Analytical Applications

Published on: 
Pharmaceutical Technology, Pharmaceutical Technology-11-01-2012, Volume 2012 Supplement, Issue 6

Developing analytical methods and performing related testing is crucial for ensuring the quality of a pharmaceutical product.

Sharanya Reddy, PhD, application scientist with PerkinElmer, describes the use of time-of-flight–mass spectrometry for impurity analysis of an over-the-counter drug product. Robert Mattes, application scientist with Foss NIRSystems, and Stephen Hoag, professor, and Ravikanth Kona, PhD candidate, both in the Department of Pharmaceutical Sciences, University of Maryland, explain the use of in-line moisture analysis in a laboratory-scale fluid-bed dryer using diode array near-infrared spectroscopy. Jeffrey P. Kiplinger, president, Paul M. Lefebvre, director of laboratory operations, Michael J. Rego, staff scientist, and John H. Tipping, staff scientist, all with Averica Discovery Services, explain a chiral biotransformation analysis using supercritical fluid chromatography–mass spectrometry.

Impurity analysis with time-of-flight mass spectrometry

Sharanya Reddy, PhD, application scientist, PerkinElmer

Time-of-flight (TOF) mass spectrometry (MS) provides high resolution and exact mass information over a wide mass range to identify unknown compounds. This case study describes a workflow to identify impurities in over-the-counter (OTC) drugs using proprietary technology (TrapPulse, PerkinElmer) of the AxION 2 TOF mass spectrometer (PerkinElmer). In conventional orthogonal TOF instruments, the ions are lost between pulses. In the TrapPulse mode operation of the AxION 2 TOF mass spectrometer, the ions are collected as packets before they are pulsed, which results in signal enhancement. Analysis in the TrapPulse mode prevents potential "wraparound" of higher mass ions into subsequent spectra, thereby eliminating spurious peaks in the spectrum and potentially incorrect mass assignments. Using this technology, the author improved detection limits and identified 10 impurities in OTC melatonin, including previously uncharacterized ones.

The AxION 2 TOF mass spectrometer also provides mass accuracy and isotope ratio-profile accuracy, both of which are required for elemental composition identification of unknowns. The author predicted the elemental composition of several unknown compounds in melatonin using information provided by the TOF in conjunction with elemental composition matching software (AxION EC ID, PerkinElmer). Accurate monoisotopic mass and isotope ratio information is used by the AxION EC ID software to search against databases, such as the PubChem database (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) or other databases for potential molecular formula matches. The software provides a ranked summary of the potential matches and suggestions for possible compound structures for a given elemental composition.

Materials and methods. The following materials and conditions were applied for liquid chromatographic (LC) analysis:

  • LC system: Flexar FX-10 high-performance LC

  • LC column: PerkinElmer Brownlee Supra column C18 (1.9 μm, 2.1 × 50 mm)

  • Column temperature: 25 °C

  • Flow rate: 0.4 mL/min

  • Mobile Phase A: water with 0.1% formic acid

  • Mobile Phase B: acetonitrile with 0.1% formic acid

  • Injection volume: 2 μL

  • Gradient: 20% B to 40% B over 5 min, to 70% B over 2 min.

The following were applied for mass spectrometric analysis:

  • Mass spectrometer: PerkinElmer AxION 2 TOF

  • Ionization source: PerkinElmer Ultraspray 2 (Dual electrospray ionization [ESI]source)

  • Ionization mode: ESI positive mode

  • Capillary exit voltage: + 100 V

  • Pulse mode: mass-to-charge ratio (m/z) 100–700

  • TrapPulse mode: m/z 100–700.

Sample preparation. An OTC melatonin tablet containing 1 mg of melatonin was crushed by a mortar and pestle and dissolved in 10 mL of water. The mixture was vortexed and centrifuged at 6000 rpm for 10 min. The supernatant was collected and injected on the column.

Figure 1 (TOF MS): (a) Analysis of melatonin in conventional pulse mode; (b) Analysis of melatonin in TrapPulse (PerkinElmer) mode. (FIGURES 1–3 (TOF–MS) ARE COURTESY OF THE AUTHOR (REDDY))

Results and discussion. Initial analysis in traditional pulse-mode operation showed few impurities in the melatonin tablet present at a low signal-to-noise ratio (S/N) (see Figure 1a [TOF MS]). When the analysis was performed in TrapPulse mode, the increase in S/N was almost fivefold to sevenfold higher, which resulted in the identification of 10 different impurities in melatonin (see Peaks A through J, see Figure 1b [TOF MS]). The comparison highlights the advantage of analyzing the sample in TrapPulse mode versus pulse mode.

Figure 2 (TOF MS): (a) Base peak ion chromatogram (BIC) (mass-to-charge ratio (m/z) of 265.132 ± 0.050 Da) shows the elution position of the Impurity Isomers A and D in a melatonin tablet. (b) BIC (m/z of 265.132 ± 0.050 Da) shows the co-elution of N1-acetyl-N2-formyl-5-methoxykynuramine (AFMK) with Peak D when spiked into a melatonin tablet extract. (c) Spectrum of Peak A Impurity. The accurate mass of [M + H]+ and the fragments of Peak A are within 2.0 ppm of theoretical value. (d) The mass spectrum of Peak D. The accurate mass of [M + H]+ and the fragments are within 2.5 ppm of theoretical value. (FIGURES 1–3 (TOF–MS) ARE COURTESY OF THE AUTHOR (REDDY))

Using accurate mass, isotope-profile information, and EC ID software, the author was able to identify all unknown drug impurities. The workflow used for identification of unknowns with the example of the drug impurities labeled Peak A and D are highlighted (see Figure 2 [TOF MS]). Both Peaks A and D (see Figure 2a [TOF MS]) have identical accurate masses, but have different spectra and elution times, which suggest that they are most likely isomers (see Figures 2c and 2d [TOF MS]). The elemental composition predicted by the EC ID software is C13H16N2O4 (see Figure 3 [TOF MS]). In vivo studies with melatonin have shown a minor metabolite, N1-acetyl-N2-formyl-5-methoxykynuramine (AFMK), to be produced, which has an elemental composition and exact mass identical to Peak D. For these reasons, the author suggests that the structure of Peak D to be AFMK. The structure of Peak D was confirmed by analyzing a synthesized standard of AFMK, which matched both the retention time and accurate mass spectrum of Peak D (see Figures 2a, 2b, and 2d[TOF MS]). Using a similar approach, the author identified the remaining impurities Peaks B to J as summarized in Table I(TOF MS).

Figure 3 (TOF MS): Software (AxION EC ID, PerkinElmer) lists the elemental composition of C13H16N2O4 with the top score for the accurate mass 265.1188 and the observed relative isotope abundances. (FIGURES 1–3 (TOF–MS) ARE COURTESY OF THE AUTHOR (REDDY))

Conclusion. Several impurities in OTC tablets of melatonin that were not visible in the conventional pulse mode were identified using the AxION 2 TOF in TrapPulse mode. High mass accuracy and accurate isotope-profile ratio information provided by the TOF and the elemental composition matching software (AxION EC ID) facilitated the identification of several unknown drug impurities.

Table I (TOF MS): Structures of impurities identified by time-of-flight mass spectrometer (AxION, PerkinElmer).

Near-infrared spectroscopy

Robert Mattes, application scientist, FOSS NIRSystems. Stephen Hoag, professor, and Ravikanth Kona, PhD candidate, both in the Department of Pharmaceutical Sciences, University of Maryland

Top-spray granulation in a fluid-bed dryer is a common method of increasing particle size to increase flow characteristics and API content homogeneity. After spraying the liquid into the formulation and forming the granule, the product must be dried to the proper moisture level. If the granules are overdried or underdried, damage to the formulation may occur, thereby causing problems with subsequent processing and problems with product stability during storage (1). Samples are typically withdrawn from the fluid bed with a thief during processing and analyzed off line in a laboratory for moisture content. Commonly, there is a delay before analysis results are available to the operator, which results in processing decisions being made without optimal product-moisture information. Top-spray granulation end point is often based on time or product temperature and not actual moisture content.

Near-infrared spectroscopy (NIRS) is a rapid, nondestructive technique for in-process analysis of moisture in a manufacturing environment (2). Real-time measurements are made with no sample preparation, and data can be analyzed and stored automatically. NIRS fits in well with FDA's process analytical technology initiative (3). Using NIRS, the process can be monitored for low levels of residual moisture and other process constituents to yield better process control and end-point determination (4). Laboratory-scale fluid-bed dryers are often used in research at the university level and in process development to better understand formulation processing. This study shows the use of NIRS for monitoring residual moisture in laboratory-scale equipment.

Methods and materials. The NIR instrument used to collect spectra was the ProFoss Diode Array (FOSS NIRSystems). Spectra were collected in the reflectance mode from 1100 nm to 1650 nm with 0.5-nm data intervals, and 32 scans were co-added to produce a single spectrum. A fluid-bed probe, specifically for fluid-bed dryer applications, was inserted into a fluid-air granulator at a 45 ° angle to the central axis of the product container as seen in Figure 1 (NIRS). The collection "spoon" and purge vents are located on the probe tip (see Figure 2 [NIRS]). After each NIR spectrum was collected, the software sent a "data complete" signal that energized an air purge exiting through the ports in the probe, thereby clearing the "spoon" for a new sample. The insert in Figure 2 (NIRS) shows the probe with the sample collected.

Figure 1 (NIRS): The fluid-bed dryer with the near infrared probe inserted at a 45° angle. Also shown is the black purge line on the left and the sample thief on the right. (FIGURES 1–5 (NIRS) ARE COURTESY OF THE AUTHORS (MATTES ET AL.))

A charge of lactose monohydrate (Pharmatose 110M, DMV-Fonterra Excipients) and microcrystalline cellulose (Avicel PH 102, FMC) was prepared and loaded into the product container. The product was fluidized for 5 min to blend and dry the mixture to homogeneity. An aqueous solution of 10% polyvinyl pyrrolidone (Kollidon K30, BASF) was added by top spray. NIR spectra were collected every 50 s during the blending operation. Samples for loss on drying (LOD) analysis were withdrawn with the sample thief at approximately 5-min intervals to be later correlated with spectra acquired at the same time.

Figure 2 (NIRS): The specially designed spoon probe with purge ports. The insert shows sample collected on the probe. (FIGURES 1–5 (NIRS) ARE COURTESY OF THE AUTHORS (MATTES ET AL.))

Results and discussion. Figure 3 (NIRS) shows the second derivative of the sample spectra. The second-derivative mathematical treatment is commonly used in NIR spectroscopy to minimize baseline offset caused by scattering and to enhance absorbance peaks. Due to the second derivative treatment, the moisture increases downward in this region. Water absorbs strongly in the NIR between 1400 and 1450 nm as evidenced by the peaks in that region.

Figure 3 (NIRS): Second derivative mathematically treated dryer spectra. (FIGURES 1–5 (NIRS) ARE COURTESY OF THE AUTHORS (MATTES ET AL.))

A two-factor partial-least-squares regression model was developed with spectra from a calibration run and LOD reference values. The second derivative intensity over the range 1100–1650 nm was used to develop a prediction model with an R2 value of 0.9519 and a standard error of calibration of 0.7358%. Figure 4 (NIRS) shows a calibration plot of NIR predicted versus LOD % moisture.

Figure 4 (NIRS): Scatter plot of the near infrared (NIR) predicted values versus the loss-on-drying (LOD) values. (FIGURES 1–5 (NIRS) ARE COURTESY OF THE AUTHORS (MATTES ET AL.))

Figure 5 (NIRS) is a typical analysis output trend chart showing the moisture decrease during the drying cycle. The operator is aided with real-time graphical output such as this in making the decision to end the drying operation before the product is damaged or degraded. The delay caused by waiting for laboratory results before the product can be released for subsequent processing can be minimized or eliminated. Output from the NIR computer is used by the fluid-bed dryer's programmable logic controller for closed-loop process-control decisions. The correct NIR probe must be placed in the product container in a manner that provides sufficient sample contact with the probe-tip window. Correct probe design and proper placement in process equipment is of high importance (4).

Figure 5 (NIRS): Trend plot of moisture on subsequent run. (FIGURES 1–5 (NIRS) ARE COURTESY OF THE AUTHORS (MATTES ET AL.))

References (NIRS)

1. A.G. Rogers, "Granulation and Drying Principles," Hands-on Postgraduate Course in Tablet Technology, Univ. Tennessee (Memphis, 2003).

2. R.A. Mattes et al., "Process Analytical Technology" supplement to Pharm. Technol. 28 (9), s17–s20 (2004).

3. A.M. Afnan, J. Proc. Anal. Technol.1 (1), 8–9 (2004).

4. R.A. Mattes, D.E. Root, and A.P. Birkmire, "The Role of Spectroscopy in Process Analytical Technologies" special issue to Spectrosc. 20 (1), 14–17 (2005).

Supercritical fluid chromatography–mass spectrometry

Jeffrey P. Kiplinger, president, Paul M. Lefebvre, director of laboratory operations, Michael J. Rego, staff scientist, and John H. Tipping, staff scientist, Averica Discovery Services

Supercritical fluid chromatography (SFC) is a well-characterized technology for analytical and preparative chiral resolution (1). It is useful for small- to mid-scale production of single enantiomers for pharmaceutical discovery, where competitive assays of enantiomers can help validate mechanisms and improve the precision of lead-compound assessment (2). Current FDA guidance speaks to the desirability of comparing enantiomers early in pharmaceutical R&D (3). SFC, however, has seen limited use in areas dominated by highly selective high-performance liquid chromatography assays due to perceptions of low sensitivity, interfacing difficulties with detectors such as mass spectrometers, and incompatibility with hydrophilic analytes and matrices. The authors present an example in which chiral SFC–mass spectrometry (MS) is shown to be an expedient analytical approach to solving a bioanalysis problem.

Materials and methods. A compound developed in a pharmaceutical lead-optimization project as a racemic mixture was separated into constituent Enantiomers A and B using SFC with ultraviolet (UV) detection of 230 nm. The enantiomers were tested competitively in rats, and plasma was drawn from the animals for pharmacokinetic assays. In the course of the efficacy study, unexpected off-target effects were observed, and the team suspected in vivo racemization. Unfortunately, insufficient compound remained for further in vivo work. Only residual samples in storage vials of a mixture of the two enantiomers (not racemic, simply a mixture generated for testing) and of the active Enantiomer A were available.

Because an SFC method for rapid separation of the enantiomers had already been developed, a rapid SFC survey of the plasma samples for enantiomeric excess was desired. Unfortunately, detection by UV absorbance is problematic with plasma extracts due to interferences. Extensive sample preparation was undesirable because a low recovery might jeopardize detection in the analytical SFC systems used. SFC with selective MS detection was therefore attempted on crude extracts from the remaining rat-plasma samples.

The residual samples of the mixtures of Enantiomers A and B and Enantiomer A were used as standards, and plasma samples were treated only by deproteinization with acetonitrile and centrifugation prior to analysis. The standards, estimated at approximately 100 μg, were taken up in 1.0 mL of acetonitrile for analysis by SFC–MS. Injection volumes were 10 μL. For sample preparation, 450 μL acetonitrile were added to 150 μL of plasma (containing 1–5 mg/mL drug, as estimated by liquid chromatography–mass spectrometry–mass spectrometry. The tubes were sonicated and centrifuged, and 550 μL of supernatant were removed. The samples were dried under nitrogen and reconstituted with 500 μL of acetonitrile for analysis by SFC–MS. Injection volumes for samples were 50 μL.

The SFC analytical method (see Figures 1-3[SFC–MS) used a 4.6 × 100 mm RegisPack 5μm Kromasil column (Regis Technologies) using a 5-min isocratic elution with 60% carbon dioxide, 40% cosolvent (methanol:isopropyl alcohol (1:1) w/ 0.1% isopropylamine) at 4.0 mL/min.

Figure 1 (SFC–MS): Analysis of a mixture of the enantiomers by a supercritical fluid chromatographic–mass spectrometric method. (FIGURES 1–3 (SFC–MS) ARE COURTESY OF THE AUTHORS (KIPLINGER ET AL.))

The custom SFC–MS interface in the laboratory used in this study split the flow from the SFC system 20:1 immediately after the system's backpressure regulator, and a makeup solvent (methanol: water (1:1) 0.5% formic acid, 1.0 mL/min) that facilitated electrospray ionization was added post split. The mass spectrometer (ZQ mass spectrometer, Waters) operated in a positive electrospray ionization mode using a selected ion monitoring mode at a mass-to-charge ratio (m/z) of 330.

Figure 2 (SFC–MS): (a) Comparison of the supercritical fluid chromatography (SFC)–ultraviolet (UV) (320 nm) and (b) SFC–mass spectrometric method (mass-to-charge ratio of 330) using the plasma sample and the same SFC method. The UV signal at this wavelength, selected for noninterference by other components in plasma, is below the detection limit. (FIGURES 1–3 (SFC–MS) ARE COURTESY OF THE AUTHORS (KIPLINGER ET AL.))

Results and discussion. Standard samples of the mixture of Enantiomers A and B (see Figure 1 [SFC–MS]) and of the active Enantiomer A (not shown) were analyzed using the previously developed resolution method to define enantiomer retention times and to verify detection by MS. Sequential injections of the racemate and Enantiomer A indicated that carryover of Enantiomer B (and thus presumably A) was negligible. Figure 2 (SFC–MS) compares detection of the compound in rat plasma at 1.85 min by MS and UV detection at 320 nm. As predicted, with UV detection the signal is below the limit of detection. Nine plasma samples were analyzed using this method and detection methodology. Figure 3 (SFC–MS) shows that the drug did not racemize to a detectable degree in the in vivo study. SFC–MS, used with a chiral separation method identical to the one used to produce the tested enantiomers proved useful for studying their potential racemization in pharmacokinetic studies.

Figure 3 (SFC–MS): The lack of appearance of the other enantiomer in this plasma sample chromatogram indicates no in vivo racemization is occurring. (FIGURES 1–3 (SFC–MS) ARE COURTESY OF THE AUTHORS (KIPLINGER ET AL.))

References (SFC–MS)

1. M. Venturea et al., J. Chromatogr. A 1036 (1), 7–13 (2004).

2. J.D. Pinkston et al., Anal. Chem. 78 (21), 7467–7472 (2006).

3. FDA, Guidance for Industry: Drugs: Development of New Stereoisomeric Drugs (Rockville, MD, April 2011).