Cleaning analytical methods are used for the detection and quantification of process residues from cleaned equipment surfaces and are a vital part of any cleaning program. If a cleaning process leaves previous process residue on equipment, that residue poses a risk of cross-contamination and can adulterate product and have adverse consequences on patient health. As the control of cross-contamination is paramount, the reliability of cleaning sample results analyzed using these methods is critical and must be demonstrated through appropriate validation. Despite this, we often observe cleaning analytical methods incorrectly identified as assay methods based on the misconception that it is product being quantified rather than an impurity. In this memo, we will discuss how to validate cleaning analytical methods in alignment with regulatory guidances.
The amount of product residue that is considered safe to remain on equipment surfaces after cleaning must be below established cleaning limits. While the method may be quantifying product residue, after equipment has been cleaned any residue found on the equipment surfaces are now contaminants. Any method where we are trying to detect trace amounts of an analyte below a certain limit, is an impurity method.
To ensure these methods are suitable to detect contaminants from equipment surfaces, cleaning analytical methods must be fully validated by demonstrating the acceptability of all performance characteristics required for impurity methods. Performance characteristics are the attributes of an analytical method that can quantitatively define the reliability of results generated by that method. According to guidelines outlined by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) and United States Pharmacopeia (USP), the following performance characteristics must be demonstrated to validate an impurity method:
Accuracy
Precision
Specificity
Limit of Detection (LOD)
Limit of Quantitation (LOQ)
Linearity
Range
Accuracy refers to how close a measured result is to the true value. For instance, if our method gives a result of 99 when the true value is 100, it is accurate within 1%. However, an analytical method is only accurate within a specific range. Beyond this range, accurate measurements become difficult due to either too much or too little analyte.
The lower end of this range is the LOQ, below which, there isn't enough analyte to produce reliable measurements. For example, in UV spectroscopy, at low concentrations, there aren't enough molecules in solution to absorb the light passing through the cuvette, making it hard to distinguish the absorbance measurements from background noise. Conversely, at high concentrations, analyte molecules may "shade" each other, obstructing the light path and leading to inaccurate readings.
Testing measurement across the specified range is essential. If only one concentration is measured, there's no assurance that the method is accurate at different concentrations. Therefore, accuracy should be tested at a minimum of three concentrations within the upper and lower bounds of the specified range, such as at the LOQ, 100%, and 120% of the specification limit. This is crucial to ensure that the results, especially those near the limit, are accurate, confirming that the sampled surface doesn't require additional cleaning.
Precision refers to how closely individual test results align with each other, rather than how closely they align with the true value. For example, if our measurements are 50, 49, and 51, and the true value is 100, the method is considered precise, but not accurate. Precision has two key components:
Repeatability: This measures how consistently a method produces similar results under the same conditions over a short period. The degree of agreement is calculated by the percent relative standard deviation (RSD). In our example above, the measurements (50, 49, and 51) has an RSD of 2%, which based on the acceptance criteria specified in the protocol of no more than (NMT) 10%, indicates repeatability. Demonstrating repeatability in cleaning analytical methods is crucial to ensure that measurements of cleanliness remain consistent across different samples.
Intermediate Precision: This evaluates how consistent the results are when considering variations within a laboratory, such as different analysts, instruments, or days of analysis. For example, if analyst A measures 50, 50, and 51, and analyst B measures 49, 47, and 51, these results have a combined RSD of 3% and fall within our NMT 10% acceptance criteria. Intermediate precision ensures that any variations observed in cleaning samples are not due to these intra-laboratory factors but reflect actual conditions of cleanliness.
Specificity is the ability of a method to unequivocally assess the analyte in the presence of other components that are expected to be present in the sample, such as buffers or excipients. Depending on the stage of production (intermediate, API, finished product), product residues may be relatively pure or contain multiple excipients and even other products. The specificity of the method must be demonstrated against everything that is used in that process step. For example, we are formulating final product and our input materials are microcrystalline cellulose, lactose monohydrate, product A, and product B. The product A cleaning analytical method should demonstrate specificity against microcrystalline cellulose, lactose monohydrate, and product B. Showing the specificity of a method is imperative to have certainty that results are not impacted by other compounds in the sample. It should be noted that not all methods are specific, such as total organic carbon (TOC). In these cases, the demonstration of specificity is not required, but its exclusion should always be documented and justified in the validation protocol.
The Limit of Detection (LOD) is the lowest level of analyte that can be reliably detected, but not necessarily quantified. For cleaning analytical methods, this will be the determining factor between what is considered detected residue, and what may just be instrument noise. Similarly, the Limit of Quantitation (LOQ) is the lowest amount of analyte that can be measured with accuracy and precision. Essentially, the lowest amount of quantifiable residue.
Linearity is the ability of an analytical procedure to elicit test results that are directly proportional to the concentration of analyte in a sample. Essentially, samples with more analyte produce a higher signal response. Much like accuracy, the relationship between concentration and signal is only proportional within a defined range. This is why it is important to establish the linearity of our methods, to ensure that we have defined and are operating within concentrations that are measurable with suitable linearity.
The Range of an analytical procedure is the interval between the upper and lower levels of analyte that can be determined with suitable accuracy, precision, and linearity. The range of the method should include the LOQ and extend to no less than the specification limit of the product or residue (i.e., if our LOQ is 1 ppm, and our specification is no more than 10 ppm, the range of the method should measure from 1 ppm to at least 10 ppm). This is important to establish for cleaning methods as it will let us know which concentrations can reliably be measured with the method.
A cleaning analytical method is an impurity method and cannot be fully validated without demonstration of all required performance characteristics [1]. The validation of cleaning analytical methods is not just a regulatory requirement but a critical component to ensure the safety and efficacy of our products. Properly validated analytical methods provide confidence in the assessment of our equipment cleanliness, thereby protecting patients from potential contamination and yielding high quality products.
References
ICH. (2023). Q2 (R2). Validation of Analytical Procedures.
USP. (2017). Chapter <1225> Validation of Compendial Procedures.
USP. (2022). Chapter <857> Ultraviolet-Visible Spectroscopy
Contributors: Alec Fufidio, Joanna Joseph, and Jenna Carlson