Pittcon 2009, the 60th Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy, was held in the massive McCormick Place in Chicago, Illinois, on March 8–13, 2009, the sixth time the Conference has been held there. This year's event hosted nearly 1000 instrument manufacturers and laboratory suppliers in more than 2200 booths. In addition to attending the exposition, the conferees listened to 2500 technical presentations, checked numerous company seminar rooms, or attended one or more of 100 short courses. 

Undoubtedly, Pittcon remains the most important yearly international analytical exhibition, where companies introduce their latest instruments, instrument accessories, software, columns, sample preparation equipment, and other consumable products. Because many past attendees have purchased one or more new products within three months after attending the show, most exhibitors attempt to maximize their booth traffic to meet as many potential customers as possible. 

The purpose of this report is to provide information about many of the new separation consumables and accessory products that were displayed at Pittcon 2009. In some cases, products that were introduced during 2008 but after Pittcon 2008 (1,2) might be included for reasons of completeness. The information is based upon manufacturers' responses to a questionnaire mailed in December 2008. Because of space limitations and the fact that some manufacturers did not respond to the questionnaire, this report cannot be considered an exhaustive listing of all new products that were introduced in Chicago. However, over the years, these Pittcon introduction summaries have provided a good source of information that would be difficult for one individual to gather during the four days of the exhibition. In addition, the products introduced have shown definite correlations to current research, development, and application activity in the separation sciences. 

As in previous years, columns and other products recommended by their manufacturers primarily for biomolecule separations or sample preparation are denoted in the tables with the designation BIO. Some of these products can be used for general high performance liquid chromatography (HPLC) separations as well, but their main emphasis is for biological samples.   

In this month's coverage, I will look at gas chromatography (GC) columns, sample preparation products, and hardware, accessories and kits for chromatography, and sample preparation. Last month, I described new introductions in the areas of HPLC; reversed-phase, normal- and bonded-phase, ion-exchange and ion, size-exclusion, and large- and preparative-scale chromatography; specialty chromatography columns; and thin-layer chromatography (TLC). 

Although GC is considered to be a relatively mature separation technique, new GC columns continue to be introduced each year at Pittcon (Table I), 11 in all for this year. A major portion of the GC columns were the wall-coated open tubular (WCOT) format but this year three new porous layer open tubular (PLOT) columns were shown. PLOT columns contain a thin layer of solid support on the internal surface of a fused-silica or metal capillary. Recent advances have come up with new technologies to ensure that the tiny stationary phase particles do not flake off of the column causing downstream problems such as detection spikes and flow fluctuations. The three PLOT columns introduced by Restek (Bellefonte, Pennsylvania) have molecular sieve, alumina, and porous polymer particles affixed to the capillary surface. PLOT columns are used generally for the separation of small volatile molecules such as solvents, hydrocarbons, and a wide range of gases including permanent gases. 

In recent years, there has been a trend in the introduction of low bleed and very inert columns that can be used for the analysis of increasingly smaller concentrations of polar compounds. For example, trace level semivolatile organics analysis using methods such as the EPA Method 8270 are important tools for accessing environmental contaminants worldwide. Thus, new levels of inertness are needed to tackle increasingly lower concentrations. This year two companies introduced new lines of inert WCOT columns. Agilent Technologies (Wilmington, Delaware) extended its family of DB- and HP-Ultra Inert line of GC capillary columns with the introduction of 1- and 5-MS columns that are tested individually with probes selected to provide in-depth evaluation of inertness. Also, Abel Industries (Newark, Delaware) introduced its AB-5-MS column that showed good inertness (low tailing/high recovery) with polar test probes — 4-chlorophenol, decylamine, and 1-dodecanol. Figure 1 and 2 show results of these challenging test probes on both of these columns. 

Application-specific columns are always popular introductions at the exposition. With the recent increased prices of petroleum-based fuels, alternative bio-based fuels have received increased attention. Column sets for biodiesel analysis were introduced by two companies and a bioethanol column by another (Table I). Both the European (DIN EN) and American (ASTM D) standards committees have established testing criteria for these new types of fuels. Diesel engines are designed to run optimally with fuels with certain specifications. Such specifications for FAMES, residual methanol, residual glycerin, and other biodiesel components are important in ensuring that the fuels meet the required standards. Similar standards are published for bioethanol obtained by biofermentation reactions. Other application-specific GC capillary columns introduced at Pittcon included those for blood alcohol analysis, drugs of abuse, and brominated flame retardants. 

As indicated in Table II, Pittcon '09 was another productive year with 22 new sample preparation products or families of products displayed. Not counted were sample preparation accessories (Table III), which will be covered later. Among the more popular introductions were products to deal with increasingly smaller amounts of available sample. Many solid-phase extraction (SPE) products ranging from cartridges, to pipette tips, to 96-well plates, and solid-phase macroextraction were among the formats introduced. Just about every mode of SPE (reversed phase, strong anion and cation exchange, adsorption–normal phase, complexation, mixed mode, molecular imprinting, and hydrophilic interaction chromatography [HILIC]) was displayed. A wide variety of media were introduced at Pittcon including polymer-based, graphitized carbon black, silica-based and a number of other inorganic oxides (for example, ZrO

Some unique products were to be seen on the Pittcon floor. Products that combine protein precipitation with SPE for the sample cleanup of biological fluids were noted at several booths. The Supelco (Bellefonte, Pennsylvania) HybridSPE product, useful for the analysis of drugs in biological fluids, combines protein precipitation with removal of phospholipids using Zr-coated silica particle as an SPE medium. One of the major causes of ion suppression in bioanalysis is the presence of phospholipids during liquid chromatography–mass spectrometry (LC–MS) or LC–MS-MS analysis in the positive-ion electrospray mode. The removal of most phospholipids is accomplished in a 96-well or cartridge format. Varian (Walnut Creek, California), although not physically present at Pittcon this year, introduced a non-drip filtration plate called the Captiva NDLipids that also combines protein precipitation with phospholipid removal via a lipid-depleting filter. Agela (Newark, Delaware) uses a mixed-phase polymer with a basic functional group that they claim removes much of the matrix while leaving analytes untouched thereby giving a cleaner extract than protein precipitation only. Evolute CX, a product of Biotage (Charlottesville, Virginia), is also a mixed-mode phase that performs a similar function. 

Table III: Chromatography and sample preparation accessories 

Polymeric SPE sorbents have become popular in recent years. Polymeric SPE devices are particularly attractive because they can dry out during the conditioning steps without affecting the recovery and reproducibility of the SPE experiment. They also can be treated with more aggressive washing solvents than silica-based sorbents. Oasis HLB, a hydrophilic-lipophilic balanced N-vinylpyrrolidone-DVB SPE copolymer manufactured by Waters (Milford, Massachusetts), has been incorporated into the disk format by Horizon Technology (Salem, New Hampshire) and used for environmental samples such as organics in drinking and waste water. Disks allow much higher flow rates than cartridges due to their larger cross sectional areas. Horizon has a semi-automated instrument that can handle one-liter bottles of water sample and accommodates various types of disks. Supelco introduced Supel-Select HLB modified styrene polymer which operates mainly in the reversed-phase mode but due to its hydrophilic character can retain polar compounds. Agilent introduced an entirely new line of polymer-based SampliQ SPE products. The cartridges and 96-well plates are based upon an HLB polyamide. Cation- and anion-exchange polymers also are available. 

Products for QuEChERS were first introduced last year (2). Pronounced "catchers," QuEChERS is an acronym for Quick, Easy, Cheap, Effective, Rugged and Safe sample preparation. This method is an emerging technique becoming increasingly popular in the area of multi-residue pesticide analysis in food and agricultural products. In QuEChERS, food and agricultural samples are first extracted with an aqueous miscible solvent (for example, acetonitrile) in the presence of high amounts of salts (for example, sodium chloride and magnesium sulfate) and buffering agents (for example, citrate) to induce liquid phase separation and stabilize acid- and base-labile pesticides, respectively. After shaking and centrifugation, an aliquot of the organic phase is subjected to further clean up using SPE. Unlike traditional methods using SPE tubes, dispersive SPE cleanup is conducted by mixing bulk amounts of SPE sorbent with the extract. After sample cleanup, the mixture is centrifuged and the resulting supernatant can either be analyzed directly or can be subjected to minor further treatment before analysis. Three new QuEChERS products were submitted for inclusion into my Pittcon 2009 article. (see Table II). For those interested, a brief introduction to the technique was published in an earlier installment of "Sample Preparation Perspectives" (3). 

Beside QuEChERS, which is used in food safety analysis, other application-specific SPE phases were introduced this year. In the environmental area, the SPE technique is now well accepted by regulatory agencies as a substitute for older methodologies like liquid–liquid extraction. A number of phases specifically for environmental samples were introduced covering EPA Methods 521, 8270, and 8290. Of all SPE-type of sorbents, applications of molecularly imprinted polymers (MIPs) are the most very compound or class specific. This year MIPs, introduced by Supelco/Sigma Aldrich as the SupelMIPs, were extended to cover the isolation of amphetamines from urine and fluoroquinolones from meat products (see Table II). To illustrate the use of the MIP technologies for the investigation of amphetamines and related drugs in urine, Figure 3 shows the MRM chromatograms in spiked urine (left of Figure 3). The right chromatograms show as follows: upper, Q1 scans at 150 amu for a standard showing methamphetamime and phentermine peaks; middle, extract after SupelMIP extraction; and lower, extract after polymer SPE cleanup. The Q1 scans at 150 amu reveal interferences which are ionized and pass into the MS detector in addition to methamphetamine and phentermine. Figure 3 shows that in the 6–10 min retention window, where all the amphetamines are eluted, the MIP chromatogram is much cleaner than the polymer SPE extract. 

In recent years, SPE pipette tips with specific phases are finding use in small samples for drugs of abuse, biological compounds, oligosaccharides, and phosphopeptides. A number of them were introduced this year at Pittcon. As an alternative to 96-well plates, this format is quite amenable to automation as many liquid handling systems are adapted to moving liquids through the tips; the flow through the tips can be bidirectional, which means that they can be loaded from the top or liquids can be drawn up from their base. Phases are often embedded onto the walls through chemical "gluing" processes. A slightly different approach to conventional SPE is the DPX (Dispersive Pipette Extraction) system from DPX Labs (Columbia, South Carolina). The DPX pipette tips have loose sorbent rather than fixed (embedded) sorbent and the sorbent is contained within the tip by a barrier on the top and a stainless steel screen at the pointed end. As depicted in Figure 4, liquid can be dispensed or drawn into the "void" volume of the pipette tip and allowed to come in intimate contact with the loose packing. In this approach, all of the SPE steps are performed by dispensing–drawing the various liquids into the pipette tip. The DPX technology has been automated by Gerstel (Linthicum, Maryland). 

Filtration is one of the most important sample preparation techniques for HPLC, particularly as the column dimensions and particle sizes decrease. Syringe filters are by far the most widely used sample cleanup product and filtration is often the last step before injection into the liquid chromatograph. Removal of particulates can prolong column life and prevent inlet frits from clogging creating pressure problems. This year a number of new filtration devices were introduced. An interesting and simple filtration device, designed mainly for inorganic digests but likely applicable to organic samples as well, is the Digifilter (SCP Science, Champlain, New York). Figure 5 depicts how the filter works. Basically, a liquid sample is added to a tube (Digitube) that is then connected to a membrane filter holder with a plug at one end. A syringe is affixed to the holder; the plug removed and as the plunger is withdrawn liquid is directed to a collection tube below. Filters are constructed of PTFE with 0.45- or 1.0-μm porosity.  

Chromatography and Sample Preparation Accessories

Table V lists 32 new hardware products and accessories for HPLC, GC, capillary electrophoresis (CE), and sample preparation. This year I have extended the columns and sample preparation accessories to include dedicated instruments that are designated to automate some of the sample preparation techniques reported in Table II. All of the products introduced were practical devices designed to make the chromatographer's life easier. I will cover each area individually. 

HPLC, LC–MS, and ion chromatography products: 

With the current worldwide shortage of acetonitrile, chromatographers are looking for ways to use less solvent. So the introduction of two solvent recycling systems for isocratic mobile phases could be welcome. These devices work in conjunction with a detector signal. When a peak is detected, a signal goes to a diverter valve that directs solvent to waste and when there is a baseline only signal the solvent (which should be free of any peaks or impurities) is recycled back into the reservoir. Pump modules for sample introduction, infusion, and other liquid pumping applications were introduced. A new system controller and method development software, along with a column selection system, make method development and solvent screening easier. With the current interest in ultrahigh-pressure liquid chromatography (UHPLC), peripherals capable of handling high pressures have began to pop up. This year, Optimize Technologies (Oregon City, Oregon) has extended its Opti-Guard EXP guard column system with a UHPLC model. The device, pictured in Figure 6, consists of a holder and cartridge column and is rated to 20,000 psi with hand-tight replaceable cartridges. It will interface to any UHPLC column with an auto-adjusting EXP Titanium Hybrid ferrule and special Opti-Lok EXP fitting to provide a zero-dead-volume connection. The total sweep volume (excluding the packed bed) is only 1.4 μL. Cartridges are available with internal diameters of 1.0 and 2.1 mm and lengths of 5 mm. 

With the world's supply of helium dwindling and becoming more expensive, users are looking for other carrier gas sources that can provide an inexpensive, safe, pure and plentiful gas supply. Hydrogen gas has superior properties to helium in terms of diffusivity and viscosity but users are often concerned about having additional cylinders of hydrogen around the laboratory. Hydrogen generators can provide carrier gas without fear of safety because only a few tens of milliliters are actually present at any given time. A new hydrogen generator was introduced this year. Just in case, your system does develop a leak, a new leak detector will spot it quickly. If your gas cylinder gets a little low on gas pressure, a new low gas alarm system alerts you by an audible signal or can wirelessly send a message to your PC alerting you to take action.  

New autosamplers with sample preparation capabilities should help to improve throughput and take away some of the manual sample preparation drudgery. Three companies have introduced such instruments for GC or HPLC. Bar-coded vials should help tracking valuable samples in these automated instruments. A new vial cap crimper will undoubtedly lessen carpal tunnel syndrome for those who have to prepare hundreds of vials a day.  

Pyrolysis is a useful technique for studying polymer degradation to understand breakdown in high pressure and high temperature conditions. CDS Analytical (Oxford, Pennsylvania), a long-time leader in this area, has introduced a new high pressure system that appears to be very versatile 

To go along with the filtration devices introduced in Table II, some semi-automated filtration devices are shown in Table III. A new SPE manifold system was introduced with seize-free connections. 

Devices such as evaporators, concentrators, mixers, aspirators, and digestors are designed to take away some of the drudgery of sample preparation. Such products are always found on the Pittcon floor and I list some of those shown this year with improved features over previous models. 

I would like to thank the manufacturers and distributors who kindly furnished the requested information in advance of Pittcon 2009, thus allowing a timely report on new product introductions. For those manufacturers who would like to be considered for inclusion into Pittcon 2010 coverage, please send the name of the primary company contact, the mailing address, FAX, and E-mail address to Patrick Kempf, Managing Editor, 

, c/o Advanstar Communications, 485 Rte. 1 South, Bldg. F, Iselin, NJ 08830, Attn.: Pittcon 2010 "Column Watch." 

"Column Watch" Editor Ronald E. Majors is Senior Scientist, Columns and Supplies Division, Agilent Technologies, Wilmington, Delaware, and is a member of LCGC's editorial advisory board. Direct correspondence about this column to "Column Watch," LCGC, Woodbridge Corporate Plaza, 485 Route 1 South, Building F, First Floor, Iselin, NJ 08830, e-mail 

Articles

Column Watch

Ron Majors brings readers the second part of his yearly review of all that was new and innovative at the annual Pittsburgh Conference.

New Chromatography Columns and Accessories at Pittcon 2009: Part II

: CASSS — An International Separation Science Society, 5900 Hollis Street, Suite R3, Emeryville, CA 94608; tel: 510-428-0740, fax: 510-428-0741,  

http://www.casss.org/cde.cfm?event=238060

www.cssconline.org/CSSC_Symp/Announcement.asp

38th ACS Great Lakes Regional Meeting (GLRM)

33rd International Symposium on Capillary Chromatography & Electrophoresis

: CASSS — An International Separation Science Society, 5900 Hollis Street, Suite R3, Emeryville, CA 94608; tel: 510-428-0740, fax: 510-428-0741, 

http://www.casss.org/cde.cfm?event=217045

23rd International Activated Carbon Conference

: Barbara Sherman, PACS, Inc., 409 Meade Drive, Coraopolis, PA 15108; tel. 724-457-6576; 800-367-2587, fax: 724-457-1214,  

http://pacslabs.com/conferences/iacc/iacc22.php

57th ASMS Conference on Mass Spectrometry

: ASMS, 2019 Galisteo Street Building I-1, Santa Fe, NM 87505; tel. 505-989-4517, fax: 505-989-1073,  

http://www.asms.org/Default.aspx?tabid=43

2nd Latin American Pesticide Residue Workshop (LAPRW)

http://www.world-of-photonics.net/en/laser/start

AAPS National Biotechnology Conference 2009

http://www.aapspharmaceutica.com/meetings/biotec/bt09/index.asp

64th ACS Northwest Regional Meeting (NORM)

Departments

Calendar

In the 1950s, petroleum-related gas chromatography–mass spectrometry (GC–MS) applications were one of the founding cornerstones of modern MS. Even now, "petroleomics" — not to be outdone by life science research — serves as an example of the cutting edge in liquid chromatography–mass spectrometry (LC–MS) (1). In petroleomics, high order MS techniques can differentiate between chemical species that differ by less than the mass of a single electron. Such astonishing precision has come about as the magnetic-sector mass spectrometers of previous decades have yielded their place to modern, time-of-flight mass spectrometers, improving the utility and flexibility of analyses with high mass accuracy and resolution. 

War in the middle east and Asia exacerbates fuel and heating oil shortages in the United States, heralding the end of an era of inexpensive oil. The stock market (with the exception of oil stocks) stumbles badly, and following the president's authorization of controls on oil prices, production, and allocation, the secretary of state announces a national plan to make the country "energy independent." You could be forgiven for assuming that, except for the last two items, I am recounting news of 2008. Yet the United States' president was Richard Nixon, and it was during his tenure in the early 1970s that oil prices went from slightly more than $3 a barrel to nearly $12. Later in that decade, the country faced a resurgence of energy-related issues. As the decades passed the United States' national energy policy is still a work-in-progress. 

Because it requires little cultural adaptation on the part of us humans, blending novel power-generating technologies with renewable energy sources would seem more promising of success. But even here, besides the considerable technological hurdles involved in developing alternative energy supplies, those who promote change encounter cultural resistance. Among the nontechnology issues that plague us are lawsuits brought by abutters who find wind machines problematic for aesthetic and other reasons, interest groups for whose members abandoning the status quo would cost money, the relatively high cost of the new technologies, and geographical restrictions on wind, thermal, or solar generation of electricity on any useful scale. Wave farms for instance, planned for the coast of the state of Rhode Island (Oceanlinx, Australia), would consist of 10–14 floating cable-tethered units each unit 90 ft long, 60 ft wide reaching a height of 30 ft above sea level. 

In December 2008, 120 windmills — the largest of Europe's onshore facilities — went online in Portugal. The total output is 650 GW hours per year, or 1% of the country's total energy needs — enough to provide power to about 300,000 homes (2). A relatively poor country by Western European standards, Portugal is already recognized for its unique, commercial wave-power plant, though the plant has been, so far, slow in meeting expectations (3). 

Figure 1: Commonly used plant species in the United States are viable for widespread growth. (Figure courtesy of Russ Miller at ORNL from Bioenergy Feedstock Development Program Status Report, ORNL/TM-2000/92, Model woody and herbaceous crops chosen for study in the 1990s.) 			

To date, the world's largest onshore wind farm, the Horse Hollow Wind Energy Center, is located in the United States, in Texas, and it provides more than 700 MW (4). Portugal is less than one-fifth the size of Texas, and its population, less than half that of Texas (10.6 million versus 23.5 million) is far more dense. Its goal of achieving more than 30% of its power generated by new technologies by 2020 is commendable (according to the Guardian article cited here, that figure is more than twice the amount planned for the U.K.). Nevertheless, the cost of new projects is substantial, and it looms as a frequent issue in technology development discussions. In this instance, the Portuguese government supplies subsidies up to 40% of costs.  

Whether for improved batteries, for greater deployment of electricity, or other alternative possibilities for decreasing our global consumption of fossil fuels, the challenges of these technologies are interdependent, and must be addressed together. 

The terminology we use to describe a new technology is often imprecise, and it tends to be as changeable as the novel technology is malleable. Biofuel, biodiesel and various other terms often become incorrectly interchangeable as they evolve. One area of endeavor that looks particularly promising in the renewable bioenergy field involve biofuels made from plants. Converting soluble plant sugars into liquid fuels involves deoxygenation: removing oxygen atoms from the raw cellulosic material. Subsequent refining steps create the branched hydrocarbons on aromatic compounds used for gasoline or as longer chains for diesel fuel. 

Europe currently leads in biodiesel production where the U.S.has focused on developing ethanol. By some estimates, biodiesel production is expected to reach 12 billion L by 2010, while worldwide consumption of petroleum products escalates 35% by 2025 (5).  

Ethanol developed from corn helped institute an E85 gasoline initiative in the U.S. Nevertheless, corn-based ethanol is now giving way to cellulose-based ethanol. Vehicles today can run on 10% ethanol mixed with gasoline. But as of 2006, only six million vehicles registered in the U.S. could run on 85% ethanol — that out of 250,851,833 passenger cars, according to U.S. Department of Transportation's Bureau of Transportation Statistics (6). 

				Steven Chu, secretary of the Department of Energy, has championed the use of biodiesel, which has initiated an upsurge in the prospects for biofuels, according to reports in 

Because of the cost volatility of corn, which is the primary source of ethanol in the U.S., the commodity is no longer looked upon favorably as a precursor of biodiesel. Loss from recent storms and increased demand for ethanol production, as well as inequities in foreign exchange rates, fuel this volatility, as prices favor exporting feed. Chu, the head of the Lawrence Berkley National Laboratory and a Nobel laureate in physics, is on record as arguing against the food-crop based ethanol approach (8). A 2007 report commissioned by the governments of China and Brazil called for intensive research into cellulose-based production, which relies upon technology like isolating microbes or using large amounts of heat and steam to break down the tough bits into fuel (9).  

How Widespread is the Interest in Cellulose-Based Fuels?

A wealth of information about cellulose-based fuels is posted on the newly developed Oak Ridge National Laboratories website (10). Links, reference data, conversion data, and a glossary make it a key source of information on the topic. 

Figure 2: T08024 (top trace) was a batch that showed no detectable monoglycerides. T07032 was a batch that had detectable levels of monoglycerides. (Courtesy Doug Stevens and James Stuart.) 			

 (11–13) on an almost issue-by-issue basis might not surprise us. What is surprising, however, is that automobile enthusiasts are as current with the technology as core researchers, thanks to publications like 

, which recently made the economic arguments (Whiter Lightning, Frank Markus: 

". . . self-seeding perennials require little or no tilling, minimal nitrogen, and no phosphorous or potassium fertilizer. The initial cost to establish these energy crops is estimated at $100–$250/acre but they live 10 years and yield 12–15 tons/acre, making 65–130 gallons of ethanol per acre depending upon the crop and processing method." 

A popular magazine seems an unlikely place to find this level of technological and economic detail. That it does indicates two things. First, the need for the technology is imminent, and the effects of that need are felt throughout our population. Second, the processing method mentioned at the end of the quotation indicates we still have work to do. 

Figure 3: Comparing the improvements gained using a time-of-flight instrument instead of a quadrupole-only design indicates the clear advantage in such complex mixtures. 			

Besides the DOE-supported consortium, comprising three laboratories (with Chu's Lawrence Berkley National Laboratory in the lead), some universities have long offered programs based upon the history of petroleum research. Several companies are already well-advanced decoding plant genomes and experimenting with gene expression. One of them, Ceres (

, of Thousand Oaks, California, currently markets varieties of switchgrass and sorghum. Sold under the name Blade Energy Crops, these bioproducts thrive in various climatic conditions and are designed to meet various biothermal or biochemical processes. The company claims planting energy crops on 40 million acres of idle or marginal farmland can potentially offset half of the gasoline needs of the U.S. It also says that planting those crops builds topsoil and does not adversely affect the country's capacity to produce food, all the while netting five times the energy yield of corn-derived ethanol. 

The Department of Energy, in conjunction with the Department of Agriculture, postulates that more than 1 billion tons of biomass could be produced exclusively for energy in the U.S. each year, all with only modest changes in terrestrial crop practices. Considering only the biomass conversion technology available today, that quantity of biomass could replace about 30% of the petroleum consumed by the United States. 

Current Practice and Analytical Necessities

Universities have long taken on basic energy research. Jay Keasling, a chemical engineer at the University of California Berkley, is noted widely for his work with yeast to produce the necessary endproducts for both pharmaceutical discovery and energy-development studies. Here again, another article in the popular press highlighted the widespread interest ("Saving the World, One Molecule at a Time," People to Watch: Jay Keasling, 

, Dec 29, 2008). The article notes grants from British Petroleum, among others, underwriting Keasling's work. Keasling is now head of the newly created American Joint BioEnergy Institute and armed with $134 million funding through 2013. 

Figure 4: The formula of C27H55O4S2 for the [M+H]+ is consistent with that for mono-linolein TMS, as suggested by the EI data from the monoglycerides standard and accurate mass measurement. Isotope pattern and mass accuracy reinforce each other and strengthen confidence in assignment. (GCâTOF results courtesy of Doug Stevens of Waters Corporation, Milford Massachusetts.) 			

Head of biodiesel testing at the University of Connecticut and professor emeritus James Stuart  and colleagues have been characterizing biodiesel for many years. A number of different vegetable oils are used to produce biodiesel (referred to as ±00) and each feedstock varies by fatty acid concentration and degree of saturation. In testing the stepwise transesterification (using methanol–potassium hydroxide) of the oil's triglycerides often results in the formation of monoglycerides, as incomplete saponification products. The various monoglycerides are eluted close to the desired 24-carbon-length methyl esters, making peak identities problematic. Assigning accurate masses to these mid-eluted peaks would allow positive identification of questionable peaks as undesired monoglycerides or desired methyl esters. 

Figure 5: Comparison of an information-rich EI spectrum displaying detailed fragmentation (at the expense of conserving the molecular ion needed to assure identification) with a CI spectrum of the same sample, which conserves the energy to produce the confirming molecular ion. 			

In his recent presentation at the Eastern Analytical Symposium (November 19, 2008), Dan Walsh, a chemical engineer with Bently Tribology Services (Peabody, Massachusetts) reviewed the realities of producing biodiesel. Most of the test methods and standards were designed for petroleum products. But biodiesel behaves differently. It is more sensitive to in-laboratory environmental factors than petroleum products. Elements of edible oil testing and petroleum testing methods are being refined to address uniqueness of product. For instance, problems that occur in storage and transit arise from no single cause but rather a synergistic effect produced from these conditions: 

Chilling (product B100 has "thermal memory")

Near-specification levels of bound glycerin (monoglycerides, diglycerides, triglycerides)

Professor Stuart's experience underscores one of the analytical issues. His GC–flame ionization detection (FID) results, based upon published work with vegetable oil methylesters (VOMEs) and fatty acid methyl esters (FAMEs) (14,15), disagree with current ASTM D6584 (Test Method for Determination of Free and Total Glycerin in B-100 Biodiesel Methyl Esters by Gas Chromatography). The methyl esters of C24, which are supposed to be eluted in the 0.80–0.82 RRT range (in comparison to a tricaprin standard), are eluted in the same range as the various known monoglycerides: near 0.83 min on two different GC columns commonly used in biodiesel research. 

Figure 6: The three coeluted peaks - monoolein, monolinolein, and monolinolenin - are easily distinguishable. So is the presence of trace impurities, using a 3D display. 			

The method is designed to analyze biodiesel samples for impurities that foul, clog, and cause deposits in engines using biodiesel as fuel. As a quality control measure, the trimethylsilyl (TMS) derivatized forms of monoglycerides, diglycerides, and triglycerides are quantified, ensuring the processing of the raw oils used as feedstock is complete and efficient. The presence of glycerides above a certain level is an indicator of a process failure. 

Figure 7: High-temperature GC analysis of canola oil showing the monoolein range expanded. Since one means of characterizing complex mixtures is by "fingerprint" comparison shifts in baseline, the retention time of coelution could be misconstrued as unacceptable glyceride presence. (Figure courtesy of James Stuart.) 			

This ASTM method consists of a 30-min GC analysis with FID and a special, high-temperature, capillary GC column. Only 0.100 g (eight drops) of the B-100 is weighed. Two internal standards are added, and any free-hydroxyl groups and acids in the B-100 are derivatized at room temperature using N-methyl-N-trimethylsilyltrifluoracetamide (MSTFA) in a pyridine solvent. After a 15-min reaction time, the sample is diluted to 10 mL with 

-heptane and 1.0 μL injected. The peaks' relative retention times are then compared with those of the two internal standards. For the B-100 sample to conform to ASTM standards, the amount of free glycerin must be less than 0.020%, and the total glycerin must be less than 0.240%, by mass. 

Making the correct identification of closely related formulas in complex mixtures is among the greater challenges facing the practitioner. Kind and Fiehn covered the topic in detail in a 2008 column (16). Predicting isotopes for given chemical entities is well-characterized and several companies offer software features that compare the theoretical isotope pattern with that of the acquired data, to increase the likelihood of arriving at the correct answer. For instance, Waters' (Milford, Massachusetts) MassLynx iFit algorithm indicates an increasingly better match between the theoretical and measured data with lower iFit ranking numbers. Bruker's (Billerica, Massachusetts) SmartFormula software ranks possible candidates according to a match factor between the measured isotopic pattern and the theoretical pattern for a given formula or sigma value, which should be close to zero. 

Resolving the differences between standards and representative samples using electron ionization (EI) and chemical ionization (CI) with isobutane and a variety of comparative tools, like NIST libraries, clearly indicates that when faced with an incomplete process or contamination, the two-dimensional GC–FID data alone is insufficient to indicate the specific source of an interference. Sometimes an acceptable batch result is masked by a deteriorated septum or other casualty of common practice. The resulting reduction of process downtime, along with the prevention of needless batch rejection, means increased productivity and prevents wasteful remedial actions to the process system. 

				The 3D figure shows another technique, one similar to the use of Krevelen diagram representations for the complex data discussed in Ryan Rodgers' petroleomics work (1).			

The relatively broad impurity peak, at 15.64 min, displays the same elemental composition as monoolein TMS, at 15.94 min (the middle peak in the inset). The other two peaks are similar to monoolein but have different degrees of saturation. (Monostearin TMS is shown at 16.20 min.) 

The early eluted peak has one more double bond, while the peak eluted later has two more double bonds than monoolein. These peaks are eluted within 1–2 s of each other, but the acquisition rate of 10 spectra/s is sufficient to profile the peaks accurately and reveal the coelutants. 

Walsh says in his EAS overview that anyone can make biodiesel, but its quality can vary. Even the ASTM method is a work in progress. Equations 6–8 are not consistent with instructions in the overview's section 9.4.1, which calls for the analyst to "Prepare a calibration curve for each reference component by plotting the response ratios (rspi), as the 

-axis, versus the amount ratios (amti), as the 

-axis." Current equations in the method plot the reverse: that is, the amount ratio as the 

-axis. Once updated, test method D6584 will become consistent with section 9.4.1 and also with other ASTM test methods, such as D4815, D5580, etc. in the plotting designation. 

Improvements such as Simulated Distillation (now either ASTM D2887 or ASTM D 7391) use GC data that replaces the classical "Distillation of Petroleum Products Under Reduced Pressure" (ASTM D1160) and the calculated Cetane Index uses some of the same GC data to replace the classical ASTM D976 "Cetane Number of Diesel Fuel Oil." Even so, there are common analytical pitfalls that increased analytical capability helps resolve. 

In addition to more traditional GC tools currently being employed, the newly opened BioEnergy Center at Oak Ridge National Laboratories has been equipped with an ultrahigh-pressure liquid chromatography (UHPLC) system (ACQUITY UPLCsystem, Waters Corporation, Milford, Massachusetts) with a quadrupole mass spectrometer (Sciex, Life Technologies, Mississauga, Canada), for high throughput quantitation, and a high-definition quadrupole time-of-flight (QTOF) instrument (SYNAPT, Waters Corporation) with ion mobility capability for qualitative development. 

The use of mass spectrometry to study biofuels is examined and the advantages and disadvantages are explored.

Alternative Futures: Biofuels and Mass Spectrometry

Chromatography of Protein Therapeutic Drugs

: Jim Sullivan, Jindai Bio, tel. (302)737-2137,  

Gas Chromatography: Fundamentals, Troubleshooting, and Method Development

: Thomas A. Brettell, Cedar Crest College, tel. (610)606-4666 ext.3495,  

: Ronald Sutton, Chromatography Institute; tel: 1-800-788-9922, 

HPLC Analysis and Purifications of Proteins & Peptides

: William Champion, SLED & Co., tel. (302)475-2466,  

Fundamentals of High Performance Liquid Chromatography

Short Courses

Quality-by-design (QbD) is a methodology gaining widespread acceptance in the pharmaceutical industry. A core tenet of this methodology is the idea of establishing the design space of a product or process as a primary R&D goal. Many articles have been published recently describing the successful application of QbD to process development. By recognizing a liquid chromatography (LC) instrument as a small process-in-a-box, one can readily see the applicability of QbD to LC method development. 

ICH Q8 defines a design space as "The multidimensional combination and interaction of input variables (for example, material attributes) and process parameters that have been demonstrated to provide assurance of quality"(1). Two key elements of this definition warrant brief discussion. First, the phrase "multidimensional combination and interaction" clearly indicates that the "design space" should be characterized by studying input variables and process parameters in combination, and not by a univariate (one-factor-at-a-time) approach. Second, the term "design space" is one of many terms used in the Design of Experiments (DOE) lexicon to denote the geometric space, or region, which can be sampled statistically by a formal experimental design. Other terms in common use include design region, factor space, and "joint factor space."(2). However, the phrase "demonstrated to provide assurance of quality" clearly defines this design space as a subset region of an experimentally explored design region in which performance is acceptable. Therefore, in this article, the term "experimental design region" refers to the geometric region described by the ranges of LC parameters studied in combination by a formal experimental design. When the experimental results are of reasonable quality, DOE can translate the experimental design region into a "knowledge space" within which all important instrument parameters are identified, and their effects on method performance are fully characterized. As DOE is fundamentally a model-building exercise, this translation is accomplished by deriving equations (models) from the experimental results. Given that the equations have sufficient accuracy and precision, they then can be used to directly establish the ICH-defined design space. The instrument parameter settings in the final LC method, thus, represent a point within the design space. The design space itself represents a region surrounding the final method bounded by edges of failure; parameter setting combinations inside the bounds have acceptable method performance, parameter setting combinations outside the bounds do not. 

Many pharmaceutical companies have adopted a two-phased approach to LC method development work in which column–solvent screening experiments are done as Phase 1, followed by formal method development as Phase 2. Part II of this article series described a QbD methodology for Phase 1 in which formal experimental design is used to study column type, organic solvent type, and pH. It also introduced the use of novel Trend Responses to  overcome knowledge limitations common to column–solvent screening studies due to the compound coelution and changes in compound elution order across experiment trials (peak exchange). Figure 1 presents a QbD-based workflow proposed for Phase 2 experiments by which an LC process design space can be established. As for the column–solvent screening work, a formal experimental design approach is used in Phase 2. 

 The first step in the QbD workflow is defining the experimental region. In LC method development, knowledge of the target compound properties usually determines which instrument parameters are selected for study (Step 1.a). A mistake commonly made in Step 1.b is setting small study ranges analogous to those typically used in method validation for robustness experiments. In such an experiment, each variable's range is normally set to the expected ± 3.0 σ variation limits about its setpoint, as a robustness experiment's purpose is to test the "null" hypothesis — that the factor has no statistically significant effect on method performance across its expected noise range. However, even critical parameters will have inherently small effects across their noise ranges (low signal-to-noise ratio [S/N]), which makes a robustness experimental approach inappropriate to the QbD method development goal of deriving precise and accurate models of study variable effects. Therefore, as a rule of thumb, the experiment variable ranges should be set to a minimum of 10 times their expected noise ranges, and unless restricted by engineering constraints, should never be set to less than five times these ranges. 

Figure 1: QbD methodology for Phase 2. 			

Table I presents a template that defines a proposed experimental region for the formal method development phase. As for the Phase 1 template previously presented, this template can be modified as needed to accommodate the specific LC instrument system and compounds that must be resolved. 

Table I: Phase 2 experiment template  			

 Step 2 in the QbD Workflow first involves generating and carrying out a statistical experimental design. Selecting the statistical experimental design is a critically important step; there is a wide variety of statistical design types, each of which has specific information properties and is intended to support a specific analysis model. In practical terms, this means that not all statistical designs have the same ability to identify which variables are important and quantify variable effects. Unfortunately, it is observed very commonly that an experimenter will select a design based upon its "size" — the required number of experiment trials for a given number of study variables, and neglect the underlying model form when analyzing the data. A typical example is the widespread use of highly fractionated factorial designs such as the popular Plackett–Burman designs. These designs are two-level fractional factorial designs for studying 

 is a multiple of four. The simplest case is the 

 = 4 design for studying three variables at two experimental levels each in four trials. Figure 2 illustrates this design for three variables, designated 

3, at standardized low (—) and high (+) levels. The figure shows the low and high level settings of the three variables in the four trials, and also the derived level settings of the two-way effects terms — obtained by multiplying the level settings of the parent main effects terms in each row. These level settings are used in the data analysis to relate the variable's linear additive (main) effects and two-way (pairwise interaction) effects, respectively, to observed changes in a given result across the experiment trials. A simple examination of the level-setting patterns for these terms reveals the perfect correlation of the interaction terms with the main effects terms across the experiment trials. This correlation pattern is deliberate in Plackett–Burman designs, which are intended primarily for screening large numbers of variables to determine if the variables have any significant effects that warrant further study. Deliberate correlation such as this is termed "effects aliasing." These designs support a linear model, and the aliasing must be considered in the interpretation of results, because it cannot be eliminated by data analysis. This means that any observed effect that the linear model ascribes to, for example, 

1, might be due in whole or in part to the aliased 

3. More experiment trials are required to break the aliasing and make the correct assignments of effects. It should be clear from this discussion that care must be taken when selecting the statistical experiment design, and the information properties of the design must not be forgotten when analyzing data and reporting results. 

Figure 2: PlackettâBurman design for N = 4. 			

Careful execution of Steps 1–2.a will correctly stage the experiment to provide the scientific knowledge required to establish accurately the design space for the analytical method in a manner consistent with current ICH guidances: "The information and knowledge gained from pharmaceutical development studies and manufacturing experience provide scientific understanding to support the establishment of the design space, specifications, and manufacturing controls" (1). To illustrate this, we will describe a Phase 2 experiment carried out according to the QbD approach using the sample mix from the Phase 1 "proof-of-technology" experiment described in part two of this article series. The sample mix used in that experiment contained 14 compounds: two active pharmaceutical ingredients (APIs), a minimum of nine impurities that are related structurally to the APIs (same parent ion), two degradants, and one process impurity. The LC instrument platform, which will again be used for this experiment, is a Waters ACQUITY UPLC system (Milford, Massachusetts). Based upon the Phase 1 experiment results, the experimental design region was defined by modifying the Phase 2 experiment template in two ways. First, pH and gradient time were shown to be critical effectors with optimum settings of at 6.8 and 9.5 min, respectively. They were therefore again studied in this experiment to further characterize their effects and establish correct operating ranges. Second, because shallower gradients with high end points were indicated to perform better, the gradient slope was studied by varying the initial conditions with a constant end point. A statistical experimental design was then generated that would support using the full cubic model in the analysis of the results. 

Table II: Example Phase II experiment 			

Once the experiment was run, two unique trend responses described in Part II were derived directly from the chromatogram results (responses). These were: "No. of Peaks" — the number of integrated peaks in each chromatogram, and "No. of Peaks — 1.50 — USP Resolution" — the number of integrated peaks which are baseline resolved in each chromatogram. In addition, peak-result based Trend Responses were used to track the two APIs. Modeling these data facilitated separating them from their respective co-eluting impurities. Trend Response based peak tracking was facilitated by spiking the sample mix such that API 1 was at a significantly higher amount than API 2, which enabled the two related compounds to be distinguished easily in the experiment chromatograms. 

Analysis of the experiment data sets yielded a prediction model for each response that identified the important effectors and characterized their effects on the response. Figure 3 illustrates how such a model can be used to predict a response. The figure shows the general form of a partial quadratic model that predicts the resolution of a critical peak pair (

s) as a function of two experiment parameters, in this case, the pump flow rate (variable 

1) and the initial percent organic of the gradient method (variable 

Figure 3: Prediction of mean performance. 			

From Figure 3, one could anticipate that by iteratively entering level-setting combinations of the two study factors and evaluating the predicted results, one could identify the best method obtainable within the variable ranges in terms of the resolution response. One could then plot these iteratively predicted responses in terms of relative acceptability. For example, given a resolution goal of  ≥ 1.50, one could generate a modified contour plot of the resolution response in which study factor combinations corresponding to predicted resolution responses below 1.50 are indicated in color, as illustrated in Figure 4. 

Figure 4: Region of acceptable mean performance. 			

The dark green line in Figure 4 demarcating the shaded and unshaded regions corresponds to level-setting combinations that are predicted to exactly meet a minimum acceptability value of 1.50 for the critical-pair resolution response within the variable ranges. The demarcation line therefore represents the predicted edge of failure for this response, as defined in the ICH guidance: "The boundary to a variable or parameter, beyond which the relevant quality attributes or specification cannot be met" (3). Taken together, the prediction model (Figure 3) and the corresponding contour plot (Figure 4) numerically and graphically represent the quantitative knowledge space obtained from the DOE experiment for the resolution response. 

As discussed in part two of this column series, the models obtained for all responses can be linked to a numerical search algorithm to identify the overall best-performing study variable settings considering all responses simultaneously. In addition, a contour plot like the one in Figure 4 can be generated for each modeled response, and these plots can be overlaid to visualize the global region of acceptability, as illustrated in Figure 5. However, the region of acceptability is consistent with the ICH definition of a design space from a mean performance perspective only. 

Figure 5: Global region of acceptability. 			

 It must be understood that each model derived from the experiment results is a single point predictor of a mean response given the input of a level setting for each study factor (that is, a candidate method). In other words, for a given candidate method, the model predicts the mean (arithmetic average) of the individual resolution values that would be obtained over many injections. The model does not predict the magnitude of the variation in the injections directly, and so cannot provide any knowledge of the relative robustness of the candidate method directly. The region of acceptability illustrated in Figure 5 is therefore only consistent with the ICH definition of a design space from a mean performance perspective. Because method performance varies, methods at or near the edge of failure will only perform acceptably on average. This means that the edges of failure must be moved inside the mean performance design space to accommodate robustness. This "reduced" design space has been referred to as the "process operating space" (3). The question is then how far inside the mean performance design space should the edges of failure be located. Moving them in too far can be overly restrictive and require a level of control that is too costly or unavailable, while not moving them in far enough increases the risk of unacceptable performance. The next section describes in detail how to determine the optimum method and the final design space in terms of both mean performance and robustness without the need to conduct additional experiments. 

Integrating quantitative robustness metrics into LC method development is critically important for the simple reason that two candidate methods can provide the same mean performance but very different robustness. This is illustrated in Figure 6 for two methods designated A and B. Both methods have the same mean performance, meaning that on repeated use, they have the same average ability to separate a critical compound pair. But method A performance varies excessively in response to inherent variation in critical LC instrument parameters, while method B performance does not. Unfortunately, it is not possible to determine the relative robustness of a given candidate method by inspecting a resulting chromatogram, and so by simple inspection method A could be identified easily as an acceptable method. 

Figure 6: Mean performance versus robustness. 			

The lack of accurately characterizing robustness in method development is a common reason why many methods must be redeveloped each time they are to be transferred downstream in the drug development pipeline to meet the stricter performance requirements that will be imposed on them. The statements reproduced in the following express how important this integration is in the view of the FDA and the ICH. Although the goal is stated clearly, the guidances do not define how to accomplish such a task. The new methodology presented in this article has been developed in response to both the stated need for integrating robustness into LC method development work and the lack of a defined "how to" approach. 

FDA Reviewer Guidance (5). Comments and Conclusions	HPL Chromatographic Methods for Drug Substance and Drug Product: 

Methods should not be validated as a one-time situation, but methods should be validated and designed by the developer or user to ensure ruggedness or robustness throughout the life of the method. 

The evaluation of robustness should be considered during the development phase and depends upon the type of procedure under study. It should show the reliability of an analysis with respect to deliberate variations in method parameters. 

To meet the needs of a working analytical laboratory the new methodology was required to meet three important requirements: 

Be based upon statistically rigorous QbD principles. 

Integrate quantitative robustness metrics without additional experiments. 

Maximize the use of automation to reduce time, effort, and error. 

In meeting these three requirements, the new methodology represents the automation of a best-practices approach in which LC methods can be developed rapidly and simultaneously optimized for mean chromatographic performance and method robustness. This new methodology employs Monte Carlo simulation, a computational technique by which mean performance models are used to obtain predictions of performance variation. A detailed description of Monte Carlo simulation is beyond the scope of this paper. For purposes of illustration, the technique is outlined in the following five basic execution steps and illustrated in Figure 7 for the two study factors previously discussed. It is important to note that this approach correctly represents a study factor's variation as random, normally distributed setpoint error, and that the entire error distribution of each factor is represented simultaneously in the robustness computation. 

Figure 7: Prediction of performance variation. 			

A setpoint variation distribution is generated for each study factor using a normal (Gaussian) distribution template with ±3.0 σ limits set to the ±3.0 σ variation limits expected for the factor in normal use. For example, a given target LC system might have expected ±3.0 σ variation limits of ±2.0% about the endpoint percent organic defined in a gradient method. 

A candidate method is selected. The method defines the setpoint level setting of each study factor. 

For each study factor the setpoint variation distribution is centered at the setpoint level setting, which, thus becomes the effective mean value of the distribution, and a very large number of level settings — say 10,000 — are then obtained by randomly sampling the variation distribution. 

The mean performance model then generates 10,000 predicted results — one for each of the 10,000 variation distribution sampling combinations of the study factors. Note that this is a correct propagation of error simulation, because all study factor random variations are propagated simultaneously through the model. 

The variation distribution of the 10,000 predicted results is then characterized, and the ±3.0 σ variation limits are determined. 

For a given candidate method, the Monte Carlo simulation approach provides a quantitative measure of the variation in a modeled response in the form of predicted ±3.0 σ variation values. This is a good starting point; what is needed then is an independent scale for determining whether the magnitude of the predicted variation is acceptable. For this, we employ the process capability index (

) — a Statistical Process Control (SPC) metric widely used to quantify and evaluate process output variation in terms of critical product quality and performance characteristics. 

 is the ratio of the process tolerance to its inherent variation, and is computed as shown in equation 1. Coupling the simulation result with the calculated 

 enables a direct comparison of the relative robustness performance of alternative methods (7). 

Note that tolerance limits can be used in equation 1 rather than the more traditional specification limits, because there might or might not be absolute specifications for acceptable variation in critical method performance attributes such as resolution to which this calculation will be applied. 

In equation 1, USL and LSL are the upper and lower specification limits for a given response, and the 6σ variation is the amount of the total variation about the mean result bounded by the ±3 σ variation limits. 

 is therefore a scaled measure of inherent process variation relative to the tolerance width. Figure 8 illustrates the 

 calculation elements for the critical pair resolution response described previously given a mean resolution (X) of 2.00 and specification limits of ±0.50. In classical SPC, a process is deemed capable when its measured 

 is ≥1.33. The value of 1.33 means that the inherent process variation, as defined by the 6σ interval limits, is equal to 75% of the specification limits (4/3 = 1.33). Conversely, a process is deemed not capable when its measured 

 is ≤1.00, as the value of 1.00 means that the 6σ interval limits are located at the specification limits. 

Figure 8: Critical pair resolution response. 			

 metric can be applied directly to each modeled response to determine the relative robustness of a candidate method in terms of the response and evaluate the robustness on a standardized acceptability scale. However, it is apparent from equation 1 that the 

 value computed for a given candidate method directly depends upon the tolerance limits defined for the response. One should therefore use the following basic rules listed in the following when specifying the specification limits to be used in the 

Specification limits should be defined in the units of the response. 

Specification limits should be defined as a symmetrical delta (±Δ) which delineates a relative tolerance range and not as absolute USL and LSL values. 

The ±Δ limits will be applied to different candidate methods to determine their relative robustness by computing their 

 values. Absolute USL and LSL values cannot be used, because the mean response will vary across a set of candidate methods being evaluated. 

Validation Viewpoint

The guest columnists continue their examination of how statistically rigorous QbD principles can be put into practice.

A Quality-by-Design Methodology for Rapid LC Method Development, Part III

Approaches to Singleton Achiral Purification of Difficult Samples for Discovery Research Support in the Pharmaceutical Industry

Chromatographic resolution of pharmaceutical compounds is one of the most efficient approaches for generating small quantities of pure material for biological testing in early pharmaceutical research and development (1–3). The use of chromatographic resolution from a purification group allows more time for synthetic chemists to make more compounds for research. Hence, separation using preparative chromatography rapidly has become a standard approach for the generation of test substances in pharmaceutical research and development in recent years (4,5). 

Discovery purification support is a challenging task. Purification scientists often are confronted with large sample loads and short turnaround time. The ability to develop analytical separation methods rapidly and the subsequent ability to scale up separations to preparative columns are critical for meeting sample turnaround timelines. Difficult samples require more time for analytical method development and more effort in handling these compounds on preparative scale. Difficult samples are defined as follows: minimal retention on the regular reversed-phase columns due to high polarity, minimal separation on the reversed-phase columns due to structurally similar impurities such as diastereomers and regioisomers, requirement for large sample amount for numerous injections, and degradation in aqueous solution.  

This article addresses three methods for dealing with difficult samples and their challenges. The first method used hydrophilic interaction liquid chromatography (HILIC) stationary phases to increase interaction between the polar functional group of the sample and its columns (6). HILIC columns operate in aqueous normal-phase mode. Therefore, compounds that have no retention on the reversed-phase columns generally have strong retention on the HILIC columns. An inversed gradient of 100% acetonitrile to 50% acetonitrile in water usually was performed on this type of column. As a result, retention of polar ionic compounds increased as the amount of organic solvent in the mobile phase decreased. The separation mode used an eluant containing a water-miscible solvent such as acetonitrile to promote hydrophilic interaction between the analyte and its hydrophilic stationary phase. Based upon the obtained results, HILIC is a technique comparable to traditional normal-phase chromatography and orthogonal to reversed-phase chromatography.  

The second approach involved using hydrophilic endcapping columns to retain highly polar compounds. These columns retain polar compounds more efficiently due to a unique hydrophilic endcapping. The combination of a moderately covered hydrophobic group and an additional polar group gives selectivity when compared to a traditional reversed-phase column — yet it allows for more retention of polar compounds. These columns can operate in a highly aqueous solution such as 100% water. Under this condition, some polar compounds can be retained on the column for two or three voids. Under some situations, this retention time should be sufficient for isolating desired components from its crude form.  

The third approach pertained to supercritical fluid chromatography (SFC) for achiral purifications. SFC has been proven as a very efficient tool for separation of various compounds (7,8). It is used widely in the pharmaceutical industry to support discovery research. The low viscosity and high diffusivity of supercritical fluid allows the process to work at a higher linear velocity when pass through the column. SFC separations are normally fast and use less organic solvent compared with HPLC. Therefore, SFC is suitable for large-scale separations from grams to kilograms. Gram-scale samples can be time consuming to purify using reversed-phase HPLC–mass spectrometry (MS) columns. SFC not only speeds up the purification process, but also avoids sample degradation in aqueous solution. This is because SFC operates in normal-phase mode. A variety of columns can be used for SFC achiral purifications, including reversed-phase C-18 columns, normal-phase silica gel columns, and pyridine and bonded-phase columns (cyano, diol, and amino). SFC also is used very commonly for chiral separation. Chiral stationary phases also can be used to isolate the structurally similar impurities such as diastereomers and regioisomers.  

Difficult samples obtained from Pfizer discovery research presented a challenging task. However, the use of HILIC, hydrophilic endcapping reversed-phase columns, and normal-phase SFC technologies offset these challenges. The following article is a discussion of the analytical method developments, the preparative experimental conditions, and the final purification results obtained from each of the three experiments. The purity and yield of each experiment also are discussed. 

: The compounds purified in this paper were the pharmaceutical substances belonging to and synthesized at Pfizer Global Research and Development (Groton, Connecticut).  

The mobile phases used in all studies varied. All were reagent grades from a variety of sources. The stationary phases and columns also were obtained from various sources.  

The analytical HPLC system was an LC–MS system from Agilent (Palo Alto, California). This system was used for preliminary analytical screening purposes. The six reversed-phase HPLC columns were Luna C-18, Phenylhexyl, Polar RP, Bonus RP, PFP, and Gemini  (Phenomenex, Torrance, California) in either acidic or basic condition. The flow rate in each column was 1.5 mL/min. A general linear gradient from 5% acetonitrile to 95% acetonitrile in water was performed on these columns. If the compound had little or no retention on these columns, the compound was then forwarded to specialty columns (HILIC columns, type-C silica columns, or hydrophilic endcapping columns) for further method development. If the sample was larger in scale or showed very tight separation, SFC methods were then used to purify the compounds.  

: The preparative HPLC systems were HPLC and MS systems from Shimadzu (Columbia, Maryland) or from Waters (Milford, Massachusetts). In this study, the preparative HPLC runs were performed using a prepacked HILIC column from Waters with an inner diameter of 30 mm and a length of 100 mm or a hydrophilic endcapped column (Atlantis D-C18) also from Waters with the same column size. The mobile phase was acetonitrile in water with 0.1% formic acid. The flow rate was 40 mL/min. The sample was dissolved in acetonitrile or an acetonitrile–water solution. The sample solution was then filtered before injecting into the system. 

 The analytical SFC systems used were Berger systems from Thar Technology (Pittsburg, Pennsylvania). One analytical SFC system contained six chiral columns including: ChiralPAK-AD, ChiralPAK-AS, Chiralcel-OD, Chiralcel-OJ, Whelk-O, and ChrialPAK-IA columns (Chiral technology, Exton, Pennsylvania). Six solvent systems were used on these columns. These solvents were methanol, ethanol, isopropanol, acetonitrile, methanol with 0.2% isopropylamine, and acetonitrile with 0.2% trifluoroacetic acid. Another analytical SFC system contained six achiral columns including: Pyridine, Benzamide, Cyano, Diol, Silica, and Pyridine amide (Princeton, Cranbury, New Jersey). Six solvent systems also were used on these achiral columns including: ethyl acetate, acetonitrile, methanol, ethanol, methanol with 0.2% isopropylamine, and acetonitrile with 0.2% trifluoroacetic acid. The flow rate was 2.5 mL/min. Pressure was 120 bar and the temperature was 40 °C. A gradient of 5% to 50% cosolvent was performed on these columns for screening purposes. 

Preparative SFC runs were performed on an Investigator system (Thar Technology) using various preparative columns with a diameter of 10 mm and a length of 250 mm. The mobile phase varied in each experiment. The flow rate was 10 mL/min. Samples were dissolved in mobile phase and then filtered before being injected into the system. 

: Our laboratory received several polar samples. From the initial analytical column screening results, the samples had little or no retention on the columns. Therefore, the separation method was redeveloped on the HILIC or type-C columns. The first example was a very polar compound with no retention on reversed-phase C18 columns. The crude sample weighed ~1.0 g and was soluble in acetonitrile. DMSO was not used on the HILIC column because it also could be retained on the column for ~ 2–3 column voids resulting in coelution. The sample can be dissolved in 100% acetonitrile, a water–acetonitrile mixture, or methanol. The compound had little absorption at 254 nm, but significant absorption at 210 nm. The initial purity of the desired peak was ~ 55% by HPLC area at a wavelength of 210 nm. The compound contained an amino group and a carboxylic acid group with no aromatic structure presented. An inversed gradient from 100% acetonitrile to 65% acetonitrile with 0.1% formic acid was performed on the HILIC column. It was discovered that the compound had good retention and also good resolution on the HILIC column. The result is displayed in Figure 1. From Figure 1, the desired peak (peak 1) is well separated from the peaks of other two impurities. All three peaks were retained on the HILIC column with a retention time of 3.5–6.0 min. A total of ~ 0.45 g of the purified material was recovered. The purity was better than 98% on the detection wavelength at 210 nm. The yield was ~ 82%. This yield is defined as the desired component yield ([the amount of the desired component recovered after the purification/the amount of the desired component present in the crude] x 100%). 

Figure 1: The retention of polar compound on a HILIC column in acidic condition.  

The second example was also a very polar compound with little retention on the regular reversed-phase columns. The sample mass was ~ 100 mg. The sample contained an aromatic group and an ester group. The initial purity of the desired peak was ~ 80% by HPLC area at a wavelength of 210 nm. Synthetic chemist noted that this sample was unstable in the presence of acid or base modifiers. Therefore, a HILIC column in neutral condition was used for purifying the compound. It also was discovered that HILIC column could not be stable at either highly basic conditions or highly acidic conditions (pH < 2). This sample was dissolved in acetonitrile at a concentration of ~ 50 mg/mL. The column was loaded with a good amount of sample (~ 45 mg) for each injection. The impurities also were separated. Three injections were made. The results are shown in Figure 2. In Figure 2, the desired peak is the main peak. All the peaks were retained on the column. ~ 58 mg of the purified material was obtained at a purity of better than 97% by HPLC area at a wavelength of 210 nm. The yield was ~ 73%. 

 Figure 2: The separation of sample with a HILIC column under neutral condition.  

Retaining polar compounds using hydrophilic endcapping columns

: Some polar compounds also were retained on hydrophilic endcapped columns. Water (100%) can be used as the mobile phase on these columns. However, the columns should be washed with 100% acetonitrile to prevent the phase collapse before running the 100% water on the column. One example obtained was a polar compound with multiple amine groups and an aromatic group. The sample was already in DMSO solution and estimated a crude weight of ~ 200 mg. The initial purity was ~ 30% by HPLC area at a wavelength of 210 nm. Because the sample was under urgent request, a rapid turn around time was required. A HILIC column was not a good choice because DMSO also could be retained on the column. Therefore, a hydrophilic endcapped column (D-C18) was used for purification. The results are shown in Figure 3. In Figure 3, the desired peak is the third peak. The first peak is the DMSO peak. A gradient with a holding time of 5 min at 100% water was used to elute the desired peak. Then, a quick gradient to 100% acetonitrile was used to elute the rest of the impurities. The column was loaded with a significant amount of sample (20 mg) for each injection. A total of 10 injections were made. Approximately 46 mg of the purified material was obtained at a purity of better than 95% by HPLC area at a wavelength of 210 nm. The yield was ~ 75%. 

Figure 3: Purification with a hydrophilic endcapped column.  

 SFC is one of the most successful methods for separating chiral molecules. Recently, it has gained acceptance for achiral purifications in drug discovery research in the pharmaceutical industry. With faster separation, higher sample throughput, less organic solvent usage, and normal-phase mode purification, SFC technology is sometimes more advantageous than reversed-phase HPLC–MS technology for achiral purifications. However, SFC still lacks mass-triggered detection and system reliability, which makes it difficult to use SFC as a primary purification tool for achiral samples. In our laboratory, the SFC systems were used mainly for chiral separations and as a back-up process for achiral separations of large samples, unstable samples, and samples with no resolution on HPLC–MS systems. 

A few of the samples in our laboratory had little or no separation from the initial reversed-phase HPLC screening columns. Therefore, SFC was used for the separation. The first example was a diastereomer with two stereo-centers. Its stereo-center contained a pyridine group and a chlorinated group. The sample mass was ~ 500 mg. Various chiral stationary phases were screened. It was found that a total of four peaks (one pair of isomers for each diastereomer) could be isolated on the ChiralPAK-AD-H column. The results are shown in Figure 4. A gradient of 5–30% of methanol over 8 min at 120 bar and 40 °C was used for the separation. All four peaks were isolated. Approximately 10 mg of crude sample was loaded on a preparative ChiralPAK-AD-H column (250 mm x 10 mm) at each injection. A total of 50 injections were made. The isolated second peak had a purity of ~ 90%. The other three peaks had a purity of > 98%.  

Figure 4: Two pairs of diastereomers separated on an SFC chiral column.  

The second example was a regioisomer containing an aromatic group and a secondary amine group. The sample mass was ~ 2g. No separation on the reversed-phase HPLC column screening was obtained. However, separation did occur and was discovered after screening through SFC achiral columns. The best separation was evident on the  2-ethylpyridine column with 20% methanol as cosolvent at 120 bar and 35 °C. The results are illustrated in Figure 5. In Figure 5, the two regio-isomers are in 1:2 ratios. Both peaks were separated on an SFC achiral column. Approximately 30 mg of sample was loaded on the preparative column (250 mm x 10 mm) at each injection. A total of 70 injections were made. The purity of the isolated isomer was ~ 99% for first peak and ~ 98% for the second peak.  

Figure 5: Regioisomer separation on an achiral column of 2-ethylpyridine with SFC.  

Several approaches for purifying difficult samples more efficiently for discovery research support are mentioned in this paper. These approaches use mass triggered HPLC on various specialty columns.

LCGC North America-04-01-2009

New Chromatography Columns and Accessories at Pittcon 2009: Part II

Calendar

Alternative Futures: Biofuels and Mass Spectrometry

Short Courses

A Quality-by-Design Methodology for Rapid LC Method Development, Part III

Approaches to Singleton Achiral Purification of Difficult Samples for Discovery Research Support

Calibration Curves, Part II: What are the Limits?