Name: a workflow for lipid nanoparticle (lnp) formulation optimization using designed mixture-process experiments and self-validated ensemble models (svem)
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Description: 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

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

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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 approach that is both easier to implement and tends to produce better models than traditional modeling approaches13. Furthermore, the graphical analyses that are based on the prediction formulas for each response are easily interpretable by scientists, giving a clear summary of the marginal behavior of the response over individual factors as well as small groups of factors without requiring the interpretation of highly correlated parameter estimates from a regression model. This allows scientists to focus on assessing practical significance across study factors after SVEM has automatically removed statistically insignificant effects.
The workflow has been used in practice to systematically vary lipid composition and formulation parameters such as N/P ratio, flow rate, and mixing ratio for optimization and to select the best helper lipid types, ionizable lipid types, and buffer types. The goals across these examples usually include maximizing in vivo or in vitro potency and encapsulating varying payloads like mRNA or DNA for relevant in vivo targets such as liver cells, or sometimes across multiple cell-types in the case of in vitro applications. For specific applications, we may need to balance biophysical properties such as size, PDI, zeta potential, and percent encapsulation while examining in vivo potency. Additionally, the goal is to find a potent, yet well-tolerated formulation and so we may include responses such as change in body weight, cytokine response, or elicitation of liver enzymes such as AST/ALT in the analysis. Patterns have emerged from numerous LNP experiments. Notably, alterations in the molar ratio of the ionizable lipid and the N/P ratio seem to significantly impact RNA encapsulation. Moreover, changes in the PEG molar ratio appear to affect particle stability, as indicated by influences on size and PDI. In general, an excess of PEG in the LNP core tends to have a detrimental effect on potency in mice.
Performance improvements are especially noticeable when more than one response is targeted: even if the benchmark already performs well with respect to the primary response (e.g., potency), joint optimization typically maintains or improves the behavior with respect to the primary response while simultaneously improving behavior with respect to other responses (minimizing PDI, size, or bodyweight loss). We validate the authenticity of these improvements with confirmation runs, wherein we prepare and directly compare the benchmark formulation (possibly with a replicate) and new candidate formulations.
The design phase of this workflow has several critical steps. First, ensure that the factors and their ranges are correctly entered into the space-filling design platform. Second, use graphics and subject matter knowledge to confirm the feasibility of each resulting formulation before initiating the experiment. Finally, execute the experiment following the randomized order specified by the design table. Adhering to this sequence helps prevent unmeasured covariates - such as the order of formulation production or ambient temperature - from confounding the factors under study. The space-filling designs are easier to construct - with less potential for user error than optimal mixture-process designs, which require extra decisions during setup that may frustrate inexperienced users and discourage them from using designed experiments. Nevertheless, after working through this protocol, scientists may benefit from additional reading on how optimal designs could potentially replace space-filling designs in the protocol, such as described in Chapter 6 of Goos and Jones (2011)27. Especially for follow-up studies that &#34;zoom in&#34; on an optimal region - where there is less concern about failures along the mixture boundaries - D-optimal designs can be more efficient than space-filling designs.
Likewise, the analysis phase of this workflow has several critical steps. First, ensure that the model specifies an appropriate set of candidate effects, including interactions, rather than only the main (first-order) effects of the factors. Second, employ SVEM Forward Selection as the modeling framework. Third, disable the default No Intercept option and avoid forcing mixture main effects. Finally, correctly set the desirability functions for the responses before initiating optimization. For users without access to SVEM, the best approach is to use traditional forward selection (targeting minimum AICc) for the regression problem12. The protocol mentions that it is also possible to use SVEM Lasso: on average, this approach gives similar results to SVEM Forward Selection, though for particular datasets the two approaches may produce slightly different optimal formulations that could be compared with confirmation runs12. However, SVEM Lasso will give inferior modeling results if the user makes the easy mistake of forgetting to disable the default No Intercept option12: for this reason, we have used SVEM Forward Selection as the default method, since it is more robust to this option.
The primary limitation of this method is that there will be occasional studies with greater complexity that will benefit from the help of a statistician for design and analysis. Situations where the run budget is more limited than usual (below the minimum heuristic), the responses are binary, there are a large number of categorical factors or levels of a single categorical factor, where a research goal is to consider eliminating one or more mixture factors from the recipe, or where there are additional constraints on the factor space may be approached differently by a statistician, such as by using optimal or hybrid12,28 designs or by adding additional structure to the design. Specifically, a hybrid design could be formed by creating a space-filling design with most of the budgeted runs and then &#34;augmenting&#34; the design with the remaining runs (usually 2-4) using a D-optimal criterion. Another hybrid approach is to generate a space-filling design over the mixture (lipid) and continuous (process) factors, and then afterward to add any categorical factors using an &#34;optimal&#34; allocation of factor levels. Nevertheless, the simplified space-filling design approach taken in the protocol has been developed over the past few years in the process of running dozens of LNP formulation optimization experiments, and we believe it offers a robust approach that will work successfully in most cases while also giving scientists confidence in their ability to utilize designed experiments.

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.

<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. Table 1 depicts the corresponding enhancements in mouse liver luciferase expression observed over the benchmark control performance throughout two optimization phases (an initial study and a subsequent follow-up study). In the first phase, focus was on optimizing the lipid ratios while keeping other factors constant. In the follow-up study, an additional helper lipid type was introduced and optimization was performed considering both the lipid ratio composition and the helper lipid type. Consequently, the newly introduced helper lipid type was selected to be used with the associated optimized lipid composition. The significant enhancement in potency suggests that these optimized compositions may exhibit superior endosomal escape capabilities25.
Simulations can be used to show the expected quality of the optimal candidate produced by this procedure. Within the framework of the example experiment used in the protocol, we can repeat the simulation many times for different run sizes and evaluate the results according to the simulated process-generating function. A JMP script for this purpose is provided in Supplementary File 4. Specifically, a space-filling design was generated and the response columns were populated with values from our generator functions, plus noise representing analytical and process variation. We fit these simulated responses with different analysis techniques (including SVEM Forward Selection) to produce a corresponding candidate optimal recipe. The candidates from each analysis method are then compared to the value of the true optimum from the generating functions. Figure 19 illustrates the average percent of the maximal theoretical response achieved by each of the three analysis methods using space-filling designs of size given on the horizontal axis. The full model, which includes all candidate effects and does not reduce the model based on the statistical significance of those effects, performs the worst. Much of the additional work that traditionally goes into fitting regression models for mixture-process experiments involves modifications (removing the intercept, forcing the mixture main effects, precluding the use of pure quadratic mixture effects, etc.) that are required to fit this full model9, and from this perspective, those procedures are unnecessary12. Furthermore, this model cannot be fit until the design size reaches the number of effects in the model. At smaller experimental sizes, we can fit the traditional forward selection method, which outperforms the full model with respect to the average performance of the optimal candidate formulation for each fixed experimental size. Likewise, the SVEM modification to this forward selection approach further improves the performance of the optimal candidates. This plot reveals that using SVEM-Forward Selection12,13 to analyze a 24-run space-filling experiment achieves the same average quality typically requiring 50 runs when analyzed with a traditional forward selection (targeting minimum AICc) model. Although actual performance will vary from process to process, this simulation - along with published results on SVEM12,13,16,17,26- demonstrates the potential of this modeling procedure for formulation optimization.
<img alt="Figure 18" class="xfigimg" src="/files/ftp_upload/65200/65200fig18.jpg" /> 
Figure 18: Improvement in liver luciferase expression following two rounds of experimentation. Round 0 shows the liver luciferase reading for the benchmark formulation; Round 1 shows the liver luciferase reading after the first experiment which optimizes the LNP constituent lipid molar ratios; Round 2 shows the liver luciferase reading after the second experiment which further optimizes the constituent molar ratios while also considering an additional helper lipid type. <a href="https://www.jove.com/files/ftp_upload/65200/65200fig18large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 19" class="xfigimg" src="/files/ftp_upload/65200/65200fig19.jpg" /> 
Figure 19: Quality of optimal formulation as a function of experimental size and statistical model. The vertical axis represents the percentage of theoretical maximum desirability, and the horizontal axis represents the size of the space-filling design. Each point shows the mean over 150 simulations. The blue line (triangles) represents the full model (without any elimination of statistically insignificant effects), the amber (circles) line represents the traditional AICc-based forward selection model (with an intercept and without forcing mixture main effects), and the green line (upside down triangles) represents the SVEM-based forward selection model (with an intercept and without forcing mixture main effects). <a href="https://www.jove.com/files/ftp_upload/65200/65200fig19large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Round</td>
			<td>Particle ID</td>
			<td>Luciferase expression in the Liver (photon/sec)</td>
		</tr>
		<tr>
			<td>0</td>
			<td>Control Benchmark</td>
			<td>8.E+06</td>
		</tr>
		<tr>
			<td>1</td>
			<td>Optimized over Lipid Ratios</td>
			<td>2.E+09</td>
		</tr>
		<tr>
			<td>2</td>
			<td>Optimized over Lipid Ratios and Helper Lipid Type</td>
			<td>8.E+10</td>
		</tr>
	</tbody>
</table>
Table 1: Systematic improvement in luciferase expression through Design of Experiment (DOE) optimization. This table illustrates the significant enhancement in the expression of luciferase, with an up to 10,000-fold improvement on the photon/second scale, from the initial benchmark to the final &#34;optimal candidate&#34;.
Supplementary File 1: 04APR2023 Summary.docx - This document provides a record of the study including its purpose, the responses assessed, the factors considered, and the total number of runs executed. <a href="https://www.jove.com/files/ftp_upload/65200/04APR2023 Summary.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 2: 23_run_simulated_experiment.jmp - A JMP file with the simulated experiment and its results. This file also includes attached analysis scripts compatible with JMP 17 Pro. <a href="https://www.jove.com/files/ftp_upload/65200/23_run_simulated_experiment_2.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 3: 23_run_simulated_experiment.xlsx - An Excel file that includes the simulated experiment and its results, suitable for readers who may not have access to JMP. <a href="https://www.jove.com/files/ftp_upload/65200/23_run_simulated_experiment_3.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 4: mixture simulation 20DEC22.jsl - This is a JMP 17 Pro script used to simulate LNP formulation experiments and evaluate the performance of different analysis methods. The script uses the SVEM-Forward Selection (no intercept) approach, which is the key analysis method used in this workflow. <a href="https://www.jove.com/files/ftp_upload/65200/mixture simulation 20DEC22.zip" target="_blank">Please click here to download this File.</a>

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

Nanyang Technological University

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 ...

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 ...

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 ...

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

Conducting Miller-Urey Experiments

Curtain Flow Column: Optimization of Efficiency and Sensitivity

Encapsulation of Cancer Therapeutic Agent Dacarbazine Using Nanostructured Lipid Carrier

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

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

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