screening in the presence of second-order effects.</a> Journal of Quality Technology. 43, 1-15 (2011).</li><li>Cornell, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data. Wiley Series in Probability and Statistics. , (2002).</li><li>Jones, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proper+and+improper+use+of+definitive+screening+designs+(DSDs).">Proper and improper use of definitive screening designs (DSDs).</a> JMP user Community. , (2016).</li><li>Myers, R., Montgomery, D., Anderson-Cook, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Response Surface Methodology. , (2016).</li><li>Lekivetz, R., Jones, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+flexible+space-filling+designs+for+nonrectangular+regions.">Fast flexible space-filling designs for nonrectangular regions.</a> Quality and Reliability Engineering International. 31, 829-837 (2015).</li><li>Czitrom, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One-factor-at-a-time+versus+designed+experiments.">One-factor-at-a-time versus designed experiments.</a> The American Statistician. 53, 126-131 (1999).</li><li>Karl, A., Wisnowski, J., Rushing, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=JMP+Pro+17+remedies+for+practical+struggles+with+mixture+experiments.">JMP Pro 17 remedies for practical struggles with mixture experiments.</a> JMP Discovery Conference. , (2022).</li><li>Lemkus, T., Gotwalt, C., Ramsey, P., Weese, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-validated+ensemble+models+for+design+of+experiments.">Self-validated ensemble models for design of experiments.</a> Chemometrics and Intelligent Laboratory Systems. 219, 104439 (2021).</li><li>Gotwalt, C., Ramsey, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Model+validation+strategies+for+designed+experiments+using+bootstrapping+techniques+with+applications+to+biopharmaceuticals.">Model validation strategies for designed experiments using bootstrapping techniques with applications to biopharmaceuticals.</a> JMP Discovery Conference. , (2018).</li><li>Xu, L., Gotwalt, C., Hong, Y., King, C. B., Meeker, W. Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+the+fractional-random-weight+bootstrap.">Applications of the fractional-random-weight bootstrap.</a> The American Statistician. 74 (4), 345-358 (2020).</li><li>Ramsey, P., Levin, W., Lemkus, T., Gotwalt, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SVEM:+A+paradigm+shift+in+design+and+analysis+of+experiments.">SVEM: A paradigm shift in design and analysis of experiments.</a> JMP Discovery Conference Europe. , (2021).</li><li>Ramsey, P., Gaudard, M., Levin, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accelerating+innovation+with+space+filling+mixture+designs,+neural+networks+and+SVEM.">Accelerating innovation with space filling mixture designs, neural networks and SVEM.</a> JMP Discovery Conference. , (2021).</li><li>Lemkus, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-Validated+Ensemble+modelling.">Self-Validated Ensemble modelling.</a> Doctoral Dissertations. 2707. , (2022).</li><li>Juran, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services. , (1992).</li><li>Yu, L. X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+pharmaceutical+quality+by+design.">Understanding pharmaceutical quality by design.</a> The AAPS Journal. 16, 771 (2014).</li><li>Simpson, J. R., Listak, C. M., Hutto, G. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+for+planning+and+evidence+for+assessing+a+well-designed+experiment.">Guidelines for planning and evidence for assessing a well-designed experiment.</a> Quality Engineering. 25, 333-355 (2013).</li><li>Daniel, S., Kis, Z., Kontoravdi, C., Shah, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quality+by+design+for+enabling+RNA+platform+production+processes.">Quality by design for enabling RNA platform production processes.</a> Trends in Biotechnology. 40 (10), 1213-1228 (2022).</li><li>Scheff&#233;, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experiments+with+mixtures.">Experiments with mixtures.</a> Journal of the Royal Statistical Society Series B. 20, 344-360 (1958).</li><li>Brown, L., Donev, A. N., Bissett, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=General+blending+models+for+data+from+mixture+experiments.">General blending models for data from mixture experiments.</a> Technometrics. 57, 449-456 (2015).</li><li>Herrera, M., Kim, J., Eygeris, Y., Jozic, A., Sahay, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Illuminating+endosomal+escape+of+polymorphic+lipid+nanoparticles+that+boost+mRNA+delivery.">Illuminating endosomal escape of polymorphic lipid nanoparticles that boost mRNA delivery.</a> Biomaterials Science. 9 (12), 4289-4300 (2021).</li><li>Lemkus, T., Ramsey, P., Gotwalt, C., Weese, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-validated+ensemble+models+for+design+of+experiments.">Self-validated ensemble models for design of experiments.</a> ArXiv. , 2103.09303 (2021).</li><li>Goos, P., Jones, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Optimal Design of Experiments: A Case Study Approach. , (2011).</li><li>Rushing, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DOE+Gumbo:+How+hybrid+and+augmenting+designs+can+lead+to+more+effective+design+choices.">DOE Gumbo: How hybrid and augmenting designs can lead to more effective design choices.</a> JMP Discovery Conference. , (2020).</li></ol>

The experimental design strategy underpinning this workflow has been employed in two patent applications in which one of the authors is an inventor. Additionally, Adsurgo, LLC is a certified JMP Partner. However, the development and publication of this paper were undertaken without any form of financial incentive, encouragement, or other inducements from JMP.

Modern software for the design and analysis of mixture-process experiments makes it possible for scientists to improve their lipid nanoparticle formulations in a structured workflow that avoids inefficient OFAT experimentation. The recently developed SVEM modeling approach eliminates many of the arcane regression modifications and model reduction strategies that may have previously distracted scientists with extraneous statistical considerations. Once the results are collected, the SVEM analysis framework offers an appro...

Lipid nanoparticle (LNP) formulations for in vivo gene delivery systems generally involve four constituent lipids from the categories of ionizable, helper, and PEG lipids1,2,3. Whether these lipids are being studied alone or simultaneously with other non-mixture factors, experiments for these formulations require &#34;mixture&#34; designs because - given a candidate formulation - increasing or decreasing the ratio of any one of the lipids necessarily leads to a corresponding decrease or increase in the sum of the ratios of the other three lipids.
For illustration, it is supposed that we are optimizing an LNP formulation that currently uses a set recipe that will be treated as the benchmark. The goal is to maximize the potency of the LNP while secondarily aiming to minimize the average particle size. The study factors that are varied in the experiment are the molar ratios of the four constituent lipids (ionizable, cholesterol, DOPE, PEG), the N:P ratio, the flow rate, and the ionizable lipid type. The ionizable and helper lipids (including cholesterol) are allowed to vary over a wider range of molar ratio, 10-60%, than PEG, which will be varied from 1-5% in this illustration. The benchmark formulation recipe and the ranges of the other factors and their rounding granularity are specified in Supplementary File 1. For this example, the scientists are able to perform 23 runs (unique batches of particles) in a single day and would like to use that as their sample size if it meets the minimum requirements. Simulated results for this experiment are provided in Supplementary File 2 and Supplementary File 3.
Rampado and Peer4 have published a recent review paper on the topic of designed experiments for the optimization of nanoparticle-based drug delivery systems. Kauffman et al.5 considered LNP optimization studies using fractional factorial and definitive screening designs6; however, these types of designs cannot accommodate a mixture constraint without resorting to the use of inefficient &#34;slack variables&#34;7 and are not typically used when mixture factors are present7,8. Instead, &#34;optimal designs&#34; capable of incorporating a mixture constraint are traditionally used for mixture-process experiments9. These designs target a user-specified function of the study factors and are only optimal (in one of a number of possible senses) if this function captures the true relationship between the study factors and responses. Note that there is a distinction in the text between &#34;optimal designs&#34; and &#34;optimal formulation candidates&#34;, with the latter referring to the best formulations identified by a statistical model. Optimal designs come with three main disadvantages for mixture-process experiments. First, if the scientist fails to anticipate an interaction of the study factors when specifying the target model, then the resulting model will be biased and can produce inferior candidate formulations. Second, optimal designs place most of the runs on the exterior boundary of the factor space. In LNP studies, this can lead to a large number of lost runs if the particles do not form correctly at any extremes of the lipid or process settings. Third, scientists often prefer to have experimental runs on the interior of the factor space to gain a model-independent sense of the response surface and to observe the process directly in previously unexplored regions of the factor space.
An alternative design principle is to target an approximate uniform coverage of the (mixture-constrained) factor space with a space-filling design10. These designs sacrifice some experimental efficiency relative to optimal designs9 (assuming the entire factor space leads to valid formulations) but present several benefits in a trade-off that are useful in this application. The space-filling design does not make any a priori assumptions about the structure of the response surface; this gives it the flexibility to capture unanticipated relationships between the study factors. This also streamlines the design generation because it does not require making decisions about which regression terms to add or remove as the desired run size is adjusted. When some design points (recipes) lead to failed formulations, space-filling designs make it possible to model the failure boundary over the study factors while also supporting statistical models for the study responses over the successful factor combinations. Finally, the interior coverage of the factor space allows for model-independent graphical exploration of the response surface.
To visualize the mixture factor subspace of a mixture-process experiment, specialized triangular &#34;ternary plots&#34; are used. Figure 1 motivates this usage: in the cube of points where three ingredients are each allowed to range from 0 to 1, the points that satisfy a constraint that the sum of the ingredients equals 1 are highlighted in red. The mixture constraint on the three ingredients reduces the feasible factor space to a triangle. In LNP applications with four mixture ingredients, we produce six different ternary plots to represent the factor space by plotting two lipids at a time against an &#34;Others&#34; axis that represents the sum of the other lipids.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65200/65200fig01.jpg" /> 
Figure 1: Triangular factor regions. In the space-filling plot within the cube, the small grey dots represent formulations that are inconsistent with the mixture constraint. The larger red points lie on a triangle inscribed within the cube and represent formulations for which the mixture constraint is satisfied. <a href="https://www.jove.com/files/ftp_upload/65200/65200fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In addition to the lipid mixture factors, there are often one or more continuous process factors such as N:P ratio, buffer concentration, or flow rate. Categorical factors may be present, such as ionizable lipid type, helper lipid type, or buffer type. The goal is to find a formulation (a mixture of lipids and settings for process factors) that maximizes some measure of potency and/or improves physiochemical characteristics such as minimizing particle size and PDI (polydispersity index), maximizing percent encapsulation, and minimizing side effects - such as body weight loss - in in vivo studies. Even when starting from a reasonable benchmark recipe, there may be interest in re-optimizing given a change in the genetic payload or when considering changes in the process factors or lipid types.
Cornell7 provides a definitive text on the statistical aspects of mixture and mixture-process experiments, with Myers et al.9 providing an excellent summary of the most relevant mixture design and analysis topics for optimization. However, these works can overload scientists with statistical details and with specialized terminology. Modern software for the design and analysis of experiments provides a robust solution that will sufficiently support most LNP optimization problems without having to appeal to the relevant theory. While more complicated or high-priority studies will still benefit from collaboration with a statistician and may employ optimal rather than space-filling designs, our goal is to improve the comfort level of scientists and to encourage optimization of LNP formulations without appealing to inefficient one-factor-at-a-time (OFAT) testing11 or simply settling for the first formulation that satisfies specifications.
In this article, a workflow is presented that utilizes statistical software to optimize a generic LNP formulation problem, addressing design and analysis issues in the order that they will be encountered. In fact, the method will work for general optimization problems and is not restricted to LNPs. Along the way, several common questions that arise are addressed and recommendations are provided that are grounded in experience and in simulation results12. The recently developed framework of self-validated ensemble models (SVEM)13 has greatly improved the otherwise fragile approach to analyzing results from mixture-process experiments, and we use this approach to provide a simplified strategy for formulation optimization. While the workflow is constructed in a general manner that could be followed using other software packages, JMP 17 Pro is unique in offering SVEM along with the graphical summary tools that we have found to be necessary to simplify the otherwise arcane analysis of mixture-process experiments. As a result, JMP-specific instructions are also provided in the protocol.
SVEM employs the same linear regression model foundation as the traditional approach, but it allows us to avoid tedious modifications that are required to fit a &#34;full model&#34; of candidate effects by using either a forward selection or a penalized selection (Lasso) base approach. Additionally, SVEM provides an improved &#34;reduced model&#34; fit that minimizes the potential for incorporating noise (process plus analytical variance) that appears in the data. It works by averaging the predicted models resulting from repeatedly reweighting the relative importance of each run in the model13,14,15,16,17,18. SVEM provides a framework for modeling mixture-process experiments that is both easier to implement than traditional single-shot regression and yields better quality optimal formulation candidates12,13. The mathematical details of SVEM are beyond the scope of this paper and even a cursory summary beyond the relevant literature review would distract from its main advantage in this application: it allows a simple, robust, and accurate click-to-run procedure for practitioners.
The presented workflow is consistent with the Quality by Design (QbD)19 approach to pharmaceutical development20. The result of the study will be an understanding of the functional relationship that links the material attributes and process parameters to critical quality attributes (CQAs)21. Daniel et al.22 discuss using a QbD framework specifically for RNA platform production: our workflow could be used as a tool within this framework.

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			<td>JMP Pro 17.1</td>
			<td>JMP Statistical Discovery LLC</td>
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			<td></td>
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a workflow for lipid nanoparticle (lnp) formulation optimization using designed mixture-process experiments and self-validated ensemble models (svem)

We present a Quality by Design (QbD) styled approach for optimizing lipid nanoparticle (LNP) formulations, aiming to offer scientists an accessible workflow. The inherent restriction in these studies, where the molar ratios of ionizable, helper, and PEG lipids must add up to 100%, requires specialized design and analysis methods to accommodate this mixture constraint. Focusing on lipid and process factors that are commonly used in LNP design optimization, we provide steps that avoid many of the difficulties that traditionally arise in the design and analysis of mixture-process experiments by employing space-filling designs and utilizing the recently developed statistical framework of self-validated ensemble models (SVEM). In addition to producing candidate optimal formulations, the workflow also builds graphical summaries of the fitted statistical models that simplify the interpretation of the results. The newly identified candidate formulations are assessed with confirmation runs and optionally can be conducted in the context of a more comprehensive second-phase study.

This approach has been validated across both broadly classified lipid types: MC3-like classical lipids and lipidoids (e.g., C12-200), generally derived from combinatorial chemistry. Compared to a benchmark LNP formulation developed using a One Factor at a Time (OFAT) method, the candidate formulations generated through our workflow frequently demonstrate potency improvements of 4- to 5-fold on a logarithmic scale, such as shown in the mouse liver luciferase readings in Figure 18. Tab...

Watch this Scientific Journal Video about A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM at JoVE.com

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

This protocol provides an approach to formulation optimization over mixture, continuous, and categorical study factors that minimizes subjective choices in the experimental design construction. For the analysis phase, an effective and easy-to-use modeling fitting procedure is employed.

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

We present a Quality by Design (QbD) styled approach for optimizing lipid nanoparticle (LNP) formulations, aiming to offer scientists an accessible ...

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4.1K Views.  Adsurgo, LLC. This protocol provides an approach to formulation optimization over mixture, continuous, and categorical study factors that minimizes subjective choices in the experimental design construction. For the analysis phase, an effective and easy-to-use modeling fitting procedure is employed.

Video: A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS can be subdivided into three domains: the distal O-polysaccharide, a core oligosaccharide, and the lipid A domain consisting of a lipid A molecular species and 3-deoxy-D-manno-oct-2-ulosonic acid residues (Kdo). The lipid A domain is the only component essential for bacterial cell survival. Following its synthesis, lipid A is chemically modified in response to environmental stresses such as pH or temperature, to promote resistance to antibiotic compounds, and to evade recognition by mediators of the host innate immune response. The following protocol details the small- and large-scale isolation of lipid A from gram-negative bacteria. Isolated material is then chemically characterized by thin layer chromatography (TLC) or mass-spectrometry (MS). In addition to matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS, we also describe tandem MS protocols for analyzing lipid A molecular species using electrospray ionization (ESI) coupled to collision induced dissociation (CID) and newly employed ultraviolet photodissociation (UVPD) methods. Our MS protocols allow for unequivocal determination of chemical structure, paramount to characterization of lipid A molecules that contain unique or novel chemical modifications. We also describe the radioisotopic labeling, and subsequent isolation, of lipid A from bacterial cells for analysis by TLC. Relative to MS-based protocols, TLC provides a more economical and rapid characterization method, but cannot be used to unambiguously assign lipid A chemical structures without the use of standards of known chemical structure. Over the last two decades isolation and characterization of lipid A has led to numerous exciting discoveries that have improved our understanding of the physiology of gram-negative bacteria, mechanisms of antibiotic resistance, the human innate immune response, and have provided many new targets in the development of antibacterial compounds.

Isolation and characterization of the lipid A domain of lipopolysaccharide (LPS) from gram-negative bacteria provides insight into cell surface based mechanisms of antibiotic resistance, bacterial survival and fitness, and how chemically diverse lipid A molecular species differentially modulate host innate immune responses.

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS ...

Conducting Miller-Urey Experiments

In 1953, Stanley Miller reported the production of biomolecules from simple gaseous starting materials, using an apparatus constructed to simulate the primordial Earth&#39;s atmosphere-ocean system. Miller introduced 200 ml of water, 100 mmHg of H2, 200 mmHg of CH4, and 200 mmHg of NH3 into the apparatus, then subjected this mixture, under reflux, to an electric discharge for a week, while the water was simultaneously heated. The purpose of this manuscript is to provide the reader with a general experimental protocol that can be used to conduct a Miller-Urey type spark discharge experiment, using a simplified 3 L reaction flask. Since the experiment involves exposing inflammable gases to a high voltage electric discharge, it is worth highlighting important steps that reduce the risk of explosion. The general procedures described in this work can be extrapolated to design and conduct a wide variety of electric discharge experiments simulating primitive planetary environments.

The Miller-Urey experiment was a pioneering study regarding the abiotic synthesis of organic compounds with possible relevance to the origins of life. Simple gases were introduced into a glass apparatus and subjected to an electric discharge, simulating the effects of lightning in the primordial Earth&#8217;s atmosphere-ocean system. The experiment was conducted for one week, after which, the samples collected from it were analyzed for the chemical building blocks of life.

In 1953, Stanley Miller reported the production of biomolecules from simple gaseous starting materials, using an apparatus constructed to simulate the ...

Encapsulation of Cancer Therapeutic Agent Dacarbazine Using Nanostructured Lipid Carrier

The only formula of dacarbazine (Dac) in clinical use is intravenous infusion, presenting a poor therapeutic profile due to the low dispersity of the drug in aqueous solution. To overcome this, a nanostructured lipid carrier (NLC) consisting of glyceryl palmitostearate and isopropyl myristate was developed to encapsulate Dac. NLCs with controlled size were achieved using high shear dispersion (HSD) following solidification of oil-in-water emulsion. The synthesis parameters, including surfactant concentration, the speed and time of HSD were optimized to achieve the smallest NLC with size, polydispersion index and zeta potential of 155 &#177; 10 nm, 0.2 &#177; 0.01, and -43.4 &#177; 2 mV, respectively. The optimal parameters were also employed for Dac-loaded NLC preparation. The resultant NLC loaded with Dac possessed size, polydispersion index and zeta potential of 190 &#177; 10 nm, 0.2 &#177; 0.01, and -43.5 &#177; 1.2 mV, respectively. The drug encapsulation efficiency and drug loading reached 98% and 14%, respectively. This is the first report on encapsulation of Dac using NLC, implying that NLC could be a new potential candidate as drug carrier to improve the therapeutic profile of Dac.

The most commonly used method for nanostructured lipid carrier (NLC) synthesis involves oil-in-water emulsion, homogenization and solidification. This was modified here by applying high shear dispersion after solidification to achieve a NLC with desirable size, improved drug encapsulation and drug loading efficiency as a potential carrier for dacarbazine delivery.

The only formula of dacarbazine (Dac) in clinical use is intravenous infusion, presenting a poor therapeutic profile due to the low dispersity of the drug ...

NMR Spectroscopy as a Robust Tool for the Rapid Evaluation of the Lipid Profile of Fish Oil Supplements

The western diet is poor in n-3 fatty acids, therefore the consumption of fish oil supplements is recommended to increase the intake of these essential nutrients. The objective of this work is to demonstrate the qualitative and quantitative analysis of encapsulated fish oil supplements using high-resolution 1H and 13C NMR spectroscopy utilizing two different NMR instruments; a 500 MHz and an 850 MHz instrument. Both proton (1H) and carbon (13C) NMR spectra can be used for the quantitative determination of the major constituents of fish oil supplements. Quantification of the lipids in fish oil supplements is achieved through integration of the appropriate NMR signals in the relevant 1D spectra. Results obtained by 1H and 13C NMR are in good agreement with each other, despite the difference in resolution and sensitivity between the two nuclei and the two instruments. 1H NMR offers a more rapid analysis compared to 13C NMR, as the spectrum can be recorded in less than 1 min, in contrast to 13C NMR analysis, which lasts from 10 min to one hour. The 13C NMR spectrum, however, is much more informative. It can provide quantitative data for a greater number of individual fatty acids and can be used for determining the positional distribution of fatty acids on the glycerol backbone. Both nuclei can provide quantitative information in just one experiment without the need of purification or separation steps. The strength of the magnetic field mostly affects the 1H NMR spectra due to its lower resolution with respect to 13C NMR, however, even lower cost NMR instruments can be efficiently applied as a standard method by the food industry and quality control laboratories.

Here, high-resolution 1H and 13C Nuclear Magnetic Resonance (NMR) spectroscopy was used as a rapid and reliable tool for quantitative and qualitative analysis of encapsulated fish oil supplements.

The western diet is poor in n-3 fatty acids, therefore the consumption of fish oil supplements is recommended to increase the intake of these essential ...

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

The precise details of the interaction of intense X-ray pulses with matter are a topic of intense interest to researchers attempting to interpret the results of femtosecond X-ray free electron laser (XFEL) experiments. An increasing number of experimental observations have shown that although nuclear motion can be negligible, given a short enough incident pulse duration, electronic motion cannot be ignored. The current and widely accepted models assume that although electrons undergo dynamics driven by interaction with the pulse, their motion could largely be considered &#39;random&#39;. This would then allow the supposedly incoherent contribution from the electronic motion to be treated as a continuous background signal and thus ignored. The original aim of our experiment was to precisely measure the change in intensity of individual Bragg peaks, due to X-ray induced electronic damage in a model system, crystalline C60. Contrary to this expectation, we observed that at the highest X-ray intensities, the electron dynamics in C60 were in fact highly correlated, and over sufficiently long distances that the positions of the Bragg reflections are significantly altered. This paper describes in detail the methods and protocols used for these experiments, which were conducted both at the Linac Coherent Light Source (LCLS) and the Australian Synchrotron (AS) as well as the crystallographic approaches used to analyse the data.

We describe an experiment designed to probe the electronic damage induced in nanocrystals of Buckminsterfullerene (C60) by intense, femtosecond pulses of X-rays. The experiment found that, surprisingly, rather than being stochastic, the X-ray induced electron dynamics in C60 are highly correlated, extending over hundreds of unit cells within the crystals1.

The precise details of the interaction of intense X-ray pulses with matter are a topic of intense interest to researchers attempting to interpret the ...

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Drying a nanoparticle dispersion is a versatile way to create self-assembled structures of nanoparticles, but the mechanism of this process is not fully understood. We have traced the trajectories of individual nanoparticles using liquid-cell transmission electron microscopy (TEM) to investigate the mechanism of the assembly process. Herein, we present the protocols used for liquid-cell TEM studies of the self-assembly mechanism. First, we introduce the detailed synthetic protocols used to produce uniformly sized platinum and lead selenide nanoparticles. Next, we present the microfabrication processes used to produce liquid cells with silicon nitride or silicon windows and then describe the loading and imaging procedures of the liquid-cell TEM technique. Several notes are included to provide helpful tips for the entire process, including how to manage the fragile cell windows. The individual motions of nanoparticles tracked by liquid-cell TEM revealed that changes in the solvent boundaries caused by evaporation affected the self-assembly process of nanoparticles. The solvent boundaries drove nanoparticles to primarily form amorphous aggregates, followed by flattening of the aggregates to produce a 2-dimensional (2D) self-assembled structure. These behaviors are also observed for different nanoparticle types and different liquid-cell compositions.

Here we introduce experimental protocols for the real-time observation of a self-assembly process using liquid-cell transmission electron microscopy.

Drying a nanoparticle dispersion is a versatile way to create self-assembled structures of nanoparticles, but the mechanism of this process is not fully ...

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen reduction reaction. Spontaneous galvanic displacement was used to deposit Pt layers onto Ni nanowire substrates. The synthesis approach produced catalysts with high specific activities and high Pt surface areas. Hydrogen annealing improved Pt and Ni mixing and specific activity. Acid leaching was used to preferentially remove Ni near the nanowire surface, and oxygen annealing was used to stabilize near-surface Ni, improving durability and minimizing Ni dissolution. These protocols detail the optimization of each post-synthesis processing step, including hydrogen annealing to 250 &#176;C, exposure to 0.1 M nitric acid, and oxygen annealing to 175 &#176;C. Through these steps, Pt-Ni nanowires produced increased activities more than an order of magnitude than Pt nanoparticles, while offering significant durability improvements. The presented protocols are based on Pt-Ni systems in the development of fuel cell catalysts. These techniques have also been used for a variety of metal combinations, and can be applied to develop catalysts for a number of electrochemical processes.

&#160; The protocol describes the synthesis and electrochemical testing of platinum-nickel nanowires. Nanowires were synthesized by the galvanic displacement of a nickel nanowire template. Post-synthesis processing, including hydrogen annealing, acid leaching, and oxygen annealing were used to optimize nanowire performance and durability in the oxygen reduction reaction.

Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen ...

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

The use of nanoprobes such as gold, silver, silica or iron-oxide nanoparticles as detection reagents in bioanalytical assays can enable high sensitivity and convenient colorimetric readout. However, high densities of nanoparticles are typically needed for detection. The available synthesis-based enhancement protocols are either limited to gold and silver nanoparticles or rely on precise enzymatic control and optimization. Here, we present a protocol to enhance the colorimetric readout of gold, silver, silica, and iron oxide nanoprobes. It was observed that the colorimetric signal can be improved by up to a 10000-fold factor. The basis for such signal enhancement strategies is the chemical reduction of Au3+ to Au0. There are several chemical reactions that enable the reduction of Au3+ to Au0. In the protocol, Good&#39;s buffers and H2O2 are used and it is possible to favor the deposition of Au0 onto the surface of existing nanoprobes, in detriment of the formation of new gold nanoparticles. The protocol consists of the incubation of the microarray with a solution consisting of chloroauric acid and H2O2 in 2-(N-morpholino)ethanesulfonic acid pH 6 buffer following the nanoprobe-based detection assay. The enhancement solution can be applied to paper and glass-based sensors. Moreover, it can be used in commercially available immunoassays as demonstrated by the application of the method to a commercial allergen microarray. The signal development requires less than 5 min of incubation with the enhancement solution and the readout can be assessed by naked eye or low-end image acquisition devices such as a table-top scanner or a digital camera.

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

In this work, a protocol for signal enhancement of nanoprobe-based biosensing is presented. The protocol is based on the reduction of chloroauric acid onto the surface of existing nanoprobes that consist of gold, silver, silica or iron-oxide nanoparticles.

The use of nanoprobes such as gold, silver, silica or iron-oxide nanoparticles as detection reagents in bioanalytical assays can enable high sensitivity ...

Analysis of Minerals Produced by hFOB 1.19 and Saos-2 Cells Using Transmission Electron Microscopy with Energy Dispersive X-ray Microanalysis

This video presents the use of transmission electron microscopy with energy dispersive X-ray microanalysis (TEM-EDX) to compare the state of minerals in vesicles released by two human bone cell lines: hFOB 1.19 and Saos-2. These cell lines, after treatment with ascorbic acid (AA) and &#946;-glycerophosphate (&#946;-GP), undergo complete osteogenic transdifferentiation from proliferation to mineralization and produce matrix vesicles (MVs) that trigger apatite nucleation in the extracellular matrix (ECM).
Based on Alizarin Red-S (AR-S) staining and analysis of the composition of minerals in cell lysates using ultraviolet (UV) light or in vesicles using TEM imaging followed by EDX quantitation and ion mapping, we can infer that osteosarcoma Saos-2 and osteoblastic hFOB 1.19 cells reveal distinct mineralization profiles. Saos-2 cells mineralize more efficiently than hFOB 1.19 cells and produce larger mineral deposits that are not visible under UV light but are similar to hydroxyapatite (HA) in that they have more Ca and F substitutions.
The results obtained using these techniques allow us to conclude that the process of mineralization differs depending on the cell type. We propose that, at the cellular level, the origin and properties of vesicles predetermine the type of minerals.

We present a protocol to compare the state of minerals in vesicles released by two human bone cell lines: hFOB 1.19 and Saos-2. Their mineralization profiles were analyzed by Alizarin Red-S (AR-S) staining, ultraviolet (UV) light visualization, transmission electron microscopy (TEM) imaging and energy dispersive X-ray microanalysis (EDX).

This video presents the use of transmission electron microscopy with energy dispersive X-ray microanalysis (TEM-EDX) to compare the state of minerals in ...

Solid Phase 11C-Methylation, Purification and Formulation for the Production of PET Tracers

Routine production of radiotracers used in positron emission tomography (PET) mostly relies on wet chemistry where the radioactive synthon reacts with a non-radioactive precursor in solution. This approach necessitates purification of the tracer by high performance liquid chromatography (HPLC) followed by reformulation in a biocompatible solvent for human administration. We recently developed a novel 11C-methylation approach for the highly efficient synthesis of carbon-11 labeled PET radiopharmaceuticals, taking advantage of solid phase cartridges as disposable &#34;3-in-1&#34; units for the synthesis, purification and reformulation of the tracers. This approach obviates the use of preparative HPLC and reduces the losses of the tracer in transfer lines and due to radioactive decay. Furthermore, the cartridge-based technique improves synthesis reliability, simplifies the automation process and facilitates compliance with the Good Manufacturing Practice (GMP) requirements. Here, we demonstrate this technique on the example of production of a PET tracer Pittsburgh compound B ([11C]PiB), a gold standard in vivo imaging agent for amyloid plaques in the human brains.

Solid Phase 11C-Methylation, Purification and Formulation for the Production of PET Tracers

We report an efficient carbon-11 radiolabeling technique to produce clinically relevant tracers for Positron Emission Tomography (PET) using solid phase extraction cartridges. 11C-methylating agent is passed through a cartridge preloaded with precursor and successive elution with aqueous ethanol provides chemically and radiochemically pure PET tracers in high radiochemical yields.

Routine production of radiotracers used in positron emission tomography (PET) mostly relies on wet chemistry where the radioactive synthon reacts with a ...