Because of advances in the fundamental understanding of gene therapy treatments and the 2017 regulatory approval of a recombinant adeno-associated viral therapy (rAAV), interest in developing rAAV-based therapies has increased. However, for patients worldwide to truly benefit from these therapies, significant improvements in the efficacy and manufacturing efficiency of rAAV are essential. We have recently seen substantial increases of inquiries regarding liquid chromatography (LC) and liquid chromatography–mass spectrometry (LC–MS) methods for the characterization and analysis of rAAV from scientists who are supporting advancements in product development. This edition of “Column Watch” highlights selected LC and LC–MS methods that can be applied to the analysis of these complex products.

Gene therapy involves altering of the genome of selected cells within a patient (1). Although CRISPR/Cas9 has received widespread attention because of the groundbreaking work done by Emmanuelle Charpentier and Jennifer A. Doudna that resulted in their receiving the 2020 Nobel Prize in Chemistry, there are a number of other gene therapy methodologies currently being studied and developed (1).

One such approach is the in vivo delivery of a genetic payload to specific patient cells through the use of a non-replicating virus (viral vector). In this approach, the primary functions of the virus capsid are to shield the encapsulated RNA or DNA from enzymatic degradation and to deliver it to the interior of the cell. The predominant virus types adopted for therapeutic development are recombinant adeno-associated virus (rAAV), lentivirus, as well as adenovirus. In this article, we focus on liquid chromatography (LC) and liquid chromatography–mass spectrometry (LC–MS) characterization and analysis of rAAV, which continues to be the most widely used viral gene therapy platform (2). This focused perspective is predicated on the wide use of rAAV and our direct experience with rAAV samples.

Before considering analytical approaches, it is not only useful to study the nature of rAAV structure, but to also understand the general manufacturing process. A rAAV consists of a ~25 nm diameter icosahedral protein shell or capsid that is composed of three different viral proteins (VP1, VP2, and VP3) arranged in pentameric substructures and in its entirety each capsid contains 60 VP proteins in a 1:1:10 proportion of VP1:VP2:VP3 (3). The VP proteins are highly homologous, where VP2 (~65 kDa) is composed of the entire amino acid sequence of VP3 (~60 kDa) with an N-terminal extension, and VP1 (~80 kDa) is an N-terminal extension of VP2. The entire rAAV protein capsid shell has a molecular weight of approximately 3.7 MDa. Although also homologous, the varied serotypes of AAV are defined by variation in their capsid protein primary structures. It is these differences that can dictate viral tropism resulting in a gene therapy more selectively targeting the desired cells of a patient. The single-stranded DNA (ssDNA) of therapeutic rAAV is located in the interior of the capsid structure and is composed of only the therapeutic gene and sequence regions that are required for the expression of the protein encoded by the therapeutic gene. Because of its size, the natural ssDNA packaging limit of rAAV is restricted to 4.7 kilobases (kb) with a molecular weight of approximately 1.55 MDa and extending that limit has been shown to be problematic (4). With the protein and ssDNA the total molecular weight of rAAV is approximately 4.3 MDa.

The rAAV production process can be quite varied and is a highly active area of study. A common production method is to transiently transfect a suspended host cell culture with plasmids that, when incorporated into the host cell, are designed to result in the host cell producing the rAAV capsid containing the desired therapeutic gene as ssDNA. In this approach, the plasmids used for the transient transfection may be considered critical reagents in the manufacturing process. As a result, these plasmid preparations may need to be well characterized and controlled (4). The harvesting and purification of rAAV generally includes cell lysis, clarification, and purification. Purification steps include a variety of extraction, immunoaffinity, chromatographic, and filtration steps (5). The influences that such a broad range of cell culture and purification steps can have on therapeutic rAAV quality are an important consideration when developing a structural characterization and product testing strategy.

In addition to the complexity of the rAAV structure, there are several additional challenges that are presented by rAAV samples compared to typical recombinant protein analyses. The low productivity of the rAAV manufacturing process results in limited sample quantities, and although this may not be a major issue for the final commercialized product, it can be very problematic when assessing process development samples where often only microgram quantities of protein and ssDNA components of rAAV are available to the analyst. Another challenge of rAAV samples is their low concentrations. As examples, a formerly approved intramuscular rAAV therapy was formulated at a concentration of 3 X 10

 vector genomes per milliliter (vg/mL) and the concentration of a current ocular rAAV therapy is 5 x 10

 vg/mL. This translates into protein concentrations of 20 μg/mL and 3 μg/mL, and ssDNA concentrations (assuming 4.7 kb ssDNA) of 13 μg/mL and 2 μg/mL, respectively. To add further complication, VP1 and VP2 each account for 8% of the total number of proteins, resulting in single to submicrogram levels of these two proteins in the formulated samples.

Although an in-depth review on all of the methods that may be included as part of rAAV product characterization and analysis is well beyond the scope of this article, we highlight several LC and LC–MS methods that we have applied to characterize rAAV, as well as some related studies. Included in this column are the anion- exchange chromatography (AEC) and size-exclusion chromatography (SEC) analyses of intact virus, the LC–MS analysis of intact VP proteins, and peptide mapping of VP proteins.

Anion-Exchange Chromatography for Intact Capsid Analysis


During the rAAV biomanufacturing process, empty capsids that do not contain the desired ssDNA are also produced. Without a consistent ssDNA payload, the efficacy and safety of rAAV therapies may be compromised (6). Several techniques have been used to quantify empty capsids in AAV samples, including analytical ultracentrifugation (AUC), static spectrophotometry, cryogenic electron microscopy (Cryo-EM) and charge detection MS (CDMS) (7–10). However, various challenges are presented by these methods including the cost of instrumentation, the expertise needed, the sample amounts required, low sample throughput, and susceptibility to interferences. As a result, the development of analytical methods to measure empty capsid levels remains an active area of research.

AEC was reported to have the potential of separating empty and full capsids based on differences in surface charge (9, 11,12). It was also shown that compared to UV absorbance detection, fluorescence detection not only increases signal-to-noise ratio, but also has much less response bias between empty and full capsids, which is in part due to minimal intrinsic fluorescence of DNA (2). Figure 1 shows the optimized separation of AAV8 empty and full capsids using 5 μm non-porous strong anion-exchange particles with a quaternary ammonium ligand. It was also observed (data not shown) that AEC retention varied by rAAV serotype indicating that optimized method conditions are serotype dependent. In addition, the percentage of empty capsids in a sample can be determined once the relative intrinsic protein fluorescence response between the empty and full capsids is determined.

In the chromatographic overlay, we also observe that for the 98.5% empty sample (as confirmed by CDMS, data not shown) that a substantial portion of the empty capsids were coeluted with the full capsid fraction (13). This is presumed to be because of the charge heterogeneity of the empty capsid proteins or the presence of oligonucleotide fragments on the empty capsid surface (14). These results indicate that an accurate assessment of partially full AAV8 capsid may not be possible to obtain via AEC. Despite this potential limitation, AEC may provide a useful approach to guide the process and product development and monitor product quality because of its simplicity, low sample demand, and precision.

SEC for Aggregate, Fragment, and Concentration Analysis

The levels of virus aggregation may be considered to be a critical quality attribute (CQA) in therapeutic rAAV preparations (4,15). SEC is most often employed for the analysis of aggregation and fragmentation of proteins such as IgG, due in large part to the precision and sample throughput of the analysis.

There are limited reports of its use for the analysis of rAAV; in contrast, the routine use of dynamic light scattering (DLS) has been more reported (4,16,17). It is important to recognize that the upper analyte size limitation for SEC separations is approximately 100 to 200 nm in diameter, which is in the subvisible range (18). Above these limits, resolution will be low, and the analyte may be altered because of on-column shear forces, or trapped by the column. Therefore, these subvisible entities in rAAV preparations are analyzed using DLS, nanoparticle tracking analysis (NTA), and asymmetric flow field-flow fractionation (AF4) methods, among others (18,19).

As previously noted, rAAV has a total protein and ssDNA molecular weight of 4.3 MDa. However, the compact icosahedral structure has a diameter of only 25 nm (20). By comparison, IgG protein has a reported maxi- mum lateral dimension of approximately 15 nm and its dimer and lower valency high molecular weight forms (HMW) are well resolved on a column packed with 200 Å pore-size diol-bonded silica-hybrid SEC particles (21). Therefore, it was predicted that a high efficiency 450 Å pore-size version of that column should provide the required separation of rAAV HMW forms ranging in valency from dimer to tetramer. In addition to an increase in precision, SEC analysis can also provide information on the capsid fragments and other proteins in rAAV preparations in comparison to other methods.

Figure 2 shows the optimized non-denaturing SEC separations for several rAAV- CMV-GFP serotypes containing ssDNA with CMV as the promoter sequence and green fluorescent protein (GFP) as the insert gene (22). To accommodate the low concentration of the rAAV samples, a detector monitoring intrinsic protein fluorescence was deployed (23). Fluorescence provides approximately 10-fold greater signal-to-noise in comparison to UV absorbance at 280 nm or 260 nm. Alternatively, the addition of a multiangle light scattering (MALS) detector to the SEC analysis of rAAV has also been shown to provide enhanced sensitivity for virus aggregates and in addition can provide accurate information on the capsid concentration and an estimate of empty capsid levels (17). Rapid estimates of these two sample characteristics can also be derived from an SEC separation using an UV absorbance detector or a fluorescence and UV absorbance detector in series (24,25).


Interrogation of proteins that compose the capsid of rAAV is an integral aspect of rAAV characterization. At a most basic level, the 1:1:10 proportion of VP1:VP2:VP3 proteins that comprise the capsid correlate with the effectiveness of an rAAV preparation (26). Changes in this proportion are not only observed for different cell culture systems but can also be a measure of process consistency. Traditionally, sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) has been used to evaluate the relative VP protein abundance and more recently the use of SDS–capillary gel electrophoresis (SDS-CGE) has been adopted. In contrast, LC–MS separations of VP capsid proteins can not only confirm consistent VP1:VP2:VP3 ratios but can also provide useful information on VP degradation (such as deamidation and oxidation), post-translational modifications (PTMs) such as phosphorylation and SUMOylation, and engineered point mutations that may impact the efficacy of rAAV products (26,27). For rAAV sub-serotypes with engineered point mutations, LC–MS analysis of the VP proteins may also find utility as a subserotype identity test.

The most commonly used LC–MS separation method for the analysis of proteins is reversed-phase LC (RPLC), which has been effectively applied to the analysis of rAAV (28). Building on this work, a method was developed for the RPLC-MS analysis of several rAAV serotypes as shown in Figure 3 (29). The separation was optimized for a set of rAAV serotypes using a 300 Å pore size C4 column and difluoroacetic acid (DFA) as a mobile-phase modifier and analyzed using a compact time-of-flight LC–MS system (LC–TOF-MS). When deployed, fluorescence optical detection was favored over UV absorbance due to the nearly 50-fold greater sensitivity. Figure 3 also shows representative mass spectra for the major components observed, including significant levels of phosphorylation of the VP1 and VP2 proteins.

A well-executed alternative approach to LC–MS analysis of the VP proteins using a hydrophilic interaction liquid chromatography (HILIC) separation with MS detection has also been reported (30). This separation makes use of a 300 Å pore size, silica-hybrid based amide HILIC column that was optimized for the separation of glycosylated peptides and proteins (31,32). The reported HILIC separation can be applied to a wide range of rAAV serotypes, and although trifluoroacetic acid (TFA) is used as a mobile- phase modifier, MS sensitivity is enhanced by modifying the MS desolvation gas with propionic acid and isopropanol.


As discussed for intact VP proteins, the LC–MS identification and analysis of PTMs and protein degradation can provide useful information to both guide rAAV product development and monitor product quality (27). However, to evaluate site-specific variants, peptide mapping may likely be required. Additionally, a peptide mapping procedure can also be deployed to monitor for host cell proteins (HCP) that may be of concern in rAAV products (33). A peptide mapping procedure involves the proteolytic digest of a protein and may also include disulfide bond reduction and alkylation steps. The LC or LC–MS analysis of the digested sample can be comprehensive to cover as much of the protein amino acid sequence as possible or be focused on the peptides and peptide variants of interest.

In comparison to typical recombinant protein therapeutics, rAAV samples present several significant challenges during peptide map development. As noted previously, these samples are much lower in concentration in addition to presenting the difficulties presented by the homology and relative abundance of the VP proteins. Furthermore, the amount of sample that can be used for these analyses is often limited and rAAV samples are often formulated with surfactants that can significantly degrade rAAV MS signal quality. Another choice that can be made for this analysis is whether to map the capsid proteins as a single digest or to purify VP1, VP2, and VP3 prior to digestion.

Given the limited amounts of sample that are often available, a single-digest mapping method was developed. The disadvantage of this approach is that variants in the conserved sequence domains cannot be attributed to a VP protein type. The use of a single digest also presents a dynamic range challenge for UV absorbance detection of the peptide map given the greater than 10-fold abundance of VP3 domain peptides versus the peptides that comprise the N-terminal extensions of VP2 and VP1. With these considerations, a tryptic digest of 1.25 μg of rAAV was optimized and then analyzed using an LC–TOF-MS system with- out a UV absorbance detector and resulted in sequence coverages of greater than 95% for VP1, VP2, and VP3 (Figure 4).

As the clinical interest in rAAV gene therapies increases it will be critical to optimize the product efficacy and the efficiency of the rAAV manufacturing process. LC and LC–MS technologies have a long history of use in the development, characterization, and quality testing of recombinant protein therapies and will likely be valuable tools in the advancement of rAAV-based gene therapies, as well as other viral and nonviral gene therapy development. Indeed, as fairly recently introduced therapy modalities, the regulatory analytical requirements for these products are only starting to mature (34). This article has provided a brief introduction to the potential application of some of these LC and LC–MS methods to rAAV characterization and analysis. In addition, we have also seen that in comparison to recombinant protein analysis, the deployment of LC and LC–MS methods is further complicated by numerous factors such as more complex samples and limited sample amounts.

The authors would like to thank Benjamin E. Draper of Megadalton Solutions for providing CDMS results for the rAAV8 samples used for AEC method development.

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 is a Senior Scientist at Waters Corporation in Milford, Massachusetts.

Articles

Column: Column Watch

Recombinant adeno-associated viral therapy (rAAV) products are particularly complex. Are liquid chromatography and LC–MS the right tools for their characterization?

Applying Liquid Chromatography (LC) and Liquid Chromatography–Mass Spectrometry (LC–MS) Methods to the Characterization and Analysis of rAAV

High-capacity sorptive extraction is a sampling technique that involves using a probe to extract volatile and semi-volatile organic compounds (VOCs and SVOCs) from samples for analysis by thermal desorption–gas chromatography–mass spectrometry (TD– GC–MS). Samples can be taken from the headspace of a liquid or a solid, or by immersing the probe in the liquid. This study demonstrates how the technique can improve on traditional methods in the analysis of VOCs that contribute to the aroma and flavor of a breakfast cereal and in identifying other compounds of interest, such as food additives, contaminants, and potentially toxic compounds at trace levels.

Within the food industry, there is an increasing need to monitor product quality and safety to understand flavor composition and product contamination, and to determine how these relate to both consumer satisfaction and effects on human health. This analysis requires a detailed understanding of individual components of a product, but conventional approaches to sample preparation and gas chromatography–mass spectrometry (GC–MS) analysis can struggle to provide the necessary level of sensitivity.

Traditionally, solid-phase microextraction (SPME)—in which a fiber coated with an extracting phase (known as the sorptive phase)—has been used to sample volatile organic compounds (VOCs) from foods and beverages. SPME offers simple, solvent- less sampling, and is easily automated for high-throughput applications. However, it is limited by the amount of sorptive phase (to ~0.5 μL) and the fragility of the fiber (1). More recently, thicker sorptive films and stir bars with a sorptive phase have been used to enhance sensitivity; however, the latter approach is limited in scope, due to the manual interactions required by the user (2).

High-capacity sorptive extraction can overcome these issues. Firstly, the use of strong, metal-core probes supporting a higher amount of sorptive phase (~65 μL) means they are less prone to damage and provide a more robust extraction technique with improved sensitivity (3). The technique can also be fully automated, from sample extraction and post-sampling probe washing and drying through to desorption and GC injection. It is also possible to preconcentrate multiple extractions onto a single focusing trap prior to GC analysis, a process known as multi-step enrichment (MSE) (Figure 1). This increases the extraction efficiency of the technique, which leads to a higher column loading of analytes, enhancing the sensitivity and number of compounds detected.

In this study, the volatile organic chemical profile of a breakfast cereal was analyzed using high-capacity sorptive extraction first, and then combining it with MSE. This enabled a broad range of VOCs contributing to the flavor and aroma of the product to be identified. Additionally, food contaminants, potentially from the product’s packaging and the original cereal crop, were detected.

A store-bought breakfast cereal (1 g) was homogenized prior to addition of NaCl (2.5 g) and distilled water (10 mL). Headspace extraction of VOCs was performed on the Centri extraction and enrichment platform (Markes International Ltd.) using two modes: (a) HiSorb, a single-vial extraction for analysis and (b) HiSorb with MSE, loading four extractions from different, replicate sample vials to the trap prior to analysis. HiSorb probe: Short-length (48 mm) stainless steel (Markes International Ltd., part no. H1-XXABC); Incubation: 75 °C (20 min); Probe desorption: 250 °C (10 min); Flow path: 180 °C; Focusing trap: Materials Emissions (U-T12ME-2S) (Markes International Ltd.); Purge flow: 50 mL/min (1 min); Trap low: 20 °C; Trap high: 280 °C (5 min); Heating rate: MAX (>100 °C/s); Split flow: 5 mL/min.

Agilent 7890 GC system (Agilent Technologies); Column type: TG-624, 30 m × 0.25 mm × 1.4-μm (Thermo Fisher); Column flow: Helium, 1.5 mL/min (constant flow); Oven program: 50 °C (5 min), 15 °C/min to 240 °C (10 min).

Instrument: BenchTOF-Select mass spectrometer (SepSolve Analytical); Mass range: m/z 33–300; Acquisition rate: 8 Hz.

ChromSpace GC×GC software(SepSolve Analytical) was used for full instrument control and data processing.

Volatile and Semi-Volatile Organic Compounds (VOC/SVOC) Profile Analysis Cereal samples were extracted using sorptive extraction probes, initially for a single-vial analysis, followed by MSE, where four headspace extractions were taken from separate vials for one GC analysis. Figure 2 shows the total ion chromatogram (TIC) for both the enriched (upper black trace) and single (lower inverted red trace) extractions. It is clear that an enhancement in sensitivity is achieved across the entire sample profile when using enrichment compared to a single extraction, with no compromise in peak symmetry or resolution. MSE provided a more comprehensive sample profile so the results using this technique are discussed in the following sections.

In total, 150 VOC/SVOC compounds were detected by MSE. The identification of compounds that had a peak area >1% of the TIC and with a NIST match factor >750 are shown in Table I, with odor descriptors (where available) and odor threshold values (the concentration at which an aroma or taste can be detected).

In a sample this complex with significant chromatographic co-elutions (where two or more compounds do not separate chromatographically so they are difficult to see), spectral deconvolution is critical for a more confident identification of VOCs present. Deconvolution is a data processing technique that can be applied to complex elution profiles. It was carried out in this study using data analysis software with resulting spectra compared to compounds in the NIST 2017/ EPA/NIH mass spectral library (6). The inset in Figure 2 shows an example of this. In this instance, the deconvolved peaks were identified as the SVOCs dibutyl phthalate (DBP, a plasticizer) and tris(2-chloroisopropyl) phosphate (Amgard TMCP, a flame retardant).

Other compounds of interest from a food toxicity or contamination perspective (text labelling in Figure 2) were also detected, and will be discussed in more detail later.

The range of VOC/SVOC compounds identified is wide, as demonstrated in Figure 3. The bar chart shows the distribution of the number of components for each compound class. The profile was dominated by aldehydes, alkanes, alcohols, heterocyclics, and aromatics. Within these classes, the most abundant compounds were hexanal (green, grassy) at 5.6% of the TIC, piperonal (cherry, sweet) at 5.5% of the TIC, and vanillin (sweet vanilla) at 5.3% of the TIC. During the production (heating) process of the cereal, Maillard reactions (chemical reactions between amino acids and reducing sugars) occur, and therefore a large amount of these compounds would be expected. Several compounds that are known to give an unpleasant or off odor were also detected, for example, four thiols or organo-sulfurs and three phenols (Figure 3), albeit at relatively low levels.

Principal component analyses for VOCs and SVOCs are important for characterizing the aroma and flavor profiles of food products, and the odor threshold value (OTV) is an associated parameter. Therefore, detection and identification of trace VOCs can be significant, particularly for compounds with medium and low OTVs, or for problematic compounds (such as malodors) with low values, so that subsequent remedial action can be taken if necessary.

MSE provides a mechanism to increase the amount of each compound introduced to the GC, thus increasing the number of compounds detected, extending down to trace-level components. As representative examples, compounds such as dihydro-5-pentyl-2(3H)-furanone (γ-nonalactone), anethole, and γ-terpinene were detected in the single extraction but at very low levels where their spectral quality was not sufficient for a good library match. Figure 4 demonstrates how MSE enabled a more confident identification for these compounds, and this is indicated in the NIST values shown in parentheses in Table II.

Other components, such as eucalyptol, phenyl ethyl alcohol, and α-farnesene were only detected in the enriched sample, meaning they could have been missed had only a single extraction been used for analysis (Figure 5). Eucalyptol is an important component in the sample with a relatively low OTV (12 ppb), imparting a herbal or camphor aroma to the product. These compounds are listed in Table II. The NIST values represent the enriched sample with comparison to the single point extraction in parenthesis when a partial response was observed.

Additives and Compounds with Potential Toxicity


Along with VOCs related to odor and taste, several additional compounds were identified in the breakfast cereal (Figure 2, Table III). These include the antioxidants butylated hydroxytoluene and 2,6-di-tert-butylphenol, known to be added to food to prevent spoilage, and several compounds that potentially originate from the food packaging as contamination: styrene, two phthalates, and two cyclic lactams. Interestingly, compounds that are usually associated with combustion were also detected in this sample (benzene, toluene, ethylbenzene, and xylene), with the possibility that these are present due to contamination of the original cereal crop.

Furthermore, compounds present on the US EPA Toxic Substances Control Act (TSCA) list (7) were tentatively identified (Figure 2, Table III), such as butyphos (tributylphospine oxide), a herbicide, Amgard TMCP (tris(2-chloroisopropyl) phosphate), a flame retardant, and naphthalene, a PAH.

Although this analysis was only qualitative, so no indication of the concentration can be inferred from the data, the detection and tentative identification of these compounds demonstrates that high-capacity sorptive extraction can provide the ability to extract and analyze a wide range of compounds in addition to the main flavor and odor-active compounds of interest.

In this study, we have demonstrated the use of a fully automated high-capacity sorptive extraction technique for profiling the VOCs present in the headspace of a breakfast cereal using preconcentration with a single-vial extraction and multiple extractions. A wide range of compounds pertaining to taste and odor were found. Several components were found only in the enriched sample, demonstrating how multi-step enrichment provides a more comprehensive extraction, which leads to a more accurate VOC profile of a food sample. The fast MS acquisition combined with data processing further enhanced the identification of compounds, enabling co-eluted peaks in the complex profiles to be distinguished into individual analyte peaks. Moreover, the ability to discover compounds present in the sample that either contribute to off odors, have migrated from the packaging, or are contaminants from the original food commodity is important because they have potential toxicity and could affect human health. This breadth of compounds discovered demonstrates the enhanced capability of the extraction methodology. Lastly, the automated extraction and enrichment process means that no human interaction is required from sample extraction through to GC injection, allowing faster sample throughput. This ensures high productivity is achieved in industrial laboratories, such as contract service or food research.

(1) N.-E. Song, J.-Y. Lee, Y.-Y. Lee, J.-D. Park, and H.W. Jang,

(2) U. Telgheder, N. Bader, and N. Alshelmani, 

(3) https://www.markes.com/Products/ Sampling-accessories/HiSorb-sorptive-extraction.aspx

(4) The Good Scents Company Information System, www.thegoodscentscompany.com, search facility accessed on Sep. 11, 2020.

(5) Odor and Flavor Detection Thresholds in Water (ppb) – Leffingwell & Associates, http://www.leffingwell.com/odorthre.htm, accessed on Sep. 11, 2020.

(6) https://sciencesolutions.wiley. com/solutions/technique/lc-ms/nist-epa-nih-ms-ms-mass-spectral-library-2017/

(7) United States Environment Protection Agency, www.epa.gov/chemicals-under-tsca, accessed on Sep. 11, 2020.

 received an M.Sci in forensic science from the University of South Wales, UK, where her final-year project focused on fingerprinting emerging psychoactive substances using advanced techniques such as GC×GC–TOF MS, in collaboration with Markes International. She later took up the role of Thermal Desorption Product Specialist at Markes, providing technical and application sup- port to the commercial team, before taking on her current role at the start of this year, specializing in the development of applications using extraction and enrichment techniques.

 obtained her B.Sc. in chemistry and Ph.D. in organic chemistry at Bangor University, UK. After working for a chemical manufacturing company for three years, she moved to the Royal Society of Chemistry where she worked in journals publishing for six years and on Chemistry World magazine for four years. This was followed by five years as a freelance copyeditor and science writer. Her current role is Technical Copywriter at Markes International.

Focus on Food Analysis

High-capacity sorptive extraction combined with thermal desorption–gas chromatography–mass spectrometry (TD–GC–MS) can improve on traditional methods for the analysis of VOCs and other trace analytes that contribute to the aroma, flavor, and safety of food products.

A Look at Improved Aroma Profiling of Foods by High-Capacity Sorptive Extraction

These tips will help you navigate choices about GC column dimensions to achieve significant improvements in resolution.

Optimizing gas chromatography (GC) separations typically involves making informed choices around stationary‐phase chemistry and column temperature programs. However, judicious initial choices of GC column dimensions, and even a change of column dimension during method development, can lead to significant improvements in resolution. Some knowledge of the relationships between resolution and column length, internal diameter, and film thickness can lead to the best column choice to begin method development or to get you out of trouble when your stationary‐phase chemistry choice or temperature program optimization do not produce the required results.

The primary relationships between resolution and column dimensions are outlined in equation 1 and Table I:

) is a function of carrier gas type, column length (L), column internal diameter (dc). Retention factor (k) is a function of temperature, film thickness (df), and column internal diameter (dc). Selectivity (a) is a function of temperature (T) and stationary‐phase chemistry. So how can we relate the various GC column dimensions to resolution (Rs)? Combining equation 1 with the information in Table I, we can summarize the overall effects of column dimensions on chromatographic resolution using the following general relationships and caveats.


Doubling column length doubles efficiency (

), improving resolution by a factor of 1.4, and doubles analysis time (by ~1.5 to 1.75x for gradient‐temperature programming). It also increases column costs, and encourages the need to consider the effects of elution temperature with increasing column length; you may need to adjust the oven temperature program.

) affects efficiency, retention, carrier flow rate, capacity, and pressure drop across the column. It is inversely proportional to column efficiency— halve the column diameter, and double efficiency, increasing resolution by a factor of 1.4. Column head pressure is an inverse square function of column radius, so halving column diameter will increase head pressure (for an equivalent linear velocity) by a factor of 4. Column capacity increases with column internal diameter; however, capacity is also dependent on the stationary‐phase type, film thickness, and the nature of the analytes.

Stationary‐phase film thickness affects retention, inertness, capacity, resolution, and bleed. It is directly proportional to retention time (or a ratio of 1.5:1 for gradient temperature programming). Thick stationary‐phase films give retention for highly volatile analytes. Increasing film thickness allows retention of volatile analytes at temperatures at or above ambient. Doubling stationary‐phase film thickness gives an increase of ~20 °C in elution temperature. Retention of late eluted (high boil‐ ing point) analytes is reduced using thinner film columns. Early eluted analytes (

 < 2) are better resolved using thicker‐film columns. Resolution may decrease for analytes with retention factor (

) values greater than ~5 with increasing film thickness. Thicker films bleed more; upper temperature limits of thick film columns will be lower. Thicker‐film columns are more inert because the film shields the analyte from active sites on the silica tubing (therefore, better peak shape for polar and active compounds). Thicker film columns have higher analyte capacity, and so may reduce peak fronting.

This is an excerpt of a blog entry with the same title. See the full blog entry on our website (www.chromatographyonline.com) for real-life examples to illustrate these points.

Judicious initial choices of gas chromatography (GC) column dimensions, and even a change of column dimension during method development, can lead to significant improvements in resolution.

An Excerpt from "Optimizing Gas Chromatography Using Column Dimensions"

For solution-phase samples, the world of mass spectrometry defaults to electrospray ionization (ESI). ESI is used for the analysis of a broad variety of compounds, ranging from polar to moderately nonpolar. However, ESI possesses limitations that prevent the ionization of certain analytes—particularly nonpolar compounds. This study aims to compare the ionization efficiency of complementary ionization techniques, and to demonstrate that multiple methods can improve the analytical results with respect to limits of detection and matrix tolerance. Atmospheric pressure photoionization (APPI) is an ionization method that complements ESI, excelling in the analysis of nonpolar and moderately polar analytes. In this study, we optimized methods using APPI and ESI for the detection and quantitation of pharmaceuticals frequently detected in the environment, including antibiotics, beta blockers, and selective-serotonin reuptake inhibitors, and tested their matrix tolerance relative to artificial wastewater. While most of these compounds ionized preferentially by ESI, some performed significantly better using APPI.

Every day, in the world of analytical chemistry, we strive to discover better ways to analyze, to achieve goals such as lower limits of detection, broader dynamic ranges, and techniques that tolerate greater interferences. Along with the need for improved methods of quantitation, there is pressure on the field of mass spectrometry (MS) to be able to successfully detect multitudes of analytes of interest from single injections. The question becomes, “How do we improve upon the detection of analytes?” However, sometimes using a single injection leads to reducing performance because it limits us to using a single ionization method. When you can optimize not only your separation, but also your ionization method, your method may truly improve. The basic principle of MS is to ionize molecules under study into gaseous ions, separate these ions in accordance with their mass-to-charge (

) ratio, and detect them (1). Today’s commercial instruments are capable of transferring 97–99% of ions successfully from the source to the detector, so the greatest improvements in detection and methods today are focused on the source. The ultimate question is, “When ionizing a sample, will the use of complementary ionization techniques improve the figures of merit of the individual analyses, or not?” 

ESI and APPI: Complementary Ionization Techniques


By choosing the appropriate ionization source, liquid chromatography with tandem mass spectrometry (LC–MS/MS) can be used for the detection of trace levels of contaminants, such as antibiotics and endocrine disrupting compounds (EDCs) from environmental samples. The electrospray ionization (ESI) source has been used as a powerful soft ionization technique for the analysis by MS of a wide array of sample types, ranging from polar to nonpolar, and has also be used for the analysis of thermolabile molecules of high molecular weight (MW) (2). Although ESI is popularly used for the analysis of environmental pollutants, it may not be able to ionize all contaminants efficiently. Certain contaminants are either poorly ionized, or not ionized at all. ESI is limited to analytes that are of low to high polarity and moderate to high MW (3).

Atmospheric pressure photoionization (APPI), introduced in 2000, is also a soft ionization technique. APPI was found to have success with the analysis of compounds with low to no polarity, and compounds of low to moderate MW, but cannot be used on thermolabile compounds. These parameters are what makes APPI and ESI complementary to each other (4). This complementarity opens new doors for studies already utilizing the ESI method, because APPI can be used for complementary analysis of compounds that may not be detected by ESI. Additionally, APPI has shown tolerance to matrix components beyond what ESI has, due to its ionization pathway (5). APPI can be used for the analysis of a wide range of compounds, including drugs, human endogenous compounds, lipids, natural compounds, pesticides, synthetic organics, and petroleum derivatives (6). There are very few research papers reporting comparative studies of ionization efficiencies in MS for the detection of antibiotics and EDCs (7–9).

A triple-quadrupole mass analyzer was used in this study, utilizing both the full- scan mode optimization and multiple-reaction monitoring (MRM) quantitation. Full-scan mode can give qualitative analysis of a sample’s composition under study, and MRM mode is a highly selective mass monitoring mode with a wider linearity dynamic range, improved limit of quantitation (LOQ), increased sensitivity, and superior accuracy. One of the advantages of the MRM scan mode is an increase in signal-to-noise ratio because of removal of non-analyte ions and isobaric precursors by monitoring fragments.

There are different MS acquisition parameters that affect the signal intensity of ions. The MassHunter Data Acquisition software sets a default value for all acquisition parameters for each ionization source (S1).

There is a sheath gas flow chamber in the electrospray ionization source that is absent in the atmospheric pressure photoionization source. As a result, the sheath gas temperature and sheath gas flow rate parameters are present only for ESI while APPI has an additional vaporizer parameter that is not present in ESI. Fragmentor voltage, collision energy, cell accelerator voltage, gas temperature, vaporizer, gas flow (L/min), nebulizer (psi), sheath gas temperature, and sheath gas flow rate were all optimized for each analyte prior to data acquisition in this study.

The current global population is growing at the annual rate of 1.09%, and pharmaceuticals are continuing to be prescribed and consumed at an alarming rate. In 76 countries across the globe, antibiotic consumption as described in defined daily doses (DDD) increased by 65%—from 21.1 billion doses in 2000 to 34.8 billion doses in 2015—and the overall antibiotic consumption rate has increased by 39% (10). In addition to antibiotics, beta blockers and antidepressants are two classes of pharmaceuticals gaining popularity. Beta blockers are a class of drugs frequently used to treat hypertension, heart disease, and other cardiovascular events. Although the true nature of their efficacy remains to be questioned in certain studies, beta blockers are still highly prescribed due to the diverse range of clinical symptoms they can successfully treat (11,12). In addition, according to 2017 data from the National Center for Health Statistics (NCHS), the rate of antidepressant use in America has increased by 65% since 1999 (13). Unfortunately, this increase in pharmaceutical use means more pharmaceutical waste is likely to end up in the environment. While there is an urgency to know the exact harm this excess will cause, the priority is to increase our ability to detect as many pharmaceuticals in environmental samples as possible. This will then allow for proper removal techniques to be employed before the harmful substances have a chance to contaminate the environment (14).

Numerous studies have shown that some pharmaceuticals are not completely removed during wastewater treatment, and ultimately enter the environment in low concentrations. Important effects of pharmaceuticals entering the environment in low concentrations include antibiotic resistance, genotoxicity, acute or chronic toxicity, and endocrine disruption (15). Antibiotics and EDCs are emerging pollutants detected throughout the world, yet they remain unregulated by the U.S. Environmental Protection Agency (EPA) (16).

Sir Alexander Fleming, the British bacteriologist, discovered penicillin in 1928 from the fungus 

, spurring a novel era of antibiotics derived from microorganisms and antibiotic synthesis (17). The ability antibiotics have to eradicate a wide range of bacterial infections led to their increased use over time. Unfortunately, these bacteria have developed mechanisms to combat the actions of antibiotics. Thus, the over-prescribing of antibiotics, along with a lack of patient knowledge regarding the importance of correct antibiotic administration, has become an insidious issue that is known as antibiotic resistance. Antibiotic resistance arises as microorganisms develop the ability to survive against the action of antibiotics, meaning that when antibiotic-resistant bacteria infect animals and humans, the antibiotic regimen that would normally eradicate the bacteria becomes useless. This is the reason being able to successfully detect antibiotics from wastewater samples is so important. In this study, five different classes of antibiotics were used: beta-lactams, macrolides, nitroimidazoles, sulfonamides, and tetracyclines.

Antibiotics can either be bacteriostatic, meaning they prevent the growth of bacteria, or they can be bactericidal, meaning that they destroy the bacteria entirely. However, an antibiotic’s mechanism of action is more important when considering treatment options. Beta-lactams inhibit the biosynthesis of bacterial cell walls by making penicillin-binding proteins unavailable for new peptidoglycan synthesis, which causes the lysing of bacteria. The beta-lactams used in this study are ampicillin, ceftriaxone, cephalexin, and penicillin G. Macrolides inhibit protein synthesis during translocation in bacteria by dissociating peptidyl-tRNA from the middle of the 23S rRNA of the ribosome’s 50S subunit, causing early detachment of unfinished peptide chains. The macrolides used in this study are erythromycin and tylosin, an antibiotic popularly used in farm animals. Tetracyclines prevent the attachment of aminoacyl t-RNA to the A site in bacterial ribosomes by acting on the 16S rRNA of the 30S subunit inhibiting protein synthesis. Oxytetracycline and tetracycline were used in this study. Sulfonamides prevent the multiplication and growth of bacteria by inhibiting certain steps in the metabolism of folic acid. Sulfamethoxazole and trimethoprim were the sulfonamides used in this study. Nitroimidazole antibiotics inhibit nucleic acid synthesis that occurs in bacterial cells by disruption of the DNA in microorganisms. Metronidazole and 1,2 dimethyl-5-nitroimidazole were the nitro- imidazoles used in this study (18).

EDCs are natural compounds or synthetic chemicals that mimic natural hormones in the body and interfere with the action of the natural hormones (19). These compounds most profoundly cause adverse effects on reproduction, developmental, neural, and immune systems of human beings and animals. Research suggests that EDCs reduce fertility, and increase the risk of cancer, diabetes, obesity, and endometriosis (20). Among various EDCs, beta blockers (acebutolol, atenolol, metoprolol and propranolol) and SSRI antidepressants (citalopram, paroxetine and venlafaxine) were used in this study.

Make or Break for Successful Analysis: Matrix Effects and Wastewater 

Properly dealing with impurities is a necessary complication in every field of research. In MS, matrix effects variability in ionization efficiency of analytes of interest as co-eluted species serve to either enhance or inhibit the ionization process for an analyte. This issue becomes increasingly problematic when trying to discern analytes of interest from wastewater. Water that is obtained as a byproduct of agricultural, industrial, domestic, and commercial activity is termed wastewater. Wastewater contains nutrients such as calcium, iron, nitrogen, phosphorus, and potassium, and components such as fats, sugars, and proteins. In this study, synthetic wastewater was made to mimic the wastewater from the influent of a “typical” wastewater treatment plant, with its composition designed to imitate the dissolved inorganic solids and dissolved organic solids of real wastewater. The synthetic wastewater prepared in this study followed the protocol described by H. E. Gray, 2012 (21).

APPI has been found to be less susceptible to matrix effects compared to ESI. This is likely due to the fact that APPI is more selective in ionization. The photon emitted from the krypton lamp, at 10.6 eV, can ionize analytes, but potentially not the matrix components; the difference in how a sample is ionized can be the difference between more or less matrix interference (22). Using APPI involves the ejection of an electron from the analyte molecule to produce the gaseous radical cation (23). It is also possible, however, that the matrix component can act as a dopant and ionize sample components with high ionization energy through electron transfer, leading to signal enhancement.

Although matrix effects cannot be removed completely, they can be minimized by optimizing the sample preparation procedure and LC–MS parameters. Solid-phase extraction (SPE) with an appropriate sorbent can reduce matrix effects by eliminating interfering matrices. The formula for calculation of the matrix effect is:

Physicochemical characteristics of anti- biotics and EDCs help to determine the environmental fate of these compounds. Table I contains important physicochemical characteristics of antibiotics and EDCs under study, including solubility, pK

 less than 2.5 means the compound is hydrophilic and readily found in the aqueous phase.

A total of 12 antibiotics and 7 EDCs were analyzed in this study. Ceftriaxone sodium salt hemi(heptahydrate) and erythromycin with a purity of >98% were purchased from Acros Organics. Propranolol hydrochloride (99%), metronidazole (99%), acebutolol hydrochloride, metoprolol tartrate (98%), tetracycline hydrochloride (96%) and oxytetracycline hydrochloride were purchased from Alfa Aesar. TCI Co. was the main supplier of chemicals: cephalexin monohydrate (>98%), sulfamethoxazole (>98%), penicillin G potassium salt (>98%), atenolol (98%), venlafaxine hydrochloride (>98%), trimethoprim (>98%), citalopram hydrobromide (>98%) and 1,2 dimethyl-5-nitroimidazole. Ampicillin sodium salt was procured from Affymetrix Inc. Tylosin tartrate (95+%) and paroxetine hydrochloride (98+%) were bought from Ark Pharm Inc. All antibiotics and EDCs were used without further purification. All the solvents used in the analysis, like acetonitrile, methanol, and toluene, are of HPLC grade and purchased from Fisher Chemical. Potassium phosphate monobasic (99.8%) was purchased from EK Industries Inc. Sodium acetate trihydrate (100.7%), magnesium sulfate heptahydrate (99.9%), ammonium chloride (99.7%) and calcium chloride dihydrate (99.9%) were bought from Fisher Scientific. All solvents and chemicals were used without further purification.

The MS parameters were optimized as follows: 1.00 ppm sample of each analyte was analyzed for the selection of the precursor ion, optimization of fragmentor voltage, optimization of cell accelerator voltage, optimization of gas temperature, optimization of gas flow rate, and optimization of collision energy. All the ions formed were analyzed for intensity. Ions with m/z equal to and greater than the MW of each analyte were considered to determine the precursor ion. See Figure 1 for the flowchart of optimization strategy.

A calibration curve for each analyte was obtained by the internal calibration method using the optimized MS parameters. Calibration was performed in the range of 1.00 ppt to 10.0 ppm for each analyte under study. Each of the standard solutions for antibiotics was spiked with the mixture of internal standards of antibiotics (azithromycin d

) to produce a final concentration of 100 ppb of each internal standard. Each of the standard solutions for EDCs was spiked with the mixture of internal standards of EDCs (metoprolol d

) to produce a final concentration of 10.0 ppb of each internal standard.

A synthetic wastewater matrix solution was prepared by dissolving potassium phosphate monobasic, sodium acetate trihydrate, magnesium sulfate heptahydrate, ammonium chloride, and calcium chloride dihydrate in MilliQ water. The concentration and quantity of reagents used for synthetic wastewater matrix preparation is given in S2.

Analysis of the antibiotics and EDCs was performed in triplicate for each parameter using an Agilent Technologies 1290-6460 Triple Quadrupole LC–MS/MS instrument using two ionization sources: a Jet Spray ESI source and an APPI source operated in positive mode. Full-scan mode was used for the optimization of MS parameters and MRM mode was used for calibration and analysis of wastewater. Data interpretation was performed using the instrument’s MassHunter Workstation Software. HPLC parameters for analysis are given in S3.

Calibration was performed in the range of 1.00 ppt to 10.0 ppm in matrix for all the analytes under study. Internal standards were added as described previously. Limits of quantitation were calculated based on the quadratic regression lines and were calculated in the instrument software. LOQs lower than 1.00 ppt were reported as <1.00 ppt as they were below the lowest calibrator.

A setup of Waters Oasis Prime HLB cartridges and a SPE vacuum manifold was used for offline SPE to extract antibiotics and EDCs from the synthetic wastewater matrix calibration sample. Waters Oasis PRIME HLB cartridges, 1 mL barrel syringe with 30 mg universal polymeric reversed-phase sorbent, were employed. SPE pretreatment was performed by washing the column with 2 mL of HPLC grade methanol, 2 mL of Millipore deionized water, and 2 mL of Millipore deionized water at pH 2 under gravity. Then the samples were loaded on the column under vacuum at 10 to 20 mL/min rate.

Washing and Elution Step for Antibiotics Sample


After loading the sample on the column, the cartridge was washed with 2 mL Millipore deionized water for antibiotic sample. The column was then eluted first with 2 mL of methanol, and then with 1 mL of methanol: acetone (1:1) under gravity and collected and combined in test tubes.


EDCs sample cartridges were washed with 1 mL of methanol:water (5:95) solvent. After the first washing step, the column was dried for 15 to 30 min under a vacuum. The column was eluted first with 1 mL of ethyl acetate:methanol (9:1) under gravity (this eluate was collected in a test tube labeled as fraction 1), then washed with 1 mL of 5% methanol/2% acetic acid in water, then with 1 mL of 5% methanol/2% NH

OH in water under a vacuum and dried for 10 to 15 min under a vacuum. After drying, the column was eluted with 1 mL of 2% NH

OH in methanol and combined with eluate present in the test tubes labeled as fraction 1.

Wastewater samples were collected from the influent of the aeration treatment at the Environmental Resources Training Center (ERTC), a training center for drinking water and wastewater treatment at Southern Illinois University–Edwardsville. The samples were analyzed for the presence of antibiotics and EDCs to demonstrate the effectiveness of the method development on real wastewater samples.

The wastewater samples were sequentially filtered through VWR 417 (40 μm) filter paper, then through VWR 696 (1.2 μm) glass microfiber filter paper, and then through Ahlstrom 193 (0.7 μm) microfiber glass filter. The filtrate was separated into six bottles each with 250 mL of filtrate, three samples for analysis of antibiotics and three for EDCs. All samples were spiked with appropriate internal standards as described previously.

Samples were adjusted to pH 3.0 using 6.0 M sulfuric acid before performing the SPE. Waters Oasis Prime HLB cartridges (6 mL, 200 mg universal polymeric reverse-phase sorbent) were used for wastewater sample analyte extraction. The SPE method for wastewater was identical to the synthetic matrix sample preparation, except the quantity of reagent solvents used was five times greater, due to the increased volume and cartridge bed mass.

The optimized MS parameters for the precursor ion of antibiotics for ESI as an ionization source can be found in S4 and S5, while parameters for APPI are shown in S6 and S7. Ions with the highest intensity peak with a 

 equal to or greater than the MW of the analyte were selected as potential precursor ions and MS parameters were optimized using these ions.

The optimized MS parameters for the product ion of antibiotics for ESI as an ionization source can be found in S8 and S9, while parameters for APPI are shown in S10 and S11. At most, three ions with 

 less than the MW of the analyte and with the highest ion abundance were optimized to determine the optimized collision energy of the product ions.

Peer-Reviewed Article

Atmospheric pressure photoionization (APPI) and electrospray ionization (ESI) are compared for the mass spectrometry (MS) quantitation of pharmaceuticals frequently detected in environmental waters, including antibiotics, beta blockers, and selective-serotonin reuptake inhibitors.


Ionization Efficiency for Environmentally Relevant Compounds Using Atmospheric Pressure Photoionization Versus Electrospray Ionization (ESI)

The concept of data integrity is straightforward. Actually ensuring data integrity in practice, however, is a more complex topic, and involves many elements, such as the right corporate culture, good standard operating procedures, and the right laboratory setup. A key step is also second-person review of data. To understand more, we spoke to Monica Cahilly, the president and founder of Green Mountain Quality Assurance, in a podcast interview. Cahilly has more than 25 years of consulting experience, with specialized interest in and enthusiasm for data integrity assurance and data governance. A short excerpt of that interview is presented here.

What is the importance of having multiple reviewers to ensure data integrity?


Second-person review is a data-integrity requirement that goes back fundamentally to the inception of the GXP regulations. It takes into account the principle that humans are error prone. Because the work we are doing in GXP-regulated industries is so important and can potentially affect the lives of our customers (patients), it is important to ensure that we have a risk reduction strategy. Having a second person review what we do is an important element of that risk reduction strategy.

Historically, if we were filling out a laboratory worksheet or a laboratory notebook on paper, we would always have a second person review what we did to make sure they did not see any mistakes, and to make sure we are following procedures or analytical methods correctly, and to make sure we are properly trained. That reviewer would also look to see if there were things that required additional investigation and corrective and preventative actions. Maybe we deviated from our procedures or our analytical methods, or maybe there was something odd in the data set. Perhaps, in some extreme cases, there may be the potential of someone falsifying the data.

Therefore, we need to have a second person reviewing data to look for these mistakes, whether they’re intentional or unintentional. In my experience, the vast majority of data integrity risk actually is not intentional. It isn’t that people come to work intending to do something incorrectly. I believe that most people come to work trying to do the best they can, but we’re all human beings and we all make mistakes. It’s really beneficial for both the individual and the larger entity that we have someone who looks over our work, and helps us catch those mistakes and correct them before they turn into something that could potentially affect one of our patients.

Why is it important to lock the data set of results while it is going through the review and approval process?

Locking data during a data process is one of the principles of good data process design. When you think about good data process design, one of the things I try to ask myself is, “How can I prevent risk, and where in my data process are opportunities to prevent risk?” Because I know that when I leverage prevention and controls that prevent risk, it makes my life a lot easier on the detection side. As an example scenario, imagine that someone is entering data, and, as they enter that data, it’s going from a chromatography data system (CDS) into a laboratory information management system (LIMS). Then the reviewer comes in, and starts looking at the data that are in the LIMS and comparing them back to the data that are in the CDS. Or the reviewer might be looking directly at the data in the CDS and going through all the different ways that the original analyst processed the data. If, as the reviewer is doing the review, they are locking the data behind them, it prevents additional changes from taking place later that would undermine the review. It creates a secure link between the signature and the data set.

Also, once data are locked in a way that people can no longer modify data or delete data, it means that there are going to be fewer entries in audit trails. In other words, if, as an ordinary user, I am unable to modify, delete, or continue to create more data, then there are no audit trails that will be triggered by me after that locking process.

Locking processes are never 100%. There always is going to be someone who can, in fact, unlock data. However, locking data will reduce that residual risk to a much lower level, when the locking process is done concurrently with the review and approval processes.

This interview has been lightly edited for style and space.

Focus on Data Handling

A key step in ensuring data integrity is second-person review of data, and data sets must be locked during the review process. Monica Cahilly explains.

Understanding the Review Process in Ensuring Data Integrity

Charged aerosol detection (CAD) is a powerful complement to ultraviolet (UV) absorbance and mass spectrometric (MS) detection for liquid chromatography (LC), particularly for analytes that have no UV chromophore, or do not ionize well by electrospray ionization. As with all detector types, successful implementation requires an understanding of behavior and characteristics of the detector. In this installment, we discuss the characteristic nonlinear response of CAD, and approaches to deal with this, as well as mobile-phase requirements to consider for successful separations involving CAD.

Ultraviolet (UV) absorbance and mass spectrometry (MS) detectors are by far the most commonly used detectors for liquid chromatography (LC). They work exceptionally well for both qualitative and quantitative methods in many application areas. However, a significant weakness of UV-absorbance detection is that not all molecules of interest have chromophores (that is, a part of a molecule that absorbs UV light) that absorb strongly enough to make UV detection a viable option, especially in applications where detection sensitivity is an important performance metric. Over the years this weakness of UV detection has motivated research and development aimed at more “universal” detection schemes for LC (1), including refractive index detection (RID), evaporative light scattering detection (ELSD), and charged aerosol detection (CAD). Although these detection schemes have the tremendous upside that they are less dependent on the presence of a chromophore in our analytes compared to UV detection, they have their own weaknesses, including significant response dependencies on temperature, nebulization process, analyte volatility, and mobile-phase composition, as well as occasional nonlinear dependence of the detector response on analyte concentration. For users who were trained using UV or MS detection, these characteristics of RID, ELSD, and CAD can be challenging. For this month’s installment of “LC Troubleshooting,” I have invited Dr. Imad Haidar Ahmad, Associate Principal Scientist at Merck & Co., Inc., to join me in sharing some tips and tricks he has found useful for the routine use of CAD in the research and development laboratories where he has worked.

Charged aerosol detection (CAD) is a detection method that is widely and routinely used in the pharmaceutical and biopharmaceutical industries for analyses involving analytes with weak or no UV chromophore. Some well-known applications of CAD in these contexts are analyses for organic and inorganic counterions (2), sugars (3), and lipids (4). In addition to the characteristic of responding more uniformly to a broad range of analytes, CAD has other strengths, including ease of use, reliability, and wide dynamic range. These characteristics have earned CAD a place on the benches of many research and development laboratories. Moreover, CAD-based methods are also used in regulated quality control laboratories where methods are validated according to demanding ICH guidelines. Success in this environment requires good performance as measured by metrics—including accuracy, repeatability, sensitivity (as measured by limits of detection [LOD] and quantitation [LOQ]), and response linearity. However, as stated earlier, there are some characteristics of CAD in particular that require familiarization for those of us who were trained on UV and MS detectors. An excellent source of information on CAD is the recently published book edited by Gamache (5).

Coping with the Nonlinear Response of CAD


Modern UV-absorbance detectors used for LC exhibit a linear response over an analyte concentration range of more than four orders of magnitude (powers of ten). This makes quantitation very straightforward, as simple two-parameter equations (the slope and intercept of a line) can be used to describe the calibration curve. With CAD, however, the detector response to analyte concentration is not linear, and more complex relationships must be used to describe calibration curves. Several approaches have been used to overcome this limitation for quantitative applications. These include using localized linear fitting over a narrow concentration range, and plotting the response data on a logarithmic scale (log[response] vs. log[concentration]), followed by a linear fit of the data over the entire concentration range. Neither of these are particularly attractive from a practical point of view, especially in regulated environments. The former approach is limited to a narrow concentration range, or establishing multiple calibration functions and choosing one that is most appropriate to the analyte concentration in each sample that is analyzed. The latter approach involves electronic data processing and transformation in spreadsheets, and these processes need to be reviewed, validated, and approved.

An alternative approach to expand the linear dynamic range of CAD is to transform the signal using a power function. Typically, a power function is implemented by raising the signal stream of the detector to a power n. This approach can yield highly linear plots of peak area vs. analyte concentration; how- ever, in practice, it has been a challenge to determine the value of 

 [PFV]) that yields the best quantitative performance in terms of accuracy and precision. The optimum PFV can be obtained by studying the variation in the detector response factor (RF = peak area/analyte concentration) with analyte concentration. Specifically, we can look at the slope of RF vs. analyte concentration, and the relative standard deviation (RSD) of RF values over the range of concentrations studied. If the PFV were to perfectly linearize the detector response, we would expect both the slope and RSD to be zero. Figure 1 shows experimental results from a study of the dependence of this slope and RSD on PFVs. We see that the optimum PFV in this case is about 1.4, corresponding to the lowest RSD value and a slope that is closest to zero.

Figure 2 shows that, at the optimum PFV of 1.4, the relationship between peak area and analyte concentration is clearly more linear than when a PFV of 1.0 is used (that is, the default value in the software).

The conventional approach to obtain the optimum PFV involves acquiring peak areas for a standard at several concentrations, using several different PFVs, as shown in Figure 1. In our own work we have demonstrated the ability to predict the optimum PFV using only the CAD signal acquired with PFV =1.0 (6). Recently, changes to commercially–available software for use with CAD have made determination of the optimum PFV much more user friendly.

Mobile Phase Considerations that Promote Success with CAD


As discussed above, a significant virtue of CAD is that it can “see” some compounds that are practically invisible to UV-absorbance detectors. The flipside of this characteristic of CAD, though, is that it will also “see” impurities in the mobile phase, or interferences in the sample, that would otherwise not be detected by UV-absorbance detectors if they do not contain chromophores. This is similar to the dilemma we often face with MS detection with electrospray ionization for LC separations—that it is exquisitely sensitive for detection of low concentration ionogenic compounds, but it is also very prone to detection of mobile-phase impurities that can give rise to high levels of detector back- ground. Given that CAD is most sensitive to nonvolatile compounds, the background signal in CAD is strongly affected by the concentration of nonvolatile impurities in the mobile phase. Therefore, many of the recommendations made regarding use of LC systems with MS detection and preparation of mobile phases for LC–MS also apply when using CAD. These are listed below:

1. When switching from the use of non-volatile buffers with UV-absorbance detection to volatile buffers and CAD, be sure to thoroughly flush (30–60 min) the LC system with the new, volatile buffers before connecting the CAD to the column outlet.

2. Use ultrapure water with low conductivity (18.2 MΩ∙cm and less than 5 ppb total organic carbon [TOC]) for mobile-phase preparation to minimize the concentration of non-volatile impurities. It is recommended to use fresh water rather than water stored in bottles because of the possibility of sodium leaching from glass.

3. Use high purity organic solvents to pre- pare the mobile phase to minimize the concentration of non-volatile impurities. We find that LC–MS-grade solvents are generally adequate.

4. Use volatile, low molecular weight acids and bases for buffer constituents (examples include, but are not limited to, formic acid, acetic acid, ammonium for- mate, and ammonium acetate).

5. Filter samples prior to analysis whenever large particulate matter may be present in the sample.

6. Use flow rates within the recommended range of the detector.

7. Adjust the evaporation temperature of the detector to ensure efficient and consistent nebulization for different mobile-phase conditions.

8. Choose chromatographic conditions (such as temperature, mobile phase pH, or organic solvent) that minimize the likelihood of stationary phase bleed from the column.

9. Avoid sources of contamination from glassware, filters, stir bars, pH electrodes, and laboratory equipment.

If a high background signal is observed, we recommend to start troubleshooting by removing the column while running LC–MS-grade mobile phase without additives. Once the background has returned to normal, the other components (buffer and column) can be added back one at a time while tracking the signal until the source of the higher-than-expected background is discovered.

Figure 3 shows the impact on the CAD background signal of flushing the LC system and detector with high purity methanol and water after a period of use with buffered mobile phase. Comparing the background after flushing (black trace) to that before flushing (red trace), we see that there are decreases in short-term noise, magnitude of the background signal, and long-term drift of the signal.

Many LC practitioners were trained using UV absorbance or MS detectors. Other detectors, including CAD and ELSD, which are attractive for the analysis of semi- and non-volatile compounds because they do not require that analytes have a light-absorbing chromophore, have increased in prominence in recent years. In this installment of “LC Troubleshooting,” we have high- lighted some of the ways that CAD in particular is different from UV-absorbance detection, including nonlinear response to analyte concentration, and sensitivity to mobile-phase impurities. We hope that these tips are especially helpful for those new to CAD, and lead to development and implementation of successful methods involving this powerful detection technique.

(1) K. Zhang, K.L. Kurita, C. Venkatramani, and D. Russell, 

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Charged aerosol detection is a powerful complement to UV and MS, but successful implementation requires understanding a few key factors, including response dependencies on temperature, nebulization process, analyte volatility, and mobile-phase composition.

LCGC-North America-12-01-2020 | LCGC North America

Applying Liquid Chromatography (LC) and Liquid Chromatography–Mass Spectrometry (LC–MS) Methods to the Characterization and Analysis of rAAV

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Ionization Efficiency for Environmentally Relevant Compounds Using Atmospheric Pressure Photoionization Versus Electrospray Ionization (ESI)

Understanding the Review Process in Ensuring Data Integrity

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