The U.S. Pharmacopeia (USP) is a non-profit scientific organization whose mission is to improve global public health through public standards and related programs that ensure the quality, safety and benefits of medicines and foods. Vibrational spectroscopy is an analytical tool that is used in the United States Pharmacopeia and National Formulary (USP–NF) and has been since the 1950s. We explain the organization of the documentation within the USP–NF and how vibrational spectroscopy plays an important role in this regulatory document collection.

The U.S. Pharmacopeia (USP) is a nonprofit scientific organization established over 200 years ago to “improve global health through public standards and related programs that ensure the quality, safety and benefits of medicines and foods” (1). It was formed in 1820 by a small group of physicians who were concerned about the dangers of poor-quality medicines. When it was founded, Dr. Lyman Spalding noted, “the value of the pharmacopeia depends on the fidelity with which it conforms to the best state of medical knowledge of the day” (2).

Since its inception, the USP has worked collaboratively with Federal and State policymakers and other stakeholders. In 1848, Congress passed the Drug Importation Act to stop importation of poor-quality drugs from Europe that were deemed “good enough for America” (3). This legislation recognized the USP as the authority on the standards to be used to determine drug strength and purity.

The second regulatory milestone that tightened the relationship between the USP and the federal government was the passage of the Pure Food and Drug Act of 1906. This act was the start of the U.S. Food and Drug Administration (FDA), and it also officially required that drugs marketed in the United States meet USP standards.

In the 1990s, the USP recognized the need for similar standards for dietary supplements. Later, the USP expanded its scope to include food quality; ensuring food identity, purity, and quality was added to the mission of the USP in 2006 when it acquired the Food Chemical Codex.

Throughout the history of the USP, expertise and scientific advancements have been key to its mission. Spectroscopy, in particular vibrational spectroscopy, has played a key role since the early days of the techniques. This summary explains how vibrational spectroscopy fits into the 

and how these techniques are used to ensure drug quality.

The organization of the documentation within the USP and National Formulary (USP–NF) may seem unusual for someone concentrating on a particular technique. However, by remembering the mission of USP, the strategy becomes clear.

starts with General Notices, which apply to official standards recognized by USP. The heart of the USP–NF lies in the monographs. Each monograph defines a medicine, including its specifications and its details related to packaging, storage, and labeling. A mono- graph also defines the tests, procedures, and acceptance criteria for the strength, quality, and purity of a material—in other words, the compendial identity standards.

The General Chapters describe tests, techniques, and procedures that are important to the contents of the monographs. They also contain information on pharmaceutical compounding, how to interpret the requirements in the compendia, and general guidance to drug manufacturers. The General Chapters are applicable to a material through references in the General Notices, the monographs, or other chapters.

A monograph starts with general chemical information about a drug, such as the name, molecular formula, added substances, physical description, and solubility. The next section of the monograph describes identification tests. These tests may be spectroscopic in nature. Another place spectroscopy comes into play is the assay section of a monograph.

Spectroscopy with commercial instrumentation started to be used in analytical chemistry laboratories in the 1940s. Vibrational spectroscopy, in particular infrared (IR) spectroscopy, had similar beginnings and, as noted by Rabkin (4), American Cyanamid contracted with PerkinElmer to produce the first commercial instruments. By the 1950s, laboratories were well-equipped with spectrophotometers (5).

This expansion of spectroscopy went hand-in-hand with developments that were underway in the USP–NF. In 1950, USP 14 had the first entries referring to spectrophotometry. By 1960, IR spectroscopy was mentioned as distinct from UV spectroscopy. The applications grew and by the 2022 version of the USP–NF, spectroscopic techniques, including mid-IR, near-IR (NIR), UV-vis, and Raman spectroscopy, appear in over 250 general chapters and monographs. The number of general chapters and monographs do not include references via Chapter <197> “Spectroscopic Identification Tests.”

Chapter <197> Spectroscopic Identification Tests

explains that the identification tests in the 

monographs must be used to ensure the identity of a material—to establish whether it is the material named in the USP–NF. Failure of an article (material) to meet all the requirements of a prescribed Identification test indicates that the article is mislabeled, adulterated, or both.

defines the spectroscopic identification tests that can be used in a monograph. These include NIR, mid-IR, and Raman spectroscopy. For mid-IR spectroscopy, the chapter de- scribes at least seven different sampling approaches, both classical and modern, including:

thin film from neat liquids, solutions or melts between alkyl halide plates

In total, more than 1700 uses of mid-IR spectroscopy are referenced in the monographs; for UV-vis, there are approximately 350 references. Chapter <197> also contains information about applying alternative and harmonized procedures.

In addition to Chapter <197>, which defines spectroscopic identification tests, there are two more sets of paired chapters in the 

that describe and define vibrational spectroscopy–which respectively belong to the chapters numbered below 1000 and the chapters numbered above 1000 (see USP–NF General Notices 3.10. Applicability of Standards). The chapters numbered below 1000 describe specific techniques that are mandatory requirements for compliance when referenced in a monograph. These chapters include an introduction and brief description of the technique, a section on the qualification of the spectrometer that is the topic of the chapter, and procedures for using the technique. In addition, there are sections in the chapter describing validation of the method, if necessary, and verification to ensure that it is being executed with suitable accuracy, sensitivity, and precision.

These spectroscopy chapters numbered below 1000 are:

Associated with those three chapters numbered below 1000 are three spectroscopy chapters numbered above 1000. The numbers for these chapters corresponds to the scheme seen for those below 1000:

These chapters provide more in-depth descriptions of each technique. They start with the theory, explaining the principles and theoretical background of the technique, including modes of measurement. Each of these chapters includes a section that provides guidance and precautions on the factors that impact measurement, such as spectral resolution, wavenumber accuracy, photometric accuracy, sensitivity, environmental factors, and sampling for individual spectroscopies, because these guidelines are important for ensuring quality data. Another section discusses instruments, configurations, and components, while also highlighting calibration considerations. Finally, these in-depth chapters may include information about applications that may not be covered in their sub-1000 counterparts or in other general chapters or monographs.

The contents of these chapters above 1000 build on the contents of the corresponding chapter numbered below 1000. Using the mid-IR chapters as an example, the contents in Chapter <854> are:

Introduction: A summary of what mid-IR spectroscopy is.

Operational Qualification: Wavenumber Accuracy

Liquids and Solutions in Transmission Cells

The information contained in this chapter provides a practical approach to mid-IR spectroscopy for someone who is practicing the analysis.

As we move to Chapter <1854>, we now approach more of the theory and the rationale behind the practices described in Chapter <854>. The contents in <1854> are:

A theoretical discussion of the principles of mid-IR spectroscopy, which includes a discussion of molecular vibration fundamentals and different vibrational modes.

Sampling Procedures: An expanded discussion on the sampling procedures presented in <854> and separated into:

Factors that Impact Measurement Performance

This more in-depth treatment provides the theory and background to ensure that the spectroscopic techniques are applied properly. One of the most important reasons for thoroughly understanding a technique is to address the application of alternative and harmonized methods and procedures. An alternative method can be used, but it must be fully validated and provide results comparable to those of the compendial method. Alternative methods can be developed to simplify sample preparation, enhance precision and accuracy, shorten analysis time, or be better suited to automation. Without thoroughly understanding the original method, developing and applying alternative methods properly would be difficult.

Under the Federal Food, Drug and Cosmetic Act of 1938, the USP and NF are recognized as official compendia, and a drug with a name recognized in the USP–NF must comply with compendial identity standards or be deemed adulterated, misbranded, or both.

Chapter 21 of the U.S. Code of Federal Regulations (21 CFR) deals with matters related to the FDA. Parts 210 and 211 of 21 CFR describe current good manufacturing practices for finished pharmaceuticals, and indicate that if USP methods are used, reference to them will suffice for meeting requirements of the FDA; 21 CFR Part 211.194(a)(2) - Addressing Laboratory Records states:

“A statement of each method used in the testing of the sample. The statement shall indicate the location of data that establish that the methods used in the testing of the sample meet proper standards of accuracy and reliability as applied to the product tested. (If the method employed is in the current revision of the United States Pharmacopeia, National Formulary, AOAC International Book of Methods, or in other recognized standard references, or is detailed in an approved new drug application and the referenced method is not modified, a statement indicating the method and reference will suffice). The suitability of all testing methods used shall be verified under actual conditions of use.”

Thus, the information in the USP is a recognized approach to characterizing a material. The chapters on spectroscopy are essential for such characterization. Thus, the information compiled and provided in the USP has important regulatory standing.

USP’s standards contribute to ensuring the quality of medicines, dietary supplements, and foods, thus helping protect our drug, dietary supplement, and food supplies from substandard and adulterated materials. The USP–NF has served that purpose for over 200 years. Spectroscopic analysis plays an important role in the identification tests in USP–NF, and by extension, in ensuring the quality of pharmaceutical products. As technology changes the approaches change, but these changes are always with the aim to protect health and safety.

https://online.uspnf.com/uspnf/document/1_ GUID-E09DC39F-1D28-4D90-8D76- 0ED109FFB640_8_en-US?source=TOC

The Pharmacopoeia of the United States of America: 1820; By the Authority of the Medical Societies and Colleges.

 Boston: Printed by Wells and Lilly for Charles Ewer; Dec. 1820 Library Catalog; MMS ID 992564553406676; NLM ID 2567001R

The Little-known History and Global Future of Quality Medicines. 

https://www.scientificamerican.com/custom-media/little-known-history-of-quality-medicines/

(4) Rabkin, Y. M. Technological Innovation in Science: The Adoption of Infrared Spectroscopy by Chemists.

(5) Mukhopadhyay, R. The Rise of Instruments during World War II. 

 is a Principal Scientist in General Chapters Department, and 

 is a Senior Director of General Chapters Department of Science Division of United States Pharmacopeia (USP). Direct correspondence to: 

Articles

The United States Pharmacopeia–National Formulary (USP–NF) has contributed to ensuring the quality of dietary supplements, foods, and medicines for more than 200 years. This overview explains the use of vibrational spectroscopy techniques in meeting USP–NF requirements and how the information is organized.

Spectroscopy and Spectroscopic Analytical Techniques in the United States Pharmacopeia–National Formulary (USP–NF)

Many applications of process analytical technologies (PAT) use spectroscopic measurements that are based on prediction models. The accuracy of the models can be affected by a number of factors such as aging equipment, slight changes in the active pharmaceutical ingredient (API) or excipients, or previously unidentified process variations. Because models are living documents and must reflect the changes in the process to remain accurate, the models need to be updated and managed. This summary describes how one major pharmaceutical company approaches the challenges of managing these models.

Vertex Pharmaceuticals, founded in 1989 in Boston, Massachusetts, has pioneered the application of continuous manufacturing, which requires system integration, engineering of materials transfers, real-time data collection, and rapid turnaround of the manufacturing rig or system. The philosophy for development and manufacturing at Vertex includes four key concepts: quality by design (QbD), continuous manufacturing, process analytical technologies (PAT), and real-time release testing (RTRT). An important aspect of this philosophy is that the PAT models that are developed and used require maintenance, just like any other component of a system. Using the example of the drug Trikafta, which is marketed in a triple-active oral solid dosage form, we describe and illustrate here how Vertex takes on the task of model development and maintenance.

PAT is an integral part of the continuous manufacturing environment. It is a system for designing, analyzing, and controlling manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials with the goal of ensuring final product quality.

PAT systems have a number of components, all of which must interact to ensure quality products. The requirements of this system are:

Continuous improvement and knowledge management, which requires continuous learning through data collection and analysis. The data generated are available to scientifically justify post-approval process changes.

Process monitoring and in-process controls that show consistent or improved product quality, increased yields, and reduced rejects and scrap, and that enhance the effectiveness of investigations.

Real-time release testing that reduces the production cycle time and inventory and also lowers laboratory costs while increasing the assurance of batch quality.

In this process, as implemented for Trikafta, production includes intragranular blending, dry granulation, milling, extragranular blending, tableting, and coating. During the process, analysis tools such as loss-in-weight feeders, near-infrared (NIR) final blend analysis, laser diffraction particle size analysis, and weight/thickness/hardness, are key.

Figure 1 shows the building blocks of the PAT implementation used at Vertex.

In this process, NIR spectroscopy is used to measure the potency of the three active pharmaceutical ingredients (APIs). The analysis is performed on the final blend powder and there are nine chemometric models in the implementation. For each API, there is a partial least squares (PLS) model and then there are linear discriminant analysis models to classify each API as typical, exceeding low typical, or exceeding high typical.

In this strategy, NIR spectroscopy is part of the in-process controls (IPC) for final blend potency. The loss-in-weight (LIW) feeders provide a final blend potency range of 90 to 110% and within that, the NIR models are used for typical potency limits, which are 95 to 105%. This 95 to 105% limit provides a warning limit that the product is outside the typical final blend potency and if the LIW potency shows that the product is out of specifications then it is segregated from the process. These two potency calculations go hand in hand to ensure consistency and quality. These models are key.

A number of agencies, including the U.S. Food and Drug Administration (FDA), the International Council for Harmonization (ICH) of Technical Requirements for Pharmaceuticals for Human Use, the European Medicines Agency (EMA), and the U.S. Pharmacopeia (USP) all have similar documents that provide guidance for developing, using, and maintaining PAT models.

The regulatory agencies recognizes that NIR models will need to be updated and that the applying company needs to take into account how the updates will be managed, how the lifecycle is evaluated and supervised, and how changes in processes, composition, or site of manufacture all would require model updates. The challenge then becomes how to plan and carry out updates in the most efficient manner.

There are a number of known sources of variability that could impact NIR model performance. Loosely, they can be categorized as variability in:

These six categories can define the known sources of variability, and using a statistically designed experimental approach the impacts on the models can be accounted for. However, in any analytical problem, there are unknowns that will also impact the process. These unknown sources of variability are the reason for model recalibration and validation.

Models need to be robust enough to limit the update frequency because frequent updates take time and effort. It is estimated that a typical model update can take up to two months. Updates can be scheduled as the production department brings new suppliers or sites online. In addition, model updates can be incorporated into the production schedule, but all stakeholders must be cognizant of the need to perform model updates because of the time and effort involved.

Any model used in these PAT applications has five interrelated components, as shown in Figure 2. The five components to the lifecycle of a PAT model are data collection, calibration, validation, maintenance, and redevelopment.

In PAT, data collection is based on the principles of QbD: experiments are defined in unit operations using QbD, and QbD principles are also employed in integrated line experiments. Process models as well as criticality points are included. The model development approach incorporates all the variables including:

These are expected variables, but it is also recognized that there are unexpected sources of variability that cannot be initially taken into account.

In the calibration step, a number of factors are investigated. These include both pre-processing and what type of model should be used. In the Trikafta example, NIR is used for final blend potency, and the data undergo three pretreatment steps, with each step narrowing the spectral range of interest. The first, smoothing, operates on the entire spectrum (1100–2200 nm); the second preprocessing step, the standard normal variate (SNV) step, is applied to the 1200–2100 nm range; the final pretreatment, a mean centering, brings us to the prediction ranges of the model, 1245–1415 nm and 1480–1970 nm.

For each API, the model, which is a PLS–linear discriminant analysis (PLS-LDA) qualitative model, classifies the target in the typical range (95–105%), exceeding low (<94.5%), or exceeding high (>105%). The optimal model will correctly categorize with no false negatives and few false positives.

After development of the model, it is validated in a number of ways. First, a challenge set of official samples that are not used in the model and have been subject to laboratory analysis are used.

They include samples with classifications of typical, low, and high. The model must classify them correctly to pass this validation challenge. Then, two other sets are also used for secondary validation. The first step of this secondary validation set is to challenge the model with hundreds of samples of a wider range, analyzed by high performance liquid chromatography (HPLC). Finally, all the prior production data are used to challenge the model; currently, this challenge set contains tens of thousands of spectra. Also included in this final data set is lot and batch variability to ensure robustness of the model and to capture maximum variability.

After the validation, the model is documented and installed and then enters the maintenance phase. Deployed models are monitored as part of continuous process verification. During each NIR run, the results from the PLS-LDA are shown in real time. Diagnostics for each batch are produced, which include run time results for all the analyses and trending reports on the batch. Another tool used in this phase is an annual parallel testing challenge. Non-standard sampling is also done, and finally, an annual product review report is produced. Deployed models are continuously monitored to understand the health of the model.

In the diagnostics, the spectrum is examined and two statistics are produced, one representing a lack of fit to the model and the other showing a measure of the variation in the sample from the center score. If either of these diagnostics exceeds the threshold, the result is suppressed and the system alarms to inform the operator that something is wrong.

The performance of the models is continuously monitored. When the model does not perform appropriately, such as when trends indicating poor operation are seen, a determination is made about whether an update to the model is necessary. The model can be updated using either ongoing data or historical data. In the redevelopment phase, the same steps as initially done are implemented, and the model goes through an extensive validation testing and peer review. Some of the types of changes that could be implemented at this point are adding new samples to capture more variability, varying the spectral range, or changing the spectra preprocessing, all of which may require notifying the appropriate regulatory agency. Changes in the model that involve the algorithm or technology require prior approval of the appropriate regulatory agency. Generally, those changes are only made if they show a significant improvement over the existing method.

A large toolset is necessary to continue model development and evaluations. The tools include detailed documentation of the contents of the electronic laboratory notebook (ELN), access to historical and current data, process information, and information on incoming raw materials—including APIs and excipients. The models are highly documented, which allows for other analysts to take ownership of models as it becomes necessary.

Internal applications that can extract data from both current and historical data sets are available and include access to process data, spectra, and process results. These tools also allow evaluation against different versions of the model as necessary. Finally, the tools also have data mining and documentation components.

In a first example, we identified a new source of variance. The model had been installed and during trial runs, false positives were observed, but HPLC analysis indicated that the samples were within specification. Evaluations indicated that there was not a large enough variation in the calibration sets used to generate the model. Samples representing the additional variability were added and the wavelength range was adjusted. The redevelopment, validation, and implementation required five weeks.

A second example occurred as the models were transferred to a contract manufacturer. The original calibration had been completed on one rig and the equipment at the contract manufacturer was not represented. Samples from both manufacturing systems were included in the new model to address this.

A final example shows the robustness of appropriate models over a five-year period. In this case, the original work was completed in 2018, and the production started in mid-2019 using three model sets. Since the models were installed there have been updates to two of the models and one of the original models is still in use and performing well.

Chemometric models play a key role in enabling process analytical technologies in a continuous manufacturing environment. Models are managed as part of the quality management systems during all phases, including development, validation, and maintenance. The models are routinely evaluated for performance and updates are implemented based on both changes over time that may impact the model as well as on performance over time. All these processes are under change control and the model updates implemented by Vertex on this product have only required regulatory agencies be notified post-update.

 is a Process Analytics and Control Scientific Fellow at Vertex Pharmaceuticals, in Boston, Masssachusetts. Direct correspondence to: 

The accuracy of prediction models based on spectroscopic measurements can be influenced by a variety of factors, including aging equipment. Continuous monitoring is key to managing the PAT model lifecycle and ensuring that changes over time do not negatively affect prediction model performance.

Process Analytical Technology (PAT) Model Lifecycle Management

The fingerprint region in mid-infrared (IR) and Raman spectroscopy, typically between 300 to 1900 cm

, is used to characterize molecules by utilizing their vibrational and rotational changes in this region. In this study, a smaller subset of this Raman spectral region was evaluated for its significance in characterizing pharmaceutical products. This narrower region from 1550 to 1900 cm

 in the Raman spectrum will further be defined as the “fingerprint in the fingerprint” region. A spectral evaluation was conducted on active pharmaceutical ingredients (APIs) in prescription drugs, over-the-counter drugs, and pharmaceutical drug products. Qualitative and quantitative analysis was performed on these products to gain an understanding of this spectral region. The qualitative results showed no Raman spectral signals in this region for any of the common excipients. Additionally, all APIs evaluated had unique Raman vibrations and spectral signals in this region. This data indicates that the 1550–1900 cm

 Raman spectral region is ideal for API identity testing. Furthermore, the data indicate the potential of this spectral region for differentiation and classification of API content in different strengths of a given drug product.

The fingerprint region, typically between 300–1900 cm

 in Raman spectroscopy, is often used for identification or authentication testing of raw materials, excipients, and active pharmaceutical ingredients (APIs) in drug products for quality control (QC) release testing. It is also used for forensic applications, such as screening suspect or counterfeit products (1). The “fingerprint in the fingerprint” is a specific spectral region (1550–1900 cm

) in Raman spectroscopy that can be used for the identity testing of pharmaceutical products, especially APIs. This narrower spectral region can strengthen the Raman capabilities of identity testing by providing the enhanced specificity needed to identify APIs in a drug product. In this spectral region, Raman signals from a variety of prescription and over-the-counter APIs are present, whereas many pharmaceutical excipients do not have Raman signals in this region. This observation leads to the conclusion that this spectral region in Raman spectroscopy is a valuable tool that could be utilized for pharmaceutical qualitative and quantitative testing, such as identity, assay, and content uniformity.

Prior to advancements in infrared (IR) and Raman vibrational spectroscopy, identity testing of pharmaceutical products was performed via chromatography. The most widely used technique for quantitative analyses is high performance liquid chromatography (HPLC), but HPLC is typically used to quantify APIs in drug products rather than excipients (2). Monitoring excipients can be beneficial, especially when it affects quality elements like dissolution (2). Chromatography also has limitations like extensive sample preparation and long analysis times. IR spectroscopy is an alternative for chromatography techniques and is commonly used for identity testing, structural elucidation, and quantitation (3). IR spectroscopy is cost efficient, has shorter analysis times, and requires little or no sample preparation when compared to HPLC. Additionally, IR spectroscopy has high spectral resolution and good signal-to-noise (S/N) ratios, which make it an effective technique for identity testing of pharmaceutical ingredients.

Raman spectroscopy is another analytical technique that can be used for identity testing in the pharmaceutical industry. This nondestructive technique requires almost no sample preparation, provides short analysis times, and enables easy operation (4). A variety of materials can be analyzed via Raman spectroscopy such as solid dosage products, bulk products, or aqueous solutions. Solid dosage formulations are more commonly evaluated via Raman spectroscopy as opposed to aqueous solutions. However, water has a weak Raman signal that does allow for analyzing biological products if needed (4). New advancements in portable Raman spectroscopy also allows for bulk products to be tested directly through their respective packaging, such as plastic liners in drums containing raw materials or excipients or blister packs for drug products. The rapid identification of drug products (5), active ingredients, and excipients are all available using Raman spectroscopy and are being accepted more widely in the pharmaceutical industry.

In this article, the “fingerprint in the fingerprint” (1550–1900 cm

) spectral region in Raman spectroscopy is explored for both prescription and over-the-counter solid dosage forms. A solid dosage product is a powder, capsule, or tablet that contains an API and a range of excipients at varying levels (2). This narrow spectral region can be used for identification and may even be used for assay or content uniformity monitoring, which are critical quality attributes for pharmaceutical products (6). The purpose of this study is to show the importance of this spectral fingerprint region in Raman spectroscopy, and how it can be leveraged for pharmaceutical applications.

Raman spectra were collected using a Thermo Nicolet NXR 6700 FT-Raman spectrometer with a microstage attachment. The microstage was replaced with a 180 reflectance attachment for the quantitative analysis experiments. A 1064-nm laser source was focused on each sample and a laser power of approximately 0.5 W and 1.0 W were used for the microstage and 180 reflectance attachments, respectively. The scattered light was transferred through the interferometer and bench optics before being collected by an indium gallium arsenide (InGaAs) detector. The Raman signals were collected at a spectral resolution of 4 cm

. Spectral calibration was performed via the Thermo ValPro qualification system, which qualifies the spectrometer, sampling module, and analysis software for use.

Omnic spectral software (version 9.8) was used to analyze the acquired Raman data. Unscrambler (version 10.4) was used for principal component analysis (PCA) modeling. Preprocessing for the PCA modeling included standard normal variate (SNV) transformation and a first derivative transformation, which was followed by a second order polynomial fit with nine-point smoothing.

Table I lists the 15 reference excipients evaluated in this article along with their common excipient class. All reference excipients were chosen from the Thermo Scientific Pharmaceutical Excipients FT-Raman library database. This library contains 300 Raman spectra collected on materials which have passed the United States Pharmacopoeia (USP) compendium methodology testing for identity and purity (7). Table II lists the 15 reference APIs evaluated in this article along with their respective drug product categories and functions. The APIs selected ranged from prescription to over-the-counter medications, which are used to treat a variety of health concerns. They are all small molecules and are not the drug substances used in protein-based biologics products. The API spectra were collected in our laboratory via the parameters outlined in the Raman instrumentation section of this article.

The following results are classified into two categories: 1) excipients and 2) APIs. The excipients outlined in Table I were chosen based on the most common inactive ingredients listed in the formulations for 30 reviewed drug products. The APIs outlined in Table II were chosen by selecting a variety of both prescription and over-the-counter medications with a range of functions. Raman data for each category are listed; the Raman band assignments are listed in the literature (8,9).

The first group of compounds analyzed were pharmaceutical excipients. These inactive ingredients have a range of functions in drug product formulations including lubricants, coating compounds, emulsifying agents, absorbents, and diluents. Magnesium stearate was the most common excipient observed. Magnesium stearate is used as a lubricant in tablet and capsule formulations and has strong Raman bands at 1061 cm

. These bands can all be assigned to C-C stretching vibrations, both asymmetric and symmetric and CH

 deformation (8). The most common coating excipient observed was titanium dioxide. Titanium dioxide is used as a coating agent, whitener, and pigment in pharmaceutical products. Raman bands are observed at 636 cm

 bending and stretching vibrations, respectively (8). Another used excipient was lactose. Lactose is widely used as a binding agent or diluent in tablet and capsule formulations. Lactose also has a variety of Raman bands that can all be assigned to C-C, C-O, and C-H bond vibrations (8). The Raman spectra (Figure 1) of the 15 common excipients evaluated in this study, and the corresponding Raman band assignments are outlined in Table III. All 15 excipients did not have any Raman signals in the defined 1550–1900 cm

The second group of compounds analyzed were APIs. These active ingredients are used in a variety of drug products, which range from cancer and diabetes treatments to over-the-counter pain relievers and antihistamines. All these drug products have most of the common excipients listed in Table III. The main spectral differences between these compounds and excipients is that the APIs have Raman vibrations located within the 1550–1900 cm

 region. Some functional groups that correlate to API peaks within this narrow region are C=N vibrations (1610–1680 cm

), and N=N vibrations observed at approximately 1580 cm

. These Raman signals are uniquely typical for APIs and are not present in common excipients. Additionally, it was noted that API #4 had a strong Raman signal at approximately 2250 cm

 that correlates to a C≡N stretching vibrational mode. All 15 active ingredients analyzed in this article had unique Raman signals in the defined 1550–1900 cm

 spectral fingerprint region. The Raman spectra (Figure 2) and corresponding Raman band assignments (Table IV) are outlined below.

The following spectra in Figures 3 and 4 compare the Raman signals within the 1550–1900 cm

 region for both excipients and APIs. As previously mentioned, the 15 excipients analyzed did not have Raman signals within this range whereas the 15 APIs analyzed did, which is an important characteristic of the 1550–1900 cm

 spectral region because the only Raman signals residing in this region are from the APIs. There are no interferences from the excipients in this region; therefore, they can be leveraged for API identity testing in a drug product. The differences in Raman signals between the two groups of compounds are because a variety of factors such as structure, functional groups, and types of bond or group vibrations. Most excipients, with the exception of a few, were comprised of carbon, oxygen, and hydrogen. The APIs had more complex structures and comprised of additional elements such as nitrogen, phosphorus, and halogens. Figure 3 shows an overlay of the Raman spectra from the 15 excipients and 15 APIs in 1550–1900 cm

 spectral region can also be used for the identification of drug products. Like Figure 3, an overlay of 15 drug products and excipients in this region show that the primary Raman signals are from the APIs rather than the excipients. Figure 4 shows the drug products and excipients highlighted in this region. The overlays show that there is no additional interference upon formulation of the product and that the Raman spectra can identify the API component in a complex drug product mixture, which can be used for more quantitative analyses such as monitoring content uniformity and assay.

As stated above, the “fingerprint in the fingerprint” region in Raman spectroscopy can also be used for quantitative analyses such as assay or content uniformity for drug products (14). Content uniformity is a standard pharmaceutical test to ensure the API content in solid dosage products have the correct amount of API across a given manufactured drug product batch (10). A similar approach to the excipient and API evaluation was conducted using FT-Raman surface scattering measurements on pharmaceutical drug products. Additionally, PCA mathematical modeling was used in this article to differentiate and classify the API content in different strengths for a given drug product (13).

As shown in Figure 5, Raman data was collected on three different strengths of a pharmaceutical solid dosage drug product (5, 10, and 15 mg) and all three strengths have a total tablet weight of 95 mg. The total API weight (w/w) in these three strengths are 5.2, 10.5, and 15.8%, respectively. The 5 and 10 mg tablets are rectangular in shape, whereas the 15 mg tablet is round. In spite of the differences in shape, it was observed that as the tablet strength increased from 5 mg to 15 mg, the intensity of the API peaks also increased between 1550–1700 cm

. This spectral intensity variability was further studied using PCA. The combination of spectroscopy and discriminate analysis, such as PCA, is a powerful tool that can be leveraged for classifying various types of medication strengths. Multivariate modeling, such as PCA, uses multiple response variables as opposed to a univariate approach, which only uses one variable. The multivariate approach does not follow a specific signal response such as concentration in Beer’s Law. For this work, PCA modeling was conducted on the “fingerprint in the fingerprint” region to determine if the 5, 10, and 15 mg tablet strengths could be differentiated. The PCA results shown in Figure 6 show a clear separation between the three tablet strengths, yielding a definitive classification of tablet strengths in this region. For this study, 90 Raman spectra were collected for the PCA modeling where 30 Raman spectra were collected for each tablet strength (10 scans per tablet). The PCA loadings plot for PC-1 is also shown in Figure 6. The loadings plot indicates that the majority of the spectral variability used to separate the three strengths along PC-1 are from the API peaks at 1580 cm

. These peaks are mainly because of aromatic ring modes, C=O and C=C vibrations.

These results demonstrate the possibility of quantitative classification of pharmaceutical products using the conventional Raman techniques utilized in this work, and the importance of the “fingerprint in the fingerprint” for both qualitative and quantitative classifications. Although the results show definitive promise and potential for broad pharmaceutical utilization, traditional surface scattering Raman techniques do pose some limitations to accurate quantitative analyses such as content uniformity.

Mitigating some of these limitations, transmission Raman is a technique that has typically been used for quantitative analyses as opposed to conventional backscatter Raman. Transmission Raman requires limited or even no sample preparation, is nondestructive, and enables rapid analysis times (11). It can be applied during both the manufacturing and finished product stages. In this technique, a spectrometer measures a larger volume of the sample and the Raman signal is transmitted and diffused throughout the body of the sample (10). The transmittance through the sample results in a representative measurement and high efficiency (12).

Despite some of the limitations, these results clearly indicate that conventional FT-Raman spectroscopy could be utilized as a potential alternative for content uniformity testing typically performed by HPLC. Additionally, mathematical models such as partial least squares (PLS) regression can also be used to increase the accuracy of quantitative analyses like assay and content uniformity. However, PLS modeling is out of the scope of this article.

The aim of this study was to show the significance of a smaller spectral region of the Raman spectrum, defined as the “fingerprint in the fingerprint,” when evaluating pharmaceutical products. A spectral evaluation was conducted on a myriad of common excipients, APIs, and drug products. Qualitative and quantitative analysis was performed on these products to gain understanding of this spectral region. The qualitative results showed no Raman spectral signals in this region for any of the common excipients outlined in this article. Additionally, all APIs as well as drug products evaluated had unique spectral signal in this region. These data indicate that the 1550–1900 cm

 Raman spectral region is ideal and can be leveraged for API identity testing because there are no interferences from common inactive ingredients. Furthermore, the data indicate that this spectral region can also be used to differentiate and classify the content of API in different strengths of a given drug product, which was shown in the mathematical modelling. Holistically, this work has demonstrated the vast potential of Raman spectroscopy to be utilized for both qualitative and quantitative analyses in the pharmaceutical industry, as well as the importance of the ~1550–1900 cm

 spectral region, the “fingerprint in the fingerprint.”

The authors would like to thank Bristol Myers Squibb and their colleagues from the Global Quality Analytical Science & Technology Forensics team for their support.

(1) Peters, J.; Luczak, A.; Ganesh, V.; Park, E.; Kalyanaraman, R. Raman Spectral Fingerprinting For Biologics Counterfeit Drug Detection. 

(2) Griffen, J.; Owen, A.; Matousek. P. Comprehensive Quantification of Tablets with Multiple Active Pharmaceutical Ingredients Using Transmission Raman Spectroscopy--A Proof of Concept Study. 

(3) Mojica, E.; Zapata, J.; Vedad, J.; Desamero, R.; Dai. Z. Analysis of Over-the-Counter Drugs Using Raman Spectroscopy. 

(4) Kalantri, P.; Somani, R.; Makhija, D. Raman Spectroscopy: A Potential Technique in Analysis of Pharmaceuticals. 

(5) Handzo, B.; Luczak, A.; Huffman, S.; Peters, J.; Kalyanaraman, R. Benchtop and Portable Raman Spectrometers to Screen Counterfeit Drugs. 

(6) Xie, Y.; Cauchon, N. Raman Scattering as a Probe for properties of Active Pharmaceutical Ingredients in tablet Formulations. 

(7) “Pharmaceutical Excipients FT-Raman Library,” Thermo Scientific (Waltham, MD, 2007).

(8) Veij, M.; Vandenabeele, P.; Beer, T. Reference Database of Raman Spectra of Pharmaceutical Excipients. 

(9) “Raman Data & Analysis: Raman Spectroscopy for Analysis and Monitoring Application Note,” Horiba Scientific (Edison, NJ, 2021).

(10) Yang, D.; Frano, K.; J. Maticchio, J. Portable, High-Efficiency Transmission Raman Spectroscopy for At-Line Content Uniformity Testing of Pharmaceutical Tablets. 

(11) Andrews, D.; Geentjens, K.; Inge, B., et al. Analytical Method Development Using Transmission Raman Spectroscopy for Pharmaceutical Assays and Compliance with Regulatory Guidelines – Part I: Transmission Raman Spectroscopy and Method Development. 

(12) Li, Y.; Igne, B.; Drennen, J. K.; Anderson, C. A. Method Development and Validation for Pharmaceutical Tablets Analysis Using Transmission Raman Spectroscopy. 

(13) Kalyanaraman, R.; Dobler, G.; Ribick. M. Portable Spectrometers for Pharmaceutical Counterfeit Detection. 

(14) Abu Bakkar, M. et al. Raman Spectroscopy for the Qualitative and Quantitative Analysis of Solid Dosage Forms of Sitagliptin. 

 are with the Forensics and Innovative Technology (FIT) department at Bristol Myers Squibb in New Brunswick, New Jersey. Ravi Kalyanaraman will be chairing a session on Pharmaceutical Forensics at the upcoming 2022 SciX Conference in Cincinnati, Ohio (October 2–7). Direct correspondence to: 

The significance of the smaller spectral region of the Raman spectrum, defined as the “fingerprint in the fingerprint” region, cannot be overstated when it comes to active pharmaceutical ingredient identity testing (API).

Spectroscopy in Pharmaceutical Analysis

Spectroscopy and Spectroscopic Analytical Techniques in the United States Pharmacopeia–National Formulary (USP–NF)

Process Analytical Technology (PAT) Model Lifecycle Management

A Fingerprint in a Fingerprint: Raman Spectral Analysis of Pharmaceutical Ingredients