A Risk-Management Approach to Cleaning-Assay Validation 3

Results and discussion
In this study, a single analyst evaluated the analytical swab recovery from a representative set of surfaces found in the CTM manufacturing and packaging areas. The surfaces were manufactured specifically for this study to have a broad range of Ras. In addition to Ra, the effect of the material of construction, acceptance limit, compound, and method variability also were evaluated. Based upon these data sets, the authors used a strategy involving three groups of materials to represent all of the surfaces in CTM operations. Merck and Co. used a similar strategy to establish five recovery groups (9). The authors expanded on Merck's strategy by adding a detailed study supporting the groups and an approach for determining the appropriate placement of new surfaces into pre-established groups.
Roughness average (Ra). The Ra targets listed above were difficult to achieve. The intermediate Ra values were significantly lower than the target values given in the design of experiments section above. Both intermediate Ra values, initially targeted for 75 and 125 Ra, were measured to be approximately 40 μin. Although the machining process at each level yielded visually different surfaces, the measured Ra changed little from surface to surface. The authors decided to proceed with the surfaces and define smooth surfaces as Ra < 100 μin. and rough surfaces as Ra > 100 μin. This approach allowed for an assessment of the anticipated relationship between Ra and analytical recovery.

Figure 2

The Ra had little impact on the observed analytical-swab recovery, but the recovery was expected to improve with lower Ras. Figure 2 shows roughness grouped by surfaces that had a measured Ra > 100 μin. and by surfaces that had a Ra < 100 μin. Only 5- and 50-μg spikes are represented in Figure 2; the variability in the 0.5-μg spikes confused the interpretation of the data slightly, but is consistent. As Figure 2 shows, the recovery within each roughness group was approximately the same for a given analyte on a given material and did not correlate to Ra. Therefore, Ra should not be used as a predictor of analytical recovery or as a grouping criterion.

Figure 3

Material of construction. Because Ra was eliminated as a factor contributing to recovery losses, the authors performed data analysis by combining all average recovery values and assessing the effect of the material of construction. The data in Figure 3 were first separated by API, and groups were generated to represent the logical separations in recovery. Figures 3(a–c) contain the data for the 0.5-μg spikes, the 5-μg spikes, and the 50-μg spikes, respectively. The data from the 0.5- and 5-μg spikes exhibited a trend similar to that of the 50-μg spikes. The variability in the results increased as the spiked amount decreased, and the 50-μg spike results were substantially less than that of the other spike levels. For both compounds, the Type III hard anodized aluminum exhibited the poorest recovery. The next logical break point grouped bronze and cast iron. The recovery of Compound B from bronze suggested that the material was representative of Group 1. The recovery of Compound A on bronze was lower and more variable, however, so the authors placed bronze into Group 2. For the majority of the surfaces, the recovery of Compound A was lower than that for Compound B at a given limit. In some cases, the recovery was approximately the same (i.e., of 5- and 50-μg spikes on cast iron, and of the 50-μg spike on Type III hard anodized aluminum). In addition, the predominant trend was that the average recovery of a compound increased as the spiked amount increased on a given material of construction. For example, the recovery of Compound B from stainless steel 316L was approximately 74%, 90%, and 95% at 0.5-μg, 5-μg, and 50-μg swabs, respectively.

Figure 4

Ra was originally considered a variable in the experiments previously outlined and did not affect swab recovery. To understand the surface attributes that might contribute to incomplete recovery for the different materials of construction, the authors acquired SEM images for Group 1, Group 2, and Group 3 surfaces (see Figure 4). Stainless steel is a relatively smooth surface with some striations from machining (see Figure 4a). Cast iron has a pitted surface that could provide opportunities for an API in solution to be trapped during a spiking experiment (see Figure 4b). The anodization process makes Type III hard anodized aluminum, the worst recovery surface, porous, thereby creating the greatest opportunity to lose analyte (see Figure 4c). Note that polymers were grouped together with metals and might not be considered to be similar on first pass. The SEM image of Lexan in Figure 4d, however, illustrated that the polymer surface was smooth, albeit with some surface debris, which prevented the loss of analyte. The polymer surface was grouped with stainless steel in Group 1. The SEM images were good supporting evidence that the groupings were logical based upon surface characteristics.

Table II: Grouping of material surface of construction.

Table II is based on the data shown in Figure 3. The top surface in Table II represents the surface that was validated for recovery in each group. This recovery value represented all others within a given group. The groupings were supplied to the CTM areas, and the group number was included on the swab submission to the analytical laboratory so that the correct recovery factor was applied to each surface. In addition, the table served as a tool for engineering to determine whether newly purchased equipment contained a new product-contact surface.

A Risk-Management Approach to Cleaning-Assay Validation 2

Design of experiments
Several variables (i.e., roughness average, material of construction, active ingredient, and spiked amount) were evaluated in a randomized fashion to prevent systematic bias that could be introduced by going from the lowest to the highest acceptance limit, from the smoothest to the roughest surface, or from one material of construction to the next. The initial design of experiments included two active pharmaceutical ingredients (APIs), three spiked acceptance-limit levels (i.e., 0.5, 5.0, and 50 μg/swab), seven surface types, four target roughness averages (Ra < 25, 75, 125, and 150 μin.), and six replicates per surface. These Ras were targeted to evaluate whether surface recovery depended on the surface Ra. Coupons were divided into a group of polymers [i.e., Lexan (polycarbonate), acetal (Polyoxymethylene), and PTFE] and a group of metals (i.e., stainless steel 316L, bronze, Type III hard-anodized aluminum, and cast iron). These surfaces were chosen to represent a cross section of surfaces found in the CTM manufacturing and packaging areas and required 1008 swab determinations to complete the study. The remaining product-contact surfaces found in the clinical-trial manufacturing and packaging areas were evaluated according to the initial design of experiments. These surfaces included nickel, anodized aluminum, Rilsan (polyamide), Oilon (blended-oil nylon), and stainless steel 316L with a 4 × 4-in. area.
The authors chose two APIs for this evaluation on the basis of their solubility profiles to represent the most- and least-soluble compounds a company would likely manufacture. Compound A, the less soluble, is slightly soluble in methanol and insoluble across the pH range, but Compound B is soluble in all solvents. In addition, Eli Lilly (Indianapolis, IN) identified Compound A as one of the most difficult compounds to clean from equipment, based on its low solubility and staining properties. A control (i.e., stainless steel 316L, 0.5 μg/swab, Compound A) was run each day that data were generated.
Equipment and operating conditions

Table I: High-performance liquid chromatography (HPLC) operating conditions.

The authors used an Agilent 1100 high-performance liquid chromatography (HPLC) analyzer (Agilent, Santa Clara, CA) for all experiments. The HPLC operating conditions were validated according to ICH standards for precision, linearity, limit of detection (LOD), limit of quantitation (LOQ) and specificity (see Table I) (8). Precision was 1.85% and 3.13% for Compounds A and B, respectively, and was determined at 0.025 μg/mL (i.e., 25% of the lowest spike). The method was linear across the equivalent range of 0.5 μg/swab to 5 μg/swab (R = 0.999). The LOQ was calculated to be 0.005 μg/mL for Compound A and 0.008 μg/mL for Compound B. The LOD was calculated to be 0.001 μg/mL for Compound A and 0.0024 μg/mL for Compound B. Swabs and solvents did not result in interfering peaks. The authors performed swabbing consistently using Texwipe Alpha large swabs (ITW Texwipe, Kernersville, NC). First, 10 vertical swipes, then 10 horizontal swipes were performed for the 2 × 2-in. surfaces. For the 4 × 4-in. surfaces, 20 swipes were executed in each direction. Methanol was used as the swabbing solvent. Spike amounts were 0.5, 5, and 50 μg per surface and were extracted into 5 mL of mobile phase, which corresponded to 0.1-, 1.0-, and 10-μg/mL standard concentrations, respectively. The authors used a Quanta FEG 200F field-emission scanning electron microscope (SEM, FEI, Hillsboro, OR) to generate the surface images.

A Risk-Management Approach to Cleaning-Assay Validation 1

By: Brian W. Pack, Jeffrey D. Hofer
Cleaning validation and verification are based on the premise of risk management. Several regulatory and guidance documents make this clear. The International Conference on Harmonization's (ICH) guideline on risk management outlines several approaches to making and documenting risk-based decisions (1). It clearly states that risk management should be based on scientific knowledge and that personnel should evaluate the effect of potential failures on the patient. In addition, it notes that the levels of effort, formality (e.g., use of tools), and documentation of the quality risk-management process should be commensurate with the level of risk.
The US Code of Federal Regulations states that equipment and utensils shall be cleaned, maintained, and sanitized at appropriate intervals to prevent malfunctions or contamination that would alter the safety, identity, strength, quality, or purity of the drug product (2). In accordance with 21 CFR 211.67, ICH issued recommendations on equipment maintenance and cleaning (Q7A, Sections 5.20–5.26) for compliance and safety that include similar, but more detailed requirements (3).
The US Food and Drug Administration's 1993 guidance on cleaning inspections states that for a swab method, recovery should be established from the surface (4). The guidance contains no specific requirements about how to establish these recovery estimates, or the acceptance limits. It is up to the manufacturer to document the cleaning rationale (i.e., process and acceptance limits) for maintaining the quality and purity of the drug product being manufactured.
Cleaning validation and verificationCleaning verification consists of routine monitoring (e.g., swab analysis) of equipment-cleaning processes. Cleaning validation confirms the effectiveness and consistency of a cleaning procedure and eliminates the need for routine testing (5). For example, cleaning limits are established to determine the maximum allowance of Product A that can carry over to Product B. The calculation of these limits is well documented and includes factors that increase the margin of safety to protect the patient (6, 7). Because it is not feasible to swab every square inch of the equipment, swabbing locations are chosen based upon factors such as how difficult the area is to clean, the size of the equipment, and the areas where product buildup is likely. All product-contact surfaces must be considered during cleaning verification to demonstrate that equipment is clean, and a recovery value is expected to be established for each product-contact surface during method validation. The recovery is used to correct the submitted swab result for incomplete removal from the surface and to compare it with the acceptance limit. This last aspect of risk management (i.e., establishing the surface recovery) is the focus of this article.

Figure 1: (ALL FIGURES ARE COURTESY OF THE AUTHORS)

Analysts have many ways to establish the swab-recovery value for a particular product-contact surface. Stainless steel is the most common material in a manufacturing environment (see Figure 1). Some companies therefore establish a recovery value for stainless steel and apply that standard to all swab submissions. Other companies attempt to establish a recovery value for each product-contact surface for every compound. From an analytical standpoint, supporting this activity becomes arduous, if not impossible to sustain. For example, equipment in a clinical-trial materials (CTM) manufacturing area is used for many compounds in the company's portfolio. New equipment might have different product-contact surfaces. Each compound in the portfolio manufactured on a new piece of equipment would require a method revalidation to add a recovery factor for the new product-contact surface. As the number of materials of construction increases, the difficulty of sustaining that approach also increases. Grouping materials of construction for analytical-method development in support of cleaning verification and validation activities is an excellent opportunity to apply a quality risk-management approach, especially when the total product-contact surface area is considered. Stainless steel accounts for approximately 95% of the surface area in a CTM manufacturing and packaging environment. Other product-contact surfaces account for only 5% of the total surface area. When polymer surfaces are considered in a CTM packaging environment, the number of minor product-contact surfaces can grow significantly. A risk-management approach allows the majority of the time and effort to be spent on activities that ensure the cleanliness of the stainless-steel area while identifying, analyzing, evaluating, and communicating the risks associated with the small fraction of remaining surfaces. This strategy does not ignore the surfaces other than stainless steel, but divides them into three recovery groups to support analytical-method validation. By choosing representative recovery surfaces for those nonstainless-steel materials, the effort proportionally addresses the risk.

The benefits of risk-based analyses during system validation

By: Peter Woods, David BealePharmaceutical Technology Europe

Don Farrall/Getty Images

Pharmaceutical manufacturers often depend on third parties for the supply of automated systems and other manufacturing equipment. Manufacturers must demonstrate to regulatory agencies that their production processes — including any third-party elements and systems — meet the necessary standards. As a consequence, any third-party systems built to purpose must comply with documented procedures embodied in, for example, GAMP 5 and ASTM E-2500.^1,2The approach to system validation defined in GAMP guidelines can be symbolized by a model that was originally created for software development: the V model. The V model demonstrates the relationships between each phase of the development life cycle and its associated phase of testing. The model deploys a well-structured method in which each phase can be implemented by the detailed documentation of the previous phase. Using this model and its associated documentation can control risk during system validation, which is the main focus of this article.
The V model

Figure 1: The V model.

Figure 1 illustrates the V model. The left-hand arm represents the succession of specification documents. This begins with the user requirement specifications (URS) against which a functional requirement specification can be generated. In turn, a set of implementation specifications can be defined that embody how a specific solution will be engineered. The system provider is usually responsible for producing and maintaining these documents, including the URS, which will have been reviewed and agreed by both parties. Against each level on the left-hand side of the V, there is a corresponding test specification on the right arm. As the elements of the solution are implemented and brought together, the tests demonstrate that the intended functions are achieved and that the end-user requirements are satisfied.
To the test

The author says...

The testing process includes installation qualification (IQ), to demonstrate that the installed system is complete, correctly configured and has the right services supplied to it; and operational qualification (OQ), which ensures that the installed system functions safely and to specification as a subsystem. Occasionally, factors may vary from when they are qualified; for example, ambient temperature. In tropical or semitropical locations, the indoor temperature can soar late in the day once the air conditioning switches off, allowing, for example, embedded computers to overheat and causing the system to suddenly fail. In this situation, the system provider is best placed to define the IQ and OQ test protocols because these require detailed knowledge of system behaviour and the service requirements. For a substantive system, the provider should supply a building and services specification that enables the site to be properly prepared before installation. By contrast, the performance qualification (PQ), which assesses compliance with the user process requirements, must be the responsibility of the user.
Where the third-party element is a standard product, the purpose of the validation is to confirm that the system meets process needs, and that the supplied version is installed and working as intended. In these instances, the documentation can be standardized; for example, the OQ test protocol could be a standard document for the product and the IQ could be a checklist generated from a generic template. The manufacturer may also request copies of generic or type approval documentation.
Automated systems, however, must be designed for purpose, and the project to supply the final system must adhere to necessary standards and generate all documentation, including system-specific specifications and test protocols. A system provider that is already familiar with these standards can add a great deal of value in helping to draw up these documents.

HPLC Method Development and Validation for Pharmaceutical Analysis

By: Ghulam A. ShabirPharmaceutical Technology Europe

The wide variety of equipment, columns, eluent and operational parameters involved makes high performance liquid chromatography (HPLC) method development seem complex. The process is influenced by the nature of the analytes and generally follows the following steps:

• step 1 - selection of the HPLC method and initial system
• step 2 - selection of initial conditions
• step 3 - selectivity optimization
• step 4 - system optimization
• step 5 - method validation.

Figure 1: A flow diagram of an HPLC system.

Depending on the overall requirements and nature of the sample and analytes, some of these steps will not be necessary during HPLC analysis. For example, a satisfactory separation may be found during step 2, thus steps 3 and 4 may not be required. The extent to which method validation (step 5) is investigated will depend on the use of the end analysis; for example, a method required for quality control will require more validation than one developed for a one-off analysis. The following must be considered when developing an HPLC method:

• keep it simple
• try the most common columns and stationary phases first
• thoroughly investigate binary mobile phases before going on to ternary
• think of the factors that are likely to be significant in achieving the desired resolution.

Mobile phase composition, for example, is the most powerful way of optimizing selectivity whereas temperature has a minor effect and would only achieve small selectivity changes. pH will only significantly affect the retention of weak acids and bases. A flow diagram of an HPLC system is illustrated in Figure 1.

Table I: HPLC detector comparison.

HPLC method development Step 1 - selection of the HPLC method and initial system. When developing an HPLC method, the first step is always to consult the literature to ascertain whether the separation has been previously performed and if so, under what conditions - this will save time doing unnecessary experimental work. When selecting an HPLC system, it must have a high probability of actually being able to analyse the sample; for example, if the sample includes polar analytes then reverse phase HPLC would offer both adequate retention and resolution, whereas normal phase HPLC would be much less feasible. Consideration must be given to the following: Sample preparation. Does the sample require dissolution, filtration, extraction, preconcentration or clean up? Is chemical derivatization required to assist detection sensitivity or selectivity?
Types of chromatography. Reverse phase is the choice for the majority of samples, but if acidic or basic analytes are present then reverse phase ion suppression (for weak acids or bases) or reverse phase ion pairing (for strong acids or bases) should be used. The stationary phase should be C₁₈ bonded. For low/medium polarity analytes, normal phase HPLC is a potential candidate, particularly if the separation of isomers is required. Cyano-bonded phases are easier to work with than plain silica for normal phase separations. For inorganic anion/cation analysis, ion exchange chromatography is best. Size exclusion chromatography would normally be considered for analysing high molecular weight compounds (.2000).

Table II: The basic types of analytes used in HPLC.

Gradient HPLC. This is only a requirement for complex samples with a large number of components (.20–30) because the maximum number of peaks that can be resolved with a given resolution is much higher than in isocratic HPLC. This is a result of the constant peak width that is observed in gradient HPLC (in isocratic HPLC peak width increases in proportion to retention time). The method can also be used for samples containing analytes with a wide range of retentivities that would, under isocratic conditions, provide chromatograms with capacity factors outside of the normally acceptable range of 0.5–15. Gradient HPLC will also give greater sensitivity, particularly for analytes with longer retention times, because of the more constant peak width (for a given peak area, peak height is inversely proportional to peak width). Reverse phase gradient HPLC is commonly used in peptide and small protein analysis using an acetonitrile–water mobile phase containing 1% trifluoroethanoic acid. Gradient HPLC is an excellent method for initial sample analysis.
Column dimensions. For most samples (unless they are very complex), short columns (10–15 cm) are recommended to reduce method development time. Such columns afford shorter retention and equilibration times. A flow rate of 1-1.5 mL/min should be used initially. Packing particle size should be 3 or 5 μm.
Detectors. Consideration must be given to the following:

• Do the analytes have chromophores to enable UV detection?
• Is more selective/sensitive detection required (Table I)?
• What detection limits are necessary?
• Will the sample require chemical derivatization to enhance detectability and/or improve the chromatography?

Fluorescence or electrochemical detectors should be used for trace analysis. For preparative HPLC, refractive index is preferred because it can handle high concentrations without overloading the detector.
UV wavelength. For the greatest sensitivity λ_max should be used, which detects all sample components that contain chromophores. UV wavelengths below 200 nm should be avoided because detector noise increases in this region. Higher wavelengths give greater selectivity.
Fluorescence wavelength. The excitation wavelength locates the excitation maximum; that is, the wavelength that gives the maximum emission intensity. The excitation is set to the maximum value then the emission is scanned to locate the emission intensity. Selection of the initial system could, therefore, be based on assessment of the nature of sample and analytes together with literature data, experience, expert system software and empirical approaches.

Table III: HPLC optimization parameters.

Step 2 - selection of initial conditions. This step determines the optimum conditions to adequately retain all analytes; that is, ensures no analyte has a capacity factor of less than 0.5 (poor retention could result in peak overlapping) and no analyte has a capacity factor greater than 10–15 (excessive retention leads to long analysis time and broad peaks with poor detectability). Selection of the following is then required. Mobile phase solvent strength. The solvent strength is a measure of its ability to pull analytes from the column. It is generally controlled by the concentration of the solvent with the highest strength; for example, in reverse phase HPLC with aqueous mobile phases, the strong solvent would be the organic modifier; in normal phase HPLC, it would be the most polar one. The aim is to find the correct concentration of the strong solvent. With many samples, there will be a range of solvent strengths that can be used within the aforementioned capacity limits. Other factors (such as pH and the presence of ion pairing reagents) may also affect the overall retention of analytes.
Gradient HPLC. With samples containing a large number of analytes (.20–30) or with a wide range of analyte retentivities, gradient elution will be necessary to avoid excessive retention.
Determination of initial conditions. The recommended method involves performing two gradient runs differing only in the run time. A binary system based on either acetonitrile/water (or aqueous buffer) or methanol/water (or aqueous buffer) should be used.

Figure 2: The chemical structure of progesterone and Figure 3: Amount injected versus peak area of progesterone standard to demonstrate linearity.

Step 3 - selectivity optimization. The aim of this step is to achieve adequate selectivity (peak spacing). The mobile phase and stationary phase compositions need to be taken into account. To minimize the number of trial chromatograms involved, only the parameters that are likely to have a significant effect on selectivity in the optimization must be examined. To select these, the nature of the analytes must be considered. For this, it is useful to categorize analytes into a few basic types (Table II). Once the analyte types are identified, the relevant optimization parameters may be selected (Table III). Note that the optimization of mobile phase parameters is always considered first as this is much easier and convenient than stationary phase optimization.
Selectivity optimization in gradient HPLC. Initially, gradient conditions should be optimized using a binary system based on either acetonitrile/water (or aqueous buffer) or methanol/water (or aqueous buffer). If there is a serious lack of selectivity, a different organic modifier should be considered.
Step 4 - system parameter optimization. This is used to find the desired balance between resolution and analysis time after satisfactory selectivity has been achieved. The parameters involved include column dimensions, column-packing particle size and flow rate. These parameters may be changed without affecting capacity factors or selectivity.

Table IV: Accuracy/recovery of progesterone from samples of known concentration.

Step 5 - method validation. Proper validation of analytical methods is important for pharmaceutical analysis when ensurance of the continuing efficacy and safety of each batch manufactured relies solely on the determination of quality. The ability to control this quality is dependent upon the ability of the analytical methods, as applied under well-defined conditions and at an established level of sensitivity, to give a reliable demonstration of all deviation from target criteria. Analytical method validation is now required by regulatory authorities for marketing authorizations and guidelines have been published. It is important to isolate analytical method validation from the selection and development of the method. Method selection is the first step in establishing an analytical method and consideration must be given to what is to be measured, and with what accuracy and precision.
Method development and validation can be simultaneous, but they are two different processes, both downstream of method selection. Analytical methods used in quality control should ensure an acceptable degree of confidence that results of the analyses of raw materials, excipients, intermediates, bulk products or finished products are viable. Before a test procedure is validated, the criteria to be used must be determined.
Analytical methods should be used within good manufacturing practice (GMP) and good laboratory practice (GLP) environments, and must be developed using the protocols set out in the International Conference on Harmonization (ICH) guidelines (Q2A and Q2B).^1,2 The US Food and Drug Administration (FDA)^3,4 and US Pharmacopoeia (USP)⁵ both refer to ICH guidelines. The most widely applied validation characteristics are accuracy, precision (repeatability and intermediate precision), specificity, detection limit, quantitation limit, linearity, range, robustness and stability of analytical solutions. Method validation must have a written and approved protocol prior to use.⁶

Equation 1 and Figure 4: HPLC chromatograms of (a) progesterone reference standard; (b) separation of progesterone gel sample; (c) placebo formulation.

This article reviews and demonstrates practical approaches to analytical method validation with reference to an HPLC assay of progesterone (Figure 2) in a gel formulation. Progesterone is widely used for dysfunctional uterine bleeding or amenorrhoea,^7,8 for contraception (either alone or with, for example, oestradiol or mestranol in oral contraceptives) and in combination with oestrogens for hormone replacement therapy in postmenopausal women.^9,10Experimental Chemicals and reagents All chemicals and reagents were of the highest purity. HPLC-grade methanol was obtained from Merck (Darmstadt, Germany). Progesterone reference standard was purchased from Sigma Chemicals (St Louis, Missouri, USA). Deionized distilled water was used throughout the experiments.
HPLC instrumentation The HPLC systems used for the validation studies consisted of Series 200 UV/Visible Detector, Series 200 LC Pump, Series 200 Autosampler and Series 200 Peltier LC Column Oven (all Perkin Elmer, Boston, Massachusetts, USA). The data were acquired via TotalChrom Workstation (Version 6.2.0) data acquisition software (Perkin Elmer), using Nelson Series 600 LINK interfaces (Perkin Elmer).
All chromatographic experiments were performed in the isocratic mode. The mobile phase was a methanol/water solution (75:25 v/v). The flow rate was 1.5 mL/min and the oven temperature was 40 ºC. The injection volume was 20 μL and the detection wavelength was set at 254 nm. The chromatographic separation was on a 25034.6 mm ID, 10 μm C₁₈ μ-Bondapak column (Waters, Milford, Massachusetts, USA).

Table V: Demonstration of the repeatability of the HPLC assay for progesterone.

Results and discussionLinearity and range The linearity of a test procedure is its ability (within a given range) to produce results that are directly proportional to the concentration of analyte in the sample. The range is the interval between the upper and lower levels of the analyte that have been determined with precision, accuracy and linearity using the method as written. ICH guidelines specify a minimum of five concentration levels, along with certain minimum specified ranges. For assay, the minimum specified range is 80–120% of the theoretical content of active. Acceptability of linearity data is often judged by examining the correlation coefficient and y-intercept of the linear regression line for the response versus concentration plot. The regression coefficient (r²) is .0.998 and is generally considered as evidence of acceptable fit of the data (Figure 3) to the regression line. The per cent relative standard deviation (RSD), intercept and slope should be calculated. In the present study, linearity was studied in the concentration range 0.025–0.15 mg/mL (25–150% of the theoretical concentration in the test preparation, n=3) and the following regression equation was found by plotting the peak area (y) versus the progesterone concentration (x) expressed in mg/mL: y53007.2x14250.1 (r²51.000). The demonstration coefficient (r²) obtained for the regression line demonstrates the excellent relationship between peak area and concentration of progesterone. The analyte response is linear across 80-120% of the target progesterone concentration.

Table VI: Demonstration of the intermediate precision of the HPLC assay results for progesterone.

Accuracy A method is said to be accurate if it gives the correct numerical answer for the analyte. The method should be able to determine whether the material in question conforms to its specification (for example, it should be able to supply the exact amount of substance present). However, the exact amount present is unknown, which is why a test method is used to estimate the accuracy. Furthermore, it is rare that the results of several replicate tests all give the same answer, so the mean or average value is taken as the estimate of the accurate answer. Some analysts adopt a more practical attitude to accuracy, which is expressed in terms of error. The absolute error is the difference between the observed and the expected concentrations of the analyte. Percentage accuracy can be defined in terms of the percentage difference between the expected and the observed concentrations (Equation 1).
Percentage accuracy tends to be lower at the lower end of the calibration curve. The term accuracy is usually applied to quantitative methods but it may also be applied to methods such as limit tests. Accuracy is usually determined by measuring a known amount of standard material under a variety of conditions but preferably in the formulation, bulk material or intermediate product to ensure that other components do not interfere with the analytical method. For assay methods, spiked samples are prepared in triplicate at three levels across a range of 50-150% of the target concentration. The per cent recovery should then be calculated. The accuracy criterion for an assay method is that the mean recovery will be 100±2% at each concentration across the range of 80-120% of the target concentration. To document accuracy, ICH guidelines regarding methodology recommend collecting data from a minimum of nine determinations across a minimum of three concentration levels covering the specified range (for example, three concentrations, three replicates each).
In the present study, the accuracy of the method was evaluated by recovery assay, adding known amounts of progesterone reference standard to a known amount of gel formulation, to obtain three different levels (50, 100 and 150%) of addition. The samples were analysed, and mean recovery and %RSDs calculated. The data presented in Table IV show that the recovery of progesterone in spiked samples met the evaluation criterion for accuracy (100±2.0% across 80–120% of target concentrations).
Specificity Developing a separation method for HPLC involves demonstrating specificity, which is the ability of the method to accurately measure the analyte response in the presence of all potential sample components. The response of the analyte in test mixtures containing the analyte and all potential sample components (placebo formulation, synthesis intermediates, excipients, degradation products and process impurities) is compared with the response of a solution containing only the analyte. Other potential sample components are generated by exposing the analyte to stress conditions sufficient to degrade it to 80–90% purity. For bulk pharmaceuticals, stress conditions such as heat (50–60 ºC), light (600 FC of UV), acid (0.1 M HCl), base (0.1 M NaOH) and oxidant (3% H₂O₂) are typical. For formulated products, heat, light and humidity (70-80% RH) are often used. The resulting mixtures are then analysed, and the analyte peak is evaluated for peak purity and resolution from the nearest eluting peak.












Once acceptable resolution is obtained for the analyte and potential sample components, the chromatographic parameters, such as column type, mobile phase composition, flow rate and detection mode, are considered set. An example of specificity criterion for an assay method is that the analyte peak will have baseline chromatographic resolution of at least 2.0 from all other sample components. In this study, a weight of sample placebo equivalent to the amount present in a sample solution preparation was injected to demonstrate the absence of interference with progesterone elution (Figure 4).
Precision Precision means that all measurements of an analyte should be very close together. All quantitative results should be of high precision - there should be no more than a ±2% variation in the assay system. A useful criterion is the relative standard deviation (RSD) or coefficient of variation (CV), which is an indication of the imprecision of the system (Equation 2).
According to the ICH,² precision should be performed at two different levels - repeatability and intermediate precision. Repeatability is an indication of how easy it is for an operator in a laboratory to obtain the same result for the same batch of material using the same method at different times using the same equipment and reagents. It should be determined from a minimum of nine determinations covering the specified range of the procedure (for example, three levels, three repetitions each) or from a minimum of six determinations at 100% of the test or target concentration.
Intermediate precision results from variations such as different days, analysts and equipment. In determining intermediate precision, experimental design should be employed so that the effects (if any) of the individual variables can be monitored. Precision criteria for an assay method are that the instrument precision and the intra-assay precision (RSD) will be ≤2%.
In this study, the precision of the method (repeatability) was investigated by performing six determinations of the same batch of product. The resulting data are provided in Table V, which show that the repeatability precision obtained by one operator in one laboratory was 0.28% RSD for progesterone peak area and, therefore, meets the evaluation criterion.

Table VII: Stability results of progesterone samples and standard solutions (n53).

The intermediate precision was demonstrated by two analysts, using two HPLC systems and who evaluated the relative per cent purity data across the two HPLC systems at three concentration levels (50%, 100%, 150%) that covered the assay method range (0.025–0.15 mg/mL). The mean and RSD across the systems and analysts were calculated from the individual relative per cent purity mean values at 50%, 100% and 150% of the test concentration. The data are presented in Table VI, and show ≤2.0% RSD, therefore, meeting the evaluation criterion. Limits of detection and quantitation The limit of detection (LOD) is defined as the lowest concentration of an analyte in a sample that can be detected, not quantified. It is expressed as a concentration at a specified signal:noise ratio,² usually 3:1. The limit of quantitation (LOQ) is defined as the lowest concentration of an analyte in a sample that can be determined with acceptable precision and accuracy under the stated operational conditions of the method. The ICH has recommended a signal:noise ratio 10:1. LOD and LOQ may also be calculated based on the standard deviation of the response (SD) and the slope of the calibration curve(s) at levels approximating the LOD according to the formulae: LOD53.3(SD/S) and LOQ510(SD/S).
The standard deviation of the response can be determined based on the standard deviation of the blank, on the residual standard deviation of the regression line, or the standard deviation of y-intercepts of regression lines. The method used to determine LOD and LOQ should be documented and supported, and an appropriate number of samples should be analysed at the limit to validate the level. In this study, the LOD was determined to be 10 ng/mL with a signal:noise ratio of 2.9. The LOQ was 20 ng/mL with a signal:noise ratio of 10.2. The RSD for six injections of the LOQ solution was ≤2%.
Analytical solution stability Validation of sample and standard solution preparation may be divided into sections, each of which can be validated. These include extraction; recovery efficiency; dilution process when appropriate; and addition of internal standards when appropriate. Although extraction processes do not actually affect the measuring stage they are of critical importance to the analytical test method as a whole. The extraction process must be able to recover the analyte from the product; it must not lose (for example, by oxidation or hydrolysis) any of the analyte in subsequent stages, and must produce extraction replicates with high precision. For example, during analysis of an ester prodrug the extraction process involves the use of strongly alkaline or acid solutions, it may cause some of the prodrug to be hydrolysed and, therefore, give false results.
Reference substances should be prepared so that they do not lose any of their potency. Thus it is necessary to validate that the method will give reliable reference solutions that have not been deactivated by weighing so little that an error is produced; adsorption onto containers; decomposition by light; and decomposition by the solvent. If the reference is to be made up from a stock solution then it must be validated that the stock solution does not degrade during storage. Reagent preparation should be validated to ensure that the method is reliable and will not give rise to incorrect solutions, concentrations and pH values.
Samples and standards should be tested during a period of at least 24 h (depending on intended use), and component quantitation should be determined by comparison with freshly prepared standards. For the assay method, the sample solutions, standard solutions and HPLC mobile phase should be stable for 24 h under defined storage conditions. Acceptable stability is ≤2% change in standard or sample response, relative to freshly prepared standards. The mobile phase is considered to have acceptable stability if aged mobile phase produces equivalent chromatography (capacity factors, resolution or tailing factor) and the assay results are within 2% of the value obtained with fresh mobile phase.
In the present study, the stabilities of progesterone sample and standard solutions were investigated. Test solutions of progesterone were prepared and chromatographed initially and after 24 h. The stability of progesterone and the mobile phase were calculated by comparing area response and area per cent of two standards with time. Standard and sample solutions stored in a capped volumetric flask on a lab bench under normal lighting conditions for 24 h were shown to be stable with no significant change in progesterone concentration during this period (Table VII).
Robustness Robustness measures the capacity of an analytical method to remain unaffected by small but deliberate variations in method parameters. It also provides some indication of the reliability of an analytical method during normal usage. Parameters that should be investigated are per cent organic content in the mobile phase or gradient ramp; pH of the mobile phase; buffer concentration; temperature; and injection volume. These parameters may be evaluated one factor at a time or simultaneously as part of a factorial experiment. The chromatography obtained for a sample containing representative impurities when using modified parameter(s) should be compared with the chromatography obtained using the target parameters.
Conclusion Method development involves a series of sample steps; based on what is known about the sample, a column and detector are chosen; the sample is dissolved, extracted, purified and filtered as required; an eluent survey (isocratic or gradient) is run; the type of final separation (isocratic or gradient) is determined from the survey; preliminary conditions are determined for the final separation; retention efficiency and selectivity are optimized as required for the purpose of the separation (quantitative, qualitative or preparation); the method is validated using ICH guidelines. The validated method and data can then be documented.
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