Thesis
Development of novel analytical pipelines for the evaluation of ribonucleic acid therapeutics.
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2026
- Thesis identifier
- T17989
- Person Identifier (Local)
- 202265568
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Nanomedicines represent an emerging therapeutic class, which supersede traditional small molecule drugs. As nanomedicines are inherently complex, analysis of their associated critical quality attributes (CQAs) is essential to ensure accurate and representative physicochemical characterisation for downstream translation. Of these nanomedicines, lipid nanoparticles (LNP) encapsulating nucleic acid therapeutics, specifically ribonucleic acid (RNA) therapeutics demonstrate unmatched applicability for inherited conditions, treatment resistant disease and rare illness. RNA therapeutics act on protein‑mediated pathways by preventing, correcting, or modulating the production of specific proteins. Depending on the therapeutic design, they can elicit an immune response to drive antibody generation or regulate gene expression through targeted gene silencing or activation mechanisms. Unencapsulated RNA-based therapeutics face several biological delivery barriers, which have driven the widespread adoption of LNP-based drug delivery systems, and contributing to the approval of several RNA-LNP nanomedicines. However, the incorporation of RNA cargo in LNP drug delivery systems, adds an additional layer of complexity to these formulations, increasing the need for robust analytical techniques for quantifying associated critical quality attributes within early discovery and development settings. Despite the rapid growth in RNA-LNP technologies and research, advances in analytical pipelines for these therapeutics have not kept pace, resulting in a significant knowledge gap and a lag in the development of complementary and orthogonal techniques required for the comprehensive characterisation of RNA-LNP associated CQAs. Conventional analytical methods currently relied upon within the field present resolution challenges, which under characterise, complex therapeutics, creating the need for novel pipelines to ensure enhanced and robust characterisation of candidate therapeutics to advance through early development trials. Therefore, the central hypothesis underpinning this thesis is that current analytical methods used to characterise RNA and RNA-LNP formulations provide limited understanding of their physicochemical parameters that reflect the true physical state of these complex therapeutics. This limited analytical resolution in resolving the effects of formulation and process-related parameters, poses a barrier to the efficient clinical translation of novel and emerging RNA-LNP nanomedicines, in addition to pre-existing undesired immunogenicity, and lack of overall selective tissue targeting. To investigate this hypothesis, RNA drug substance and model systems were investigated across the RNA to LNP translation process, using a suite of pipeline approaches with varying resolution and orthogonality. By examining RNA prior to and following incorporation into LNP delivery systems, this work aims to improve mechanistic understanding of how formulation variables shape CQA outputs and, ultimately, therapeutic performance. Chapter 1 defines current analytical techniques, their applications and limitations, whilst introducing state-of-the-art pipelines for RNA and RNA-LNP CQA quantification, addressing the need for higher-resolution analysis of candidate nanomedicines within early development settings to inform candidate selection. Chapter 2 details the method development of hydrophilic interaction liquid chromatography (HILIC), hyphenated with online photodiode array (PDA) and mass spectrometry (MS)-based detectors. This study compared analytical resolution across different MS platform systems, comparing single quadrupole (SQ) with Orbitrap (orbi) for antisense oligonucleotide (ASO) detection and impurity quantification. Of the six oligonucleotides (ONs) tested, two ON impurity profiles matches between SQ/orbi evaluation, two ON impurity abundances were higher on the SQ than orbi and two ON impurity abundances were higher on orbi than SQ. Orthogonal techniques, including gel electrophoresis and ion-pairing reverse-phase liquid chromatography (IPRP-LC), were incorporated to support separation and quantification. Together, these results established an analytical framework capable of differentiating ASO drug-substance CQAs that are undetectable with conventional, lower‑resolution methods. Building on the ASO drug substance analytics, chapter 3 applied a multi-platform pipeline to the analysis of ASO-LNP formulation, with varying lipid nitrogen headgroup to RNA phosphorous backbone group (N/P ratio), ionisable lipid composition (SM102, MC3), microfluidics flow rate ratio (FRR), and total flow rates (TFR). Using an orthogonal analytical pipeline incorporating frit-inlet asymmetric flow field flow fraction (FI-AF4) with online UV, multiangle- and dynamic- light scattering, the study revealed marked differences in particle size, size distribution and morphology-based across the ASO-LNP formulation panel, differences that could not be fully discerned by conventional low-resolution analytical techniques. Formulations parameters such as main ionisable lipid choice and microfluidic total flow rate were key formulation parameters impacting exploratory analytical ASO-LNP CQA outputs. Findings from chapter 2 and 3 highlight CQA output changes using ASOs as a drug substance incorporated into an ASO-LNP drug product. Chapter 4 investigated Poly(A) as a model mRNA cargo encapsulated widely during LNP formulation composition and process-related studies. Despite its widespread use, Poly(A) quality attributes remain uncharacterised across commercial vendors, Poly(A) samples from three vendors were evaluated for their Poly(A) chain length and molecular weight (MW) distributions as a function of vendor, whilst comparing their associated CQAs using enhanced orthogonal analytical techniques. Poly(A) vendor CQAs and their downstream impact on LNP CQAs were evaluated to gain insights into Poly(A) CQA effects on LNP formulations. Poly(A) size and heterogeneity showed clear vendor-specific trends. However, when these Poly(A) samples were incorporated into LNP formulations under controlled microfluidics manufacturing conditions, vendor‑specific differences did not translate into measurable changes in LNP CQAs. Correlative analyses provided deeper insights into relationships between RNA attributes and formulation outputs, demonstrating the value of enhanced analytics for model RNA systems, underpinning classical colloidal stability DLVO theory. Early assessment of candidate nanomedicine formulations remains crucial for establishing changes in LNP CQAs during formulation and process development. In chapter 5, impact of formulation process parameters, short-term refrigerated stability and frozen storage stability on LNP CQAs were examined using a model DOTAP-Poly(A) LNP formulation. An AF4‑MD pipeline provided high‑resolution measurements of particle concentration, size, distribution, and shape. AF4 analysis of cationic LNPs proved challenging due to electrostatic interactions with the anionic separation membrane, leading to particle loss. Membrane preconditioning strategies were implemented to mitigate these interactions, enabling successful analysis across formulations with differing physicochemical attributes. These findings reinforce the need for robust high‑resolution analytics during early formulation and stability assessment, which show that > 20% sucrose (w/v) is needed for enhanced cryoprotectant properties to prevent DOTAP-LNP aggregation from frozen storage to ambient temperature cycling. The results presented in this chapter demonstrate that the incorporation of asymmetric-flow field-flow fractionation hyphenated to inline multidetector system (AF4-MD) enhanced RNA-LNP CQA analysis beyond lower resolution techniques. Chapter 6 applied a computational approach to further advance insights into CQAs by applying AF4-based elution profiles towards a data-processing pipeline. These insights highlight size (2-6) and RNA load (2-7) subpopulations within a panel of RNA-LNP formulations using different constituent lipids and nucleic acid payloads. In-depth analysis of these results provided higher resolution insights into internal LNP nanoarchitecture, linking size, RNA loading and morphology with key sub-populations. Advanced data-processing also highlights corresponding weight fractions of RNA per formulation and associated numbers of RNA per LNP formulation to further link with size, size distribution and morphology parameters. This approach enhanced insights beyond the scope of the RiboGreenTM encapsulation assay to determine nucleic acid loading numbers. The computational-based approach enhanced CQA insights beyond the resolution-power of AF4 separation and detection systems, by highlighting key-populations with varied RNA concentration levels, differing size, morphology and internal architectures. Collectively, this thesis advances the application of field‑flow fractionation–based analytics for RNA therapeutics, demonstrating resolution and insight that surpass existing pipelines widely used in the field. Comparative studies with mass spectrometry further highlight complementary strengths and define where each method offers the greatest analytical value. The analytical strategies developed throughout this thesis provide a foundation for improved characterisation of RNA and RNA-LNP systems, supporting more reliable development and translation of next‑generation RNA therapeutics for improved patient outcomes.
- Advisor / supervisor
- Perrie, Yvonne
- Rattray, Zahra
- Resource Type
- DOI
Relations
Items
| Thumbnail | Title | Date Uploaded | Visibility | Actions |
|---|---|---|---|---|
|
|
PDF of Thesis T17989 | 2026-06-24 | Public | Download |