Overcome Challenges in LNP Vector Analysis

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 • Surface charge • Therapeutic payload information • 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-for- purpose 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, 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: • The importance of the above-listed key attributes for developing and manufacturing safe and effective LNP-based therapies • The suite of robust orthogonal analytical tools that can help you better measure them 3 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsChapter 1: Characterizing LNP size, polydispersity, and concentration 4 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsSize 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 LNP-based therapy developers have a selection of size-measuring analytical techniques at their disposal when it comes to efficiently and reliably measuring LNP vector size — 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. 5 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsSmall Particles Intensity Time Intensity Time Intensity Intensity Time Time Intensity Time Large Particles Avalanche 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 no 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 (~20 µl) 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! 6 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsSingle Angle DLS Three angles give three different broad distributions Correlation Coefficient (g1 1 Time (µs) 0 0.01 0.11 10 1001e+03 1e+04 1e+05 1e+06 1e+07 1e+08 Size (d.nm) 0.11 10 100 1e+03 1e+04 0.2 0.4 0.6 0.8 1 Intensity Percent) 0 5 10 15 20 25 1.2 Correlation Coefficient (g1 1 Time (µs) 0 0.01 0.11 10 100 1e+03 1e+04 1e+05 1e+06 1e+07 1e+08 0100 200300 400 500 600 700800 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 0.11 10 1001e+03 1e+04 2 4 6 8 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. 7 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsLike DLS, MADLS can detect even the smallest LNPs, with a detectable size range of 10 µm to 1nm 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 (see Chapter 3: Understanding LNP vector composition for more information on compositional analysis). Figure 4: Comparison of size distributions for an LNP sample produced using single-angle DLS (blue) and MADLS (red). 8 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsImage 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 a typical NTA set up. 9 Overcoming challenges in LNP vector analysis: key tools, techniques, and considerationsMost 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. 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 50 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. What’s more, NTA often requires significant sample dilutions, so analysts will need to verify the stability of their sample (e.g., by measuring replicates of their sample 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), whe