Use of Raman Spectroscopy to Monitor In-Vitro Transcription in mRNA Manufacturing
This article explores the innovative use of Raman spectroscopy as a non-destructive analytical tool for specific detection as well as quantification of NTP bases and mRNA molecules during IVT.
Read more about
- Importance of Precise Monitoring during In-vitro Transcription
- Limitations of Conventional Approaches and Opportunities of Raman Spectroscopy in mRNA Manufacturing
- Case Study: Qualitative and Quantitative Analysis of NTP Bases and mRNA Using Raman Technology
- Empowering Efficient Process Monitoring for mRNA Vaccines and Therapeutics
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Importance of Precise Monitoring during In-vitro Transcription
The development of mRNA vaccines and therapeutics is characterized by its relative simplicity and speed. However, making an informed decision on mRNA manufacturing strategies is critical. Each manufacturing step, from pDNA synthesis to purification and formulation of the mRNA, necessitates the selection of chemicals and raw materials of suitable quality as well as the use of appropriate techniques and equipment for e.g., synthesis or purification. Furthermore, the efficient and reliable monitoring of each process step is of utmost importance.
During in-vitro transcription (IVT), the DNA template is transcribed into the desired mRNA. Precise monitoring and control of the nucleoside triphosphate (NTP) and mRNA molecules’ evolution during IVT is crucial for optimizing reaction conditions, ensuring high fidelity mRNA synthesis, and confirming the quality of the mRNA product.
By vigilantly monitoring these molecules throughout the IVT process, optimal conditions for mRNA productions can be identified. Observing the accumulation of mRNA allows for gauging the accuracy of the reaction and determining its completion. This approach helps prevent inefficiencies in non-performing reactions, ensuring an abundant supply of mRNA for mRNA therapies.
Limitations of Conventional Approaches and Opportunities of Raman Spectroscopy in mRNA Manufacturing
Conventional methods for quantifying NTP bases and mRNA molecules during IVT, such as UV absorbance, fluorescence or HPLC techniques, yield valuable data but often come with limitations including the requirement for extensive sample preparation, which can result in significant delays and potential degradation of samples.
In contrast, Raman spectroscopy is an efficient method for process monitoring and control which empowers the user to navigate mRNA manufacturing with confidence. It enables the direct monitoring of molecular vibrations, offering a detailed chemical fingerprint of NTP bases and mRNA molecules without the need for labeling or extensive sample handling. Benefits of Raman technology in IVT include:
- Direct Correlation: Raman spectroscopy establishes a direct link between the intensity of Raman peak signals and the concentration of each molecule in the solution.
- Superior Specificity: Raman spectroscopy offers high specificity and precision in quantifying NTPs and mRNA, ensuring exact measurements with every scan.
- Resource-Efficient: No consumables or solvents are needed for Raman spectroscopy measurements, making it a cost-effective and sustainable solution.
- Preservation of Samples: Non-destructive measurements of the IVT reaction safeguard sampling volumes, setting it apart from traditional methods and preserving resources.
- Multi-Molecule Quantification: Simultaneous quantification of multiple molecules allows for comprehensive analysis in a single instrument, saving time and resources.
- Seamless Calibration Transfer: A single calibration method can effortlessly be shared between instruments, providing maximum adaptability for site-to-site tech transfers, scale-ups, or increased production efficiency.
Case Study: Qualitative and Quantitative Analysis of NTP Bases and mRNA Using Raman Technology
Measurement Set-up
All measurements were performed using the ProCellics™ Raman Analyzer coupled with the ProCellics™ External Measurement Unit (EMU) enabling non-contact, non-destructive Raman measurements in quartz cuvettes. Acquisition frequency was 2 minutes and 30 seconds per sample, facilitating rapid insight into the sample composition. The described set-up facilitates non-immersive measurements of small volume samples between 250 µL and 3500 µL.
Figure 1.Experimental set-up with (A) the Raman probe in the ProCellics™ External Measurement Unit and (B) the ProCellics™ Raman Analyzer with Bio4C® PAT Raman Software.
Sample Preparation
Purified concentrated NTP bases (ATP, GTP, CTP, UTP; 100 mM in 250 µL) were diluted to the required concentration using Omnipur® sterile, nuclease-free water. Serial dilutions of pure NTPs into water were performed, generating samples containing pure NTP but also a mix of all NTPs in the same mixture to account for cross-interactions between the compounds (Figure 2).
Samples containing various mRNA concentrations were provided by Millipore® CTDMO Services. mRNA concentrations were determined using a UV reference method at 260 nm.
A total of 60 NTPs and 64 mRNA samples were measured with the ProCellics™ Raman Analyzer. The samples covered a range from 0 mM to 16.7 mM for the NTP bases and 0 mg/mL to 3.6 mg/mL for mRNA concentration.
Figure 2.Illustration of sample preparation containing NTP bases and mRNA
Spectral Interpretation
The acquired Raman spectra are preprocessed in Bio4C® PAT Raman Software using an in-house water normalization followed by a Savitzky-Golay 1st order derivative, 2nd order polynomial order, 5 points smoothing window (15 cm-1) and focusing on the 600 - 1800 cm-1 spectral range. The preprocessing step enhances the relevant information, remove potential signal noise and emphasize Raman bands linked to the molecules of interest.
Figure 3 provides an overlay of all spectral measurements taken at different concentrations for each NTP. The preprocessed spectra are colored according to each molecule regardless of the individual samples’ concentration to facilitate interpretation. Distinct Raman bands corresponding to distinct molecular vibrations are observed for each molecule attesting of the specificity of the technology, capable to discern between each NTP bases as well as mRNA.
Figure 3.Raman plot with preprocessed spectra colored according to molecules, confirming specificity of the method.
Figure 4 shows the spectral measurements at different concentrations for each NTP and mRNA individually. These Raman plots focus on bands which are unique to mRNA or the NTP in question. The preprocessed spectra are colored according to the concentrations levels of each molecule to exhibit correlations between measured Raman intensities and effective samples concentrations. It can be observed that the intensity of these specific bands increases as the concentration of the nucleotide increases, showing that the amplitude of the Raman peak is directly proportional to the molecule concentration.
Figure 4.Raman plot with preprocessed spectra colored according to the concentration of each molecule in the respective sample, showing more prominent Raman peaks at higher concentration levels.
Quantification Using Partial Least Squares (PLS) Regression
After the first set of data acquisitions, the file containing preprocessed spectra and off-line reference measurements has been imported into the Bio4C® PAT Chemometric Expert Software to create Partial Least Squares (PLS) regression models. Five PLS-1 models were built: one for each NTP, and one for mRNA.
The created models were then imported back into the Bio4C® PAT Raman Software to enable direct monitoring of new samples preparations. This step is very important to determine the accuracy of the regression models and estimate statistical criteria such as the Root Mean Squared Error of Prediction (RMSEP) and the relative error calculated based on the ratio between the RMSEP and the maximum concentration of the validation samples range for each molecule.
The graphs displayed in Figure 5 illustrate a high linearity between the reference measurements and the Raman spectroscopy readings for each molecule with R² values greater than 0.95. This important degree of linearity suggests that the regression model accurately translates the acquired Raman spectra into molecular concentrations, closely aligning with the anticipated concentrations in the solution.
Figure 5.Reference concentrations vs Raman estimated concentrations regression lines for calibration samples and validation samples, showing a good linearity between both.
The RMSEP values obtained from the validation sets as well as the relative errors are detailed in Table 1. The model validation delivers relative errors below 5% across all measured analytes, providing a high level of accuracy. Such precision enables a reliable monitoring of molecular concentrations in the analyzed samples.
Empowering Efficient Process Monitoring for mRNA Vaccines and Therapeutics
The presented case study demonstrates how each NTP and mRNA exhibit unique Raman signatures, enabling specific detection and quantification via Raman spectroscopy. The ProCellics™ Raman Analyzer’s linearity and precision facilitate efficient analysis and process monitoring of the IVT reaction with utmost confidence.
The innovative solution not only delivers high specificity and accuracy in quantifying NTPs and mRNA but also allows for rapid and non-destructive measurements. Consequently, operators can promptly intervene during manufacturing to ensure optimal process conditions for the IVT reaction. Furthermore, the presented solution eliminates the need for consumables and solvents, while preserving sample integrity and reducing environmental impact.
With seamless calibration transfer methods and simultaneous quantification of multiple molecules, this solution offers flexibility and scalability, thereby facilitating smooth tech transfers, scale-ups, and increased production efficiency.
The successful implementation of Raman spectroscopy sets the stage for a new, efficient analytical solution for monitoring NTPs and mRNA in IVT reactions with precision and efficiency, effectively overcoming the limitations of conventional techniques and enabling well-informed decisions in mRNA vaccines and therapeutics manufacturing.
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