Near-Infrared Analysis of Critical Parameters in Lyophilized Materials

February 1, 2006
BioPharm International, BioPharm International-02-01-2006, Volume 19, Issue 2
Pages: 40–45

Lyophilized, or freeze-dried, materials are challenging samples for quality assurance and quality control (QA/QC) measurement because of the inability to open the container without corrupting the product. Near-infrared analysis presents itself as the QC method of choice for lyophilized materials due to its ability to penetrate glass or plastic containers to analyze the sample in a non-destructive manner. This study demonstrates the performance of a Fourier transform near-infrared (FT-NIR) spectrometer used in analyzing lyophilized samples of thrombin, a topical coagulant commonly used in the medical and dental fields. Key stability parameters for lyophilized thrombin include moisture and potency, which can be predicted simultaneously from a single spectrum using multivariate analysis.

Lyophilized, or freeze-dried, materials are challenging samples for quality assurance and quality control (QA/QC) measurement because of the inability to open the container without corrupting the product. Near-infrared analysis presents itself as the QC method of choice for lyophilized materials due to its ability to penetrate glass or plastic containers to analyze the sample in a non-destructive manner. This study demonstrates the performance of a Fourier transform near-infrared (FT-NIR) spectrometer used in analyzing lyophilized samples of thrombin, a topical coagulant commonly used in the medical and dental fields. Key stability parameters for lyophilized thrombin include moisture and potency, which can be predicted simultaneously from a single spectrum using multivariate analysis.

Jeffrey Hirsch

Lyophilization is a common process in both the food and pharmaceutical industries that eliminates the need for sample refrigeration while dramatically increasing shelf life. Products that normally spoil after a few months of refrigeration can be rendered stable, in some instances, for years at room temperature. Lyophilization works by removing residual moisture in a sample through sublimation (the process of transitioning water from the solid to the vapor phase without passing through a liquid phase). Sublimation is the process at the core of lyophilization; if one attempted to remove water from a sample simply by heating to send the water into a vapor phase, the sample would be destroyed.

The sublimation process begins with a solution of target compound, along with various buffers and bulking agents, which are injected into a serum vial. The vial is then partially stoppered and the solution is frozen below its glass temperature (Tg) or, in the case of crystalline compounds, its eutectic temperature. The glass temperature is the temperature below which the material is essentially solid, so removal of water can proceed efficiently. The pressure is then reduced and the freeze-drying process begins with the primary drying stage. After bulk moisture is removed in this phase, the residual moisture (sometimes as great as 8% by weight) is removed in the secondary drying phase. The temperature is slowly increased as the pressure is reduced further until the desired degree of dryness is achieved. The serum vials are then stoppered and sealed.

The most fundamentally challenging issue with lyophilized materials is how to analyze them after they are sealed. Lyophilized fine chemicals or proteins have many chemical and physical properties associated with them, but without proper analytical techniques, there is no way to measure them to ensure that the product will be safe and effective. Currently, lyophilized materials are analyzed by batch sampling, where a small number of samples are pulled from a lot, opened, and analyzed for parameters such as moisture, concentration of the active pharmaceutical ingredient (API), or efficacy. Batch testing of lyophilized materials is ineffective for several reasons. Sample subsets are never guaranteed to be representative of the whole lot, and they are destructive. Protocols like titrations, polyacrylamide gel electrophoresis (PAGE) or enzyme-linked immunosorbent assay (ELISA) are laborious, complicated, and expensive.

FT-NIR spectroscopy is an easy-to-use technique that uses low- absorbing vibrational overtones and combination bands for analysis. FT-NIR analysis allows the user to scan through packing materials like polyethylene bags or glass vials to gain information about the sample. In the case of lyophilized materials, low-energy light can readily penetrate a serum vial to reach the analyte without damaging it. Once the spectral information is collected, multivariate analysis techniques can be used to retrieve information about multiple chemical or physical parameters, all from the same single spectrum. Typical scan times for a lyophilized material sample by FT-NIR are 15–30 sec which, when compared with the 30–60 min necessary for a Karl Fischer titration, is extremely efficient.

Figure 1. The Water Overtone Region for the Ten Standards.

This study details the analysis of thrombin, a lyophilized topical coagulant, by FT-NIR spectroscopy. Two critical parameters for thrombin are moisture (usually below 1.0%) and potency. Traditionally, moisture measurements are carried out by Karl Fischer titration, a USP-referenced technique (USP <921>). Potency is established using light- scattering measurements after the sample is titrated with plasma. If the sample potency is high, the thrombin will coagulate the plasma, creating particulates. Greater potency creates more particulates, resulting in increased light scattering from the sample. Both of these techniques are destructive and require a skilled operator. Both are also time-consuming and use consumables like solvents, plasma, or reagents.

Figure 2. The Calibration Curve for Moisture.

EXPERIMENTAL

Two sets of ten thrombin samples were used to establish calibrations for moisture and potency. The samples were pulled from finished product lots and analyzed through the vial using a Nicolet Antaris FT-NIR analyzer with a LyoCheck solution package from Thermo Electron Corp. The autosampler RS attachment was used to collect all sample data without operator interaction. Thirty-two scans were taken of each sample and co-averaged at a resolution of 4.0 cm–1 . Each sample took approximately 20 sec to analyze. The wavelength range scanned was 4,000 cm–1 to 10,000 cm–1 . Once the samples were analyzed on the FT-NIR spectrometer, they were analyzed destructively using the primary technique — Karl Fischer titration for the ten moisture samples and light-scattering plasma titration for potency. These primary numbers were then combined with the spectral data in multivariate analysis methods using TQ Analyst, a chemometric analysis software package.

Figure 3. The Percent Residual for Moisture.

MOISTURE RESULTS

Spectral data from the FT-NIR analyzer were combined with primary numbers from Karl Fischer titration for ten production samples. The moisture values ranged approximately from 0.5% to 0.8%. The data were combined using the analytic software to construct a chemometric model using the Stepwise Multiple Linear Regression (SMLR) algorithm. Data pretreatment of NIR spectra is common to help the various algorithms predict analytes accurately. In some cases, the primary or secondary derivative of the raw spectrum may prove more effective for measurement. Other pretreatments include pathlength algorithms like multiplicative scatter correction or standard normal variate, smoothing, or baseline corrections. In this case, the pretreatments were minimal as moisture is usually a strong absorber in the NIR range. The second derivative of the spectra was used for making the calibration curve with a 9,2 Norris smoothing filter. The regions chosen were constrained around 7,000 cm-1 , the first water overtone band. The water overtone region for the ten standards is shown in Figure 1.

Figure 4. The Cross Validation Curve for Moisture.

The calibration curve and percent residual for moisture are shown in Figures 2 and 3. The correlation coefficient (how the data is fit by a straight line; 1.0 is theoretically perfect) is 0.998 with a Root Mean Square Error of Calibration (RMSEC) of 0.005. Both parameters describe a good fit of the spectral data to the reference data. Cross-validation is an excellent way to gauge the stability of a certain method. One by one, each calibration standard is removed from a calibration and predicted against the remaining nine standards. If the method is robust, the predicted values of these removed standards will not be very far from their original values. If the calibration seems to get significantly worse upon cross-validation, then the method should be reviewed and perhaps have more standards added. Cross-validation for moisture in thrombin gave a correlation coefficient of 0.984 with a Root Mean Square Error of Cross Validation (RMSECV) of 0.018. Although cross-validation indicated some change in the line fit from the calibration curve, the RMSECV was only a few times greater than the RMSEC, suggesting a relatively stable method. The cross-validation curve for moisture is shown in Figure 4.

Figure 5. The Cross-Validation Percent Residual for Moisture.

POTENCY RESULTS

Potency analysis was conducted by correlating primary potency data derived from titration scattering measurements with spectral data from a second set of ten thrombin samples. These samples had potency values from approximately 29,000 to 33,000 units.

Figure 6. The Calibration Curve For Potency.

The pretreatment for this data set was slightly different than that for moisture. Here, a Partial Least Squares (PLS) algorithm was used on the second derivative spectra with a Norris 9,5 smoothing filter. The pathlength compensation used was a multiplicative scatter correction. The analysis region for potency was 6,000 cm-1 to 6,800cm-1 , which is in between, but not part of, the water resonances. The calibration curve for this case (Figure 6) was excellent, with a correlation coefficient of 0.999 and an RMSEC of 21.9. The percent residual (Figure 7) for nine of the calibration standards showed prediction values for potency of ±0.09%, although sample 18582 had a residual value of –0.18. The Predicted Error Sum of Squares (PRESS) also showed a reasonable trend (Figure 8). For a reasonable calibration curve, the PRESS plot should start high, tend towards a minimum and then stay flat or increase slightly. Cross-validation for this calibration showed the residual increase to approximately ±2.0%. However, the high and low standards had a greater degree of error because they were under-represented in the calibration.

Figure 7. The Percent Residual for Potency.

CONCLUSION

In this experiment, the analysis of lyophilized materials using FT-NIR spectroscopy was shown to be an effective, rapid, and non-destructive method for analyzing lyophilized materials. In this case, both moisture and potency were examined, with the primary methods used being Karl Fischer titration and colloidal light scattering, respectively. The moisture analysis calibration showed a correlation coefficient of 0.998 and an RMSEC of 0.005, with a residual of approximately ±1.5%. These data suggest that FT-NIR analysis of lyophilized materials for moisture is limited only by the error implicit in the reference method. The analysis for potency indicated a correlation coefficient of 0.999 with an RMSEC of 21.9 and an average residual of ±0.15%.

Figure 8. The Predicted Errror Sum of Squares (PRESS) Plot for Potency.

Jeffrey Hirsch is a near-infrared pharmaceutical applications scientist, Thermo Electron Corporation, 5225 Verona Rd., Madison, WI 53711, 608.276.5634; Fax: 608.273.5046; Jeffrey.Hirsch@thermo.com