The 53rd Annual American Society for Mass Spectrometry (ASMS) Conference on Mass Spectrometry and Allied Topics was held June 5–9 at the Henry B. Gonzalez Convention Center in San Antonio, TX (pictured). More than 6000 scientists attended, and some 2700 papers were presented as posters and talks.   

Each conference day opened with a plenary lecture followed by six parallel oral sessions and two periods of formal poster presentations. Plenary lecturers included Firouz Naderi of Jet Propulsion Laboratory (Pasadena, CA) speaking on the Mars landings in his lecture entitled, "Six Intense Minutes, a Hole in One, and a Salty Sea — the Landing of NASA's Mars Exploration Rovers," and Don Catlin of the UCLA Olympics Laboratory (Los Angeles, CA) discussing drug testing of athletes in his presentation, "Drugs, Sports, and Mass Spectrometry."  

Additional plenary lectures included two award recipients, James A. McCloskey of the University of Utah (Salt Lake City, UT), who received The Award for a Distinguished Contribution to Mass Spectrometry, and Gary Van Berkel of Oak Ridge National Laboratory (Oak Ridge, TN), who was awarded with the Biemann Medal. 

Continuing the trend of recent ASMS conferences, more than half of the presentations represented the areas of proteomics, drug metabolism, and other biomedical applications. While the bioanalytical applications of mass spectrometry were a significant part of the meeting, flavor and fragrance, environmental, and polymer interests groups also were well represented. The mechanisms for soft ionization techniques and fundamental ion molecule reactions were discussed, as were instrumental and ionization developments that showed improvements in resolution, sensitivity, and mass accuracy. 

The application of mass spectrometry to the field of proteomics included identification of novel proteins, biomarkers, protein conformation, and protein–protein interactions, as well as the development of new instrumentation, new software, and new strategies for isotope labeling of proteins. Portable instrumentation developments and new methods for detecting of biotoxins, neurotoxins, and pathogens relative to Homeland Security were presented. Environmental issues, such as the detection of pharmaceuticals, toxins, microorganisms, and flame retardants, also were reported.  

With such an intensive program, the conference initiated web casting as a way to provide participants the opportunity to view the presentations they missed. The presentations were made available for viewing on-line through the week following the conference. 

In addition to exhibit booths, exhibitors also hosted hospitality suites at the Marriott Hotel Rivercenter on Monday, Tuesday, and Wednesday evenings. This provided opportunities for conference registrants to relax at the end of the day and view more extensive displays of company products and services. 

The weekend preceding the conference featured 11 short courses attended by 900 participants, tutorial lectures, and customer meetings sponsored by various exhibitors. Some of the short courses included: 

LC–MS: The Techniques of Electrospray and APCI

MALDI-TOF MS: Fundamentals and Applications

Drug Discovery Using Mass Spectrometry: From Target Identification to IND Enabling Studies

Quadrupole Ion Trap Mass Spectrometry and Tandem Mass Spectrometry

The conference kicked off with an opening reception in the exhibit hall where some 170 exhibitors were represented. However, unlike most large conferences, a sea of 660 scientific posters occupied most of the exhibit hall space. These presentations changed daily for a total of approximately 2400 posters. 

Next year, the 54th Annual ASMS Conference will be held May 28–June 1 in Seattle, WA. For more information, contact ASMS at: 

Articles

A review of the five-day conference dedicated to promoting mass spectrometry and its allied topics held earlier this month in San Antonio, TX.

53rd Annual ASMS Wrap Up

In a previous column (1), I made a comment that virtually all of astronomy is spectroscopy. Well, this "Baseline" column should drive that point home. 

Glance up at the sun (quickly — I don't want to be responsible for anyone going blind!), and what do you see? A yellow-white glowing ball of light in the sky. Spectroscopists, however, don't just look at the sun. They take the light and run it through a monochromator, separating it into its constituent colors, and look for the presence or absence of particular lines of light that give hints regarding the chemical composition of the sun.  

 Figure 1: A high resolution spectrum of the sun, measured with the Fourier transform spectrometer at the McMath-Pierce Solar Facility at Kitt Peak National Observatory, near Tucson, Arizona. (Credit: N.A.Sharp, National Optical Astronomy Observatory/ National Solar Observatory /Kitt Peak Fourier Transform Spectrometer/Association of Universities for Research in Astronomy/National Science Foundation. Reprinted with permission.)			

Astronomers at the Kitt Peak National Observatory (Tuscon, AZ) have done just that. On the facing page is a high-resolution visible spectrum of the sun, from about 7000 Å to about 4000 Å, as measured by Kitt Peak astronomers. Digital versions of this spectrum are available at the National Optical Astronomy Observatory website (

). Each of the 50 individual images covers 60 Å. 

The spectrum of the sun had been observed for millennia as rainbows, but was long not recognized as such. In 1671–1672, Isaac Newton demonstrated that light from the sun was composed of a range of colors that could be separated by a prism. Previously, it was thought that the prism itself imparted color on white light. 

In 1802, English chemist William Wollaston first noted dark lines in a rainbow caused by sunlight. In 1814, German physicist Joseph von Fraunhofer devised a solar spectroscope (scooping Bunsen and Kirchhoff's development of the spectroscope by about 45 years) to study the solar spectrum. He mapped over 500 lines, measuring their wavelengths and assigning the obvious lines with capital letters A through K and weaker lines with other letters. These lines are still referred to as the Fraunhofer lines, perhaps the most famous one being the yellow sodium D lines. 

It wasn't until 1860, however, that Bunsen and Kirchhoff connected the wavelengths of light emitted or absorbed to specific chemical elements. The lines in the solar spectrum, therefore, indicated that certain chemical elements were present on the Sun absorbing light, and the Fraunhofer lines were quickly assigned to a variety of chemical elements, from H to Ti and more. In fact, helium was first detected in the solar spectrum in 1868, and assigned by English chemist Norman Lockyer to an as-yet-undiscovered element (helium was isolated on terra firma in 1895 by English chemist William Ramsey). 

It is unfortunate that the spectrum in Figure 1 does not have a scale, or that it is unannotated with identifications of the species causing each absorption. (Lists of assignments are available through the NOAO Web site, but as far as I can tell, annotated spectra are not.) Annotating this spectrum would be a major job in any case, but perhaps someone with too much time on their hands can annotate at least the major absorptions. 

How do we know what matter exists in other parts of the universe? By the spectra of other stars, of course. The NOAO Web site has similar spectra for other stars, and with corrections for red- or blue-shifts, the spectral lines show the same chemical elements out there as there are right in our own region of space. See how fundamental spectroscopy is to understanding our universe? 

is a professor of chemistry at Cleveland State University in Ohio. Many of his "Baseline" columns have been reprinted in book form by SPIE Press as 

, available through the SPIE Web Bookstore at 

, was published in August 2002. He can be reached at the Department of Chemistry, Cleveland State University, Cleveland, OH 44115, by e-mail at 

, or by phone at (216) 687-2456. His personal website is 

Columns: The Baseline

Spectroscopists separate light from the sun into spectra and look for the presence or absence of particular lines that give hints regarding its chemical composition. The same method can be applied to studying the composition of other matter in the universe.

The Baseline: The Solar Spectrum

Since the invention of scanning tunneling microscopy (STM) in 1982 (1), a family of similar techniques known as scanning probe microscopy (SPM) has developed quickly into a powerful tool for surface science. Among these techniques is atomic force microscopy (AFM), introduced in 1986 (2). Similar to STM, AFM uses a sharp probe that scans the sample in a raster-fashion to detect changes in surface properties such as topography and charge distribution. Unlike STM, AFM does not require conductive samples; rather than measuring the tunneling current, AFM senses the interactive forces (Van der Waals, electrostatic, and magnetic forces, for example) that range from 10

 N (3), between the tip and the sample surface. Excellent three-dimensional spatial resolution (nanometer scales) can be achieved, in contrast to other forms of microscopy that typically can offer only high resolution in lateral dimensions. AFM imaging operations can be conducted in ambient environments or in solution flowthrough conditions with minimal sample preparation. These advantages have made AFM a unique and popular technique to study a variety of surface phenomena. 

One of the most unique features of AFM is its ability to perform in situ experiments in fluid environments. This is useful particularly in studying the growth and dissolution process of water-soluble crystallites in which the speed of step movement commonly is the subject interest. However, measuring step speed on AFM images proves difficult and inaccurate due to the following two factors: 					

Spatial uncertainties associated with the electronics and mechanics of AFM. A common problem for sequential AFM imaging is the thermal drift of piezos that causes the scanner to move away from the original field of view (4). 

Lack of stationary reference marks within the imaging area due to surface growth–dissolution. Because the entire imaging area is subject to mass addition or reduction during growth–dissolution, most existing surface features constantly will undergo changes in sizes and positions and therefore cannot be used as fixed reference points. 

The combination of these two factors poses a practical challenge to quantifying step motion. This article describes two methods that have been used successfully in AFM studies of step kinetics. These methods either take advantage of the ubiquitous presence of imperfections in crystallites or make use of the dynamic characteristics of dissolving–growing steps. Together, they provide a pragmatic means for effective measurements of step movement. 

The two techniques presented here include a direct measurement of step speed as step displacement over time and an indirect measurement of step speed from the angles of moving steps with reference to their still positions. 			

This method measures the changes of step positions relative to a fixed reference point on consecutive images (Figure 1). Step speed then is calculated simply as the ratio of the position change to the time elapsed between the two images. It is a straightforward operation but requires effort to identify at least one immobile reference mark on the surface. A good reference mark should be insensitive to surface reactions. Specifically, its position should not shift with changing surface morphology, and its presence should not fade away with each surface layer forming or dissolving. A best candidate for this purpose is linear defects terminated at surfaces. Defects are a common occurrence in crystals. Although the actual density of dislocations estimated by different techniques can vary to a certain extent, the quantity in general ranges from below 10

 in the best germanium and silicon crystals to 10

 in highly deformed metal crystals (5).   

 Figure 1. Sequential AFM images of a growing calcite crystal face exemplifying the measurement of step displacement over time. Note that the measurements were made using the center of the spiral (that is, a screw dislocation) as a reference.			

Linear defects root inside the bulk crystals and therefore do not drift significantly within adjacent layers even if the dislocation axis is not perpendicular to the surface. A particularly ideal defect to be used as a fixed reference point is a screw dislocation (Figure 2a). Unlike other types of linear defects such as edge dislocations, screw dislocations have a displacement vector (that is, the Burgers vector) component normal to the crystal face at which they emerge (6). Frank (7) showed that the screw component makes moving steps winding in a spiraling fashion around the dislocation axis so that the defect is preserved to be a "permanent" landmark on surfaces. Screw dislocations are best recognized on atomically flat surfaces such as cleavage crystal faces. For materials that have fast growth kinetics, screw dislocations can be found relatively easily by spiking the flat sample surface using solutions with high supersaturation to quickly generate spiral hillocks (Figure 2b). The centers of the spirals where the first legs come into view usually are where the screw dislocations are located. For materials that do not respond quickly to supersaturated solutions, one must trace the edges of existing steps. Screw dislocations usually are where steps end suddenly on flat surfaces (Figure 2c).  

Figure 2. Screw dislocation shown (a) in a schematic view; (b) as growth hillocks on calcite surface; and (c) at the inward termination of a single step on feldspar surface. Dx and b in (a) are the dislocation axis and the Burgers vector. The arrows in (b) and (c) point to the approximate locations of the screw dislocations.			

After a fixed reference mark is identified, step displacement over time can be measured on consecutive images to compute step speed. However, caution must be exercised if the step speed is higher than or even just comparable to the scan rate. This is because a moving step will either chase or migrate away from the tip during an AFM scan, depending upon the original step orientation and the scan direction, so that the apparent step orientation appears differently on upward and downward scan images (Figure 3). A common technique to compensate for this error is to adjust the scan angle so that the fast scan direction is perpendicular to the step edges before steps move (that is, step edges are adjusted to be parallel to the 

-direction). This allows the step displacement to be measured as the distance in the 

-direction between the dislocation and the step edges.  

Figure 3. Consecutive AFM images of a growing spiral on calcite surface demonstrating image distortion due to the difference in scan direction. The measurements of Î¸D and Î¸U in equation 2 are given on (a) downward scan and (b) upward scan images, respectively.			

This technique makes use of the time the AFM tips take to complete an end-to-end scan on a single image. Consider a straight step that starts to move with a constant speed at the time a scan begins (Figure 4). What the tip records during the scan is the position of the step edge at different times along the pathway that the tip travels across the surface. The recorded step (that is, a line that connects the initial position of the step at the starting end and the final position at the finishing end on the completed image) therefore does not represent the actual position and orientation of the step. In fact, the apparent step orientation on the image intersects the true step direction (or the step before movement) at an angle θ. If the original step is not made perpendicular to the fast scan directions, the values of θ will differ on images of upward and downward scans (see Figure 3 in reference 8).  

Figure 4. Schematic illustration of the relationship between the actual and the apparent step directions on AFM images.			

Assuming that there are measurable values for θ on AFM images, step speed 

 are the angles between the horizontal axis and the apparent step direction on images of downward and upward scans (Figure 3); θ

 is the angle between the horizontal axis and the real step direction and can be computed through:  

The measurement and calculation can be simplified significantly if the sample orientation or the scan angle is adjusted so that the original step direction is parallel to the 

-axis (9). In this case, only the angle between the apparent step direction and the 

, is needed for step speed determination (Figure 5a). Upheld by geometry, it can be shown that step speed and θ

 should be the same on upward and downward scans (in fact, this equality is a convenient check for the truthfulness of the parallel relationship between the genuine step direction and the 

-axis). This eliminates the need to make measurements on consecutive images, as in the cases of applying equations 1 and 2. 

 is to perform an AFM scan with the slow scan axis disabled (9). This allows the tip to scan back and forth only in one dimension. For a moving step, what the tip records consequently is the movement of an individual point (more accurately, a step segment of the size of the tip diameter) on the step edge. The positions of this point at different times constitute a straight line (provided that the speed of movement is constant) and the angle between this line and the 

 (Figure 5b). At a constant scan rate, the more deviated θ

 is from 90°, the faster the step moves. For example, at a typical scanning rate of 6 Hz and a scan size of 5 μm, the measurements of θ

 step on calcite {1014} faces changed from approximately 87° to 79° when the saturation index 

 (ion activity product/solubility product) increased from ~1.6 to ~4.1, corresponding to a step speed increase from ~3 nm/s to ~11 nm/s (9). This technique is useful particularly for curved or rough steps that might not have a uniform step speed due to the occurrence of curvature at step edges. Application of equation 3 in these cases allows the determination of the speed of different step segments.  

Figure 5. Apparent step orientations on AFM images with (a) full scan and (b) slow scan disabled, showing the angles needed to compute step speed.			

Although both methods are capable of giving reliable measurements of step speed, each has its own pros and cons. The most important advantage of the indirect method is that it does not require the presence of any fixed reference frames. This often brings significant convenience to experiments. In addition, the indirect method is less prone to errors because it requires only one experimental reading (that is, θ

), as opposed to three for the direct method (two readings for distance and one for the time elapsed in between). This might be particularly important on images that have tip-related artifact. For example, when the tip of an AFM probe becomes dull and rough, or the tip breaks and results in irregular tip geometry, blurred (Figure 6a) or multiple (Figure 6b) edges might appear at the steps. This often contributes significant errors to step position measurements in the direct method, but has little effect on angle measurements because the step orientation is not affected by the artifacts.  

Figure 6. Samples of artifacts that can affect step position measurements on AFM images. (a) Broadened edges due to the tip losing sharpness. (b) Double steps (indicated by the arrows) when the tip has an irregular geometry										

As far as step motion is concerned, while the direct method serves better at low step speed, the indirect method is more effective when the step moves fast. When the step speed is so high that the targeted step can grow out of the viewing area between consecutive images, the direct method becomes difficult to use because it is not always easy to locate the same step on different images for displacement measurement. In this situation, the angle method becomes valuable. On the other hand, if step speed is so low that no measurable movement is obtainable within the time span (defined by the 

 ratio) of one AFM scan, the indirect method becomes inoperable because of the unavailability of angle measurements. Quite the opposite, the direct method fits well into this situation as the slow speed makes step displacement an easy measurement. 

It is helpful to note that AFM measurements of step kinetics often are limited to atomically flat surfaces, such as cleavage faces of crystals, where mono-molecular layers are present. For rough surfaces (for example, polished or fracture surfaces) or high index crystal faces that are characterized by high density steps or kinks, the growth or dissolution of related materials might not be controlled by a layer spreading/retracting mechanism; therefore, the kinetic processes taking place on these surfaces might not be measured effectively by the methods described in this article. 

This work was supported by the Office of Basic Energy Science, Department of Energy (Washington, DC), through Grant DE-FG02-02ER15366, and the National Science Foundation (Washington, DC), through Grant EAR 0229763. Thanks go to D. Teng for proofreading the manuscript. 

 is with the department of chemistry at George Washington University (Washington, DC). E-mail: 

1. G. Binnig, H. Rohrer, Ch. Gerber, and E. Weibel, 

2. G. Binnig, C.F. Quate, and Ch. Gerber, 

Surface Analysis: The Principal Techniques

, J.C. Vickerman, Ed. (Wiley & Sons, Chichester, UK, 1997), pp. 393–449. 

, 6th ed., (Wiley, Hoboken, NJ, 1986), p. 570. 

This paper discusses two atomic force microscopy (AFM) techniques that have been used successfully to quantify step movement: direct and indirect measurement. The benefits and difficulties associated with each method are discussed.

AFM Measurement of Step Kinetics for the Growth and Dissolution of Crystallites

Liquid chromatography-mass spectrometry (LC–MS) has been an important analytical tool in support of drug discovery and drug development for some time. LC–MS provides a combination of detection selectivity and sensitivity, speed of analysis, and robust performance that is well suited to the rapid structural characterization of candidate therapeutic substances and high-throughput screening assessment of pharmacological activity (1–3). More recently, LC–MS has been used for patient monitoring in clinical drug trials (4–6) and for the detection of drugs of abuse (7). As knowledge has grown about the relationship between genetic and environmental factors and individual responses to drugs, clinicians have sought new ways to monitor drug responses accurately to assess and tailor drug therapies more effectively. With its advantageous performance characteristics, LC–MS has proven to be a useful tool for collecting data characterizing these relationships. This, in turn, is generating broader interest in the application of LC–MS for the therapeutic monitoring of drugs and their metabolic products (8).  

Traditionally, therapeutic drug monitoring has been performed by immunoassay and LC–UV. Immunoassay is simple, rapid, and relatively inexpensive to perform. However, compared with LC–MS, it is limited in detection specificity, sensitivity, and accuracy. Moreover, only a handful of new assays have been introduced in the past decade despite the fact that many times that number of new drugs have been launched during the same period. Given the advantages and growing penetration of LC–MS, it is an open question whether any new immunoassays will be developed for future therapeutic drug monitoring applications. LC–UV determinations lack sufficient selectivity and sensitivity, making detection and quantitation problematic for the newer, more potent low-dose pharmaceuticals. In addition, the relatively broad UV absorption bands make analytical separation challenging, requiring long runtimes that compromise analytical throughput.  

Clozapine (Figure 1) is one of 20 drugs used in the Department of Clinical Pharmacology (DCP), St. Olav's Hospital, Trondheim, Norway, for the treatment of schizophrenia and psychoses. It is one of more than 200 different drugs DCP routinely monitors almost exclusively by LC–MS. Every month, approximately 200 samples are analyzed from patients treated with clozapine. Clozapine is metabolized mainly to clozapine N-oxide and desmethylclozapine. The half-life of clozapine in blood usually ranges from 6 to 36 h. Some data indicate that individuals with a high level of desmethylclozapine are more susceptible to developing agranulocytosis, a potentially lethal adverse event. This is one reason why the ability to rapidly and accurately monitor an administered drug and its metabolites is important.   

Figure 1. Structural formulas of clozapine, desmethylclozapine (clozapine metabolite), flurazepam (clozapine internal standard), and periciaizine (desmethylclozapine internal standard).			

High-throughput monitoring of clozapine and desmethylclozapine in patient serum is performed using one (out of 24) Agilent 1100 LC–MSD single-quadrupole instrument (Agilent Technologies, Palo Alto, CA). Instrument subsystems are arranged in racked assemblies with electrical and LAN connections at the rear to facilitate operational and maintenance access (Figure 2).    

Figure 2. Racked assemblies of identical LCâMS instruments used in high-throughput therapeutic drug monitoring and drug-of-abuse screening at St. Olav's Hospital.										

Samples to be analyzed are registered into the data system and prepared according to the extraction procedure for blood serum sample preparation shown in Table I. Each sample is spiked with two internal standards, (flurazepam and periciazine, Figure 1). After preparation, sample analysis vials are placed in autosampler trays, which in turn are inserted into the appropriate LC–MS system. Extracts are analyzed with the MS in selected ion monitoring (SIM) mode at the settings required to detect the appropriate qualifying ion masses. Instrument settings and conditions are shown in Table II.  

Table I. Procedure to extract clozapine and its metabolite from blood serum			

Calibration standards and internal quality control (QC) checks are run to verify both calibration and LC–MS performance. To guard against external sources of error, additional QC samples are randomly incorporated into the sample stream and treated blindly as any other sample. Calibration is performed with zero serum using a sequence of calibration standards, each containing clozapine and desmethylclozapine at the following concentrations: 0, 25, 100, 500, 1000, 2000, 3000, and 4000 nM. The calibration curves are constructed using a quadratic fit (because of the wide range of concentration) with 

 > 0.999. A series of quality control samples also are prepared with zero serum and identical concentrations of clozapine and desmethylclozapine at 500, 1000, and 3000 nM.  

Figure 3. Selected ion chromatograms for (a) 25 nM clozapine, (b) desmethylclozapine, (c) fragments from both target compounds (after neutral loss of 1-N-methylaziridine), (d) periciazine, and (e) flurazepam (9). All chromatograms show the [M+H]+ ions.			

Following analysis, results are transferred to a database. Sample data is evaluated against controls, and subsequent clinical evaluations are performed by a specialist in clinical pharmacology. These evaluations include an assessment of the correlation of therapeutic outcomes with trends in residual serum concentration of both the administered drug and drug metabolites. This information can prove helpful in fine-tuning subsequent therapy as well as providing additional information about the efficacy of the treatment protocol.  

Figure 4. Extracted ion chromatogram of blank clozapine in blood serum with internal standards (9).			

Figure 3 displays extracted ion chromatograms for the target compounds and their internal standards at the lowest concentration calibration standard (25 nM), demonstrating the limit of method sensitivity. This concentration is well below normal therapeutic monitoring levels. Up-front (or in-source) collisional-induced dissociationfurther enhances method specificity by producing additional qualifying ions for the two targets (Figure 3c). Figure 4 shows the extracted ion chromatograms of blanks containing the internal standards. Figure 5 displays the extracted ion chromatograms for a patient sample with clozapine and its metabolite measured at 366 nM and 365 nM, respectively. Despite the fact that these concentrations are lower than those found in typical patient samples, detected signals are clean and show an absence of interference.  

Table II. Instrument settings and conditions										

For a high-volume clinical analytical method to be credible, it must demonstrate acceptable sensitivity and specificity, as well as both day-to-day and long-term reproducibility across the analytical system, independent of the specific instrument employed or operator performing the analysis. Moreover, the instruments dedicated to this task must be sufficiently robust to handle the high-volume analytical load with minimal downtime. All of these objectives have been met by the system described here. Tables III and IV detail the true reproducibility of QC sample determinations across different operators and instruments for clozapine and its primary metabolite, respectively, for a single day "snapshot" and over a three-month rolling average. An assessment of month-to-month reproducibility, based upon one day/month sampling is conducted by an outside service and is shown in Figure 6.  

Table III. True reproductivity of analysis (three-month rolling average)			

Ensuring reliable, high-quality, high-throughput system performance is an ongoing process requiring continuous attention to both analytical operations and maintenance. Overall system organization resembles a set of assembly lines that facilitate rapid sample processing. Identical instrument configurations and the use of a quaternary mobile phase with four standard constituents and one of two column packings across all methods simplifies method development, reagent storage, and reagent dispensing. It also reduces the number of replacement parts and maintenance tools that must be inventoried and the time required for conducting repairs. Instrument maintenance of the 24 LC–MS and 10 gas chromatography–MS (GC–MS) systems is supported by meticulous documentation, a team of four maintenance engineers trained on-site using courses provided by the instrument manufacturer (Agilent Technologies), and by strategic in-house stocking of replacement parts. As a result, most component failures and other potential operational bottlenecks are anticipated and remedied before they seriously impact system function. The use of robust system components and the attention paid to preventative maintenance has enabled the system to achieve a long-term uptime of 95%. 

Since 1999, the LC–MS platform described here has been employed increasingly for high-volume drug screens, primarily for drugs of abuse. Each year, the capacity of the system has increased, approaching 1,000,000 determinations in 2004. The number of therapeutic drug monitoring determinations also has increased, but less rapidly, approaching 60,000 in 2004. Consistently robust and reliable high-throughput performance is now documented over a five-year period (Figure 7). Note that the first LC–MS system was purchased in 1998 and LC–MS screening was fully deployed by 1999. When compared with a corresponding immunoassay, the actual number of analytes determined by LC–MS is significantly greater because each analysis actually identifies multiple qualifying ion masses for both target compounds and the corresponding internal standards.  

Table IV. True reproductivity of analysis (one-day example)			

LC–MS versus Immunoassay in Forensic and Clinical Settings

Immunoassay lacks the identification specificity afforded by LC–MS detection of qualifying ion masses. Instead, an immunoassay indicates the presence of a target analyte and estimates its concentration by the occurrence and intensity of an antibody–antigen reaction. Substances that share common structural characteristics with the target analyte also can initiate this reaction. As a result, immunoassay is vulnerable to interferences that can produce an unacceptable number of false positive or questionable responses, especially at low analyte concentrations. The performance of the immunoassay reaction is optimized to a predetermined cutoff level that for some analytes can be as high as 1000–3000 ng/mL to avoid false positives. This tradeoff sacrifices sensitivity, thereby permitting a potentially higher incidence of false negative responses. No such sacrifice is required for LC–MS, which in contrast allows complete flexibility for the use of different cutoff values for different clinical or legal purposes, only limited by the range of the calibration curve and the limit of detection. (Quadrupole MS in single ion detection mode has a limit of detection typically at low- or even subnanogram molar levels, which can be several orders of magnitude better than immunoassay cutoff values.)  							

The improvement in both sensitivity and specificity afforded by LC–MS can qualitatively enhance clinical practice. It enables clinicians to track multiple analytes at lower detection levels rapidly and quantitatively, thereby providing a richer and more rapid assessment of response to treatment. It also makes it possible to identify and treat potentially life-threatening, drug-induced adverse events quickly, whether the causative agent is the drug being administered, one of its metabolites, or drug–drug interactions arising from prescription conflicts, self-medication by the patient, or from drug abuse.  

Figure 5. Extracted ion chromatogram of a patient sample at a typical low level (9).			

While the initial capital investment for LC–MS can be considerably higher than for corresponding immunoassay instrumentation, operating factors can mitigate these differences over time. The high volume of sample processing and lower cost of consumables associated with LC–MS can bring the cost per analysis down to a par with immunoassay. When new drugs become available, corresponding LC–MS determinations are developed fairly rapidly, often by modifying an existing method so as to identify the relevant qualifying masses. Contrast this with the extensive investment in time and resources required to develop a new immunoassay. Finally, unlike immunoassay and except where stipulated by regulation in evidentiary proceedings, an LC–MS analysis is sufficiently definitive that there is rarely a need to incur the additional costs of a confirmation by GC–MS. 

A battery of identically configured LC–MS systems operating under GLP compliant software and utilizing sample handling, analytical methods, quality control routines, and data management procedures that ensure sample integrity and chain-of custody, is shown to support a near continuous (24 h/7 day), high-throughput, high-volume, therapeutic drug monitoring and drugs of abuse screening operation. This system has demonstrated a capability for long-term, reliable, and cost-effective performance suitable for large-scale therapeutic drug monitoring in a clinical setting.  

Figure 6. 12-month control chart for external reproducibility assessment. The chart is generated by Heatcontrol/Cardiff Bioanalytical Services Ltd., Cardiff, U.K., based upon the analysis samples submitted once per month. The chart displays the bias index score (BIS), which is defined as the difference of the laboratory's measurement from the consensus mean scaled in terms of a chosen common coefficient of variation for all participants. If the exact coefficient of variation is used, a BIS value of 300 corresponds to 3 standard deviations (SD). External quality control samples are used for any therapeutic drug monitoring analyte that is available. Results shown here indicate that BIS scores correspond to a month-to-month variation 			

The LC–MS instrumentation described here utilizes a quadrupole MS system operating in SIM mode to achieve the requisite detection sensitivity. Operating in SIM mode precludes the ability to simultaneously detect and identify nontarget analytes. This limitation does not extend to ion-trap MS, which can detect multiple low-abundance analytes free of the sensitivity limitations of quadrupole MS in full scan mode. 

Implementation of an LC–MS ion trap system would make it possible to detect and identify multiple analytes in a single run, decreasing analysis times and costs and providing a considerably richer data set. With new drugs continuously being introduced and a growing patient population being treated under multiple drug regimens, LC–MS ion trap capabilities could prove valuable in clinical therapeutic drug monitoring and drugs of abuse screening. A development program utilizing an LC–MS ion trap system for screening in clinical toxicology (in LC–MS–MS mode) currently is ongoing.  

Figure 7. Number of reliable LCâMS analyses per year for drugs of abuse, 1996-2003 (10). 			

 is laboratory manager and analytical sciences director, for the Department of Clinical Pharmacology, St. Olavs Hospital, Trondheim, Norway. 

 is department director, also for the Department of Clinical Pharmacology, St. Olavs Hospital. 

 is a market development specialist with Agilent Technologies (Wilmington, DE). E-mail: 

1.  B.L. Ackerman, M.J. Berna, and A.T. Murphy, 

2.  N.J. Clarke, D. Rindgen, W.A. Korfmacher, and K.A. Cox, 

4.  J. Chen, Y.Z. Zha, K.P. Gao, Z.Q. Shi, X.G. Jiang, W.M. Jiang, and X.L. Gao, 

5.  B. Shah, S. Rohatagi, C. Natarajan, S. Kirkesseli, R. Baybutt, and B.K. Jensen, 

6. E. Stokvis, H. Rosing, L. Lopez-Lazaro, and J.H. Beijnen, 

9.  K. Zahlsen, T. Aamo, and J. Zweigenbaum, "Therapeutic Drug Monitoring by LC–MSD-Clozapine, An Example," Agilent Technologies publ. 5989–1267EN, Aug. 18, 2004 (Agilent, Wilmington, DE). 

10.  K. Zahlsen, T. Aamo, and J. Zweigenbaum, "Screening Drugs of Abuse by LC–MS-Technical Overview," Agilent Technologies publ. 5989-1541EN, Aug. 23, 2004 (Agilent, Wilmington, DE). ? 

Therapeutic drug monitoring is performed routinely by liquid chromatography–mass spectrometry (LC–MS) using instrumentation and methods originally developed and systematically configured for the high-volume, high-throughput analysis of drugs of abuse. An example of LC–MS monitoring of the drug clozapine and its metabolite, desmethylclozapine, is detailed along with a description of the overall system architecture, workflow, and maintenance routines that support a large-scale clinical therapeutic drug monitoring program. The relative advantages of LC–MS over immunoassay and LC–UV, the current standard techniques for therapeutic drug monitoring, are discussed in the light of these results.

Spectroscopy-06-01-2005

53rd Annual ASMS Wrap Up

The Baseline: The Solar Spectrum

AFM Measurement of Step Kinetics for the Growth and Dissolution of Crystallites

A High-Volume, High-Throughput LC–MS Therapeutic Drug Monitoring System