Overcome Challenges in LNP Vector Analysis

Accelerating LNP Therapeutics Your Guide to Smarter Analytics 2ND EDITIONContents Introduction 3 Chapter 1: Characterizing LNP size, polydispersity, and concentration 4 Size 5 Polydispersity 12 Concentration 14 Section summary 15 Chapter 2: Characterizing vector surfaces 16 Surface charge: zeta potential determination by ELS 17 Section summary 19 Chapter 3: Investigating thermal stability 20 Measuring thermal stability with differential scanning calorimetry (DSC) 21 Key considerations when using DSC 24 Section summary 25 Your route to analytical excellence Optimizing LNP vector characteriztion with the right tools 26 Featured & related products from Malvern Panalytical 27 Accelerating LNP therapeutics: Your guide to smarter analytics 2Introduction Lipid-based nanoparticles (LNPs) hold great promise for the treatment, cure, and prevention of a range of challenging medical conditions — from genetic diseases to cancers. Not only do these vectors enable the efficient delivery of therapeutic payloads such as RNA, but (unlike viral vectors), they can also be manufactured using cell-free production processes with the potential for rapid scaling. However, LNP therapies are analytically challenging to develop and manufacture, owing to their intrinsic structural complexity (Figure 1). Developers must accurately measure and carefully control a range of key attributes to determine their stability, and guide and inform product design, production process optimization, and release specification. Some of the key attributes of LNPs include: • Size • Polydispersity • Concentration • Zeta potential • Higher order structure (HOS) • Thermal stability Critically, traditional techniques used for the characterization of earlier, more established therapeutic modalities, such as monoclonal antibody therapies, are often not suitable for the complexity or pace of development when it comes to novel LNP-based therapies. As such, developers urgently need a suite of fit-forpurpose orthogonal analytical techniques that are complementary to traditional approaches. Such tools can help guide and optimize everything from vector design through formulation development and manufacturing, to quality control, enabling more confident decision-making for faster delivery of better medicines to patients in need. Figure 1: Illustration of a typical messenger RNA (mRNA)-LNP complex (DSPC = distearoylphosphatidylcholine). Cationic lipid DSPC PEGylated lipid Cholesterol mRNA By reading this eBook, you’ll discover: • How to get insights into the key attributes of LNPs • Robust, orthogonal analytical tools that can help you to measure LNPs with confidence Accelerating LNP therapeutics: Your guide to smarter analytics 3Chapter 1: Characterizing LNP size, polydispersity, and concentration Accelerating LNP therapeutics: Your guide to smarter analytics 4Size LNP vector size is a critical attribute for the function of LNP-based therapies, as it can determine tissue penetration (and, therefore, in vivo efficacy). In addition to giving you insight into LNP function, accurately measuring the size of your LNP vectors can also help you identify potential instability in your sample (typically displayed as aggregation or a change in particle size, for instance) due to external stresses, such as storage conditions or processing steps. Size measurements are used across all stages of creating a new LNPbased therapy, from early development, through to process and formulation development, process control, and final batch release. Sizing up your LNPs: available tools & techniques For LNP-based therapeutics, there are a number of size-measuring analytical techniques, which cover a wide range of particle sizes - namely single angle Dynamic Light Scattering (DLS), Multi-Angle Dynamic Light Scattering (MADLS), and nanoparticle tracking analysis (NTA). These techniques cover a wide particle size range. Accelerating LNP therapeutics: Your guide to smarter analytics 5Small Particles Intensity Time Intensity Time Intensity Intensity Time Time Intensity Time Avalanche Large Particles photodiode detector (APD) Digital signal processor (Correlator) Small Particles Cumulants analysis: Z-average Polydispersity index (Pdl) Particle size distribution (non-negative least squares (NNLS) analysis): Peak size, width and area Large Particles Laser Cuvette containing sample Side scatter detection angle ZS Advance Lab/Ultra Back scatter detection angle Forward scatter detection angle ZS Advance Pro/Ultra ZS Advance Pro/Ultra Figure 2: Diagram showing how dynamic light scattering works. Dynamic Light Scattering (DLS) DLS is a non-invasive, well-established technique for measuring the size and size distribution of molecules and particles dispersed or dissolved in liquid (Figure 2). In DLS, a light source illuminates a dispersion of particles. The particles then scatter a fraction of the light in all directions, with some of this scattering detected at a single, specified angle. Analyzing the scattering intensity fluctuations gives the velocity of the Brownian motion, which is then used to calculate the particle size using the Stokes-Einstein relationship. With the latest technology, DLS can detect particles ranging from 10s of µm to 1 nm and below, meaning it can help you reliably measure even the smallest mRNA-LNPs (which typically range from ~50–150 nm). One critical benefit of DLS is its wide concentration range, which is a particular advantage for LNPs as they can often occur in very high concentrations. While DLS has the lowest resolution of the three sizing techniques discussed here, it is accurate, reproducible, fast, and requires limited method development. As such, analysts often use DLS as a rapid screen for sample degradation or aggregation, providing an indication of whether deeper investigation of your LNP vector’s size is needed. What’s more, DLS also uses minimal sample volumes (~1 uL) non-destructively, meaning you can preserve your precious samples and re-use them in other assays. However, you should be aware of one critical disadvantage when it comes to single-angle (backscatter) DLS: larger aggregates tend to scatter more light in the forward angle, meaning that it can be difficult to detect the presence of LNP aggregates. For this reason, you should always quote the scattering angle used to obtain your DLS result. Briefly how it works! Accelerating LNP therapeutics: Your guide to smarter analytics 6Single Angle DLS Three angles give three different broad distributions Correlation Coefficient (g1-1) Time (µs) 0 0.01 0.1 1 10 100 1e+03 1e+04 1e+05 1e+06 1e+07 1e+08 Size (d.nm) 0.1 1 10 100 1e+03 1e+04 0.2 0.4 0.6 0.8 1 Intensity (Percent) 5 0 10 15 20 25 1.2 Correlation Coefficient (g1-1) Time (µs) 0 0.01 0.1 1 10 100 1e+03 1e+04 1e+05 1e+06 1e+07 1e+08 0 100 200 300 400 500 600 700 800 45 (Mie) 90 (Mie) 173 (Mie) 900 1000 Diameter (nm) 0.2 0.4 0.6 0.8 1 Normalised Scattering Per Particle (Mie) 1e-08 1e-06 1e-04 1e-02 1e+00 1.2 Intensity (Percent) Size (d.nm) 0 0.1 1 10 100 1e+03 1e+04 8 6 4 2 10 12 MADLS One accurate and more resolved distribution from the same data Multi-angle dynamic light scattering (MADLS) While DLS works by measuring samples at a single detection angle, MADLS measures samples at multiple angles, offering improved resolution as well as angleindependent particle size distribution (Figure 3). MADLS, therefore, has several advantages relative to single angle DLS. First, it provides a more accurate representation of the different populations present in the sample, and a higher resolution size determination of multi-modal samples (Figure 4). It can also consistently detect low numbers of larger aggregates (which are inherently harder to detect with single angle DLS, as discussed above). Figure 3: Comparison of Single angle DLS and MADLS. By combining the correlation data from several scattering angles with Mie theory, MADLS provides a single, more representative, and better-resolved size distribution relative to single angle DLS. Accelerating LNP therapeutics: Your guide to smarter analytics 7MADLS NIBS 16 14 12 10 8 6 4 2 10 100 1000 10000 Size Diameter (nm) Intensity (%) 0 Like DLS, MADLS can detect even the smallest LNPs, with a detectable size range of 10 µm to 1 nm and below. A key consideration for using MADLS is that you need to know the refractive index and absorption of your sample material and dispersant. Without the correct values, the MADLS algorithm will fail to converge on the true solution, giving you inaccurate results. Since RNA can change the refractive index of your sample, you need to know if your samples contain RNA or not. To do this, you can either calculate the refractive index from a RiboGreen assay, or you can calculate it from compositional analysis data. Figure 4: MADLS reveals a second population (shoulder) alongside the NIBS measurement, providing higer resolution overall Accelerating LNP therapeutics: Your guide to smarter analytics 8Image Capture Analyse Data NTA doesn’t require any knowledge about material constants such as RI or absorbance of the particles - orthogonal technique Polydispersity Size Flourescence Number or concentration “Relative Light Intensity” Nanoparticle tracking analysis (NTA) Nanoparticle Tracking Analysis (NTA) utilizes the properties of both light scattering and Brownian motion to obtain the nanoparticle size distribution of samples in liquid suspension (Figure 5). For this technique, particles in liquid suspension are loaded into a sample chamber, which is illuminated by a laser beam. Particles in the path of the beam scatter the light, which is then collected by a microscope and viewed with a digital camera. The camera captures a video of the individual particles moving under Brownian motion, with software analyzing many particles individually and simultaneously, particle-by-particle. By using the Stokes Einstein equation, NTA software then calculates the hydrodynamic diameters of the particles. Tracks particles in real time and reports on several characteristics Figure 5: Diagram showing how NanoSight Pro obtains a particle size distribution. Accelerating LNP therapeutics: Your guide to smarter analytics 910 10 0.0 5.0e+5 1.0e+6 1.5e+6 2.0e+6 2.5e+6 3.0e+6 5 0 10 15 20 100 100 Size Diameter (nm) Concentration (p/mL) Size Diameter (nm) Intensity (%) 1000 1000 MADLS NIBS NTA Most importantly NTA offers much higher resolution than MADLS, meaning that it is a superior technique for analyzing polydisperse LNP samples (Figure 6). With real-time monitoring capabilities, NTA can also monitor subtle changes in the characteristics of your LNP populations, which you can confirm with visual validation. One example is its ability to detect low levels of closely spaced aggregate populations—often critical for the early identification of suboptimal buffer or storage conditions that fail to protect samples from environmental or process-related stresses— thereby supporting scientists in developing longterm stability strategies. Similar to MADLS and DLS, NTA also uses very small sample volumes (1 µl, before dilution), nondestructively, and with little sample preparation needed. However, NTA has a few downsides that you should be aware of. First, NTA cannot detect LNPs below 30 nm. NTA is also less sensitive than either DLS or MADLS in detecting small populations of larger aggregates, as it is a numbers-based technique. However, this is where the techniques complement one another: NTA offers unique visibility into subtle changes in closely spaced sample populations—something DLS and MADLS often struggle to resolve. Additionally, because NTA typically requires significant sample dilution, analysts should verify sample stability, for example by measuring replicates over an extended period. Figure 6: Comparison of size distribution measurements of an mRNA-LNP sample using DLS (left, blue), MADLS (left, red), and NTA (right), where NTA demonstrates superior resolution to both DLS and MADLS. Accelerating LNP therapeutics: Your guide to smarter analytics 10Automating dynamic light scattering workflows Sample Assistant, the robotic accessory for Zetasizer Advance, boosts lab efficiency while delivering highquality, reproducible data. • Maximize throughput by fully automating sample changes • Free up your team to focus on higher-value tasks and analysis • Run size and zeta potential measurements automatically—with zero risk of crosscontamination • Switch between measurement types from a single tray, with no manual reconfiguration • Stay on track with an intuitive workflow that simplifies maintenance and minimizes downtime Choosing the right tool for the job: key considerations As we’ve seen, several tools are available for measuring the size of your LNP vectors. To ensure you make the appropriate choice, keep in mind the following factors: • Particle size and polydispersity NTA struggles to measure particles below 30 nm, whereas advanced DLS and MADLS systems can measure particles below 1 nm. When it comes to polydisperse samples, MADLS has superior resolving power relative to DLS, but NTA has the best resolving power • Concentration range See the section on concentration range (page 14) for more detail on the accessible concentration range of MADLS, NTA and SEC-LS, which provide valuable measurements across a wide range of concentrations • Stage of development and sample volume available Both DLS and MADLS require only small amounts of samples (~1 μl for DLS and 20 μl for MADLS) making the techniques a good choice for earlier stages of development (where only smaller amounts of samples may be available). While NTA requires ~ 1ml of samples, this is heavily diluted, requiring only 1 µl of undiluted LNP solution • The specific question you want to ask of your sample For instance, if you want to see if your sample has aggregated (but don’t require deeper information about the nature of these aggregates), DLS can be a rapid route to a reliable answer. If, on the other hand, you wish to understand what aggregates have formed, higher resolution techniques such as MADLS or NTA may be the best option Accelerating LNP therapeutics: Your guide to smarter analytics 11Polydispersity Polydispersity is a measure of the heterogeneity (in terms of particle size) of a sample. It’s an important attribute to measure, as it can help you assess sample stability throughout development and manufacture, as well as help track process reproducibility and monitor changes in size distribution due to environmental stress. Importantly, when investigating sample polydispersity, you can look at either the polydispersity of the whole sample (which is used to describe the presence of aggregates or agglomerates), or the polydispersity of an identified population among many populations in the same sample. Critically, different analytical tools will provide different measures of polydispersity. Tools & techniques The tools you can use to measure LNP sample polydispersity are the same as those used to measure size: DLS, MADLS, and NTA with in-line light scattering detectors. Below we discuss which tool provides which measure of polydispersity, how the measure is calculated, and how they should be interpreted. The polydispersity index (DLS) The polydispersity index (PDI) is a useful and common parameter for measuring sample heterogeneity. It’s a dimensionless measure that describes the broadness of the sample size distribution, and ranges from 0.01 (perfectly uniform) to >1 (highly polydisperse) (table 1). (The PDI is calculated from the DLS cumulants analysis method) The Span (DLS, MADLS, and NTA) The Span is another common and useful measure of sample polydispersity, indicating how far the 10% and 90% points of the distribution are from each other, normalized with the distribution midpoint. (Span = (D90 – D10)/D50) You can calculate the Span from DLS and MADLS data (but you’ll need to transform this data to volume distributions), or from NTA size distributions. The closer the Span value is to 0, the more monodisperse your sample population. Polydispersity parameter Definition Distribution type ‘Monodisperse’ ‘Polydisperse’ Uniform Narrow Moderate Broad PDI (DLS) (distribution standard deviation/distribution mean)^2 0.04 0.0–0.1 0.1–0.4 >0.4 Table 1: Values of different classes of sample dispersity. Accelerating LNP therapeutics: Your guide to smarter analytics 12Considerations Given that polydispersity is calculated using the same instruments (and uses the same measurements) as when characterizing particle size, the considerations for which tool is best are mostly the same — namely that your LNP is in the detectable size and concentration range, and that you have sufficient sample to meet the instrument’s requirements. It is also worth noting that polydispersity measurements will be inherently more variable than size measurements, given that the smallest variation in size polydispersity is not necessarily reflected in the average size value. Important tip There is no one-size-fits-all approach when it comes to deciding on the best parameter to track the polydispersity of your LNPs or determining an acceptable polydispersity level for your sample. Both ultimately depend on the type of particles you’re working with, as well as their intended end use, route of uptake, and fate in the body. As such, you’ll need to use a case-by-case approach. Accelerating LNP therapeutics: Your guide to smarter analytics 13Concentration Concentration is another important attribute to measure when characterizing and tracking LNP therapies. Though not as commonly used (the methodologies used to measure concentration are still considered immature relative to particle sizing techniques), exploring concentration during the design, formulation, and processing of your LNP therapy is highly beneficial, as it helps to monitor yield. Tools & techniques As with size and polydispersity measurements, you have multiple tools at your disposal for measuring the concentration of LNP vectors, ranging from mass-based techniques (measured in mg/ml) to numberbased techniques (measuring the number of particles in a given volume of sample). MADLS and NTA can provide valuable particle-based concentration measurements across a wide range of concentrations, offering measurements that are orthogonal to mass-based techniques. The accessible concentration ranges of each technique are detailed in Table 2. Importantly, as can be seen from Table 2, NTA can offer measurements complimentary to MADLS, overlapping with but also extending the detectable concentration range by an order of magnitude at the lower end. It is also worth noting that larger samples will show non-linear behavior at the highest concentrations, owing to increased scattering effects of larger particle sizes at higher concentrations. Technique Accessible concentration range Accessible size range Nanoparticle tracking analysis (NTA) Optimal concentration range of ~10^7 – 10^9 particles/mL 50-100 nm Multi-angle dynamic light scattering (MADLS) From 10^8 – 10^13 particles/mL. Range is determined by the amount of light scattered (directly related to vector size and refractive index). Larger vector sizes = increased scattering = lower accessible concentration. <1 nm to 10 µm Table 2: Comparison of the accessible concentration ranges of NTA and MADLS. Considerations Considerations for choosing the right concentration-measuring tools are largely the same as for measuring sample size — i.e., the size of your particles, whether you have enough sample (in terms of concentrations of particles), and how detailed you need your analysis to be (MADLS offers a quick, rough particle concentration screen), while NTA offers more detailed results. Accelerating LNP therapeutics: Your guide to smarter analytics 14Section summary Size, polydispersity, and concentration are key attributes to define and monitor when it comes to LNP-based therapy design, development, and manufacture. LNP vector size • A critical determinant of LNP vector tissue penetration, and can indicate sample instability • DLS, MADLS, and NTA offer accurate and reliable ways to measure LNP size across a range of particle size ranges, and with different resolutions (see table on page 14) • Choosing the right tool depends on several considerations, including the size and polydispersity of your LNP samples, sample concentration, available sample volume, and what you are looking to find out about your sample LNP vector polydispersity • Provides important insights into LNP sample stability and process reproducibility • DLS, MADLS, and NTA can all be used to effectively measure polydispersity, although in different ways • Optimal tool (and therefore parameter) choice ultimately depends on the same considerations as when measuring particle size (the tools are the same) LNP vector concentration • An important attribute to track, informing you about yield • MADLS and NTA provide valuable particlebased concentration measurements across a wide range of concentrations, and offer measurements orthogonal to mass-based techniques (see table on page 14) • Technique choice depends on the size of your LNPs, the amount of sample you have available, and how detailed you need your analysis to be (MADLS offers a quick screen while NTA can access lower concentrations with more precision for polydisperse samples) Accelerating LNP therapeutics: Your guide to smarter analytics 15Chapter 2: Characterizing vector surfaces Accelerating LNP therapeutics: Your guide to smarter analytics 16Zeta potential a particle/molecule acquires in a particular medium. - + Voltage Electrode Electrode Capillary - + Slipping plane Electrical double layer Stern layer Diffuse layer Figure 7: Diagram showing how electrophoretic light scattering system works with zeta potential or indication of particle in a solution. Zeta potential (indicative of surface charge in a certain environmental condition e.g. pH/salt) is a key attribute in the development of LNP therapies. It is one of the most important determinants of an LNP’s solubility and interaction with cellular membranes. (Importantly, the optimal zeta potential value depends on your target tissue). Knowledge of the surface charge can therefore help you predict the in vivo fate and activity of your LNP therapy. Additionally, surface charge information can offer insight into its surface chemistry, as well as its colloidal stability in solution and its tendency to form aggregates in solution (and any modifications it may undergo). Several factors can influence the measured zeta potential: • Changes in pH • Ionic strength • Concentration of other components in the solution (such as additives, coagulants, and surfactants) As such, to ensure reproducible zeta potential measurements, you should always report the sample buffer you’ve used, as well as the conductivity that the instrument measured for the zeta potential value. Zeta potential determination by ELS Electrophoretic light scattering (ELS) is a key tool that you can use to measure the zeta potential of your LNP samples. The fundamental physical principle of ELS is electrophoresis (Figure 7). A dispersion is introduced into a cell containing two electrodes, and an electrical field is applied across them. Particles with a net charge migrate towards the oppositely charged electrode with a velocity (known as the electrophoretic mobility) related to their zeta potential. A laser is passed through the bottom of the cell, with the charged particles producing scattered light that is frequency shifted in proportion to their velocity. By detecting the frequency shifts, we can then calculate the zeta potential. When it comes to LNP samples, ELS is most often used to validate the apparent surface charge of your LNPs — for example by measuring the zeta potential of your LNPs in PBS, or a 10x diluted version of your sample, to evaluate your LNP formulations (e.g., for stability, predicted uptake efficiency in target tissues). Electrophoresis = movement of a charged particle relative to the liquid it is suspended in under the influence of an applied electric field Zeta potential = electrical potential at the slipping plane Zeta potential or indication of particle charge in a solution Accelerating LNP therapeutics: Your guide to smarter analytics 17Figure 8: Overview of the DBM for ELS measurements. With the DBM, gel electrophoresis loading pipettes are used to insert a small ‘plug of sample’ into the cell. A key challenge of measuring zeta potential: high-conductivity samples High conductivity samples pose significant challenges when it comes to measuring zeta potential, as they can lead to inaccurate measurements. This is important, as LNP therapies are prepared in physiological buffers, which are high conductivity. High-conductivity samples interfere with accurate zeta potential measurements in four ways: Joule heating effects, electrode polarization, electrode degradation, and sample degradation (simply applying a voltage across a high-conductivity sample can cause it to aggregate). Overcoming challenges of high-conductivity samples with the diffusion barrier method To ensure your zeta potential measurements are accurate and precise when working with high-conductivity samples, you can use the diffusion barrier method (Figure 8), which mitigates the above-mentioned adverse effects. The diffusion barrier separates the particles in your sample from the electrodes by inserting a small ‘plug’ or aliquot (~20–100 ul) of sample into a folded capillary cell that already contains the same buffer that the sample is prepared in. The sample is therefore isolated due to the physical distance between the sample and the electrodes. The time it takes for the particles to diffuse to the electrode is significantly longer than the duration of the measurement itself. Since the sample is not directly in contact with the electrodes, sample integrity is maintained, and electrode degradation is minimized. Furthermore, the diffusion barrier method (DBM) also reduces the amount of sample required for zeta potential measurements. Traditional fill method ~750 ul Diffusion barrier fill method ~20-100 ul Patented by Malvern Panalytical Assessment of charge differences between formulations Accelerating LNP therapeutics: Your guide to smarter analytics 18Important tip While the diffusion barrier method can mitigate the risk of sample degradation, ELS is still an invasive measurement. Accordingly, to ensure sample integrity is not adversely impacted by the measurement, you should measure the size of your sample both before and after ELS measurements (for example, using five repeat DLS measurements). Section summary Zeta potential is an important attribute of LNPs that can indicate colloidal stability and help predict cellular membrane interactions • Zeta potential can also give insight into the surface chemistry of LNPs • Electrophoretic light scattering (ELS) is a valuable tool to measure the zeta potential of your LNP samples, helping you to evaluate the stability of your formulations and optimize target tissue uptake • High-conductivity samples can make accurate and precise zeta potential measurements challenging - the DBM technique helps you overcome this • Since ELS is an invasive technique, analysts should measure the particle size of their samples before and after to see if sample degradation has occurred Accelerating LNP therapeutics: Your guide to smarter analytics 19Chapter 3: Investigating thermal stability Accelerating LNP therapeutics: Your guide to smarter analytics 20T=20°C Folded T=90°C Unfolded Temperature (°C) T m N D Excess heat capacity (C p ) 20 40 60 80 100 H Area= cal T onset T 1/2 Figure 13: Schematic of a typical DSC instrument. LNPs are non-covalent assemblies of lipids and mRNA with mRNA being a crucial structural component. Structure and structural stability are key properties ensuring desired function and safety of LNP vectors and their cargo. Temperature change is a common stress factor for LNP-based therapies throughout production, storage, and application. Being able to measure and compare thermal stability profiles helps you to assess the inherent structural stability of LNPs and their mRNA cargo. Such insights can help you track changes between batches and stress conditions, helping you know if you are producing the same higher order structure, or whether those particles change (or have changed) structurally due to certain stressors. Differential scanning calorimetry (DSC) is a valuable and well-established tool for monitoring the thermal stability and thermally induced transitions of biomolecules and biomolecular assemblies including lipid-based delivery vectors and nucleic acids. How does DSC work? Broadly, DSC works by measuring the heat change associated with a sample’s structural transitions when heated at a constant rate. The thermal core of a DSC system consists of two cells — a reference cell, and a sample cell (Figure 13). The device is designed to maintain the two cells at the same temperature as they are heated. To perform a DSC measurement, you must first fill a reference cell with buffer and the sample cell with the sample solution. The instrument heats these at a constant scan rate. The absorption of heat that occurs when a molecule undergoes a structural transition causes a temperature difference (ΔT) between the cells, resulting in a thermal gradient across the Peltier units (or thermoelectric modules). This sets up a voltage, which is converted into power and is used to control the Peltier to return ΔT to 0°C. Accelerating LNP therapeutics: Your guide to smarter analytics 21Cp (kJ/mol/K) Temperature (°C) -2.78E-4 -1.78E-4 -7.84E-5 2.16E-5 1.22E-4 2.22E-4 3.22E-4 20 40 60 80 100 Loaded 120 Empty vs loaded Cp (kJ/mol/K) Temperature (°C) -2.31E-4 -1.31E-4 -3.11E-5 6.89E-5 1.69E-4 2.69E-4 3.69E-4 4.69E-4 5.69E-4 40 50 60 70 80 90 100 110 120 Different mRNA amounts Empty Figure 14: Illustration of mRNA-LNP thermograms. DSC thermograms – what they can tell us about a sample? The output of a DSC measurement is a thermogram (Figure 13), which provides multiple parameters for describing the thermally induced transitions of samples: • T m (thermal transition midpoint): This is the so-called melting temperature of the sample, denoted by an endothermic peak in the DSC thermogram. The higher the thermal transition midpoint, the more stable the sample. Shifts in Tm can indicate structural heterogeneity or degradation • T onset (thermal transition onset): The onset temperature of the first thermal transition event is an important factor to consider. A lower Tonset means that molecules are more likely to unfold at this temperature, which increases the likelihood of aggregates forming. By understanding the Tonset of your sample, you can identify temperature ranges that will minimize the risk of aggregation and maximize sample stability • T 1/2 (the width of thermal transition at half-height): The T1/2 reflects the extent of cooperativity of the thermal transition. The narrower the transition, the more cooperative it is • Enthalpy change (∆H): The enthalpy change is the total energy spent in a thermal transition. This is represented by the area under the thermogram and reflects the relative amount of native biomolecule in your sample • Higher order structure (HOS): In addition to these parameters, the entire thermogram shape can give us a fingerprint of the molecule’s HOS. The HOS is a valuable descriptor that can help inform you about the stability profile of your biomolecules during product characterization, formulation, and comparability analysis • Reversibility: While not a feature of the thermogram itself, the reversibility of thermal transitions is another key aspect of structural transitions observed with DSC. Reversibility reveals the ability of biomolecules to re-adopt their native structure upon cooling. Low reversibility is characteristic of unfolding events accompanied by aggregation and/or chemical degradation Accelerating LNP therapeutics: Your guide to smarter analytics 22Taking a deeper look at RNA with DSC RNA is a structurally flexible molecule that can exist in a variety of primary, secondary, and tertiary structures. These structural differences can have significant effects on function, as well as impacting RNA-LNP complexation. By revealing information about primary and secondary structure (though T m values and HOS), DSC results can give us insights into the functional efficiency, stability and degradation, modifications, and half-life of the nucleic acids and oligonucleotides in question, helping you to design and select optimal mRNA variants for your therapies. Thermogram parameters can also reveal information about solvent conditions and hybridization efficiency (primarily through the Tm value). And, as with LNP vectors, you can also measure ΔH values and explore the RNA’s HOS to understand structural heterogeneity and possible degradation. Overall, DSC offers a high-throughput (although low resolution) option for structural analysis. DSC traces can inform you about the sample structure in a broad range of concentrations and solution conditions without being limited by high absorbance limitations and without hypo/hyperchromicity artifacts. With this technique, you can better design and select mRNA-LNPs and their components, identify the conditions beneficial to the structural stability of mRNA-LNP formulation, processing, and storage, as well as complement and enhance physico-chemical characterization of your lipidbased delivery vectors. Using DSC to characterize LNPs and nucleic acids When it comes to LNP-based therapies, DSC can provide a wealth of information, from overall thermal stability and the effect of drug loading on thermal stability, to information about LNP phase structure. By detailing the HOS of your samples, DSC also allows you to compare batches of drug substance and drug product, helping you ensure you’re delivering the same product consistently. It’s important to remember that while mRNALNP assemblies are complex, RNA itself is also complex, and can impact therapeutic protein expression. It is therefore important to explore and characterize your RNA payload. Accelerating LNP therapeutics: Your guide to smarter analytics 23Key considerations when using DSC When studying samples as complex as LNPs, it is important that you can get the best possible instrument performance — high resolution and accurate output that you can rely on for confident conclusions about thermogram features and differences. However, lipid formulations can lead to carry-over effects, which can reduce the accuracy and resolution of DSC traces. To minimize the impact of these carry-over effects, it is vital that you properly clean the DSC cell and syringe between scans. With most DSC instruments, you can soak DSC cells and rinse syringes with detergent after each scan. But this is not always sufficient to eliminate sample carryover effects, especially if the cleaning procedure is carried out manually. Advanced DSC systems, however, allow you to soak the syringe in detergent during scanning, as well as fully automate the cleaning process, eliminating sample carryover effects while also saving time (Figure 15). Indeed, since cleaning takes place during measurements, the instrument is ready for the next sample immediately after the first measurement is finished. Figure 15: Comparison of LNP sample carry-over effects with (left) and without (right) enhanced syringe cleaning capabilities. • SCAN after each LNP sample • Syringe clean* after each LNP sample * New feature available with upgraded PEAQ-DSC software (BSL2 support) Carry-over DSC s/w pre V1.6 (no BSL2 cleaning enabled) • Cell can be soaked in detergent • Syringe can only be rinsed DSC s/w post V1.6 • Cell can be soaked in detergent • Syringe can soaked during scans DP Temperature (°C) DP Temperature (°C) Enhanced cleaning for higher resolution DSC and LNPs Accelerating LNP therapeutics: Your guide to smarter analytics 24Section summary • Differential scanning calorimetry (DSC) is a valuable and wellestablished tool for monitoring the thermal stability and phase behavior of biomolecules • DSC thermograms reveal a wealth of information about biomolecules, from thermal stability to higher order structure (HOS) • DSC helps inform the design and selection of mRNA variants and LNP vectors, identify optimal conditions for structural stability of mRNA-LNPs during formulation, processing, and storage, and can enhance physico-chemical vector characterization • Critically, by revealing the HOS of your LNP therapies, DSC can aid batch comparability analysis • Analyzing lipid formulations with DSC can lead to carry-over effects. However, some systems now feature enhanced and automated cleaning capabilities (BSL2) that can eliminate this Accelerating LNP therapeutics: Your guide to smarter analytics 25Your route to analytical excellence Optimizing LNP vector characteriztion with the right tools LNP delivery systems offer much promise for cell and gene therapies and novel vaccines. Developing and manufacturing LNP-based therapies, however, is fraught with difficulty. As an LNP-based therapy developer, you must overcome the challenges posed by the intrinsic structural complexity of LNPs to reliably measure their critical quality attributes. Only then can you optimally guide their development, enable consistent manufacture, and ensure safe delivery to patients. A suite of robust, accurate, and highly reproducible biophysical techniques is available to help you to overcome these challenges and better characterize the critical quality attributes of your LNPs — from size, polydispersity, concentration and stability, to surface charge and composition. These tools offer powerful, complementary approaches to tracking the development and manufacture of your LNP-based therapies, delivering deeper insights while also offering opportunities to minimize sample use, save time, and reduce costs. Ultimately, with the right suite of insightful biophysical analytical tools, you can carve a swifter path to analytical success in a complex and competitive field. Want to learn more about how you can better characterize your LNP-based therapies? Then reach out to our team of analytical experts, or visit https://bit.ly/3ZtVEob. Accelerating LNP therapeutics: Your guide to smarter analytics 26Featured & related products from Malvern Panalytical Explore how Malvern Panalytical’s best in class analytical instruments can help you understand the structure, stability and affinity of your nanodrug delivery systems. NanoSight Pro View here Zetasizer Advance View here MicroCal DSC range View here Zetasizer Sample Assistant View here Accelerating LNP therapeutics: Your guide to smarter analytics 27