Needle-trap devices (NTDs) are another sorbent-based tool in the haystack of methods used in analytical extractions. Syringe needles, similar to those used for gas chromatography (GC) injection, can be partially filled with suitable sorbents and are used for extracting and collecting volatile organics, followed by injection into a GC instrument via thermal desorption. Although NTDs share many similarities and advantages of solid-phase microextraction (SPME), the larger sorbent bed provides robustness and offers potentially exhaustive extractions. This month, we take a look at the principles and applications of NTDs, and recent developments in their use.

In the early 1990s, Janusz Pawliszyn and his research group at the University of Waterloo (Canada) introduced solid-phase microextraction (SPME), and a new era in sample preparation for gas chromatography (GC) began. With SPME, analytes could be sampled by adsorption onto a stationary phase via direct immersion into a liquid sample or via headspace sampling of volatiles. Sample collection, extraction, clean-up, and concentration are combined into a single operation prior to injection into a chromatograph. Although SPME has seen applications with liquid chromatography (LC), the technique really made its mark as a sample preparation device for GC. We’ve somewhat recently provided an update to developments in SPME (1).

Perhaps most impressive about the development of SPME is its versatility, evidenced by new embodiments of the technique and spurring other sorptive-based extraction methods, such as thin-film microextraction (TFME), coated-blade spray (CBS) for mass spectrometry (MS), or stir-bar sorptive extraction (SBSE). Another of these unique geometries for SPME is the concept of needle-trap devices (NTDs) introduced by Koziel, Odziemkowski, and Pawliszyn in 2001 (2). Although different geometries exist, NTDs typically use syringe needles, generally the 23-gauge needles common for GC injection, packed with solid sorbent particles. NTDs are not to be confused with other packed-needle or syringe-tip approaches, like disposable pipet tips (DPX) or microextraction by packed sorbent (MEPS), which bear greater resemblance to conventional solid-phase extraction (SPE). Although, in theory, an NTD could be used with liquid samples, in practice the flow resistance limits these applications. An NTD configuration is displayed in Figure 1.

FIGURE 1: Schematic diagram of a needle-trap device (NTD) featuring an adsorbent packed inside a syringe needle, with an (optional) side hole in the syringe to facilitate air flow during sampling and carrier gas flow during desorption. Modified from reference (3) with the Creative Commons license.

With NTDs, given the greater amount of sorbent compared with conventional SPME approaches, diffusion of analytes into the needle and to the sorbent bed is not subject to equilibrium limitations. In fact, needle-trap extraction (NTE) is often considered exhaustive. The exhaustive nature of NTE removes the calibration considerations found with SPME and other non-exhaustive equilibrium extractions. The extraction mode typically occurs in conjunction with dynamic headspace sampling. This active sampling approach is possible because of the lack of diffusion limitations and serves to increase the speed of extraction. However, passive sampling approaches are also possible. Like SPME, NTE is a single-step procedure and solvent free (discounting the sorbent phase, which acts as the extracting solvent). Following extraction, the NTD can be stored for extended periods with little loss of analyte, then taken to the gas chromatograph, where, upon insertion into the injection port, analytes are thermally desorbed from the sorbent bed and move to the GC column. The needles packed as NTDs often have a side hole that provides three advantages: providing a portal for introducing the sorbent during the packing process; allowing for flow during dynamic headspace sampling; and, most importantly, facilitating flow of carrier gas in the GC inlet to stimulate quantitative transfer of desorbed analyte to the GC column. Some needle configurations feature a conical or extended tip to further direct analyte to the column.

Perhaps the biggest limitation of NTE is breakthrough volume: Once the sorbent bed becomes saturated with analyte, additional analyte is not collected in the needle trap. Breakthrough volume will primarily be a function of sorbent amount and surface area, and a plot of amount extracted versus analyte concentration in the sample is linear until breakthrough occurs. This allows quantitative extraction. Compared with standard SPME, NTE addresses concerns with fragility and adsorption capacity of the SPME fiber. Because there is more sorbent in an NTD, partition coefficient limitations are addressed, allowing extraction of analytes with smaller log P values. The time for an NTE is shorter than both SPME and traditional sorbent-based sampling tubes. All of the attributes of NTDs render them useful for on-site sampling or in- field analysis; for example, with occupational exposure studies. These features, combined with its solvent-free nature, give NTE serious green chemistry attributes. A recent review (4) provides a thorough discussion of the principles and practice of NTDs. NTDs have evolved to where they are now offered commercially by PAS Technology, Shinwa, PerkinElmer, and CTC Analytics.

Perusal of the NTD literature since 2020 reveals interesting trends. Particularly interesting are the extraction of aerosol droplets and particles, use of metal organic frameworks (MOFs) as sorbent phases, and breath analysis, including for disease diagnosis.

Extraction of Droplet-Bound and Gaseous Sample Components

Typically, the capture and analysis of droplet-bound and gaseous sample components, such as in breath analysis, are performed separately. Zeinali and Pawliszyn (5–8) have reported on combining a filter in front of the sorbent bed for a unified approach to this analytical challenge. The filter (for example, divinylbenzene) collects particulate matter and aerosol droplets, while Carboxen or other sorbent phases trap the volatile, gaseous compounds. They studied the stability of the droplets and gases in the air- sampling bags and obtained detection limits down to 0.05 mg/mL for their analysis. The method was applied with great success to pesticide aerosols, air from (burning) scented candles, and in the breath of volunteers following exposure to air freshener spray, fragrance mists, cannabis smoking, and incense.

Metal Organic Frameworks (MOFs) as Sorptive Media in NTDs

Development of newer phases to comprise the sorbent bed in NTDs is of ongoing interest. One type of media seeing wide development are MOFs. Table I summarizes selected recent applications of this technology. As a general rule, this approach provides sub-ng/mL limits of detection and exhibit minimal loss of analyte when the NTDs are stored at sub-ambient temperatures.

In addition to MOFs, other sorptive phases have been recently presented for NTD applications. A reduced graphine oxidemelamine formaldehyde phase created a superhydrophobic sorbent (19) with a reported 0.2 μg/L detection limit for chlorobenzenes. The NTD was reused over 200 times. An NTD with a Schiff base network-1/single-walled carbon nanotube (SNW-1/SWCNT) was created for phenolics analysis with limits of detection down to 0.002 ng m/L (20).

NTE for bovine respiratory disease diagnosis via cattle breath analysis was developed, though not enough evidence was obtained to create a useful profile of volatile organic compounds (VOC) (21). VOC analysis via NTE of fecal and tissue samples from cattle and goats identified a bacterial culture (22). With optimization, VOC emissions could be monitored for diagnosis of paratuberculosis in livestock.

As a unique embodiment of SPME, NTE presents a versatile, exhaustive extraction device for the sample preparation of volatile analytes prior to GC analysis. Continued development of needle configurations and interfacing with a GC inlet and types of sorbent media, including filter-sorbent combinations, will advance NTE into the everyday realm for occupational exposure, disease diagnostics, aerosol-bound analytes, and breath analysis.

(1) Raynie, D.E., Recent Advances in Solid-Phase Microextraction, Part I: New Tricks for an Old Dog. 

(2) Koziel, J.A.; Odziemkowski, M.; Pawliszyn, J. Sampling and Analysis of Airborne Particulate Matter and Aerosols Using In-Needle Trap and SPME Fiber Devices. 

(3) Cheng, W.-H.; Lai, C.-H.; Tzeng, W.-J.; Her, C.; Hsu, Y.-H. Gaseous Products of Incense Coil Combustion Extracted by Passive Solid Phase Microextraction Samplers. 

(4) Zeinali, S.; Khalilzadeh, M.; Pawliszyn, J.; The Evolution of Needle-Trap Devices with Focus on Aerosol Investigations. 

(5) Zeinali, S.; Pawliszyn, J. Needle-Trap Device Containing a Filter: A Novel Device for Aerosol Studies. 

(6) Zeinali, S.; Pawliszyn, J. Determination of Droplet-Bound and Free Gas-Phase Fragrances Using a Filter-Incorporated Needle-Trap Device and Solid-Phase Microextraction Technologies. 

(7) Zeinali, S.; Ghosh, C.; Pawliszyn, J. Simultaneous Determination of Exhaled Breath Vapor and Exhaled Breath Aerosol Using Filter-Incorporated Needle-Trap Devices: A Comparison of Gas-Phase and Droplet-Bound Components. 

(8) Zeinali S.; Pawliszyn, J. Effect of Household Air Pollutants on the Composition of Exhaled Breath Characterized by Solid-Phase Microextraction and Needle-Trap Devices. Anal. Bioanal. Chem. 2022, 414(18), 5573–5583. DOI: 

(9) Javanmardi, H.; Abbasi, A.; Bagheri. H. The Geometrical Characteristics of Nickel-Based Metal Organic Framework on its Entrapment Capability. 

(10) Firoozichahak, A.; Bahrami, A.; Ghordani Shahna, F.; Aizadeh, S.; Nematollahi, D.; Farhadian, M. Development of a Needle Trap Device Packed with Titanium-Based Metal-Organic Framework Sorbent for Extraction of Phenolic Derivatives in 

(11) Javanmardi, H.; Abbasi, A.; Bagheri, H. Roles of Metal, Ligand, and Post Synthetic Modification on Metal Organic Frameworks to Extend their Hydrophobicity and Applicability Toward Ultra-Trace Determination of Priority Organic Pollutants. 

(12) Noorpoor, Z. The Needle Trap Extraction Capability of a Zinc-Based Metal Organic Framework with a Nitrogen Rich Ligand. 

(13) Rahimpoor, R.; Firoozichahak, A.; Nematollahi, D.; Alizadeh, S.; Alizadeh, P.M.; Langar, A.A.A. Determination of Halogenated Hydrocarbons in Urine Samples Using a Needle Trap Device Packed with Ni/Zn–BTC bi-MMOF via the Dynamic Headspace Method. 

(14) Saedi, N.; Bahrami, A.; Ghorbani Shahna, F.; Mohraz, M.H.; Faradian, M.; Alizadeh, S. A Needle Trap Device Packed with MIL-100(Fe) Metal Organic Frameworks for Efficient Headspace Sampling and Analysis of Urinary BTEXs. 

(15) Rahimpoor, R.; Firoozichahak, A.; Nematollahi, D.; Alizadeh, S.; Alizadeh, P.M.; Langari, A.A.A. Bio-Monitoring of Non-Metabolized BTEX Compounds in Urine by Dynamic Headspace-Needle Trap Device Packed with 3D Ni/Co- BTC Bimetallic Metal-Organic Framework as an Efficient Absorbent. 

(16) S. Soury, A. Firoozichahak, D. Nematollahi, S. Alizadeh, H. Kakaei, and A. Abbasi. Needle-Trap Device Packed with the MIL-100(Fe) Metal–Organic Framework for the Extraction of the Airborne Organochlorine Pesticides. 

(17) Rahimpoor, R.; Firoozichahak, A.; Alizadeh, S.; Soleymani-Ghoozhdi, D.; Mehregan, F. Application of a Needle Trap Device Packed with a MIP@MOF Nano-Composite for Efficient Sampling and Determination of Airborne Diazinon Pesticide. 

(18) Rahimpoor, R.; Firoozichahak, A.; Alizadeh, S.; Nematollahi, D.; Urinary Bio-Monitoring of Amphetamine Derivatives by Needle Trap Device Packed with the Zirconium-Based Metal–Organic Framework. 

(19) Zare, F.D.; Allahdadlalouni, M.; Baktash, M.Y.; Bagheri, H. Reduced Graphene Oxide–Melamine Formaldehyde as a Highly Efficient Platform for Needle Trap Microextraction of Volatile Organic Compounds. 

(20) Safarpour Khotbesara, N.; Bahrami, A.; Habibi Mohraz, M.; Afkhami, A.; Farhadian, M. Development of a Needle Trap Device Packed with the Schiff Base Network-1/Single-Walled Carbon Nanotube for Sampling Phenolic Compounds in Air.

(21) Haddadi, S.; Koziel, J.A.; Engelken, T.J. Analytical Approaches for Detection of Breath VOC Biomarkers of Cattle Diseases- A Review. 

(22) Safarpour Khotbesara, N.; Bahrami, A.; Habibi Mohraz, M.; Afkhami, A.; Farhadian, M. Development of a Needle Trap Device Packed with the Schiff Base Network-1/Single-Walled Carbon Nanotube for Sampling Phenolic Compounds in Air. 

 is a Department Head and Associate Professor at South Dakota State University. His research interests include green chemistry, alternative solvents, sample preparation, high-resolution chromatography, and bioprocessing in supercritical fluids. He earned his PhD in 1990 at Brigham Young University under the direction of Milton L. Lee. Raynie is a member of 

’s editorial advisory board. Direct correspondence about this column via e-mail to 

Articles

Continued development of needle-trap extraction (NTE) has expanded
its versatility in applications such as disease diagnosis, aerosol-bound analytes, and breath analysis.

Overview of Recent Development of Needle-Trap Devices for Analysis of Volatile Compounds

Stationary phase chemistry, polarity, and selectivity have been of ongoing interest since the inception of gas chromatography (GC) in the 1950s. In the early days when most analyses were performed on packed columns, there were hundreds of stationary phase materials available. Today, with modern capillary columns, most GC analyses are performed with a few stationary phases, with a wide array of choices for specialty applications. Stationary phases are often classified using the broad term polarity, with polar stationary phases recommended for separating polar analytes and nonpolar stationary phases recommended for nonpolar analytes. In this installment, we examine the idea of stationary phase polarity. We examine the assumptions inherent in the most popular stationary phase polarity evaluating systems—McReynolds constants and the polarity scale. We see that high polarity does not always mean greater retention or higher selectivity.

 are probably the most used modifiers when describing stationary phases in gas chromatography (GC). We have all heard about stationary phases, such as polydimethylsiloxane (PDMS), being called nonpolar, cyano-modified PDMS being called moderately polar, and polyethylene glycol (PEG) being called polar. We also all remember the definitions of polar molecules and polar bonds from undergraduate chemistry, so we think about electronegativity differences and the presence of heteroatoms in the structure as a sign of a more polar compound. Translated to GC, we then use the old maxim “like dissolves like” to think about which stationary phase to choose to retain a given analyte or set of analytes. The stationary phase that seems to have a structure most similar to the analytes or the stationary phase that is on hand is often the eventual choice.

Whenever I am told that a compound is polar, I ask, “Compared to what?” As we are in winter here in the eastern United States, I am reminded of a common and often entertaining social situation—talking about the weather. Depending on who you talk with, a nice January day in New York City, with an average temperature of 39 °F or 4 °C, may be either hot or cold, depending on who is discussing it. My friend from Florida might think it is cold, whereas my other friend from Vermont might think it is warm. So which is it? Polarity must be thought of in a similar fashion in that the word by itself is meaningless without a standard for comparison.

In GC, there are two common measures for providing this comparison, both of which we discussed in an earlier column (1). These are Rohrschneider-McReynolds constants, developed in the 1960s, and a polarity scale developed by Mondello in 2011 (2–4). As discussed in the previous column, the Mondello polarity scale is based on a sum of McReynolds constants, so both measures have the same basis. Both methods can easily lead to the common assumption that higher numbers for the constants indicate greater retention and greater selectivity for compounds that have a similar structure to the test probes.

To examine this assumption, consider three stationary phases (PDMS, PEG, and an ionic liquid, SLB-IL-100) whose structures are shown in Figure 1. A quick examination of the structures shown in Figure 1 shows a different chemistry for each stationary phase. We can expect that PDMS retains analytes by dispersive interactions, indicating strong retention for analytes containing high hydrocarbon content. PEG, with a high hydroxy content, indicates strong retention of analytes capable of hydrogen bonding. Finally, SLB-IL-100, a molten salt, has complex retention properties, but because it is a salt, we can expect that it will not strongly retain hydrocarbons. Although a long hydrocarbon chain is seen in the structure, it seems likely that this chain, being flexible, bends to a conformation that exposes more of the polar and ionic character of the structure.

FIGURE 1: Structures of nonpolar, polar and very polar stationary phases. (a) poly-dimethylsiloxane, PDMS (nonpolar), (b) polyethylene glycol, PEG (polar), (c) SLB-IL-100 (very polar).

Table I shows McReynolds constants and the sum of the constants for the three stationary phases seen in Figure 1. The constants and the total are much higher for the PEG and ionic liquid stationary phases than for PDMS. Details on how the McReynolds constants are determined using Kovats retention indexes and how the sum of the McReynolds constants are used to determine polarity numbers are provided in a previous column (1).

Kovats retention indexes and McReynolds constants are determined isothermally, so the temperature also has a major impact on the result and must be known. Figure 2 shows a chromatogram of four of the test probes used for determining McReynolds constants plus the necessary alkanes on a PDMS stationary phase, simulated using ProEZ GC online software (5). To calculate the McReynolds constant for an analyte, such as benzene (peak 2), in the chromatogram, the adjusted retention times of hexane (peak 1), benzene, and heptane (peak 5) must be known. The actual Kovats retention index for benzene in this chromatogram is 655, indicating that it is eluted about halfway between hexane (600) and heptane (700). The McReynolds constant for benzene would then be determined by subtracting the Kovats retention index for benzene on squalene from the Kovats retention index on PDMS.

FIGURE 2: Pro EZGC chromatogram modeler for McReynolds probes and alkanes on PDMS. Column: PDMS, 30.00 m, 0.25 mm i.d., 0.25 μm, carrier gas: helium, constant flow @ 2.03 mL/min, Average velocity: 41.37 cm/sec, outlet pressure: 14.70 psi, oven temperature: 40 °C. Sample: 1) hexane (C6), 2) benzene, 3) 1-butanol, 4) 2-pentanone, 5) heptane (C7), 6) pyridine, and 7) octane (C8).

Benzene is an aromatic hydrocarbon that undergoes both dispersive and pi–pi interactions. McReynolds constants are determined by the difference between the Kovats retention indexes measured on the stationary phase of interest and on a classical nonpolar stationary phase, squalene, which is the liver oil from sharks. One challenge when working with McReynolds constants today is the availability of columns made using squalene, which were popular in packed columns but not used in capillary columns. Our nonpolar stationary phase in this discussion, PDMS, is considered the most nonpolar stationary phase in GC today.

Looking more closely at the constants for benzene shown in Table I, we see that the constants are much higher for PEG and SLB- IL-100, implying that the more polar stationary phases should retain benzene much more strongly. However, this is not the case.

Table II shows the retention factors, McReynolds constants, Kovats retention indexes, and polarity numbers for benzene on the three stationary phases. The retention factors were determined at 60 °C. With the thin film columns used, we see that the retention factors are low, indicating that retention is not strong. We quickly make a couple of interesting observations. First, benzene is more strongly retained, as seen by the higher retention factor on PDMS, the least polar column than on SLB-IL-100. This observation may not be too surprising as PDMS primarily exhibits dispersion forces which are prevalent in benzene while SLB-IL-100 does not.

If benzene is less retained on SLB-IL-100, then why are the McReynolds constants so much higher? Looking at the Kovats retention index data in Table II, we see that the hydrocarbons that are used as standards in the calculation must shift to much lower retention factors themselves as the stationary phase becomes more polar, whereas the retention factor of benzene remains roughly the same or decreases. On PDMS, benzene is eluted between hexane and heptane. On PEG, it is eluted between nonane and decane. On SLB-IL-100, it is eluted after undecane. In short, the 

-alkanes have shifted to a much shorter retention, but retention of the analyte remains approximately the same. If this is the case, is it appropriate to then state that the stationary phase is more polar, more retentive of the analyte, or has higher selectivity for similar analytes?

Does a high polarity, indicated either by large McReynolds constants or by a high polarity number, always indicate that a stationary phase will be retentive, giving long retention times? From the benzene example, a high polarity number or a high McReynolds constant does not always mean long retention times or high retention. Although the constants are a convenient and often illustrative example, we must consider how they are generated and whether the constants change because of a shift in retention of the analyte or of the 

Does a high polarity number or a high McReynolds constant mean that a column will also be highly selective? Because the polarity number scale is derived from a combination of several McReynolds constants, it is not useful in determining selectivity. It is just an overall measure of polarity, and because it considers several intermolecular interactions, it cannot be used to determine the effectiveness of the stationary phase to separate based on any one of them. A high McReynolds constant can be used as an indicator of selectivity for compounds that exhibit similar properties as the test probe, but this should be used with caution. If the high McReynolds constant results from a shift in alkane retention, rather than analyte retention, then the stationary phase might not exhibit the expected selectivity.

-alkanes as standards in the Kovats retention index calculation and by extension for the McReynolds constants and the polarity scale presents an interesting problem. Alkane retention provides a measure of dispersive interactions which are major components of retention in both gas chromatography and reversed-phase (RP) high performance liquid chromatography (HPLC). Because 

-alkanes themselves exhibit high dispersive interactions, using them as standards effectively subtracts dispersive interactions out of the discussion of retention.

, are by far the most seen intermolecular interaction in GC. We remember from undergraduate general chemistry that they occur in nonpolar molecules or in the nonpolar portion of larger or polar molecules. They occur because electron clouds are rarely evenly distributed around the molecule, generating momentary dipoles. This is especially evident in aliphatic hydrocarbons, which contain only nonpolar carbon–hydrogen bonds, arranged in a tetrahedral orientation. As the hydrocarbon chains get longer, dispersive interactions between molecules of the hydrocarbon increase, generating an increased boiling point. Similarly, increased dispersive interactions between analyte molecules and the stationary phase lead to longer retention times in GC.

-alkanes with our three stationary phases, we easily see that 

-alkanes will be most strongly retained on PDMS, followed by PEG and then by SLB-IL-100, because of the strongest dispersion forces being with PDMS, followed by PEG and very little dispersion force with SLB-IL-100. In the case of SLB- IL-100, the stationary phase repels alkanes so strongly that undecane, with a normal boiling point of 384 °C, has a retention factor of approximately 0.3, with 70% of the undecane molecules in the vapor phase at 60 °C, indicating very little retention.

The first solution to this problem is to consider the actual structure of the analytes and the expected intermolecular interactions qualitatively and carefully with the stationary phase, rather than simply looking at the McReynolds constant related to that interaction. Stronger retention can still be expected from stationary phases that exhibit similar intermolecular interactions, such as dispersion, pi–pi, or hydrogen bonding of the analytes. If a more rigorous analysis is needed, thermodynamic retention indexes, which are based on classical thermodynamics of separations and form the basis for chromatography modeling software, can be used, but these can also be tedious (5,6). To date, there is still no systematic way to fully predict separation power of a given stationary phase other than experimentation and classical method development.

Polarity and polar are common and easily misunderstood terms used to describe stationary phases in GC. When polar is used to describe a stationary phase or a solvent, the question, “Compared to what?” must be asked. PEG would be considered more polar compared to PDMS but less polar when compared to SLB-IL-100. The common measures of stationary phase polarity—McReynolds constants and the polarity scale—are not always accurate predictors of retentiveness or selectivity because of the use of 

-alkanes as retention time standards. A truly systematic and easy-to-use means for predicting retentiveness and selectivity based on stationary phase chemistry remains elusive.

(1) Snow, N.H. Stationary Phase Selectivity: The Chemistry Behind the Separation. 

(2) Rohrschneider, L. Eine Methode zur Charakterisierung von Gas- chromatographischen Trennflüssigkeiten. 

(4) Mondello L.; Ragonese C.; Sciarrone D.; et al. Evaluation of a medium-polarity ionic liquid stationary phase in the analysis of flavor and fragrance compounds 

(6) Dose E. Simulation of Gas Chromatographic Retention and Peak Width Using Thermodynamic Retention Indexes. 

 graduated with a MS from the Department of Chemistry and Biochemistry at Seton Hall University in 2021. She holds a BS degree in Chemistry from Veer Narmad South Gujarat University in India. Direct correspondence to: 

 is the Founding Endowed Professor in the Department of Chemistry and Biochemistry at Seton Hall University, and an Adjunct Professor of Medical Science. During his 30 years as a chromatographer, he has published more than 70 refereed articles and book chapters and has given more than 200 presentations and short courses. He is interested in the fundamentals and applications of separation science, especially gas chromatography, sampling, and sample preparation for chemical analysis. His research group is very active, with ongoing projects using GC, GC–MS, two-dimensional GC, and extraction methods including headspace, liquid–liquid extraction, and solid-phase microextraction. Direct correspondence to: 

The common measures of stationary phase polarity—McReynolds constants and the polarity scale—are not always accurate predictors of retentiveness or selectivity in GC.

Does High Polarity Mean High Retention on Stationary Phases in Gas Chromatography?

Stationary phase chemistry, polarity, and selectivity have been of ongoing interest since the inception of gas chromatography (GC) in the 1950s. In the early days when most analyses were performed on packed columns, there were hundreds of stationary phase materials available. Today, with modern capillary columns, most GC analyses are performed with a few stationary phases, with a wide array of choices for specialty applications. Stationary phases are often classified using the broad term polarity, with polar stationary phases recommended for separating polar analytes and nonpolar stationary phases recommended for nonpolar analytes. In this instalment, we examine the idea of stationary phase polarity. We examine the assumptions inherent in the most popular stationary phase polarity evaluating systems–McReynolds constants and the polarity scale. We see that high polarity does not always mean greater retention or higher selectivity.

Nonpolar, moderately polar, and polar are probably the most used modifiers when describing stationary phases in gas chromatography (GC). We have all heard about stationary phases, such as polydimethylsiloxane (PDMS), being called 

, and polyethylene glycol (PEG) being called 

. We also all remember the definitions of polar molecules and polar bonds from undergraduate chemistry, so we think about electronegativity differences and the presence of heteroatoms in the structure as a sign of a more polar compound. Translated to GC, we then use the old maxim “like dissolves like” to think about which stationary phase to choose to retain a given analyte or set of analytes. The stationary phase that seems to have a structure most similar to the analytes or the stationary phase that is on‑hand is often the eventual choice.

Whenever I am told that a compound is polar, I ask a simple questioncompared to what? As we are in winter here in the eastern United States, I am reminded of a common and often fun social situation—talking about the weather. Depending on who you talk with, a nice January day in New York City, with an average temperature of 39 °F or 4 °C, may be either hot or cold, depending on who is discussing it. My friend from Florida might think it is cold, whereas my other friend from Vermont might think it is warm. So which is it? Polarity must be thought of in a similar fashion in that the word by itself is meaningless without a standard for comparison.

In GC, there are two common measures for providing this comparison, both of which we discussed in an earlier column (1). These are Rohrschneider-McReynolds constants, developed in the 1960s and a polarity scale developed by Mondello in 2011 (2–4). As discussed in the previous column, note that the Mondello polarity scale is based on a sum of McReynolds constants, so both measures have the same basis. Both methods can easily lead to the common assumption that higher numbers for the constants provide greater retention and greater selectivity for compounds that have a similar structure to the test probes.

To examine this assumption, consider three stationary phases (PDMS, PEG, and an ionic liquid, SLB‑IL-100) whose structures are shown in Figure 1. A quick examination of the structures shown in Figure 1 shows a different chemistry for each stationary phase. We can expect that PDMS retains analytes by dispersive interactions, indicating strong retention for analytes containing high hydrocarbon content. PEG, with a high hydroxy content, indicates strong retention of analytes capable of hydrogen bonding. Finally, SLB-IL-100, a molten salt, has complex retention properties, but because it is a salt, we can expect that it will not strongly retain hydrocarbons. Although a long hydrocarbon chain is seen in the structure, it seems likely that this chain, being flexible, bends to a conformation that exposes more of the polar and ionic character of the structure.

Table 1 shows McReynolds constants and the sum of the constants for the three stationary phases seen in Figure 1. The constants and the total are much higher for the PEG and ionic liquid stationary phases than for PDMS. Details on how the McReynolds constants are determined using Kovats retention indexes and how the sum of the McReynolds constants are used to determine polarity numbers are provided in a previous column (1).

Kovats retention indexes and McReynolds constants are determined isothermally, so the temperature also has a major impact on the result and must be known. Figure 2 shows a chromatogram of four of the test probes used for determining McReynolds constants plus the necessary alkanes on a PDMS stationary phase, simulated using ProEZ GC online software (5). To calculate the McReynolds constant for an analyte, such as benzene (peak 2) in the chromatogram, the adjusted retention times of hexane (peak 1), benzene, and heptane (peak 5) must be known. The actual Kovats retention index for benzene in this chromatogram is 655, indicating that it elutes about halfway between hexane (600) and heptane (700). The McReynolds constant for benzene would then be determined by subtracting the Kovats retention index for benzene on squalene from the Kovats retention index on PDMS.

Looking more closely at the constants for benzene shown in Table 1, we see that the constants are much higher for PEG and SLB‑IL-100, implying that the more polar stationary phases should retain benzene much more strongly. However, this is not the case.

Table 2 shows the retention factors, McReynolds constants, Kovats retention indexes, and polarity numbers for benzene on the three stationary phases. The retention factors were determined at 60 °C. With the thin film columns used, we see that the retention factors are low, indicating that retention is not strong. We quickly make a couple of interesting observations. First, benzene is more strongly retained, as seen by the higher retention factor on PDMS, the least polar column than on SLB-IL-100. This observation may not be too surprising as PDMS primarily exhibits dispersion forces that are prevalent in benzene while SLB-IL-100 does not.

If benzene is less retained on SLB‑IL-100, then why are the McReynolds constants so much higher? Looking at the Kovats retention index data in Table 2, we see that the hydrocarbons that are used as standards in the calculation must shift to much lower retention factors themselves as the stationary phase becomes more polar, whereas the retention factor of benzene remains roughly the same or decreases. On PDMS, benzene elutes between hexane and heptane. On PEG, it elutes between nonane and decane. On SLB-IL-100, it elutes after undecane. In short, the n-alkanes have shifted to a much shorter retention, but retention of the analyte remains approximately the same. If this is the case, is it appropriate to then state that the stationary phase is more polar, retentive of the analyte, or has higher selectivity for similar analytes?

Does a high polarity, indicated either by large McReynolds constants or by a high polarity number, always indicate that a stationary phase will be retentive, giving long retention times? From the benzene example, a high polarity number or a high McReynolds constant does not always mean long retention times or high retention. Although the constants are a convenient and often illustrative example, we must consider how they are generated and whether the constants change because of a shift in retention of the analyte or of the n-alkane standards.

The use of n-alkanes as standards in the Kovats retention index calculation and by extension for the McReynolds constants and the polarity scale presents an interesting problem. Alkane retention provides a measure of dispersive interactions that are major components of retention in both gas chromatography and reversed-phase high performance liquid chromatography (HPLC). Because n-alkanes themselves exhibit high dispersive interactions, using them as standards effectively subtracts dispersive interactions out of the discussion of retention.

, are by far the most seen intermolecular interaction in GC. We remember from undergraduate general chemistry that they occur in nonpolar molecules or in the nonpolar portion of larger or polar molecules. They occur because electron clouds are rarely evenly distributed around the molecule, generating momentary dipoles. This is especially evident in aliphatic hydrocarbons, which contain only nonpolar carbon–hydrogen bonds, arranged in a tetrahedral orientation. As the hydrocarbon chains get longer, dispersive interactions between molecules of the hydrocarbon increase, generating an increased boiling point. Similarly, increased dispersive interactions between analyte molecules and the stationary phase lead to longer retention times in GC

Looking at n-alkanes with our three stationary phases, we easily see that n-alkanes will be more strongly retained on PDMS than PEG than SLB-IL-100 because of the strongest dispersion forces being with PDMS, followed by PEG, and very little dispersion force with SLB‑IL-100. In the case of SLB-IL-100, the stationary phase repels alkanes so strongly that undecane, with a normal boiling point of 384 °C, has a retention factor of approximately 0.3, with 70% of the undecane molecules in the vapour phase at 60 °C, indicating very little retention.

The first solution to this problem is to consider the actual structure of the analytes and the expected intermolecular interactions qualitatively and carefully with the stationary phase, rather than simply looking at the McReynolds constant related to that interaction. Stronger retention can still be expected from stationary phases that exhibit similar intermolecular interactions, such as dispersion, pi–pi, or hydrogen bonding of the analytes. If a more rigorous analysis is needed, thermodynamic retention indexes, which are based on classical thermodynamics of separations and form the basis for chromatography modelling software, can be used, but these can also be tedious (5,6). To date, there is still no systematic way to fully predict separation power of a given stationary phase other than experimentation and classical method development.

 are common and easily misunderstood terms used to describe stationary phases in GC. When polar is used to describe a stationary phase or a solvent, the question, “compared to what?” must be asked. PEG would be considered more polar compared to PDMS but less polar when compared to SLB-IL-100. The common measures of stationary phase polarity, McReynolds constants and the polarity scale, are not always accurate predictors of retentiveness or selectivity because of the use of n-alkanes as retention time standards. A truly systematic and easy-to-use means for predicting retentiveness and selectivity based on stationary phase chemistry remains elusive.

Snow, N. H. Stationary Phase Selectivity: The Chemistry Behind the Separation. 

Rohrschneider, L. Eine Methode zur Charakterisierung von Gaschromatographischen Trennflüssigkeiten. 

, 6–22. DOI: 10.1016/S0021-9673(01)97064-5

McReynolds, W. O. Characterization of Some Liquid Phases. 

Mondello, L.; Ragonese, C.; Sciarrone, D.; et al. Evaluation of a Medium-Polarity Ionic Liquid Stationary Phase in the Analysis of Flavor and Fragrance Compounds. 

 Restek Corporation. https://ez.restek.com/proezgc (accessed December 2022).

Dose, E. V. Simulation of Gas Chromatographic Retention and Peak Width Using Thermodynamic Retention Indexes. 

graduated with a MS from the Department of Chemistry and Biochemistry at Seton Hall University, New Jersey, USA, in 2021. She holds a BS degree in chemistry from Veer Narmad South Gujarat University in India. Direct correspondence to:hetalrana711@gmail.com

 is the Founding Endowed Professor in the Department of Chemistry and Biochemistry at Seton Hall University, and an adjunct professor of medical science. During his 30 years as a chromatographer, he has published more than 70 refereed articles and book chapters and has given more than 200 presentations and short courses. He is interested in the fundamentals and applications of separation science, especially gas chromatography, sampling, and sample preparation for chemical analysis. His research group is very active, with ongoing projects using GC, GC–MS, two-dimensional GC, and extraction methods including headspace, liquid–liquid extraction, and solid-phase microextraction. Direct correspondence to: amatheson@mjhlifesciences.com

This instalment examines the idea of stationary phase polarity in detail. The assumptions inherent in the most popular stationary phase polarity-evaluating systems—McReynolds constants and the polarity scale—are discussed.

Does High Polarity Mean High Retention on Stationary Phases in Gas Chromatography (GC)?

Analytical methods are used in the biopharmaceutical industry to ensure the quality, efficacy, and safety of drug substances and drug products. One of the fundamental measures of the quality of a drug substance or drug product, including biopharmaceuticals, is the stability of the active pharmaceutical ingredient (API). In fact, the first International Council for Harmonization (ICH) guideline on quality, 

, is for drug stability. In this column, we look at drug stability in general, differences between large- and small-molecule stability (

), as well as the analytical methods used to measure the stability of a product. Although there are several analytical methodologies that can be used, and we touch on those briefly, we focus mostly on the chromatography and mass spectrometry methods used to characterize drug stability.

Analytical methods are used in the biopharmaceutical industry to ensure the quality, efficacy, and safety of drug substances and drug products. One of the fundamental measures of the quality of a drug substance or drug product, including biopharmaceuticals, is the stability of the active pharmaceutical ingredient (API). In fact, the first International Council for Harmonization (ICH) guideline on quality, ICH Q1, is for drug stability. In this column, we look at drug stability in general, differences between large- and small-molecule stability (

At the center of everything we do in the biopharmaceutical industry should be patients and ensuring that their medicines are high quality, safe, and effective. A cornerstone of any drug substance or drug product quality assessment is stability. Ensuring the stability of a biologic (or any pharmaceutical) gives patients confidence that their drugs are efficacious, perform as expected, and are available when needed. Stability ensures that patients receive the same high-quality product throughout the product’s expiry period (2).

, both pharmaceutical and biopharmaceutical, can be defined as the ability of a drug substance or product to retain the same properties and characteristics, within specified limits, that it had at the time of manufacture (3). Biopharmaceuticals are complex drugs that comprise thousands of molecules and a complex amino acid chain that is folded into complex structures. As a first-year biochemistry student can tell you, that complex structure directly impacts protein function. Thus, in biopharmaceutical stability, we are looking for the correct structure of a given protein therapeutic, such that its function, or its specific therapeutic effect, is achieved. Ensuring the stability of the drug substance or product ensures a high-quality product, accurate dosing, identification of degradants, mitigation of impurity-induced adverse events, and increased patient compliance.

In the case of a biopharmaceutical, instability is most commonly referring to degradants in terms of fragments or aggregation (protein aggregation was discussed in a previous set of columns in 2015 (4,5). Product instability can lead to a loss of potency or efficacy. Degradation products can lead to impurities and contaminants and safety and toxicity issues. They can also lead to problems with immunogenicity, an unwanted immune response against oneself (6), and alterations of bioavailability. In addition, product instability can affect appearance, smell, feel, taste, and precipitation, which may impact both product efficacy and safety but also patient compliance.

This all highlights the importance of understanding drug stability. How do we characterize drug stability? To determine the stability of a biopharmaceutical, a stability testing program is developed and implemented throughout the life cycle of the product. This stability program can comprise various components, which we will not go into in any great detail. However, briefly, one might consider long-term stability testing, accelerated stability testing, annual (follow-up) stability testing, and discrepancy testing as key components of a stability testing program. Long-term testing of the drug substance or drug product serves as the basis of the recommended storage conditions and shelf-life. Accelerated stability testing provides information on degradation pathways that the product is most vulnerable to. Annual testing provides additional information to guide long-term storage conditions. Discrepancy testing can confirm the stability of specific batches. In addition to these tests, other types of studies commonly seen in stability testing are cumulative expiry (for biologics, supports processing time, temperature, freeze, thaw, and refiltration), temperature cycling, photostability, reconstitution, comparability, and in-use studies (2,7). Thus, one of the major end goals of a stability testing program is determining shelf life or expiry under specific conditions, which are determined to support the maximum stability of the drug over time. The United States Food and Drug Administration (U.S. FDA) defines an expiration date as “the time period during which the product is known to remain stable, which means it retains its strength, quality, and purity when it is stored according to its labeled storage conditions” (8).

With a general understanding of drug stability in hand now, we can briefly discuss international guidelines for drug stability and how the differences between small (pharmaceuticals) and large (biopharmaceuticals) molecules are characterized. Over the years, there have been several guidelines developed by international consortia to individual drug authorities, World Health Organization (WHO) and others (2). Here, we focus on guidelines developed by one of those international consortia, the International Council for Harmonization (ICH), specifically the 

 series of guidelines, like all ICH guidelines, promotes a science-based, risk-based evaluation of drug substances and products related to stability. In addition, they allow for flexibility in their implementation so that they can be broadly implemented across the world. 

 indicating revision 2 of the guideline) is the foundational ICH guideline related to drug stability. The purpose of 

 is to provide basic guidance on how to ensure the quality of a drug substance or product under four general key considerations: selection of batches; study design; evaluation and outcome; and stability commitment. For the selection of batches, 

 provides guidance on what is considered a primary batch and production batch, how to select batches for specific testing, and how to consider storage conditions such as container closure. It provides guidance on formal stability studies, supporting data to be considered, how to conduct stress studies, and storage condition studies (for example, temperature and humidity), including long-term, intermediate, and accelerated testing and frequency of testing. 

 provides guidance on how to evaluate stability data and its outcomes, including specifications and significant changes. 

 also provides guidance on the appropriate stability commitment to propose and implement. It is important to note, that 

 specifically addresses pharmaceutical products (small molecule drugs), but more on that later (7).

 guidelines were developed and implemented in support of 

 was developed to address the possibility that light exposure can affect product quality. Specifically, 

 provides guidance to characterize the intrinsic photostability of new drug substances and products. It also offers guidance on the evaluation of photostability data to ensure light exposure does not result in changes to the drug substance or product. 

, one of the shortest ICH guidelines, reiterates the application of 

 provides guidance for using bracketing and matrixing in stability studies. 

 is defined as evaluating samples on the extremes of certain design factors at all time points. 

 is defined as evaluating a subset of the total number of possible samples for all factor combinations tested at a specific time point. 

 provides guidance on how to perform stability data evaluation and on such topics as extrapolation (using a known data set to infer information about future data sets) (10–13). And finally, 

 was withdrawn at the Yokohama June 2006 ICH meeting and definitions of Climatic Zones III and IV were left to the respective regions and the WHO guidelines (9). For reference, Table II provides the climatic zones used in temperature and humidity stability studies, including Zones III and IV from WHO.

 were written with pharmaceutical (small molecule) products in mind and are largely applicable to biopharmaceuticals. However, as we know, there are significant differences between large and small-molecule drugs. Briefly, more traditional pharmaceuticals are comprised of a defined chemical structure of identical copies and are synthesized chemically. There is rarely biological contamination, there are defined standards and specifications for impurities, there are sensitive, well-defined standards and discriminating analytical characterization methods, and stability programs that are well-defined and can be modeled. On the other hand, biopharmaceuticals are comprised of complex chemical structures that are comprised of thousands of atoms making up heterogeneous macromolecules made in living cells. Biopharmaceuticals are prone to biological contamination because of contamination for the production process using living cells. There are also process and product-related impurities with no fixed acceptable threshold and multiple orthogonal analytical methods used for characterization (see next section). And finally, biopharmaceuticals are susceptible to stability loss through temperature, shear, and light (14). These differences lead to the development of specific guidance, 

, related to biopharmaceutical stability.

 provides guidance on stability testing of biotechnological and biological products and applies to well-characterized proteins and polypeptides isolated from tissues, body fluids, cell cultures, or if they are produced using recombinant deoxyribonucleic acid (DNA) technology. A well-characterized biologic is defined by its product quality attributes, such as appearance, purity, activity and quantity, and structure. Many aspects of stability testing contribute to elements of a well-characterized biologic, such as specifications, analytical characterization, product trending, process controls, and monitoring and adherence to current Good Manufacturing Practices (cGMP). Specifically, 

 provides specific guidance for biologics around a selection of batches, stability-indicating profile, storage conditions, test frequency, specification, and labeling. In addition, 

 in sections 5 and 8 provides guidance on test methods to detect stability-indicating profiles (15).

To determine the stability of a biopharmaceutical, several orthogonal analytical techniques are employed for both the drug substance and the drug product. Generally speaking, these stability monitoring techniques must be able to detect changes in identity, purity, and potency. Methods are validated at the time of market approval.

 (15) recommends cell-based bioassays or using enzyme-linked immunosorbent assay (ELISA)-based binding assays for both drug substances and drug products. To monitor purity, 

 recommends monitoring size variants and charge variants. To monitor charge variants for both drug substance and drug product several methodologies are suggested including size exclusion chromatography-high performance liquid chromatography (SEC-HPLC), sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and capillary electrophoresis (CE)-SDS. Of note, SEC-HPLC is particularly useful in monitoring stability because it can differentiate aggregates and high molecular weight species, which are indignant of protein instability (16). Ion exchange (IEX)-HPLC and isoelectric focusing are recommended for monitoring charge state variants for drug substances and products. 

 also recommends monitoring the appearance and protein concentration for drug substance stability. For drug product stability, 

 recommends monitoring the appearance, color, clarity, protein concentration, subvisible particles (aggregates), container closure integrity, stabilizers, and preservatives.

In addition to these analytical methods, liquid chromatography–mass spectrometry (LC–MS) has become increasingly used in monitoring biopharmaceuticals and biopharmaceutical stability. A recent review by Vallejo and others (1) highlights the use of LC–MS as a tool to monitor biomolecular stability. This review gives a thorough look at MS-based techniques used to monitor protein stability from native MS to ion mobility–MS (IM–MS). However, for this column, let us review some of the trends in biotherapeutics.

For biopharmaceuticals, in particular monoclonal antibodies (mAbs), structurally sensitive MS methods have become more widely used, critical perhaps, to rapidly analyze high molecular weight species. Some of these trends include variable-temperature MS, footprinting MS, IM–MS, and collision-induced unfolding (CIU). Figure 1 provides representative thermal stability, MS, and IM–MS stability data for mAbs, biosimilars, fusion proteins, antibody–drug conjugates (ADCs), and bispecific antibodies. Variable-temperature experiments can monitor protein stability through monitoring melting temperatures whereas gas-phase technologies, such as MS, can be used to detect changes in gas-phase mAb structures in correlation with variable temperature experiments or changes in CIU mode. In variable temperatures, MS mAb samples are heated within the ESI source and the detection of degradation products and non-native disulfide bonds can be used to characterize the mAb stability (1,17). Footprinting MS techniques, such as hydrogen deuterium exchange (HDX)-MS or covalent labeling (CL)-MS, can provide information on structural integrity, changes in high molecular weight species, and antigen interactions.

FIGURE 1: Data and information content that can be expected from variable-temperature, MS, and IM data sets for the following biotherapeutic modalities: (a) mAbs, (b) biosimilars, (c) fusion proteins, (d) antibody−drug conjugates, and (e) bispecific antibodies. For variable temperature experiments, shifts to lower Tm values indicate a decrease in stability and higher values indicate an increase in stability. Changes in mass spectrometry indicate different structures or stoichiometries. For IM, shifts to lower CCS values indicate more compact structures while larger values indicate larger, often unfolded structures. By applying activation energy and monitoring unfolding, for example biosimilar IM, shifts in stability can be monitored by shifts in the IM peak relative to the activation energy.

IM–MS has been shown to be a powerful tool to characterize disulfide bonding patterns, structural differences between a biosimilar and its reference product, glycosylation patterns, and several other key structural elements linked to stability. Variable temperature IM–MS can also be used to monitor structural changes where the temperature of the IM-MS drift gas is adjusted. These techniques in parallel to CIU methods can help with monitoring domain structure and anion and cation adduction and binding. In addition, Figure 2 shows representative data of how CIU can monitor disulfide bonding, glycosylation patterns, ADC drug loading, domain exchange, HDX uptake, light-chain variants, bispecific stoichiometries, and differences between reference products and biosimilars (1).

FIGURE 2: CIU applications for biotherapeutic mAbs. (a) Differentiation of monoclonal IgG subclasses by disulfide bonding patterns and difference in CIU unfolding because of domain exchange. (b) Biosimilar antibodies have qualitatively similar fingerprints, but contemporary CIU analyses can quantitate subtle differences in stability. (c) Bispecific antibodies present CIU characteristics centered between the precursor structures. (d) Shifts in CCS and stability can be quantified as a function of increasing drug load in ADC biotherapeutics.

Biopharmaceutical stability is a fundamental part of ensuring product quality, safety, and efficacy. It refers to the ability of a drug substance or product to retain the same properties and characteristics, within specified limits, that it had at the time of manufacture (3). Several different organizations, such as ICH, produce guidance on how to monitor drug stability throughout a product’s life cycle. The monitoring of biopharmaceutical stability requires several orthogonal techniques that include LC (for example, SEC, reversed-phase LC) and MS. LC methodologies are well established for monitoring stability and MS methodologies are becoming increasingly used. Regardless of the stability program in place, and the analytical technologies used in that program, drug stability is a cornerstone of ensuring that patients get the drugs they need when they need them.

(1) Vallejo, D.D.; Rojas Ramirez, C.; Parson, K.F.; Han, Y.; Gadkari, V.V.; Ruotolo, B.T. Mass Spectrometry Methods for Measuring Protein Stability. 

(2) Bajaj, S.; Singla, D.; Sakhuga, N. Stability Testing of Pharmaceutical Products. 

Handbook of Pharmaceutical Analysis by HPLC, vol. 6

. (Separation Science and Techonology, Mountain View, 2005), pp. 335–358.

(4) Berkowitz, S.; Rathore, A.S.; Krull, I.S. Challenges in the Determination of Protein Aggregates, Part II. 

(5) Rathore A.S.; Krull, I.S. Challenges in the Determination of Protein Aggregates, Part I. 

Immunogenicity of Protein-based Therapeutics

https://www.fda.gov/vaccines-blood-biologics/biologics-research-projects/immunogenicity-protein-based-therapeutics

(7) International Conference on Harmonization, 

ICH Q1A(R2), Stability Testing of New Drug Substances and Drug Products

https://www.fda.gov/drugs/pharmaceutical-quality-resources/expiration-dates-questions-and-answers

WHO Guidelines on Stability Testing of Active Pharmaceutical Ingredients and Finished Pharmaceutical Products

https://www.who.int/publications/m/item/who-guidelines-on-stability-testing-of-active-pharmaceutical-ingredients-and-finished-pharmaceutical-products

(10) International Conference on Harmonization, 

Photostability Testing of New Drug Substances and Products

(11) International Conference on Harmonization, 

ICH Q1C, Stability Testing for New Dosage Forms

(12) International Conference on Harmonization, 

ICH Q1D, Bracketing and Matrixing Designs for Stability Testing of New Drug Substances and Products

(13) International Conference on Harmonization, 

(14) Hong, M.S.; Severson, K.A.; Jiang, M.; Lu, A.E.; Love, J.C.; Braatz, R.D. Challenges and Opportunities in Biopharmaceutical Manufacturing Control. 

(15) International Conference on Harmonization,

 ICH Q5C, Quality of Biotechnological Products: Stability Testing of Biotechnological/Biological Products

(16) Hong, P.; Koza, S.; Bouvier, E.S.P.; Size-Exclusion Chromatography for the Analysis of Protein Biotherapeutics and Their Aggregates. 

(17) Brown, C.J.; Woodall, D.W.; El-Baba, T.J.; D.E. Clemmer, D.E.; Characterizing Thermal Transitions of IgG with Mass Spectrometry. 

 is the Vice Provost of Research Economic Development and Director of Bioinnovation at Northeastern University, in Boston, Massachusetts.

 is a professor in the Department of Chemical Engineering at the Indian Institute of Technology in Delhi, India.

A drug stability program is a fundamental part of ensuring product quality,
safety, and efficacy. Here, we summarize essential guidelines, differences between large- and small-molecule stability, and the analytical methods used.


Analytical Methods to Determine the Stability of Biopharmaceutical Products

Robots outperform humans in five areas, and thus can be particularly beneficial for performing sample preparation for HPLC.

Tony Taylor is Group Technical Director of Crawford Scientific Group and CHROMacademy. His background is in pharmaceutical R&D and polymer chemistry, but he has spent the past 20 years in training and consulting, working with Crawford Scientific Group clients to ensure they attain the very best analytical science possible. He has trained and consulted with thousands of analytical chemists globally and is passionate about professional development in separation science, developing CHROMacademy as a means to provide high-quality online education to analytical chemists. His current research interests include HPLC column selectivity codification, advanced automated sample preparation, and LCâMS and GCâMS for materials characterization, especially in the field of extractables and leachables analysis.

 website (1) contrasting the performance of robots in industry against those of humans. The article highlighted five areas where robots perform better than humans: a) handling tedium; b) extreme sensing; c) strength and speed; d) unwavering focus; and e) perfect, objective recall, as well as three areas where humans still outperform robots: a) empathy; b) flexibility; and c) acceptability and trust.

It strikes me that, considering these points within the laboratory context, we may not be taking full advantage of the positive aspects of robotics, or indeed working to address the negative aspects. This is especially true when considering sample preparation and sample manipulation.

Most laboratory workers with more than just a few samples to process will be familiar with the daily routine of preparing samples, setting up equipment, running a system suitability, checking fitness for purpose, and then starting the “batch” of samples just before leaving work for the evening. Modern instruments have very much reduced the amount of finger crossing required on the journey home, and that sense of anticipation as one enters the laboratory the next morning to see if the “run has worked” or not, but those feelings of anxiety have not been completely eliminated. Automated robotics have the power to change this paradigm, with samples being prepared in a “just-in-time” fashion so that the analysis can begin at the start of the day, and much of it will be done as we leave work, preparation and analysis times permitting. We could then even rack up enough samples to do the same thing overnight, hence improving our throughput, if required.

Of course, we need the automated solutions to be very robust and reliable, and this is perhaps where the industry needs to evolve, to ensure that our engineering is optimized to the point at which fallibility is not a consideration. Again, most of us are familiar with situations where campaigns have been halted overnight because of a vial not being gripped or picked up properly, or a vial tolerance that has caused the autosampler to reject a particular sample, or even simply to drop the vial. Here, evolution in the flexibility and “learning” of the instruments could be improved. Provided that the rejected vial does not lie in a dangerous or obstructive position, the robot should be able to move to the next operation and simply flag the failure in the batch report, provided the sample is not a key System Suitability or QC, which may render the remainder of the analyses invalid.

There are myriad situations in which robotics can be used for sample preparation in high performance liquid chromatography (HPLC) and produce a better result than the human laboratory worker might otherwise manage. Operations such as sample dilution (including serial dilution), filtration, and derivatization are all possible. There are sample robotics that can handle weighing, mixing, and even centrifugation, yet I don’t see these solutions being employed in many HPLC laboratories, and I wonder why. I can state with certainty that the robot will follow a sample preparation or extraction protocol with much greater precision than a human if it has been properly “trained.” It will also follow the standard operating procedure (SOP) every time. Can we honestly say that we follow the SOP for every sample preparation that we undertake?

Always shaking for the precise time, using the same method of agitation, using the same spot in sonic bath, weighing or pipetting using exactly the correct technique...I could go on! Robots are infallible in terms of recall and repeatability; we are not.

Revisiting the title of this piece, do we need to evolve or die? Of course, it’s a fact of every species on the planet. How true is this of automation of sample preparation for chromatographic applications? Well okay, I like to use a shocking title to draw in the reader; however, the flexibility of modern automated systems and the range of tasks they can complete has been revolutionized, even in the past decade. Their unfaltering accuracy and repeatability is proven, and their ability to reduce worker exposure to both tedium and hazardous reagents or operations can also be clearly demonstrated.

Do we then just fear the “rise of the robots”? Surely not in our modern society, and I encourage you to further investigate the upsides of automation that, hopefully, I’ve been able to point out in this article.

5 things robots do better than humans (and 3 things they don’t)

https://www.therobotreport.com/5-things-robots-better-humans/

 is the Chief Scientific Officer of Arch Sciences Group and the Technical Director of CHROMacademy. Direct correspondence to: 

Viewpoints: The Rise of Automation—Evolve or Die?

Online monitoring of odour and taste components that occur at parts-per-trillion (ppt) levels in industrial process waters requires specialized analytical hardware that is generally not compatible with the harsh environmental conditions in these typical industrial settings. An alternative instrumental method is proposed that uses dynamic extraction in combination with gas chromatography (GC) equipped with a simple flame ionization detector (FID) to achieve these extremely low detection limits. The extraction process was fully automated by means of online solid-phase extraction (SPE). The combination of online SPE and GC–FID was used to monitor the quality of process water contaminated with 2-methylisoborneol and geosmin, which are two notorious odour and taste components, in volumes up to 1 L.

-1,10-dimethyl-trans-9-decalol) are two well-known off-flavours that can be produced by a wide variety of micro-organisms (1,2). They result, for example, in the typical stale odour that you come across in water that has been left unattended for a while in an uncooled plastic bottle. They are also responsible for the cork taint off-odour of wine (3,4). Because of their unpleasant odour and the fact that they are characterized by extremely low odour threshold levels, typically at the low ng/L level, both components are an important cause of product quality issues in multiple industries. Aquaculture production units and drinking water production facilities are particularly susceptible to significant production losses when either 2-MIB and/or geosmin are perceived in the final products they deliver. Therefore, these industries must regularly measure 2-MIB and geosmin concentrations to anticipate any potential quality issue well before product release. Unfortunately, accurate analysis of 2-MIB and geosmin at parts-per-trillion (ppt) concentrations—particularly in a monitoring scheme with reasonable time resolution—is far from straightforward. Obviously, sensitive and selective analytical instrumentation is mandatory, and because of the volatility of both components, gas chromatography (GC) in combination with mass spectrometry (MS) or MS/MS are the first choice. Moreover, a suitable enrichment technique is required as well, to amplify detector response well above background noise levels. Suitable techniques in that respect include headspace solid-phase microextraction (HS-SPME), which has been used quite extensively, but also stir-bar sorptive extraction (SBSE) (5–8). Both techniques are much alike and apply microextraction at equilibrium conditions. In the case of SPME, sampling is performed in headspace mode, whilst for SBSE the sampling device is submerged in the water sample and applied like a magnetic stir-bar, which improves mass transfer but complicates end‑to-end process automation. In the SPME procedure, 2-MIB and geosmin are promoted to the headspace by means of salting out, shaking, and incubating the sample at elevated temperature (40–60 °C) (7). Once in the headspace, components are trapped on the SPME fibre, which is coated with a small amount of a suitable polymeric material, such as carboxen/polydimethylsiloxane and divinylbenzene/carboxen/polydimethylsiloxane (9,10). After sampling—which can take up to 60 min for maximal sensitivity—the fibre is transferred to a hot GC inlet where the trapped components are quickly desorbed and swept to the GC column for subsequent analysis. Because of the small amount of material, these injections can be performed in splitless mode without significant peak tailing.

To prevent accidental release of 2-MIB and geosmin into the production process, online monitoring at regular time intervals would be of great industrial benefit. Unfortunately, the intended production facilities, such as water treatment plants and aquaculture production, are not properly equipped or controlled (dirty, high temperature, and relative humidity) to adequately accommodate the intended GC–MS instrumentation in close vicinity to the production line. In addition, the MS hardware, pumps, and electronics are not designed to be exposed to these harsh conditions 24/7 via continuous monitoring. In addition, the mass spectrometer requires trained operators to tune, use, and properly maintain the device. From our perspective, it would make more sense if the MS detector was replaced by a more robust and far less complicated device, such as the flame ionization detector (FID). Unfortunately, FID is less sensitive than MS, necessitating another, more effective procedure to concentrate the analytes prior to analysis. Driven by the nature of both samples and analytes, solid-phase extraction (SPE) seems to be a reasonable candidate to achieve the required degree of preconcentration, predominately because it is—contrary to SPME and SBSE—not an equilibrium technique but allows exhaustive extraction of target analytes from large sample volumes. Unsurprisingly, both 2-MIB and geosmin have been extracted successfully from aqueous samples using SPE with C18 packed cartridges (11,12). After extraction, both analytes were conveniently eluted from the cartridge by means of a suitable organic solvent, such as ethyl acetate, and the extract analyzed by GC–MS. Concentration factors of 2000 are reported in the literature (12).

An important factor that needs to be considered when applying SPE—particularly when compared with SPME and SBSE—involves the loss of sensitivity, as only a small portion of the obtained extract is injected, even when it is evaporated and/or when large-volume injection is applied. To compensate for this inherent sensitivity loss, users have only one option and that is to proportionally increase the volume of sample sent over the SPE cartridge. Unfortunately, this is not the most effective way of working, as increasing the sample volume requires the amount of packing material to be increased as well, to achieve exhaustive extraction without analyte breakthrough, which in turn increases the volume of extraction solvent for quantitative elution. To preserve the potential of SPE preconcentration, the entire extract needs to be transferred quantitatively to the GC column rather than only a portion, which is only feasible when the SPE cartridge is placed inline to the GC system during elution. Online SPE–GC is not an easy solution, mainly because of solvent incompatibility reasons when it is applied to process aqueous samples. Nonetheless, several attempts have been made, with varying success, including microextraction by packed sorbent (MEPS) and micro SPE (µSPE) in miniature cartridges (10,13). Both solutions are micro-options by design, very much aligned with common automated sample handling systems, and, therefore, direct injection of the entire elution solvent on the GC inlet is readily achievable. However, employing micro-scale configurations with minute amounts of packing material (only a few milligrams) for processing large sample volumes appears unsuited.

Initial investigation of online SPE–GC–MS for measuring pollutants in aqueous samples has been conducted by Bulterman et al. (14,15). More recently, the analysis of mineral oil hydrocarbons (MOH) in food, fats, and oils has gathered a lot of attention, mainly because of the potential risk that these components may have on public health (16). As a result, several instrument manufacturers have developed hardware solutions in which these analyses are performed using online liquid chromatography (LC)–GC–FID (16). Here, a long normal-phase silica column is used to fractionate the mineral oil saturated hydrocarbons (MOSH) from the mineral oil aromatic hydrocarbons (MOAH) and transfer both fractions to the GC system using an elegant combination of selection and backflush valves. Since both fractions are eluted with hexane and dichloromethane, respectively, quantitative transfer of both fractions to the GC system (parallel column setup, injection volumes 400 µL each) is in fact relatively easy. By the same philosophy, we developed a similar setup, replacing the LC column with a SPE cartridge, but maintaining the capability to transfer the fraction eluting from the cartridge column quantitatively to the GC system. Measures were taken to address the incompatibility between the aqueous sampling phase and subsequent elution with non-aqueous GC‑friendly solvents. In this article, we present our results on this new online SPE–GC–FID system for the preconcentration and analysis of 2-MIB and geosmin in process water.

A short, stainless-steel cartridge (30 × 2.1 mm × ¼ outside diameter [o.d.]., Restek, Interscience) was packed with 80 mg adsorbent particles (held in place with two metal frits) and served the purpose as a SPE cartridge. The cartridge was contained within a custom valve box (Interscience), equipped with two 6-port 2-position rotary valves (VICI International) to direct sampling, purge, and elution flows. Before sampling, the bed was preconditioned by rinsing with ethyl acetate (> 99%, Sigma Aldrich) and subsequently with an excess of ultrapure water (MilliQ, Sartorius). Analyses were performed with a 25 ppt 2-MIB and geosmin solution in 1 L ultrapure water prepared from a 2-MIB and geosmin stock solution (100 µg/mL mix solution, Sigma Aldrich).

An Ultimate 3000 HPG-3200BX binary pump (Thermo Fisher Scientific, Interscience) was used to control the flow over the packed SPE bed. Pump A was used to draw the water sample and push it over the SPE bed (1 L at 50 mL/min). After sampling, the adsorbed bed was dried with nitrogen (N5.0) and the flow paths were rinsed with ultrapure water and ethyl acetate consecutively. Finally, pump B was used to deliver 150 µL ethyl acetate over the adsorbent bed (at 300 µL/min)to elute the components from the packing bed and transfer the analytes quantitatively to the GC part of the instrument.

The outlet of the SPE cartridge was connected to a Trace 1300 GC (Thermo Fisher Scientific, Interscience) by means of a Y-piece, which also served as inlet point for the GC carrier gas (helium, N5.0, supplied at 5 mL/min). The remaining outlet part of the Y-piece was connected to 15 m of a 0.53 mm internal diameter (i.d.) fused silica capillary column (uncoated, except for the last 3 m with 0.25 µm polydimethylsiloxane, Thermo Fisher Scientific, Interscience), which served as retention gap to capture the entire volume of ethyl acetate eluting from the SPE cartridge. To remove the excess ethyl acetate prior to analysis, the retention gap was not connected directly to the analytical column (15 m × 0.25 mm, 0.25-µm Rxi-5 MS Mega, Interscience), but indirectly by means of a T-piece that also included a heated solvent vapour exit (SVE) valve. Briefly, during solvent transfer—that is, adsorbent bed elution—the SVE valve was opened so that all the ethyl acetate vapours were conveniently vented away. Just before almost all the ethyl acetate was removed, the SVE valve was closed, thereby connecting the retention gap with the analytical column, and chromatography was able to start. Flow rates between 30–50 mL/min were used to evaporate the solvent. The SVE temperature was kept at 150 °C. The GC oven was operated with a ramped temperature starting at 55 °C for 4 min before ramping to a final temperature of 240 °C at 50 °C/min. The entire unit was controlled by Chromeleon CDS (Thermo Fisher Scientific, Interscience).

 An online SPE–GC–FID analyzer was developed that consists of three distinct units: the pumping unit, the trapping unit, and the analysis unit. The pumping and trapping unit are designed to process large volumes of water, preconcentrate any dissolved analytes of interest, and subsequently elute the analytes in any GC-compatible solvent. At its heart, the instrument contains a small amount of an appropriate adsorbent that is recycled continuously for maximal uptime. The trapping unit contains several switching valves to appropriately direct sample, elution, and purge flows over the cartridge. The analysis unit consists of a GC–FID system. Analyte transfer was achieved by means of a setup that is similar to large volume on-column injection.

Preconcentration Process in Pump and Trapping Unit:

 The adsorbent bed was used to enrich 2-MIB and geosmin from water. The minimal sample volume required was determined by the instrument detection limit (IDL). In the case of GC–FID, this implied that at least 0.1 to 1 ng of both analytes must be trapped by the adsorbent and quantitatively transferred. In the case of 2-MIB and geosmin, the desired detection limit in water was close to 1 ppt; this corresponded to sample volumes from 1 to 0.1 L (for 0.1 ng and 1 ng IDLs, respectively).

The sampling process was fully automated by a combination of two 6-port rotary valves and a binary pump with wide applicable flow rate range (Figure 1). The aqueous sample was transferred from the sampling bottle over the packed bed via the first 6-port rotary valve. After passing over the packing material, the sample was discarded to waste by means of the second 6-port rotary valve (Figure 1[a]). Several solvents—acetone, hexane, toluene, ethyl acetate—were evaluated to quantitatively recover 2-MIB and geosmin from the extraction bed, and ethyl acetate was selected for the highest recovery in minimal volume. To avoid pressure inconsistencies because of the insolubility of water and ethyl acetate, the packed bed was dry purged with a high flow of nitrogen to remove any residual water prior to administering the ethyl acetate (Figure 1[b]). Afterwards, both 6-port rotary valves changed position to desorb the packed bed (Figure 1[c]). The entire extraction volume (150 µL ethyl acetate) containing the recovered analytes was quantitatively transferred to the GC column.

The required limit of detection (LOD) of the online SPE–GC–FID method was maximally 1 ppt. In an initial set of experiments, the method was evaluated in offline mode to evaluate to what extent this LOD could be achieved with reasonable sample volumes. A typical chromatogram resulting from an offline SPE extraction of 2-MIB and geosmin from a 1 L water sample, spiked to a concentration of 25 ppt, is depicted in Figure 2. Ethyl acetate elution volume was 0.15 mL and GC injection volume was 1 µL in splitless mode, which corresponds with 0.16 ng on-column. The corresponding peak for 2-MIB and geosmin were clearly distinguished from the background and achieved a signal-to-noise ratio of 4.4 for 2-MIB and 37.1 for geosmin.

A major benefit of online SPE is that the total volume of ethyl acetate required to recover the trapped components (150 µL) can be introduced onto the GC column, compared with only 1 µL when performing a standard splitless injection, giving rise to the sensitivity gain aimed for. To remove the excess of ethyl acetate prior to analysis, an SVE valve was employed in the setup, meaning that the SVE time is an important parameter that needs careful optimization. If the SVE valve is closed too soon, the amount of ethyl acetate transferred to the analytical column will be far too high, giving rise to column overload and severely disturbed chromatography. On the contrary, if the SVE valve is closed too late, there is a severe risk that not only all the solvent but also all of the analytes of interest will be lost. To determine the optimal SVE time, an offline procedure was applied in which variable volumes of ethyl acetate were introduced using an autosampler rather than using the online setup. The advantages of this approach are apparent. First, disconnecting the SPE module from the GC–FID system allowed the SVE time to be optimized without considering any flow rate restrictions imposed by the system. Very small sample volumes were difficult to generate using the high performance liquid chromatography (HPLC) pump, let alone be transferred to the GC system. Next, improper SVE settings did not severely impact the integrity of the SVE valve, nor did they result in system contamination. SVE time was determined experimentally for injection volumes of 10, 25, 50, and 100 µL. A linear correlation was obtained with a R² of 0.99 between injection volume and SVE time, allowing the SVE time to be extrapolated to 150 µL, that is, the volume of ethyl acetate required for quantitative elution of 2-MIB and geosmin. Final SVE time was 1.3 min. When applied to real samples, the online SPE method exhibited repeatability (n = 6) of 3.13% and 3.93% relative standard deviation (RSD) for 2-MIB and geosmin, respectively, at a concentration of 50 ppt.

Online Solid-Phase Extraction–Gas Chromatography–Flame Ionization Detection System for Monitoring Contaminants at Parts-Per-Trillion Concentrations in Process Waters