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

<ol>
	<li>Dolgin, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Better+lipids+to+power+next+generation+of+mRNA+vaccines.">Better lipids to power next generation of mRNA vaccines.</a> Science. 376 (6594), 680-681 (2022).</li><li>Hou, X., Zaks, T., Langer, R., Dong, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+nanoparticles+for+mRNA+delivery.">Lipid nanoparticles for mRNA delivery.</a> Nature Reviews Materials. 6 (12), 1078-1094 (2021).</li><li>Huang, 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=The+landscape+of+mRNA+nanomedicine.">The landscape of mRNA nanomedicine.</a> Nature Medicine. 28, 2273-2287 (2022).</li><li>Rampado, R., Peer, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+of+experiments+in+the+optimization+of+nanoparticle-based+drug+delivery+systems.">Design of experiments in the optimization of nanoparticle-based drug delivery systems.</a> Journal of Controlled Release. 358, 398-419 (2023).</li><li>Kauffman, K. 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://jove.unicartagenaproxy.elogim.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.

<table><tbody><tr><td>JMP Pro 17.1</td><td>JMP Statistical Discovery LLC</td><td></td><td></td></tr></tbody></table>

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 ...

a-workflow-for-lipid-nanoparticle-lnp-formulation-optimization-using

Universidad de Cartagena UDEC

Research

JoVE Journal

Chemistry

4.3K 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 ...

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional units (e.g., domains). Traditional consensus-building models fail to account for interpositional dependencies &#8211; functionally required covariation of residues that tend to appear simultaneously throughout evolution and across the phylogentic tree. These relationships can reveal important clues about the processes of protein folding, thermostability, and the formation of functional sites, which in turn can be used to inform the engineering of synthetic proteins. Unfortunately, these relationships essentially form sub-motifs which cannot be predicted by simple &#8220;majority rule&#8221; or even HMM-based consensus models, and the result can be a biologically invalid &#8220;consensus&#8221; which is not only never seen in nature but is less viable than any extant protein. We have developed a visual analytics tool, StickWRLD, which creates an interactive 3D representation of a protein alignment and clearly displays covarying residues. The user has the ability to pan and zoom, as well as dynamically change the statistical threshold underlying the identification of covariants. StickWRLD has previously been successfully used to identify functionally-required covarying residues in proteins such as Adenylate Kinase and in DNA sequences such as endonuclease target sites.

Synthetic protein sequences based on consensus motifs typically ignore co-evolving residues, that imply interpositional dependencies (IPDs). IPDs can be essential to activity, and designs that disregard them may result in suboptimal results. This protocol uses StickWRLD to identify IPDs and help inform rational protein design, resulting in more efficient results.

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional ...

Curtain Flow Column: Optimization of Efficiency and Sensitivity

Active Flow Technology (AFT) is a form of column technology that increases the separation performance of a HPLC column through the use of a specially purpose built multiport end-fitting(s). Curtain Flow (CF) columns belong to the AFT suite of columns, specifically the CF column is designed so that the sample is injected into the radial central region of the bed and a curtain flow of mobile phase surrounding the injection of solute prevents the radial dispersion of the sample to the wall. The column functions as an 'infinite diameter' column. The purpose of the design is to overcome the radial heterogeneity of the column bed, and at the same time maximize the sample load into the radial central region of the column bed, which serves to increase detection sensitivity. The protocol described herein outlines the system and CF column set up and the tuning process for an optimized infinite diameter 'virtual' column.

Here, we present a protocol for the operation and optimization of Active Flow Technology (AFT) column in Curtain flow (CF) mode for enhanced separation performance.

Active Flow Technology (AFT) is a form of column technology that increases the separation performance of a HPLC column through the use of a specially ...

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 ...

PLGA Nanoparticles Formed by Single- or Double-emulsion with Vitamin E-TPGS

Poly(lactic-co-glycolic acid) (PLGA) is a biocompatible member of the aliphatic polyester family of biodegradable polymers. PLGA has long been a popular choice for drug delivery applications, particularly since it is already FDA-approved for use in humans in the form of resorbable sutures. Hydrophobic and hydrophilic drugs are encapsulated in PLGA particles via single- or double-emulsion. Briefly, the drug is dissolved with polymer or emulsified with polymer in an organic phase that is then emulsified with the aqueous phase. After the solvent has evaporated, particles are washed and collected via centrifugation for lyophilization and long term storage. PLGA degrades slowly via hydrolysis in aqueous environments, and encapsulated agents are released over a period of weeks to months. Although PLGA is a material that possesses many advantages for drug delivery, reproducible formation of nanoparticles can be challenging; considerable variability is introduced by the use of different equipment, reagents batch, and precise method of emulsification. Here, we describe in great detail the formation and characterization of microparticles and nanoparticles formed by single- or double-emulsion using the emulsifying agent vitamin E-TPGS. Particle morphology and size are determined with scanning electron microscopy (SEM). We provide representative SEM images for nanoparticles produced with varying emulsifier concentration, as well as examples of imaging artifacts and failed emulsifications. This protocol can be readily adapted to use alternative emulsifiers (e.g. poly(vinyl alcohol), PVA) or solvents (e.g.&#160;dichloromethane, DCM).

We describe the production and characterization of nanoparticles and microparticles composed of poly(lactic-co-glycolic acid) using vitamin E-TPGS as an emulsifier. By varying formulation parameters such as the concentration of emulsifier, it is possible to produce nanoparticles with mean diameters ranging from 220 nm to&#160;1.98 &#181;m.

Poly(lactic-co-glycolic acid) (PLGA) is a biocompatible member of the aliphatic polyester family of biodegradable polymers. PLGA has long been a popular ...

Bioengineering

Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device

The development of functional lipid nanoparticles (LNPs) is one of the major challenges in the field of drug delivery systems (DDS). Recently, LNP-based RNA delivery systems, namely, RNA-loaded LNPs have attracted attention for RNA therapy. In particular, mRNA-loaded LNP vaccines were approved to prevent COVID-19, thereby leading to the paradigm shift toward the development of next-generation nanomedicines. For the LNP-based nanomedicines, the LNP size is a significant factor in controlling the LNP biodistribution and LNP performance. Therefore, a precise LNP size control technique is indispensable for the LNP production process. Here, we report a protocol for size controlled LNP production using a microfluidic device, named iLiNP. siRNA loaded LNPs are also produced using the iLiNP device and evaluated by in vitro experiment. Representative results are shown for the LNP size, including siRNA-loaded LNPs, Z-potential, siRNA encapsulation efficiency, cytotoxicity, and target gene silencing activity.

Microfluidic-based lipid nanoparticle (LNP) production methods have attracted attention in drug delivery systems (DDSs), including RNA delivery. This protocol describes the fabrication, LNP (siRNA-loaded LNP) production, and LNP evaluation processes using our original microfluidic device named iLiNP.

The development of functional lipid nanoparticles (LNPs) is one of the major challenges in the field of drug delivery systems (DDS). Recently, LNP-based ...

Biochemistry

Formulating and Characterizing Lipid Nanoparticles for Gene Delivery using a Microfluidic Mixing Platform

Lipid-based drug carriers have been used for clinically and commercially available delivery systems due to their small size, biocompatibility, and high encapsulation efficiency. Use of lipid nanoparticles (LNPs) to encapsulate nucleic acids is advantageous to protect the RNA or DNA from degradation, while also promoting cellular uptake. LNPs often contain multiple lipid components including an ionizable lipid, helper lipid, cholesterol, and polyethylene glycol (PEG) conjugated lipid. LNPs can readily encapsulate nucleic acids due to the ionizable lipid presence, which at low pH is cationic and allows for complexation with negatively charged RNA or DNA. Here LNPs are formed by encapsulating messenger RNA (mRNA) or plasmid DNA (pDNA) using rapid mixing of the lipid components in an organic phase and the nucleic acid component in an aqueous phase. This mixing is performed using a precise microfluidic mixing platform, allowing for nanoparticle self-assembly while maintaining laminar flow. The hydrodynamic size and polydispersity are measured using dynamic light scattering (DLS). The effective surface charge on the LNP is determined by measuring the zeta potential. The encapsulation efficiency is characterized using a fluorescent dye to quantify entrapped nucleic acid. Representative results demonstrate the reproducibility of this method and the influence that different formulation and process parameters have on the developed LNPs.

Lipid nanoparticles are developed using a microfluidic mixing platform approach for mRNA and DNA encapsulation.

Lipid-based drug carriers have been used for clinically and commercially available delivery systems due to their small size, biocompatibility, and high ...

Testing the In Vitro and In Vivo Efficiency of mRNA-Lipid Nanoparticles Formulated by Microfluidic Mixing

Lipid nanoparticles (LNPs) have attracted widespread attention recently with the successful development of the COVID-19 mRNA vaccines by Moderna and Pfizer/BioNTech. These vaccines have demonstrated the efficacy of mRNA-LNP therapeutics and opened the door for future clinical applications. In mRNA-LNP systems, the LNPs serve as delivery platforms that protect the mRNA cargo from degradation by nucleases and mediate their intracellular delivery. The LNPs are typically composed of four components: an ionizable lipid, a phospholipid, cholesterol, and a lipid-anchored polyethylene glycol (PEG) conjugate (lipid-PEG). Here, LNPs encapsulating mRNA encoding firefly luciferase are formulated by microfluidic mixing of the organic phase containing LNP lipid components and the aqueous phase containing mRNA. These mRNA-LNPs are then tested in vitro to evaluate their transfection efficiency in HepG2 cells using a bioluminescent plate-based assay. Additionally, mRNA-LNPs are evaluated in vivo in C57BL/6 mice following an intravenous injection via the lateral tail vein. Whole-body bioluminescence imaging is performed by using an in vivo imaging system. Representative results are shown for the mRNA-LNP characteristics, their transfection efficiency in HepG2 cells, and the total luminescent flux in C57BL/6 mice.

Testing the In Vitro and In Vivo Efficiency of mRNA-Lipid Nanoparticles Formulated by Microfluidic Mixing

Here, a protocol for formulating lipid nanoparticles (LNPs) that encapsulate mRNA encoding firefly luciferase is presented. These LNPs were tested for their potency in vitro in HepG2 cells and in vivo in C57BL/6 mice.

Lipid nanoparticles (LNPs) have attracted widespread attention recently with the successful development of the COVID-19 mRNA vaccines by Moderna and ...

Synthesizing Lipid Nanoparticles Using CIJ and &#181;MIVM Mixer Technologies

Synthesizing Lipid Nanoparticles by Turbulent Flow in Confined Impinging Jet Mixers

Lipid nanoparticles (LNPs) have demonstrated their enormous potential as therapeutic delivery vehicles, as evidenced by the approval and global usage of two COVID-19 messenger RNA (mRNA) vaccines. On a small scale, LNPs are often made using microfluidics; however, the limitations of these devices preclude their use on a large scale. The COVID-19 vaccines are manufactured in large quantities using confined impinging jet (CIJ) turbulent mixers. CIJ technology enables production at a laboratory scale with the confidence that it can be scaled to production volumes. The key concepts in CIJ mixing are that the mixing length and time scale are determined by the turbulence intensity in the mixing cavity and that the nanoparticle formation occurs away from walls, eliminating the problem of deposition on surfaces and fouling. This work demonstrates the process of making LNPs using confined impinging jet mixer technology with two geometries: the two-jet CIJ and the four-jet multi-inlet vortex mixer (MIVM). The advantages and disadvantages of each mixing geometry are discussed. In these geometries, LNPs are formed by rapid mixing of an organic solvent stream (usually ethanol containing the ionizable lipids, co-lipids, and stabilizing PEG-lipids) with an aqueous anti-solvent stream (aqueous buffer containing RNA or DNA). The operating parameters for the CIJ and MIVM mixers are presented to prepare reproducible LNPs with controlled size, zeta potential, stability, and transfection effectiveness. The differences between LNPs made with poor mixing (pipetting solutions) compared to CIJ mixing are also presented.

Author Spotlight: Enhancing Lipid Nanoparticle Formation Through Turbulent Mixing in Confined Geometries

A detailed protocol for synthesizing lipid nanoparticles (LNPs) using confined impinging jet (CIJ) mixer technologies, including a two-jet CIJ and a four-jet multi-inlet vortex mixer (&#181;MIVM), is demonstrated. The CIJ mixers generate reproducible, turbulent micro-mixing environments, resulting in the production of monodisperse LNPs.

Lipid nanoparticles (LNPs) have demonstrated their enormous potential as therapeutic delivery vehicles, as evidenced by the approval and global usage of ...

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

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Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device

Formulating and Characterizing Lipid Nanoparticles for Gene Delivery using a Microfluidic Mixing Platform

Testing the In Vitro and In Vivo Efficiency of mRNA-Lipid Nanoparticles Formulated by Microfluidic Mixing

Synthesizing Lipid Nanoparticles by Turbulent Flow in Confined Impinging Jet Mixers