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2UTILITY OF HIGH-RESOLUTION MASS SPECTROMETRY FOR NEW DRUG DISCOVERY APPLICATIONS
William Bart Emary and Nanyan Rena Zhang
2.1 INTRODUCTION
The pharmaceutical industry has used high-resolution mass spectrometry (HRMS) for many years for the determination of the molecular formula of a compound, and this has been critical to the research and development process. Instrument compa-nies have made significant technological advancements in HRMS over the past two decades, and this has created much excitement in the world of bioanalytical chem-istry. New systems can perform more sophisticated scans and are easier to use because of more stable calibration and new software tools. Instrument costs of the latest systems are decreasing and becoming more competitive with triple quadru-pole mass spectrometers (TQMS), which only provide unit mass resolution. Quan-titation with a TQMS is typically carried out using selected reaction monitoring (SRM), which requires optimization of each analyte.
Drugs are extraordinarily expensive to discover and bring to market, in part due to the high attrition rate of compounds during the multi-year testing process [1]. The inflation rate of drugs cannot continue to increase at the same pace and phar-maceutical companies must find ways to increase the probability of success. Scien-tists create data and convert it into knowledge. Analytical laboratories are at the center of this effort and these laboratories need to undergo continuous process improvement. The latest generation of HRMS provides more information with less effort than TQMS. In the future, many SRM assays will be performed instead on an HRMS system.
Mass Spectrometry for Drug Discovery and Drug Development, First Edition. Edited by Walter A. Korfmacher.© 2013 John Wiley & Sons, Inc. Published 2013 by John Wiley & Sons, Inc.
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2.2 QUALITATIVE/QUANTITATIVE WORKFLOW IN DRUG METABOLISM
2.2.1 Instrumentation
While instruments based on magnetic and electric sectors dominated HRMS for decades, two other types of systems supplanted them since about 2000. Makarov introduced the orbitrap, which is based on electrostatically trapped ions circulating between two oval electrodes (Fig. 2.1) [2]. Each m/z has a characteristic harmonic oscillation frequency that induces a current on a detection electrode. The total detector signal is complex and a summation of all frequencies and intensities of the trapped ions. An inverse Fourier transform is performed on the total current signal, generating a frequency to ion intensity spectrum, which can then be converted to a mass spectrum. The orbitrap mass analyzer is frequently preceded by a linear quad-rupole ion trap for the purpose of conducting collision-induced dissociation (cID). The orbitrap analyzer is stable enough to use for accurate mass work for weeks without needing recalibration.
Wong examined the effects of parameters such as resolution, maximum injection time, and automatic gain control on discovery assay quantitative performance [3]. While the lower limit of quantitation (lloQ) could be decreased by increasing the length of time that ions were injected into the analyzer, this came at a sacrifice of linearity at the high end of the concentration curve. The lloQ for most compounds in mouse plasma was in the 1–3 nM range and the dynamic range was three to four orders of magnitude. A resolution of 20,000 full width at half maximum (FWHM) was found to strike the best balance of signal to noise. The results were competitive
Figure 2.1 orbitrap mass spectrometer.
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QUAlITATIvE/QUANTITATIvE WoRKFloW IN DRUg METAbolISM 39
to many triple quadrupoles but illustrated the need for thoughtful consideration of parameter values to meet study objectives.
The second type of HRMS system commonly used today is based on the time-of-flight (ToF) design. Similar to the orbitrap, the high-resolution device can be preceded with a quadrupole for cID or other mass filter purposes. Researchers have used ToF for many years with pulsed ion sources but the resolution of such systems was relatively low until the 1990s [4]. The combination of a delayed extraction pulse of ions in the source plus a reflectron (Fig. 2.2) significantly increased resolution, and 30,000 FWHM is readily achievable today on commercial instruments [5]. Although the calibration must be carried out more frequently than with an orbitrap, ToF is automated and, in general, seamlessly integrated into the unknown sample run batches. Tandem ToF/ToF systems have also been used to provide simultane-ous high-resolution analysis of both parent and fragment ions [6].
2.2.2 Applications
Adoption of high-performance liquid chromatography-mass spectrometry (HPlc-MS) occurred quickly after the introduction of robust atmospheric pressure ioniza-tion (API) sources in the early 1990s. This advancement occurred at a time when pharmaceutical companies were building better tools to improve the accuracy of human pharmacokinetic (PK) predictions and reduce the chance of drug–drug interactions. larger organizations’ workload increased exponentially and their labo-ratories specialized in either quantitative or qualitative experiments. We are now beginning to see a reversal of this trend because better hardware and software tools for both qualitative and quantitative data allow us to consider high-level process changes to increase efficiency. Hardware ruggedness and software ease of use are
Figure 2.2 Synapt g2™ (Waters corporation, Milford, MA) ion mobility-ToF mass spectrometer.
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critical features for laboratories that analyze thousands of samples each day. like-wise, scientists charged with metabolite characterization rely on sophisticated tools so that they can rapidly provide feedback to medicinal chemists regarding metabolic soft spots. Scientists’ energy must be focused toward problem solving as much as possible and neither specialist can afford to take a step backwards when these workflows merge together.
Implementation of HRMS to combine qualitative/quantitative (Qual/Quan) workflows has already begun for routine in vitro metabolic stability and character-ization experiments. Although this can also be performed on a low-resolution TQMS, it is less efficient because it requires compound tuning for the SRM portion of the experiment, and multiple injections to provide similar levels of information. State-of-the-art ultra-performance liquid chromatography (UPlc)-HRMS systems can do this with a single experiment and injection [7]. If the experiments are designed appropriately, data can be reprocessed later to answer new questions that come up after the first examination of the data. Post-acquisition data processing software using mass defect and biotransformation filters allow for the discovery of new metabolites that might have initially been missed, and even estimation of their concentrations [8]. Several laboratories have expanded the concept to in vivo studies [9, 10]. Ramanathan used a Q-ToF for routine discovery PK profiling of 20 propri-etary compounds [11]. The lloQ of the parent compound and PK parameters were comparable to SRM, with no loss of metabolite information. The authors asked, “Is it time for a paradigm shift from SRM to HRMS?”
This is indeed happening in several laboratories and one must ask, “To what extent will this occur for all routine drug metabolism discovery assays?” While the transformation should begin for many assays, there is still anecdotal information that indicates that for some compounds, SRM results in lower lloQs. Maximum sensitivity is needed sometimes in drug discovery for a variety of reasons. For example, drug potency is continuously being reduced to minimize risk of adverse events in humans. chemists are always challenging bioanalytical groups to increase compound PK screening throughput, and for some laboratories in vivo cassette dosing is an essential tool. Researchers typically dose five to seven compounds per cassette and a proportional reduction in dose of each compound is warranted as the number of compounds increase. Instrumentation is constantly evolving and expen-sive. Each analytical laboratory will be challenged to decide the best mix of systems depending on their circumstances and budgets. The laboratory of tomorrow will likely include both HRMS and TQMS systems. Analytical scientists will also lever-age other skills in their toolbox, namely extraction and chromatography.
Xia et al. performed extensive experiments and looked at parameter effects on quantitative performance measures for over 153 drugs in human plasma. They found a resolution of 20,000 and extracted ion chromatogram (XIc) less than +20 ppm generally provided good conditions for quantitation [12]. The number of endoge-nous peaks and interferences were characterized at different resolution and extracted ion chromatogram (EIc) windows. Their findings demonstrated a continued need for chromatography to complement HRMS (Fig. 2.3). Kaufmann measured concen-trations of over 100 veterinary drugs in animal tissues using an orbitrap and com-pared this with the European Union method using SRM detection. Results (precision, accuracy, and dynamic range) were comparable, although in a few cases, SRM was more sensitive [13]. In many cases, one wants to screen for as many drugs as possible
bIoMARKERS 41
in the unknown. In this case, HRMS is superior because it requires only one injec-tion and did not require tuning and SRM transitions for all possible drugs.
Hopfgartner reported a new Q-ToF technique called sequential window acquisi-tion of all theoretical fragment-ion spectra (SWATH). He used it to improve per-formance and quality of data for Qual/Quan experiments of drugs (e.g., talinolol, a beta-blocker) [14]. In SWATH, Q is set to band pass a narrow mass range approxi-mately 20 Da wide. low and high collision energy spectra are generated over 25 ms and combined into a single data scan providing a spectrum that contains significant parent ion intensity plus fragment ions. by limiting the parent ion mass window to a small range, correlation of fragments from parents are much easier to make. This facilitates structure elucidation of metabolites.
The first example using HRMS for regulated analysis was recently published for prednisone and prednisolone in human plasma [15]. The lloQ of 5 ng/ml (resolu-tion 20,000) meets most needs but is still not as good as several reported SRM methods. Signal to noise is good even with a relatively wide mass filter window of +40 ppm. The Jetstream™ spray device increased ion signal strength by focusing ions using a supersonic nitrogen stream. This increased sensitivity approximately 10-fold. Henry recently developed clinical methods for 17 drugs on both the orbi-trap and TQMS in the SRM mode [16]. Performance measures were similar for both systems, and they highlighted the benefit of full scan MS to diagnose problems such as standard curve nonlinearity.
2.3 BIOMARKERS
biomarkers encompass physiological (e.g., blood pressure) and biochemical (e.g., cholesterol) domains. Undesirable biochemical changes are the root of disease, and
Figure 2.3 Total number of endogenous chromatographic peaks in human plasma detected in the extracted ion chromatogram windows of 153 drugs. Data analysis in (a) profile mode; (b) centroid mode.
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also the cause of drug side effects. In a recent survey of development candidates that failed during testing, over 66% were due to lack of efficacy and 21%, to unac-ceptable safety margins [1]. The high rate of failure years after first synthesis is the impetus for biomarker research. Measurement of biochemical biomarkers is being utilized earlier in research since changes can occur before positive or negative effects manifest themselves. There are thousands of potential chemical biomarkers and this, in combination with natural variation and response to other stimuli, makes identification of relevant ones very difficult. bioanalytical labs are increasingly asked to add one or more biomarker concentration measurements to their PK drug candidate assays for the purpose of differentiating between several interesting leads. The transition of work between biomarker discovery and its application to selection of drug candidates must be seamless.
Examples of HRMS for biomarker discovery and compound assessment are beginning to appear in the literature. Differential mass spectrometry (DMS) has been developed as a tool within Merck to locate potential Alzheimer’s biomarkers in preclinical models [17]. Multiple samples were taken from each subject and served as their own control. The strategy utilized in-house software routines to map spectral components between samples, and those that changed significantly were identified using statistical analysis tools. It did not require stable label tools, which are frequently used to measure protein concentration changes. Tests were conducted using secretase inhibitors dosed to rhesus monkeys and confirmed changes in A-beta, a known biomarker of the disease. The findings by DMS correlated well with enzyme-linked immunosorbent assay (ElISA).
Drug companies stop development of many compounds after good laboratory practice (glP) preclinical multi-day toxicity testing. These studies are relatively late in the discovery process and are expensive. The industry recognizes that to reduce attrition rates as early as possible, it is critical to identify important safety genomics and metabonomics biomarkers that can be implemented earlier [18]. For example, muscle toxicity was observed after the treatment of ibipinabant to dogs. Zhang et al. used UPlc-HRMS and a plasma pooling strategy to find several statistically significant metabolome biomarkers in dogs after 6 weeks of dosing. Sample cleanup was straightforward and the chromatographic run times were relatively short. In-house software was created to carefully compare control and study sample spectral intensities. changes in several acylcarnitine concentrations were identified [19]. The importance of continued research like this to better understand toxicity and link them to biochemical biomarkers cannot be understated.
In the small-molecule lead optimization world, the challenge we are facing is “How do we measure concentrations of drug candidates and an increasing array of biomarkers efficiently?” In the past, we have optimized our processes around mol-ecules in a relatively small physical–chemical space first highlighted by lipinski [20]. biomarkers significantly expand this space and create new challenges. In order to build the best possible PK/pharmacodynamic (PD) correlations, we must generate information from the same subject/sample. Many transgenic animal models are in mouse, which limits blood volumes after serial sampling. Different diseases, and complexity of biochemical events, may dictate that many simultaneous biomarker measurements are necessary to achieve acceptable correlation of PD to drug PK [21]. For these applications, HRMS becomes increasingly attractive compared with SRM in order to maintain simplicity and to allow post-acquisition data mining.
siRNA AND PRoTEIN THERAPIES 43
2.4 TISSUE
Since the majority of pharmacological targets are in tissues, concentrations of drugs and metabolites are important to help understand toxicity and efficacy or lack thereof. one important evolving MS field is the use of ion or laser beams to directly probe tissue and provide complementary information to radiolabel drug disposition studies [22, 23]. It can even supplant radiolabel to answer early discovery questions before radiolabel is available. Ubiquitous chemical noise makes interpretation of low-resolution MS data challenging. Most reports mapping drug and metabolite to tissue location use SRM to provide good signal to noise, but this limits the informa-tion in the raw data set. HRMS will play a critical role because full scan MS data will be generated with a high level of specificity. Spengler et al. developed a high spatial and mass resolution system demonstrating the utility [24]. They were able to distinguish between different types of endogenous compounds in kidney tissue, separating isobaric phospholipid ions, both from themselves and matrix ions.
Mann et al. recently reported measurement of 30 colon cancer biomarkers from tissue using laser capture microdissection followed by lc-HRMS. They were able to monitor several thousand proteins in a single analysis from as few as 1000 cells [25]. Addition of polyethylene glycol or dextran carriers increased peptide yield critical to reduce cell count. These carriers add complexity to spectra, but this can be compensated with high mass resolution. Similar strategies may be employed in the future for preclinical oncology models to better understand their relevance to humans, and to improve tools for compound selection.
2.5 siRNA AND PROTEIN THERAPIES
While most drugs are small organic molecules, drug companies are developing an increasing number of medicines with molecular weights (MWs) greater than 1000. The hormone Epogen (Amgen, Thousand oaks, cA) and oncology antibody Her-ceptin (genentech, South San Francisco, cA) are examples of protein therapies. Researchers use ElISA for most protein quantitative assays because it provides high throughput and extremely low lloQ. It requires time-consuming antibody preparation, and assays can sometimes suffer from specificity problems if cross reactivity occurs with metabolites. MS is now used to provide early candidate dis-position assessment in preclinical studies and before investment in ElISA is war-ranted. Protein drugs are challenging to work with from an MS perspective because they are large and multiply charged, and their glycosylation is heterogeneous [26]. In order to improve PK properties, chemical moieties such as polyethylene glycol polymers are added and this only makes MS spectra more complex. Much of this work is carried out with digestions and sample clean-up followed by SRM. While chromatography is critical, HRMS allows the work to be carried out faster because of greater specificity, diverse acquisition modes, and powerful data processing tools [27]. Ruan reported that 60-K mass resolution was needed to provide good selectiv-ity for measurement of intact protein with MWs of 15 k Dalton in plasma (Fig. 2.4) [28]. The effect of different extracted ion windows on isobaric analyte and endog-enous interference signal is shown in Figure 2.5.
Researchers are testing another class of high MW therapies made of small inter-fering RNA (siRNA). While small-molecule drugs bind reversibly to receptor
44 UTIlITy oF HIgH-RESolUTIoN MASS SPEcTRoMETRy
Figure 2.4 Effect of resolving power on separation of isotopic peaks of octuply charged lysozymes: (A) resolving power of 2 k, (b) resolving power of 10 k, (c) resolving power of 30 k, and (D) resolving power of 60 k.
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proteins to cause the desired outcome, siRNA bind to expressed messenger RNA so that it cannot be transcribed into the protein associated with the disease. The MWs of siRNA duplexes are usually 10,000 to 14,000 Da. Robust HPlc-MS assays are challenging to develop in a high-throughput format because unique analytical conditions are required for the passenger and guide stands, which are negatively and multiply charged due to the phosphate backbone of nucleotides. As with other drugs, it is necessary to characterize the siRNA molecules to determine structure and stability. It is also important to understand their PK and metabolism. Q-RT-PcR methods are the standard for siRNA quantification, but they cannot always distinguish small metabolic changes from parent molecule, and require reporter probe reagent preparation [29]. li showed that HPlc-HRMS and MS/MS comple-mented qPcR and used it in PK studies to confirm parent concentration, and also provided metabolic information of the siRNA candidates [30]. Zou differentiated between two potential siRNA metabolites with molecular mass difference less than 1 Da [31]. Takami characterized metabolites in plasma, and then used a linear ion trap quadrupole–Fourier transform (lTQ-FT) to quantify both parent and the metabolite concentrations [32]. We have also used HRMS to confirm siRNA qPcR concentrations and also to optimize PK of lipid nanopartilces (lNP), which deliver the active molecules to the liver target (unpublished) [33]. lNPs are complex,
DRUg METAbolITE IDENTIFIcATIoN USINg MASS DEFEcT FIlTERS 45
multicomponent vehicles that prevent the active siRNA molecule from being degraded by nucleases during transport into the cells [34]. Understanding their PK and distribution is important to maximize target engagement.
2.6 DRUG METABOLITE IDENTIFICATION USING MASS DEFECT FILTERS
In order to more fully assess safety risks to humans, increasing scrutiny is being paid to the level of exposure of metabolites in preclinical glP studies [35]. The science is still evolving and events leading to manifestation of toxicity are complex. For example, factors such as projected human dose level need to be accounted for to increase the confidence level of toxicity prediction and to avoid unnecessarily eliminating good drug candidates [36]. Many organizations are implementing new and more sophisticated HRMS hardware and software technologies in order to
Figure 2.5 Effect of extracted ion chromatogram (EIc) window on assay selectivity and sensitivity for lysozome in human plasma: (A) ±0.5 m/z, (b) ±30 ppm, (c) ±10 ppm, (D) ±1 ppm.
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move the metabolite characterization process further upstream in the research process, and to do it efficiently. Mass resolution greater than 30,000 and accuracy of ±5 ppm from current high-resolution mass spectrometers allow unambiguous assignment of elemental composition. Rapid characterization of drug candidate metabolites is critical in order to successfully influence new compound synthesis and to improve the PK and structure–activity relationships (SAR). chemists need to make decisions quickly, and will do so based on whatever timely information is available.
characterization of metabolites early in discovery is typically carried out without the aid of radiolabels. For many years, researchers have used advanced SRM systems with intelligent software to control scan functions and automate manual steps in metabolite characterization. They require some knowledge of fragmentation pat-terns of the parent compound and common metabolic pathways. Mortishire-Smith was among the earliest researchers to characterize metabolites in a new way, using HRMS with mass defect filtering (MDF). Minute mass differences of drug and metabolites can be used to distinguish themselves from isobaric endogenous com-pounds. Mortishire-Smith et al. studied commercial and proprietary compounds in metabolic stability studies and were able to automate data interrogation routines and fully characterize drugs with less work [37]. MDF also allowed them to identify less common metabolic products. bateman expanded upon this work and generated parent and fragment information in a single injection on a Q-ToF MS [38]. The quadrupole was used as a band pass filter and interleaved scans at low and high energy produced mass spectra that contained either parent or a combination of parent and fragment ions. This provided more structure information in addition to the molecular formula.
once putative metabolites are characterized, the focus turns to the concentration level of more abundant ones because this may impact selection between candidates or, at a minimum, affect design of future studies. These questions arise before authentic standards are available, and while selectivity is a hallmark strength of MS, it can also be viewed as a weakness because endogenous compounds eluting near the metabolite can cause suppression or enhancement of ionization and skew the measured concentration. yang et al. attempted to correct for the weakness by cou-pling an ultraviolet (Uv) detector to HRMS [39]. Uv detectors at low wavelengths (200–230 nm) tend to show a more uniform response for different compounds. They reduced the error of metabolite concentration from eight- to two- to threefold.
Zhang et al. increased metabolite hit quality by adding control sample back-ground subtraction routines to MDF. They applied it to highly complex in vivo samples such as bile, urine, and plasma (Fig. 2.6) [40]. The tool was used to charac-terize metabolites of troglitazone, a compound withdrawn from the market after idiosyncratic hepatotoxicity was observed [41]. The combination of MDF with improved subtraction routines provided complete characterization of all metabo-lites, including critical ones implicated in the toxicity. They have continued making improvements to detect low concentration metabolites by adding more sophisti-cated scan techniques on the latest hardware [42].
glutathione (gSH) drug conjugates are phase 2 metabolic products, typically generated by the organism to protect itself from xenobiotic electrophilic species. Some electrophiles may be associated with idiosyncratic drug reactions, causing medicines to be removed from the market after approval. While gSH conjugates
Figure 2.6 Troglitazone metabolites in rat urine: (a) full scan raw total ion chromatogram data, (b) full scan extracted ion chromatogram using mass defect filtering and background subtraction of two control samples.
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are present in many safe medicines, they are of heightened interest during drug development. Phase 2 metabolites such as glucuronide and sulfate conjugates can easily be detected using neutral loss scans on quadrupole instruments. In contrast, gSH metabolites do not always fragment with the same neutral loss (e.g., 129), making it more challenging to find all of them on TQMS. chen et al. used back-ground subtraction and MDF tools to investigate in vitro formation rates of gSH conjugates from a series of compounds containing a thiophene moiety, a known structural alert [43]. Their results demonstrated the utility of MDF for rapid iden-tification of electrophiles.
The process of identifying a metabolite is not complete until the biological sample is tested along with an authentic, synthetic standard. Discovery programs move quickly, and some degree of uncertainty is inevitable before a standard is available. Holman characterized metabolites of a drug candidate that underwent oxidation of a sulfur atom [44]. one particular metabolite underwent unusual intra-molecular rearrangement after cID on a TQMS. This initially led to an incorrect assignment of the metabolite structure. Holman performed HRMS and hydrogen/deuterium exchange experiments that showed unresolved fragment ions and ulti-mately led to a change in the proposed metabolite structure. This is a clear demon-stration of improved data quality in early preclinical space.
2.7 FUTURE TRENDS
Improvements in software are at the forefront of current HRMS research. Manu-facturers are attempting to bring together the best of both qualitative and quanti-tative packages of the past. Different types of ion filtering devices are also being introduced into newer instruments. orthogonal gas phase separation can be done by coupling an ion mobility separation (IMS) to the ToF (Fig. 2.2) [45]. This is a powerful combination, and in some cases, cross-sectional differences in the drift tube allow rapid (MS) separation of isobaric ions such as isomeric peptides (Fig. 2.7) [46]. Dwivedi built an IMS-ToF device and then used it to analyze the meta-bonome in human blood. Figure 2.8 shows two-dimensional mobility-m/z plots and the segregation of the various classes of compounds found in blood. These new, powerful techniques should find utility identifying biomarkers [47]. New fragmen-tation tools have also been added to the orbitrap. Electron capture dissociation (EcD) and electron transfer dissociation (ETD) have demonstrated benefits in peptide sequencing compared with cID, and preserved post-translational modifica-tions of proteins [48].
Most routine PK and metabolite characterization work is done using 2.1–4.6 mm inner diameter (ID) columns with flow rates greater than 0.5 ml/min. The metabo-nomics and proteomics communities have used lower flows and nano-liquid chro-matography and ionization sources for many years. They had to use these systems because some compound concentrations were very low, creating a demand for maximum sensitivity. Drug metabolism scientists do not use the systems routinely for PK and drug metabolism studies because the number of analyzed samples is larger and the flow paths become blocked more frequently. Instrument companies have modified ion source designs to bridge the gap between nano- and conventional flow rates. Ramanathan used the captiveSpray™ (bruker-Michrom, Auburn, cA)
coNclUSIoNS 49
Figure 2.7 Ion mobility separation of isobaric peptide ions.
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ion source because it provided benefits associated with nano-spray, but at higher flow rates [49]. Ramanathan et al. did routine bile metabolic profiling using a 75 micron ID column running at 0.3 μl/min [50]. Samples were diluted up to 1000-fold, but this was counterbalanced by a gain in sensitivity of about 1000-fold due to the smaller column and spray device. No special workup was used other than high speed centrifugation at 13,000 RPM. They found that lower RPM was insufficient for sample cleanup. comparable metabolic profile data were generated with signifi-cantly less sample and solvent consumption. Ramanathan found that columns lasted for months, which meets the need of most laboratories.
2.8 CONCLUSIONS
The creation of new analytical instrumentation is one of the keys to opening new doors in science [51]. Instrumentation companies and academic and industrial ana-lytical scientists have worked well together to develop and use new systems in the pursuit of new medicines. Pharmaceutical scientists are making high-resolution mass spectrometers one of their most important tools in drug discovery.
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Figure 2.8 Ion mobility-ToF two-dimensional plot of the metabonome from human blood.
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290 390Mass to charge ratio (m/z)
Mobility-Mass correlation Curves
490 590 690 790
100 150m/z
200
0.80
0.65
0.50
0.3590 120 150
1/Ko of Amino acdis in blood1/Ko of Amino add standards
Mass-to-charge ratio of ions180 210 2401/
Ko
(cm
2 V–1
s–1)
250
ACKNOWLEDGMENTS
We acknowledge the following for providing their original artwork: Drs. Prabha Dwivedi, Michaela Scigelova, Qin Ji, Mohammed Jemal, Mark Wrona, Haiying Zhang, and Iain campuzano. We also acknowledge Drs. Walter Korfmacher, Kathy cox, and lucinda cohen for critical reading of the manuscript.
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