Description:

Method for validating the stability of mRNA vaccines under storage conditions
Field of the Invention
[0001] The present disclosure relates to pharmaceutical validation methods, more particularly, to methods for validating the stability of mRNA vaccines under defined storage conditions through multi-parametric analysis.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Messenger RNA vaccines have emerged as transformative platforms for infectious disease prevention and therapeutic applications. Their rapid development timelines and flexible design architectures have enabled accelerated responses to global health challenges. Despite demonstrated efficacy, the stability of mRNA vaccines remains a critical concern, as messenger RNA molecules are inherently prone to hydrolytic degradation, enzymatic cleavage, and structural instability under suboptimal storage conditions. Lipid nanoparticle carriers provide protection to encapsulated RNA, yet stability can be influenced by lipid composition, pH, ionic environment, and temperature.
[0004] Conventional stability validation practices for vaccines rely on evaluating physical and chemical parameters over storage durations. Protein-based vaccines often undergo well-established stability assays; however, such methodologies are insufficient for nucleic acid vaccines, as RNA exhibits faster degradation kinetics and sensitivity to environmental fluctuations. Existing validation methods may lack sensitivity in detecting subtle degradation events, thereby limiting predictive accuracy of shelf life. Moreover, there is inadequate integration of physicochemical data with biological potency measurements, resulting in incomplete stability assessments.
[0005] Regulatory agencies require rigorous validation of vaccine stability to ensure safety and efficacy throughout the distribution chain. mRNA vaccines, in particular, require ultra-cold storage conditions, yet such requirements pose logistical challenges in global distribution. Without validated stability data, distribution at higher temperatures cannot be authorized, thereby limiting accessibility. Additionally, current approaches often fail to leverage computational modeling for correlating degradation with potency outcomes, restricting predictive value for long-term stability.
[0006] Accordingly, there exists a pressing need for comprehensive methods that integrate periodic sampling, physicochemical evaluation, biological potency assessment, and computational modeling into a unified workflow. Such a framework would enable generation of stability validation profiles that define acceptable storage conditions, predict shelf life, and support regulatory compliance. The disclosed method addresses this need by combining multi-parametric assays, environmental monitoring, and advanced modeling to provide robust validation of mRNA vaccine stability.
Summary
[0007] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[0008] The following paragraphs provide additional support for the claims of the subject application.
[0009] The disclosure pertains to a method for validating the stability of mRNA vaccines under defined storage conditions is disclosed. The method comprises providing a batch of mRNA vaccine formulations encapsulated within lipid nanoparticles, subjecting them to storage environments including ultra-cold freezers, refrigerators, and ambient conditions, and periodically sampling aliquots over defined time intervals. Each sample is subjected to physical testing to measure nanoparticle size, charge, and encapsulation efficiency, chemical testing to evaluate mRNA integrity through chromatographic and electrophoretic techniques, and biological potency assays to quantify protein expression after transfection.
[00010] Data derived from physical, chemical, and biological assessments are integrated into computational models, including Arrhenius-based kinetics and machine learning regression frameworks, to correlate degradation profiles with potency retention. This enables prediction of stability under accelerated and real-time conditions. Environmental sensor data from temperature and humidity monitors are incorporated into the workflow to ensure traceability of conditions during testing.
[00011] The method generates a stability validation profile comprising degradation kinetics, acceptable storage conditions, maximum shelf life at each temperature, and expiration date assignments. In one embodiment, accelerated stability studies predict outcomes at standard storage conditions, enabling reduction of validation timelines. In another embodiment, real-time longitudinal studies validate results under regulatory standards. Integration of experimental data and predictive models provides a robust framework for regulatory compliance and global distribution.
Brief Description of the Drawings
[00012] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00013] FIG. 1 illustrates a system architecture diagram of the disclosed validation method, depicting the integration of storage environments, sampling units, analytical modules, computational models, and stability profile generation within a unified framework, in accordance with the embodiments of the present disclosure.
[00014] FIG. 2 illustrates a sequence diagram of the operational workflow, showing the chronological interactions between storage, sampling, physical testing, chemical testing, biological potency evaluation, computational modelling, and stability report generation, in accordance with the embodiments of the present disclosure.
[00015] FIG. 3 illustrates a data flow diagram depicting how raw data from environmental monitoring, physical assays, chemical integrity analyses, and potency assays are processed through computational engines to generate a stability validation profile, in accordance with the embodiments of the present disclosure.
Detailed Description
[00016] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00017] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00018] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00019] The disclosed method for validating the stability of mRNA vaccines under storage conditions shall now be described in technical detail. The method is designed as a systematic validation framework combining multi-parametric analysis, computational modelling, and environmental monitoring to ensure robust determination of vaccine stability and shelf life.
[00020] The method begins with preparation of representative batches of mRNA vaccines encapsulated within lipid nanoparticles. Said batches are divided into multiple aliquots, each designated for storage under defined conditions including ultra-cold freezers at approximately –80°C, refrigerators at 2–8°C, and controlled ambient environments ranging from 20–25°C. Aliquots are stored with environmental sensors configured to log temperature and humidity throughout the storage period.
[00021] At predefined time intervals, aliquots are retrieved for analysis. The time intervals include short-term sampling at daily or weekly points and long-term sampling at monthly or quarterly points. This ensures that both accelerated and real-time degradation pathways are observed. The retrieved aliquots are subjected to a tiered testing process.
[00022] The first tier comprises physical characterization of lipid nanoparticles. Dynamic light scattering is employed to determine hydrodynamic diameter and polydispersity index. Zeta potential measurements assess surface charge stability. Nanoparticle tracking analysis provides high-resolution particle count distributions. Collectively, these measurements reveal aggregation, destabilization, or fusion events within the lipid nanoparticles.
[00023] The second tier comprises chemical integrity analysis of the encapsulated mRNA. Reverse transcription polymerase chain reaction assays quantify intact nucleotide sequences. High-performance liquid chromatography separates intact and degraded RNA fragments. Capillary electrophoresis resolves strand length distributions, thereby providing detailed profiles of degradation. These analyses directly evaluate chemical stability of mRNA molecules across storage conditions.
[00024] The third tier comprises biological potency assessment. Representative aliquots are used to transfect suitable mammalian cell lines. Transfected cells are evaluated for protein expression using enzyme-linked immunosorbent assay, flow cytometry, or luminescent reporters. Potency data provides functional correlation to physical and chemical stability, ensuring biological relevance.

[00025] The multi-tier results are transferred into computational models. Arrhenius-based kinetic models utilize temperature-dependent degradation rates to predict stability under alternative conditions. Machine learning regression models integrate multi-parametric datasets to capture non-linear correlations between nanoparticle stability, RNA degradation, and potency outcomes. Survival analysis is applied to predict time-dependent loss of functional potency. These computational tools convert raw data into predictive models of shelf life.
[00026] The integration of physical, chemical, biological, and computational data generates a stability validation profile. Said profile includes maximum permissible storage duration at each condition, predicted degradation timelines under accelerated scenarios, and regulatory expiration dates. The profile provides comprehensive evidence for defining cold-chain requirements and justifying distribution at varying temperature regimes.
[00027] In a first embodiment, the method is implemented for accelerated stability testing. Aliquots are exposed to elevated temperatures including 25°C and 37°C to accelerate degradation. Experimental data collected over weeks is extrapolated to predict years of stability under refrigerated conditions. This embodiment shortens validation timelines while maintaining scientific rigor.
[00028] In a second embodiment, the method is implemented for real-time stability testing. Aliquots are stored at regulatory-defined conditions for extended periods, with periodic sampling over months and years. Data is accumulated to confirm accelerated models and provide long-term validation. This embodiment ensures compliance with regulatory standards and provides definitive shelf-life determination.
[00029] In a third embodiment, the method is configured for distributed validation studies across multiple sites. Aliquots are shipped to geographically distinct laboratories equipped with environmental monitoring systems. Data collected from diverse conditions is aggregated into cloud-based databases. Machine learning algorithms continuously retrain on multi-center data, improving predictive robustness. This embodiment supports global regulatory submissions and harmonization of stability data.
[00030] Operational flows are reiterated across contexts. In clinical trial supply chains, accelerated profiles provide rapid deployment of mRNA vaccines under emergency conditions. In commercial distribution, real-time validation ensures reliability of shelf life for marketed products. In global distribution, multi-center validation provides confidence in stability across variable cold-chain infrastructures.
[00031] The disclosed method delivers technical benefits including comprehensive multi-parametric analysis, predictive modeling of degradation, integration of environmental monitoring, and regulatory compliance. Each operational step contributes to a unified framework that ensures safety, efficacy, and accessibility of mRNA vaccines. By uniting experimental rigor with computational forecasting, the method establishes a robust foundation for stability validation and global distribution.
[00032] Figure 1 represents a system architecture diagram illustrating the modular composition of the validation method for mRNA vaccine stability. The diagram begins with storage environments comprising ultra-cold, refrigerated, and ambient chambers, each configured with environmental sensors. Samples are routed to a sampling unit that defines time-based collection. Outputs from the sampling unit are transmitted to three analytical modules: physical integrity module, chemical integrity module, and biological potency module. Data from all three modules are directed to a computational modeling engine, which applies kinetic and machine learning models. The computational engine communicates with a stability profile generator, which produces validation outputs including shelf-life estimation, degradation kinetics, and recommended storage conditions. This architectural representation emphasizes modularity, traceability, and integration of multiple analytical dimensions. Each module is presented as an independent component, yet functional interconnectivity ensures comprehensive validation. The architecture illustrates how environmental control, empirical analysis, and predictive computation operate as a cohesive framework.
[00033] Figure 2 represents a sequence diagram describing temporal interactions of the validation method. The sequence begins with initiation of storage at defined environmental chambers. At scheduled intervals, sampling units extract aliquots and transfer them sequentially to physical, chemical, and biological analysis. The physical module measures nanoparticle size and charge, the chemical module assesses RNA strand integrity, and the biological module quantifies potency. Results are forwarded to computational models, which calculate degradation rates and predict shelf life. The final step is generation of a stability validation report that is reviewed and archived for regulatory compliance. The sequence diagram highlights the chronological structure of the workflow, showing interdependence between empirical assays and computational predictions. It further clarifies how repeated temporal sampling enables monitoring of both immediate and long-term stability.
[00034] Figure 3 provides a data flow diagram showing how multi-parametric datasets converge into a unified validation profile. Raw environmental monitoring data flows from sensors into the validation pipeline. Physical integrity measurements, chemical analysis outputs, and potency data are transferred into a centralized computational model. Within the model, Arrhenius-based kinetics quantify degradation rates, while machine learning regression integrates multiple data dimensions. Outputs from the computational system flow into the stability validation module, where reports are compiled containing expiration assignments, permissible storage conditions, and degradation curves. The data flow diagram illustrates the convergence of heterogeneous inputs into harmonized outputs. This representation underscores the technical benefit of data integration, as it enables predictive, reproducible, and regulatory-compliant stability profiles.
[00035] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00036] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A method for validating the stability of mRNA vaccines under storage conditions, comprising: providing a representative batch of an mRNA vaccine formulation encapsulated within lipid nanoparticles; subjecting said vaccine formulation to predefined storage environments including ultra-low temperatures, refrigerated conditions, and ambient conditions; periodically sampling said vaccine formulation over a plurality of time intervals; measuring physical, chemical, and biological integrity parameters of said vaccine formulation, wherein parameters include mRNA strand length preservation, lipid nanoparticle encapsulation efficiency, and biological potency in vitro; analyzing degradation kinetics using computational models configured to correlate physicochemical data with biological activity; and generating a stability validation profile configured to determine acceptable storage conditions and shelf life of said mRNA vaccine formulation.
2. The method of claim 1, wherein the periodic sampling comprises collection of aliquots at daily, weekly, and monthly intervals, thereby enabling comprehensive evaluation of both short-term and long-term stability characteristics.
3. The method of claim 1, wherein measuring physical parameters comprises evaluating particle size distribution, zeta potential, and polydispersity index using dynamic light scattering and nanoparticle tracking analysis.
4. The method of claim 1, wherein measuring chemical parameters comprises assessing nucleotide integrity through reverse transcription polymerase chain reaction, high-performance liquid chromatography, and capillary electrophoresis, thereby quantifying strand degradation.
5. The method of claim 1, wherein measuring biological potency comprises transfecting cell lines with sampled formulations and quantifying protein expression levels through enzyme-linked immunosorbent assay, flow cytometry, or luminescence assays.
6. The method of claim 1, wherein the computational models comprise Arrhenius-based kinetic models, machine learning regression models, or survival analysis, thereby correlating temperature, time, and degradation rate with potency retention.
7. The method of claim 1, wherein the stability validation profile further comprises determination of maximum permissible storage duration at each condition, prediction of accelerated degradation rates, and assignment of expiration dates.
8. The method of claim 1, wherein the method further comprises integrating environmental sensor data from temperature loggers and humidity controllers, thereby ensuring traceability of storage environments during validation studies.
9. The method of claim 1, wherein the method is performed under compliance with regulatory guidelines for vaccine stability validation, thereby providing quality assurance for clinical deployment.
10. The method of claim 1, wherein integration of multi-parametric testing, kinetic modeling, and environmental monitoring within a unified validation workflow establishes a robust framework for determining stability and shelf life of mRNA vaccines.

Method for validating the stability of mRNA vaccines under storage conditions
Abstract
A method for validating the stability of mRNA vaccines under storage conditions is disclosed. The method comprises providing vaccine formulations encapsulated within lipid nanoparticles, subjecting them to ultra-cold, refrigerated, and ambient storage conditions, and periodically sampling aliquots. Physical properties including particle size and charge, chemical integrity of mRNA strands, and biological potency through protein expression are measured. Data are analyzed using computational models including kinetic and machine learning approaches to correlate degradation with potency retention. A stability validation profile is generated, defining acceptable storage conditions, maximum shelf life, and expiration dates. Environmental monitoring data are integrated for traceability. The method establishes a comprehensive validation workflow for regulatory compliance and global distribution of mRNA vaccines by uniting multi-parametric testing, computational modelling, and environmental monitoring.
Fig. 1

  , Claims:Claims
I/We Claim:
1. A method for validating the stability of mRNA vaccines under storage conditions, comprising: providing a representative batch of an mRNA vaccine formulation encapsulated within lipid nanoparticles; subjecting said vaccine formulation to predefined storage environments including ultra-low temperatures, refrigerated conditions, and ambient conditions; periodically sampling said vaccine formulation over a plurality of time intervals; measuring physical, chemical, and biological integrity parameters of said vaccine formulation, wherein parameters include mRNA strand length preservation, lipid nanoparticle encapsulation efficiency, and biological potency in vitro; analyzing degradation kinetics using computational models configured to correlate physicochemical data with biological activity; and generating a stability validation profile configured to determine acceptable storage conditions and shelf life of said mRNA vaccine formulation.
2. The method of claim 1, wherein the periodic sampling comprises collection of aliquots at daily, weekly, and monthly intervals, thereby enabling comprehensive evaluation of both short-term and long-term stability characteristics.
3. The method of claim 1, wherein measuring physical parameters comprises evaluating particle size distribution, zeta potential, and polydispersity index using dynamic light scattering and nanoparticle tracking analysis.
4. The method of claim 1, wherein measuring chemical parameters comprises assessing nucleotide integrity through reverse transcription polymerase chain reaction, high-performance liquid chromatography, and capillary electrophoresis, thereby quantifying strand degradation.
5. The method of claim 1, wherein measuring biological potency comprises transfecting cell lines with sampled formulations and quantifying protein expression levels through enzyme-linked immunosorbent assay, flow cytometry, or luminescence assays.
6. The method of claim 1, wherein the computational models comprise Arrhenius-based kinetic models, machine learning regression models, or survival analysis, thereby correlating temperature, time, and degradation rate with potency retention.
7. The method of claim 1, wherein the stability validation profile further comprises determination of maximum permissible storage duration at each condition, prediction of accelerated degradation rates, and assignment of expiration dates.
8. The method of claim 1, wherein the method further comprises integrating environmental sensor data from temperature loggers and humidity controllers, thereby ensuring traceability of storage environments during validation studies.
9. The method of claim 1, wherein the method is performed under compliance with regulatory guidelines for vaccine stability validation, thereby providing quality assurance for clinical deployment.
10. The method of claim 1, wherein integration of multi-parametric testing, kinetic modeling, and environmental monitoring within a unified validation workflow establishes a robust framework for determining stability and shelf life of mRNA vaccines.