Analytical Applications

On-line TOC analysis

Jonathan Yourkin, global pharmaceutical product manager GE Analytical Instruments (Boulder, CO)

Total organic carbon (TOC) is a critical water-quality attribute. Reliance on periodic laboratory grab samples taken from the process is comparatively inefficient and less reliable than using on-line TOC analyzers located at critical locations within the water-distribution system. An important element of implementing on-line TOC is ensuring that the process analyzers use an analytical method that is equivalent to or better than the established laboratory method. Recognizing that most compendial methods were never intended for, nor designed to qualify continuous-process analyzers, the US Food and Drug Administration's guidance on process analytical technology (PAT) suggests that users consider any process analyzer used for real-time data as an "alternate analytical method" (1). Simply stated, the user must establish the suitability of the process analyzer for the intended use through prescribed method-validation procedures. Beyond establishing the suitability of on-line TOC methods, several important process-validation elements should be considered for complying to current good manufacturing practices, including evaluating variables within a measurement system.

APOSTROPHE PRODUCTIONS, PHOTODISC, GETTY IMAGES

Evaluating variables in a measurement system. A key challenge in any shift to on-line TOC analysis is ensuring that the measurement system that measures an analyte on-line provides the same or better data quality as the laboratory-based measurement system. Often, analytical methods used to produce on-line and laboratory data are different. These challenges can be solved when all analytical methods used are essentially the same or the analytical capabilities of the laboratory-based and on-line TOC methods have proven to be like-for-like. Once method comparability is established, any variability observed between laboratory and on-line TOC results can be attributed to different sampling processes.

When comparing TOC data from similar samples analyzed by on-line and laboratory TOC instruments, observed variation can be attributed to two major sources: the sampling process used to introduce the sample to the measuring device and the inherent analytical capabilities of the analytical method. Given the ubiquitous presence of organics in the environment, grab-sample collection of TOC samples is subject to numerous error sources. With the on-line method, sampling-process variability is minimized or eliminated because the analyzer is physically integrated within the process-water stream for continuous quality-assurance monitoring. In addition, this evaluation quantifies sampling-process variation at TOC concentrations produced by the pharmaceutical water system, which are typically an order of magnitude lower than the TOC standard concentrations used to validate the analytical method. Sampling process variation is generally much more pronounced at these lower TOC concentrations.

Equivalency testing. Equivalency testing demonstrates the sameness of two measurement systems based upon the analytical results the methods produce. This type of testing differs from instrument-validation testing, which typically assesses various attributes using standards of known concentration. Validation protocols are not designed to identify differences in the test-sample data quality. Therefore, it is possible to validate two methods with the same protocols using the same criteria, yet have the methods yield nonequivalent analytical results under actual conditions of use. It is particularly important in on-line TOC implementations to augment validation protocols with equivalency testing. The fundamental hypothesis supporting the justification for measurment-system equivalency testing is that, given the sameness of on-line and laboratory analytical methods, any lack of equivalency observed between the two data sets can be attributed to aspects of the measurement system rather than the core analytical method.

This type of analysis is typically performed using a Gage Repeatability and Reproducibility (Gage R&R) study to quantify measurement-system variation relative to variation of the process as a whole. The Gage R&R addresses several components affecting variability, including the gauge (the instrument), the operator, and the sample itself. Ordinarily, a full-scale Gage R&R would be used to assess variability contributed by all components. This example uses a simplified Gage R&R approach based on the degree of sameness of the analytical methods and the unique differences in the operational or environmental conditions associated with laboratory and on-line measurements. By pairing the on-line and laboratory data collection, process water variability remains common between the two systems. In addition, the inherent accuracy of the instruments remains common, based on like-for-like methods that have been validated appropriately.

Figure 1 (TOC) is an example of a 30-day measurement-system equivalancy test protocol from a pharmaceutical company. In this protocol, the statistical parmameters of mean and standard deviation represent data-quality equivalency. The mean is determined from repeated measurements of the process. This average includes contributions from the pharmaceutical water itself, the sampling process, and the inherent accuracy of the measurement device. By pairing the on-line and laboratory data collection, the process water variability remains common between the two systems. In addition, the inherent accuracy of the instruments remains common, based on previous method validation results. The mean serves to quantify the added TOC contributions of the sampling process itself. Additionally, the standard deviation is used to assess the repeatability of the measurement system, taking into account the variation of the sampling process and the measurement device.

Figure 1 (TOC): A 30-day measurement-system equivalency test of off-line and on-line total organic carbon (TOC) analysis. (FIGURE 1 (TOC) IS COURTESY OF GE Analytical Instruments)

TOC section reference

1. FDA, Guidance for Industry: PAT (Rockville, MD, 2004).

Ultra-fast liquid chromatography

Simon Robinson, HPLC product manager, Shimadzu Scientific Instruments (Columbia, MD)

Over-the-counter cold medicines often contain multiple active ingredients. These actives include combinations of decongestants, antihistamines, pain relievers, cough suppressants, and expectorants in addition to numerous vitamins and herbal extracts—all of which exhibit different chemical properties. A wide range of analyte polarities often makes developing chromatographic methods challenging. Separating ingredients in a cold medicine is important to understand the content of each medication and product quality. A high-speed liquid chromatographic system (Prominence UFLCxr with a SPD-M20A photodiode array detector, Shimazdu Scientific Instruments) was used to analyzed 20 common cold medcines.

Methods. A standard mixture of cold-medicine ingredients was prepared (dissolving them in Mobile Phase A/acetonitrile = 1/1 (v/v), 100 mg/L, each) and analyzed using the conditions shown in Table I (UF LC). The mixture consisted of thiamine, acetaminophen, caffeine, riboflavin, hesperidin, ethenzamide, chlorpheniramine, ambroxol, noscapine, isopropamide, ispropylantipyrine, dextromethorphan, glycyrrhizin, bromhexine, clemastine, and ibruprofen. The contents of a capsule of cold medicine was dissolved in 100 mL of Mobile Phase A/acetonitrile = 1/1 (v/v), or in the case of tablet form, a single tablet was dissolved in 50 mL of the previously described mobile-phase acetonitrile solution. The resultant solution was passed through a 0.22 µm pore membrane filter and analyzed using the conditions in Table I (UFLC).

Figure 1 (UFLC): Results of five serial ultra-high-speed liquid chromatographic analyses. (FIGURE 1 (UFLC) IS COURTESY OF SHIMAZDU SCIENTIFIC INSTRUMENTS)

Results. Figure 1 (UFLC) shows the results of five serial ultra-high-speed analyses of the cold medicine. Using UFLC, the autosampler cycle time becomes a critical factor. The injection speed of the autosampler was 10 s, and the total time for all five analyses took no more than 5 min. This cycle time, in conjunction with a reduction in carryover, increased sample throughput. The run time of a single analysis was shortened, and the total cycle time of the injection sequence and run time was optimized.

Table I (UFLC): Analytical conditions. (TABLE 1 (UFLC) IS COURTESY OF SHIMAZDU SCIENTIFIC INSTRUMENTS)

Rapid microbial testing

Lori Daane, PhD, vice-president of Celsis Rapid Detection, and Judy Madden, vice-president of Celsis International (Chicago)

Microbiological tests may fall into several categories. Final product-release testing includes microbial limits testing of nonsterile drug products and sterility testing of sterile products. Environmental monitoring tests air, water, surfaces, and personnel for viable microorganisms. Bulk drug products, including active pharmaceutical ingredients and excipients, may be tested for bioburden (1). Rapid microbial screening is used to detect the presence or absence of microbial contamination within 24 h compared with 3–7 days using traditional microbial testing. For example, Celsis AKuScreen is a rapid microbial method that can detect contamination from bacteria, yeast, or mold definitively within 24 h.

ATP bioluminescence. Rapid microbial methods can be based on adenosine triphosphate (ATP) bioluminescence, which is commonly known as the firefly luciferase reaction as follows, where AMP is adenosine monophosphate and PPi is inorganic phosophate (2):

ATP drives the reaction, which emits light that is measured in a luminometer. ATP is present in all living cells. If microbial contamination is present, the reaction moves forward, and light is emitted. Because definitive detection is based on having a sufficient quantity of microbial ATP present to generate a light signal that is distinguishable from sample background, detection becomes more difficult if the sample contains nonmicrobial ATP. Enzyme and molecular-based technologies, such as adenylate kinase (AK) or AK-amplified assays (i.e., Celsis AKuScreen, Celsis), allow bacteria to be detected in 18 h and yeast and mold in 24 h. The AKuScreen assay uses microbial adenylate kinase (AK) to generate ATP in a reaction that is not constrained by the finite amount of metabolic ATP available in the standard bioluminescence assay. The generation or amplification of ATP beyond that inherent in the assayed sample is accomplished via the enzyme-catalyzed and reversible reaction as follows:

The AK reaction uses adenosine diphosphate (ADP) and microbial AK to catalyze the production of ATP. The produced ATP is detected and measured using the traditional firefly luciferase reaction. The longer the AK reaction is allowed to proceed in the presence of an adequate supply of ADP, the more ATP is generated and the greater the bioluminescence signal and the resulting assay sensitivity. Assuming the ratio of AK and ATP in a typical bacterium and a reaction turnover number of 40,000, approximately 40 times as much ATP as originally represented by microbial ATP can by generated per minute by the AK reaction (2, 3). Similarly, if the reaction is allowed to continue for 25 min, the amount of ATP can be 1000 times more than what was originally present, allowing the test-enrichment period and time-to-result to be reduced.

Case study : in-process sterility testing. Because of the ubiquity of AK in living organisms, AK-amplified assays can detect bacteria, molds, and yeasts, and this microbial testing is used in sterility testing to determine product release (4, 5). A company was testing its sterile, in-process materials using a 14-day sterility test. A comparability test was performed to see if equivalent results could be obtained using a rapid method. Spiking studies were conducted by inoculating samples at a level approximating 1 colony-forming unit (CFU). Following inoculation, the samples were enriched for 24 h, and contamination was detected using a rapid microbial system (Celsis AKuScreen). Microbial hold times and the corresponding time for quarantined inventory was 48 h, a savings of 12 days over traditional microbial testing.

Case study: mold detection. Pharmaceutical companies use microbial specifications that are based on United States Pharmacopeia, European Pharmacopoeia, and Japanese Pharmacopoeia guidelines, product history, and risk profile. With respect to mold, this typically means the ability to detect Aspergillus brasiliensis. Slow-growing, molds are often the rate-determining step for product release. AK-amplified assays (e.g., Celsis AKuScreen) can detect yeast and mold in 24 h compared with 48 h if standard bioluminescence is used or 4–7 days if using traditional methods.

One company used standard ATP screening placed microbial specifications on certain products to include the detection of Penicillum expansum. The company modified its bioluminescence protocol using a rapid microbial system. A study was performed on a water-based lotion, examining how broth type, incubation temperature, and conditions affect A. brasiliensis and P. expansum detection using standard bioluminescence and AK-amplified reageants (e.g., Celsis AKuScreen). Preliminary studies indicated that static incubation at room temperature optimized the growth of P. expansum. Table 1 (rapid microbial testing) summarizes the study data in which the samples were inoculated with < 50 spores of P. expansum and incubated statically at room temperature (24–27 °C). The modifications of room temperature incubation and broth changes to letheen or malt extract broth (MEB) enabled detection with the AKuScreen reagents within 24 h. Detection with standard bioluminescence required 48 h using MEB and 72 h in letheen under the same incubation conditions.

Table I (rapid microbial testing): Detection times for slow-growing mold.* (TABLE 1 (RAPID MICROBIAL TESTING) IS COURTESY OF CELSIS)

Rapid microbial testing section references

1. B.S. Riley, Am. Pharm. Rev, 7 (2), 28–31 (2004).

2. D.J. Squirrel et al., Anal. Chim. Acta457 (1), 109–114 (2002).

3. D.J. Squirrel and M.J. Murphy, "A Practical Guide to Industrial Uses of ATP-Luminescence" in Rapid Microbiology (Cara Technology, Lingfield, Surrey, UK, 1997).

4. L. Noda, Adenylate Kinase: The Enzymes (Vol. 8) ( Academic Press, New York, 1973), p. 279–305.

5. G.E. Shultz, Cold Spring Harbor Symposia on Quantitative Biology, 52 , 429–439 (1987).

6. L. Dane, "Study Results: Modifications to Detect P. expansum in 24 Hours Using AKuScreen Reagents" (Celsis, Chicago, 2006).

DSC–Raman spectroscopy

Kevin P. Menard, PhD, business manager of thermal analysis at PerkinElmer (Shelton, CT)

Differential scanning calorimetry (DSC) and Raman spectroscopy are well-known techniques in pharmaceutical analysis. DSC determines glass transitions, amorphous content, melting temperatures, and enthalpy and estimates the degree of crystallinity. In its fast-scanning mode, DSC also suppresses kinetic changes (i.e., polymorphism and decompositions). This allows measurements to be made before kinetic changes can occur, allowing one to determine the initial polymorphic form as well as measure melting and heat capacity on materials before they decompose. Raman spectroscopy is able to detect changes in chemical composition as well as positional and stereochemical changes in a sample; this allows it to identify specific polymorphic states. Raman spectroscopy, however, has some difficulties in measuring temperature-dependent reactions such as dehydrations and polymorphic rearrangements. These reactions are sensitive to the temperature applied, and care must be taken so that the energy added by the Raman's laser does not affect the data by causing changes in the sample temperature (1). A combination of dual furnace (i.g., power-compensated) DSC and a shuttered laser in an Eschelle Raman spectrometer were found to give minimal increases in bulk sample temperatures (2).

As a hyphenated technique, however, DSC–Raman spectroscopy works well in detecting the changes in polymorphic materials as a function of temperature (3). For example, running a sample of acetaminophen in heating showed a series of peaks corresponding to changes in polymorphic forms, but the changes in the material corresponding to those thermal events are inferred (2). For example, an endothermic peak in the DSC themogram can be a melt, a water loss, or a polymorphic change. Raman spectroscopy allows one to confirm what the transition is; the chemical and structural changes detected from the Raman spectra clarify the DSC data. Because the DSC can performed under a wide range of thermal conditions, including heating and cooling rates from 0.1 to 750 ° C/min, the combined techinques can be used to characterize the materials' kinetic behavior under a wide variety of conditions. For example, crystallization processes can be studied using DSC–Raman during cooling experiments. Other work has been reported on hydrates and pseudo polymorphs as measuring the reaction of materials by tracking changes in assigned bands in the Raman spectra and the energetic changes (4, 5).

DSC–Raman spectroscopy section references

1. R. Alexander et al., Proceedings of the North American Thermal Analysis Society (NATAS) Annual Conf. (Atlanta, 2008), pp. 131–136.

2. A Dennis, K. Menard, and R. Spragg, Proceedings of the Annual Technical Conf. of the Society of Plastic Engineers (Chicago, 2009), pp 647–653.

3. K. Menard et al., Am. Lab. 42 (1), 21–23 (2010).

4. N. Redman et al., Furey, , Proceedings of NATAS Annual Conf. (Albuquerque, NM, 2003), pp. 120–129;

5. A. Bigalow-Kern et al., J. ASTM,2 (7), 42–61 (2005).