Pharmaceutical analysis for moisture content in products has traditionally relied on well-established laboratory methods, often involving wet chemistry techniques, such as Karl Fischer titration and gravimetric loss on drying. While these methods have been the gold standard for many years, they are not without their limitations. They can be time-consuming, require extensive sample preparation, analyst training, and involve the use of hazardous reagents. Additionally, the destruction of the sample during analysis poses a significant drawback, especially when working with limited quantities of valuable substances. Near-Infrared (NIR) spectroscopy, a non-destructive and rapid analytical technique that has revolutionized pharmaceutical analysis. NIR spectroscopy operates on the principle that molecules absorb specific wavelengths of light in the NIR region, resulting in characteristic absorption patterns that can be used for quantitative analysis. This method offers several advantages that make it particularly appealing in the pharmaceutical industry.
In a new study published in the Journal of Pharmaceutical and Biomedical Analysis, led by Amrish Patel, Dr. Chunguang Jin, Brittany Handzo, and Dr. Ravi Kalyanaraman from the Forensics and Innovative Technology, Global Quality Analytical Science & Technology at Bristol Myers Squibb, the researchers conducted an extensive study to develop and validate a method for determining moisture content in pharmaceutical tablets using handheld NIR spectrometer.
The researchers recognized that accurate measurement of moisture content in pharmaceutical tablets is crucial for quality control. To address the issue of sample variability, which is a major source of error in NIR measurements, the researchers carefully selected tablet samples for their study. They acknowledged that variations in tablet composition and the random nature of light in turbid media could affect measurement accuracy. They also acknowledged the impact of environmental conditions, particularly temperature and humidity, on moisture content measurement. They designed their study to span three months, during which they monitored temperature and maintained it within a specified range (20–22 ℃). They also monitored humidity, which fluctuated between 30% and 50%, representing typical laboratory conditions.
Understanding that NIR signals are not specific to a single component in a mixture, the authors addressed the compositional variation in the tablets. They randomly selected calibration samples from different batches of tablets, ensuring that the samples represented the diversity of tablet compositions encountered in routine use. They also considered the impact of moisture exposure on tablet composition. The authors recognized the strong water absorbance band at 1940 nm in NIR spectra. They conducted experiments to compare the performance of the NIR calibration model using two different spectral ranges: (a) 1900–2000 nm and (b) 1596–2396 nm. Based on their findings, they selected the wider spectral range (1596–2396 nm) to develop the calibration model. The researchers employed various preprocessing techniques, including normalization by 1-Norm to correct optical drift, standard normal variate (SNV) to remove scatter effects, and mean centering to further enhance data quality. They compared these preprocessing methods to optimize their calibration model. Moreover, they successfully developed a robust calibration model for moisture content prediction. They used Partial Least Squares (PLS) regression to build the model with optimal number of PLS factors.
To validate the accuracy of their NIR method, the team applied the calibration model to an independent sample set with moisture content spanning the same range as the calibration samples. They compared the predicted moisture content values with reference measurements obtained through loss on drying. Statistical analyses, including linear regression, t-tests, and F-tests, were used to assess accuracy, and the method was confirmed to be equivalent to conventional methods. The authors estimated the LOD and LOQ for the NIR method to understand its sensitivity. These parameters were calculated based on standard deviation and slope, providing insight into the method’s detection capabilities. To ensure method robustness, the authors systematically evaluated factors that could impact the method’s performance, including sample presentation and environmental conditions. They demonstrated the method’s resilience by comparing measurements taken with different types of glass vials and under varying room temperature and humidity conditions. Moreover, they emphasized the importance of monitoring spectral quality to ensure that sample spectra align with the calibration model. They introduced model diagnostic measures, including Q residuals and Hotelling’s T2, to detect any outlying or non-conforming spectra. These measures helped identify samples that fell outside the established range of variation.
The researchers recognized the need for ongoing verification of method performance. They established a comprehensive program for monitoring method performance, including the use of statistical process control charts. Regular parallel testing, comparing NIR results with traditional loss on drying reference method results, was an integral part of this verification process. Throughout their research, the authors followed Analytical Quality by Design (Analytical QbD) principles, emphasizing risk assessment, robustness, and adaptability. They demonstrated a comprehensive approach to developing, validating, and continuously verifying the NIR method for moisture content determination in pharmaceutical tablets. Their careful attention to detail and rigorous scientific methods highlights the reliability and potential of NIR spectroscopy in pharmaceutical analysis for moisture content.
Patel A, Jin C, Handzo B, Kalyanaraman R. Measurement of Moisture Content in Pharmaceutical Tablets by Handheld Near-Infrared Spectrometer: Adopting Quality by Design Approach to Analytical Method Lifecycle Management. J Pharm Biomed Anal. 2023;229:115381. doi: 10.1016/j.jpba.2023.115381.