Хроматомасс-спектрометр LCMS-IT-TOF — комбинация жидкостного хроматомасс-спектрометра, ионной ловушки и времяпролетного масс-спектрометра:

• Высокое разрешение, чувствительность, точность, производительность
• Новая система фокусировки и фрагментации ионов в ионной ловушке
• Ионизация при атмосферном давлении
• Трехступенчатая система вакуумирования
• Температурный контроль пролетной трубки
• Встроенный ESI интерфейс
• Двухступенчатый рефлектрон
• MСn анализ до МС10

Разрешение и точность
Двухступенчатый рефлектрон (DSR) и баллистическая экстракция ионов (BIE), запатентованные Шимадзу, обеспечивают высокое разрешение (R>10000) и высокую точность (5 ppm) во всех режимах работы: МС, МС/МС и МС/МС/МС.

Чувствительность
Компрессионная инжекция ионов (CII — патент Шимадзу) в систему ионной оптики обеспечивает высокую эффективность ввода ионов в ионную ловушку и высокую чувствительность в МСn-режимах. (Чувствительность по резерпину в МС2 режиме 5 пикограмм при соотношении сигнал/шум 50:1).

Производительность
Переключение между режимами работы с положительными и отрицательными ионами и регистрация спектра всего за 100 миллисекунд.

Квадрупольная ионная ловушка
Для достижения высокой чувствительности использована новая технология “охлаждения” ионов в квадрупольной ионной ловушке (QIT). Запатентованное Шимадзу устройство баллистической экстракции ионов (BIE) обеспечивает высокое разрешение и чувствительность во всех MC и MCn-режимах.

Октопольные линзы
Патентованное устройство компрессионной инжекции ионов (CII) в систему ионной оптики эффективно вводит ионы элюированных компонентов пробы из жидкостного хроматографа в ионную ловушку.

Трехступенчатая дифференциальная система откачки
Обеспечивает высокочувствительные измерения благодаря оптимальному давлению на каждой стадии масс-спектрометрии.

Ионный источник ESI
Технологии ионизации и транспортировки ионов, развитые для жидкостных хромато масс-спектрометров общего назначения, в данном приборе были дополнительно улучшены.

Как и все приборы Шимадзу, жидкостной хромато масс-спектрометр LCMS-IT-TOF внесен в ГОСРЕЕСТР РФ, имеет Государственный Метрологический Сертификат РФ.

Intelligent Auto MSn Function

Samples cannot be recaptured once injected for LC/MS analysis; therefore, it is vital that instruments can automatically select the appropriate precursor ions. With the LCMS-IT-TOF, a variety of precursor ion selection criteria is available, such as the selection of ions in order of intensity or m/z, as well as intelligent automatic precursor selection, such as a monoisotopic peak selector and charge-state filtering.

Principles of Neutral Loss Survey
MS3 measurements are automatically performed if the specified neutral loss Is observed in the MS2 spectrum.

With the Neutral Loss Survey, only the target ions are measured in MS3, enabling one to obtain desired information effectively without loss of time.

As detailed information about target ions is obtained using the neutral loss survey function, it can become a powerful tool for supporting the identification of compounds (e.g., phase 2 metabolites for drug discovery research.)

Example Highlighting the Neutral Loss Survey Function
The combination of the neutral loss survey and MS3 measurement provides accurate mass information that can lead to highly reliable structural analyses of phospholipids.

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Formula Predictor Software

Formula Predictor (Optional Product)
Effectiveness of Accurate MSn

In composition prediction, target constituents having a small mass and high mass accuracy associated with their measured values, provide fewer numbers of candidates and greater prediction reliability. When using information-rich MSn data with the Formula Predictor software, formula predictions starts with the product ion having the smallest mass, and uses that result in the effective prediction of the parent ion by reducing the number of candidates.

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Ionization Options (APPI, APCI)

The LCMS-IT-TOF adopts the widely used ESI method for ionization, but the APPI (Atmospheric Pressure Photoionization) and APCI (Atmospheric Pressure Chemical Ionization) methods have been added to provide more ionization options for low polarity substances. APPI (Atomospheric Pressure Photoionization) is ionization in which samples are ionized using ultraviolet light. APCI (Atmospheric Pressure Chemical Ionization) is ionization in which the analytical sample is subjected to a corona discharge. APPI achieves good ionization with low to moderate polarity compounds, while APCI is suitable for moderately polar substances, so further expansion of LCMS-IT-TOC applications can be expected using these ionization methods. Selection of the ionization method is based on the sample properties.

APPI-ITTOF (Atmospheric Pressure Photoionization Kit for LS-IT-TOF: APPI + APCI)
APPI, APCI, APPI/APCI combination modes are supported.
APPI employs ultraviolet light, while APCI achieves ionization using the ion — molecule reaction with the reactant ion (solvent origin).
For analysis of low to moderately polar compounds (polycyclic aromatics, some mycotic toxins, etc.)
Instrument control and data processing with LCMSsolution software for LCMS-IT-TOF.

APCI-ITTOF (Atomospheric Pressure Chemical Ionization Kit for LCMS-IT-TOF)
Supports APCI.
APCI achieves ionization using the ion – molecule reaction with the reactant ion (solvent origin).
For analysis of moderately polar substances (steroids, lipids, sugars, pesticides, etc.)
Instrument control and data processing with LCMSsolution software for LCMS-IT-TOF.

Principles and Components of APPI and APCI Ionization Methods
Atmospheric Pressure Photoionization (APPI)
This is a new ionization method in which the sample is ionized using ultraviolet light. The APPI method excels in high sensitivity ionization of low polarity compounds. The APCI heater and nebulizer are used to vaporize the sample. (Ionization by a combination of APPI and APCI is also possible using simultaneous UV radiation and the corona discharge.)

Direct APPI
If the analytical target ionization energy is lower than the photon energy, the target ion M+ that was ionized due to the photons receives a proton from the hydrogen in the solvent, to become (M+H)+.Sensitivity can sometimes be increased by adding a compound called a dopant, which has lower ionization energy than the analytical target.

Atmospheric Pressure Chemical Ionization (APCI)
This is an ionization method in which the sample is ionized using an ion – molecule reaction with a reactant ion. Sample solution is nebulized by the N2 nebulizer gas to form a spray as it enters the heater (at about 400°C), and both sample and solvent molecules are vaporized to a gaseous state. The solvent molecules are ionized by the corona discharge, and stable reactant ions are formed. Protein transfer occurs between these reactant ions and sample molecules (ion – molecule reaction), and the sample molecules either add or lose protons to become ions. This ion – molecule reaction is known to occur in a variety of patterns, such as protein shift reactions, electrophilic addition reactions, etc. Similarly as with ESI, primarily protonated molecules (or deprotonated molecules) are detected, so it is used for analysis of highly lipid-soluble compounds and compounds that do not ionize in solution.

Measurement Example
MTDATA [4,4′,4″-tris (3-methyl-phenylphenylamino) triphenylamine] is a typical material used as an organic EL element. Using the APPI mode, the observed base peak was the molecular ion M+, while in the APCI mode, it was the protonated molecule [M+H]+. It was possible to identify it as MTDATA based on each of the accurate masses. Using APCI, m/z 530.2628, 607.3000 and 698.3422 were observed as cleavage fragmentation ions of MTDATA, while only the molecular ion was observed with APPI, without any cleavage occurring.

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Metabolite Structural Analysis Software MetID Solution

MetID Solution compares the pre- and post-metabolized sample data, and a search is then performed for expected and unexpected metabolites. (See the Profiling SolutionMetabolomics Software AM+ for a description of the statistics-driven in-depth search function that pinpoints changes among huge amounts of data.) Peaks generated with the metabolized sample that do not occur in the pre-metabolized sample are recognized a possible metabolites, and isotopic pattern comparison in conjunction with the composition prediction tool powerfully support highly accurate metabolite identification. As for the unexpected metabolite candidates (substances not expected in the metabolic pathway), this statistical multivariate analysis is applied to the MSn spectrum in conjunction with composition prediction tool to efficiently filter the candidates and identify the metabolites. This technique can be applied not only to metabolite analysis but to detection and identification of similar compounds in contaminant identification and natural substance chemistry.

Easy operation
Comparison of pre- and post-metabolized sample
Metabolite candidate detection by multivariable analysis
Synchronization with Composition Prediction Tool
Lhasa Meteor Support
Comparison of Pre-metabolized and Post-metabolized Samples
MetID Solution can be used to compare pre- and post-metabolized samples to check the types of metabolites that are formed. Following is an explanation of the data obtained when the compound with the composition formula C18H11N2 is metabolized through an oxidation reaction. The compound changes as shown at right due to oxidation.

A comparison of the mass spectrum at m/z 289.0739 in the pre-metabolized and post-metabolized samples reveals a peak that is only observed in the post-metabolized sample. Thus, the substance is judged to be a metabolite. By comparing the peaks, any substances with masses in the vicinity of metabolites may be detected as metabolites. However, with the high-resolution, high-accuracy Shimadzu LCMS-IT-TOF, since comparisons are made between chromatograms drawn using precise mass width (XIC: Extracted Ion Chromatogram), assessments are made with high reliability.

mass spectrum at m/z 289.0739

Metabolic pathways often follow typical patterns, such as oxidation reaction, etc., and the presence of derived metabolites can be judged using MetID Solution. However, as less typical metabolic pathways also exist, the mass chromatograms are compared over the entire measurement mass range, and the differences between their respective samples are also assessed. Since composition prediction is applied to these differences, it can be judged as to whether or not they could have metabolized from the parent compound. These results are displayed in a window like that shown below.

Metabolite Candidate Detection by Multivariate Analysis
As there is often some type of correlation between the MSn spectrum (n(2) of the parent compound and that of the metabolite, statistical analysis (PLS method: Partial Least Squares method) is used to analyze the correlation between the MSn spectrum of the parent compound and that of each precursor ion.

Basically, if the parent compound is metabolized, there is a strong possibility that part of the metabolites will maintain a structure consistent with that of the parent compound. Therefore, by utilizing the ability to find commonalities between the product ions and neutral loss in the metabolite MSn spectra with that of the parent compound, high-correlation precursor ions are automatically extracted as metabolite candidates.

The analysis results appear as shown in the following window. The correlation between each precursor ion and the parent compound ion are plotted at the upper left. High-correlation precursor ions are distributed along the X axis in the region greater than 0. In addition, these precursor ions are displayed in the list at the bottom of the window.

MetID Solution can automatically generate the method for acquiring MS/MS spectra. The retention times, mass spectra, MS spectra and MS/MS spectrum accurate mass information can be obtained by a single analysis, and in addition to identifying expected metabolites using fewer analyses than previously, it enables reliable and efficient detection and prediction of unexpected metabolites.

Composition Prediction of Metabolite Candidates
Even for unknown metabolites, metabolic clues can be obtained from the differences between the composition formula candidates and the administered drug using the Composition Prediction Tool. The precise masses are used to automatically predict the compositions of unknown metabolites. High-accuracy prediction is possible through comparison of the isotopic patterns of MS spectra.

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Example of Metabolomic Analysis

Profiling Analysis of Green Tea Leaves
The example below introduces profiling analysis on green tea leaves as a method for determining the components that contribute to tea quality.

1.Pretreatment and Analysis
Analytical samples were prepared from nine types of high-grade green tea leaves ranked at a tea fair, and analyzed by Prominence UFLC/LCMS-IT-TOF.
2. Peak Extraction and List Creation – Peak extraction by Profiling Solution software
Peaks were extracted from the results of MS1 analysis and peak lists created using the peak signal intensity values. Retention time sorting, isotope peak elimination, and p-Value filtering were applied to the 3798 peaks detected to extract 479 significant peaks. The data were then exported to commercial multivariate analysis software (SIMCA-P by Umetrics).
3. Multivariate Analysis
PCR score and PCR loading plots were produced. The green tea leaves that were highly ranked and lowly ranked at the tea fair are divided to the left and right of the PCR score plot, such that the primary component axis indicates differences in rank.The PCR loading plot of the primary component axis (PC1), which indicates the contribution of each component to tea quality, shows the components contained in large quantities in highly ranked green tea as positive values.
4.Formula Prediction for Unknown Compounds
The formula was predicted for the Peak X candidate compound, which was selected as a typical unknown compound that contributes to tea quality. The formula was predicted from the accurate mass information obtained in MS3 analysis. The formula prediction software (Formula Predictor) indicated Peak X to be C14H16O10.
5.Candidate Compound Prediction of Unknown Compounds
A search for the formula C14H16O10 in a database published on the Internet suggested that the compound may be a polyphenol called theogallin. Attributing a mass spectrum based on the structure of theogallin suggested that Peak X was theogallin.

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Profiling Solution Metabolomics Software

Metabolomic methods to detect differences in the complex data files obtained from an extremely large number of samples or compounds provide a powerful tool for both academic research and applied fields, including pharmaceuticals, foods, and the environment, by clarifying metabolic pathways and control mechanisms.

Automatically detecting and listing the peaks in large volumes of data is said to be the most important step in metabolomics and differential analysis.

Profiling Solution software automatically aligns the retention times in high-mass accuracy data obtained by the LCMS-IT-TOF mass spectrometer and exports the resulting lists (matrices) to commercial multivariate analysis software.

This significantly reduces the burden of peak picking and list creation, a process that can take an extremely long time due to the large number of data points and sample components.

Note 1) Profiling Solution software does not offer multivariate analysis functions.

Note 2) MetID Solution is recommended for comparing samples before and after metabolism to aid in searching for both predicted and unknown metabolites.

Features

1. Automatic extraction and display of peaks from a large number of data files.
2. Aligning chromatograms offers more accurate peak evaluation.
3. Simultaneous display of multiple chromatograms.

Support for pooled QA/QC samples. Filtering of peak information displayed in tables.
Example of Metabolomic Analysis: Quality Evaluation of Green Tea Leaves
Automatic extraction and display of peaks from a large number of data files
Simply drag and drop multiple data files and click the [Run] button to perform peak picking and list the m/z values, retention times, and signal intensities for each peak. This table can be used as data for multivariate analysis by commercial statistical calculation software such as SIMCA-P by Umetrics.

Automatic correction (alignment) of the chromatograms for slight changes in retention time permits more accurate peak evaluation, even when fluctuations exist from run to run.

Simultaneous display of multiple chromatograms
Chromatograms and mass spectra can be displayed for all data. Operations (division, integration, logic operations) and filtering functions for the signal intensities are provided to allow comparison and overview of the data.

Support for pooled QA/QC samples
Support for pooled QA/QC, which involves analyzing controls made by mixing small quantities of target samples, helps effectively discover differences in peaks. Normalization of peak area values (logarithmic display, relative intensity display), operations, and filtering enhance and visualize areas of difference to track relative quantitative changes.

Example of analysis using pooled QA/QC samples (Shimadzu poster for ASMS 2009)

Accelerating lipid profiling of human samples for biomarker discovery using UFLC-IT-TOF

Global profiling studies in tumor bearing mouse models using high mass accuracy MSn analysis

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Shimadzu’s Metabolomics Solution

Shimadzu strongly supports metabolomics research. Tools for high resolution separation of compounds in various biological samples, a tool for online accurate mass measurement of these compounds, and tools for efficiently extracting the desired information from huge amounts of acquired data are all available. The instrumentation used for these are designed to make full use of leading edge technology to achieve high-accuracy and high-speed processing.

[1] What is metabolomics?
Metabolomics is one of the important elements of systems biology which includes the analysis of low molecular weight compounds such as organic acids and amino acids as metabolites (study of the metabolome). Thus, while genomics focuses on the study of DNA, transcriptomics the study of mRNA, and proteomics the study of proteins, metabolomics focuses on the study of metabolites overall.

DNA and proteins are subject to such environmental influences as food, medicines, movement, and a variety of stresses. The results of interaction with these influences are reflected in the metabolites that remain behind, so metabolomics can be said to measure the activities of the genome and proteins within the body as well as the sum total of the environmental influences. Since metabolomics can be used to garner valuable information about biological functions, its application has extended to wide-ranging fields of research, from biomarker discovery projects (drug metabolism and dynamics, drug safety, pharmacologic toxicity, etc.) to disease diagnosis, lifestyle habits and health.

[2] Metabolomics Analysis Project Flow
A rough flow might be as follows:

• Step 1 Project idea
• Step 2 Experiment design
• Step 3 Discovery of changed metabolites
• Step 4 Identification of changed metabolites
• Step 5 Hypothesis, verification, conclusion

[Step 1 Project idea]
To start a project, there must be some sort of question to be addressed, such as a drug, food or environment-related question (for example, «why is this drug toxic, or why does this food show antihypertensive action»), or a disease-related question, or a genome / proteomics-related question (for example, «what is the function of this gene, or protein»). This question then becomes the project idea.

[Step 2 Experiment design]
A variety of approaches are possible in metabolomics, so it is important that the experiment be designed so that when the project is analyzed, meaningful results will have been obtained. For example, should cultured cells be used for the pre-clinical sample or should an animal disease model be used, and for the clinical sample, should a healthy human sample (Phase I) be used or a disease sample (Phase II and later), and should the sample itself consist of tissue and cells or a biological fluid such as plasma, serum or urine. In addition, it is necessary to decide the number of samples taking into consideration the biological variability of the system.

[Step 3 Discovery of changed metabolites]
The approaches for discovering changed metabolites can be broadly divided into two categories.

(1)Non-targeted metabolomics

This refers to the search for changed metabolites among all of the compounds, and the emphasis on in-depth search techniques make this a strong approach. It involves relative quantitative analysis.

(2)Targeted metabolomics

In this approach, known metabolites are selectively analyzed. Relative quantitative analysis is taken into consideration, and multiple sample concentration profile analysis is possible.

There are various technology-related approaches in metabolomics, including the use of chromatography — mass spectrometry, and NMR, etc.

When chromatography-mass spectrometry is used, the measurement data advances the analysis starting with retention time and mass. If retention time calibration and alignment are required, iterative measurement using the same sample is conducted. Finding the changed metabolites in the case of a drug, for example, will necessarily involve the generation of large quantities of data. This is due to the production of multiple data sets associated with acquisition at timed intervals after the drug is administered, as well as the possibility of biological variability and parent population variability. Because of the huge volume of data generated from so many samples submitted to analysis, analytical software becomes virtually indispensable.

Thus, metabolite discovery is normally conducted using specialized software applications to handle processing of acquired data, such as calibration of retention time and mass, alignment and normalization, as well as to perform such operations as statistical analysis and data mining.

[Step 4 Identification of changed metabolites]
To identify the metabolites discovered in step 3, the compounds are cross-checked with those registered in databases, and their structures are determined through compound information analysis. Various databases are in development both domestically and internationally, which contain MS/MS and other reference spectra, as well as compounds associated with metabolic maps.

Since it will take some time before identification of all metabolites will become possible through database referencing, structural identification is attempted based on the information on changed metabolites in addition to that obtained from analytical instrumentation like the MS and NMR.

[Step 5 Hypothesis, verification, conclusion]
After the changed metabolites are discovered and identified, the project idea is revisited, a hypothesis is framed and verified, and a conclusion is drawn. The framing of a meaningful hypothesis and drawing conclusions requires reproducible data, a means of separating each of the metabolites from complex samples (normally, a combination of mass resolution and chromatographic resolution), abundant data for ID verification (a combination of mass accuracy and MSn information), and software that can extract from huge amounts of data the information which fulfills the research objective.

[3] Shimadzu’s Metabolomics Solution
The combination of chromatography and mass spectrometry is widely used in metabolomics because of the high compound resolution obtained with this technique.

The information that is most essential for discovering changed metabolites is retention time. Excellent retention time reproducibility is critical, and the Shimadzu HPLC Prominence Series which receives high acclaim for its exceptional performance is perfectly suited for metabolomics research. The combination of the Shimadzu LCMS-IT-TOF, with its ability to obtain MSn accurate mass via high-speed measurement, with the Prominence HPLC can provide the accurate information required to satisfy these objectives.

To efficiently extract the information required to fulfill research objectives from the huge quantities of data acquired with these instruments, a system comprising these in combination with the Profiling Solution Metabolomics Software , developed by Phenomenome Discoveries Inc. (Canada) with its widely recognized experience and expertise in metabolomics and bioinformatics, is offered as Shimadzu’s Metabolomics Solution.

Making use the 0.1-second high-speed mass spectral measurement of the LCMS-IT-TOF, when this instrument is configured online with the HPLC, a single measurement is all that is required to obtain not only an MS spectrum, but MS/MS and MS/MS/MS spectra, as well. In addition, after pinpointing changed metabolites, the MSn accurate mass spectra can be measured by re-injecting the same sample. The LCMS-IT-TOF Composition Prediction Program (Patent Pending) effectively narrows the number of candidates using not only isotopic patterns, but also the MSn spectra accurate mass information. That’s what makes this system extremely useful in tackling the most formidable of tasks in metabolomics, the identification of discovered metabolites, and to powerfully support the success of projects using metabolomics, such as the search for biomarkers.

High throughput metabolite analysis is also required during lead compound optimization which occurs in the depth search stage of drug discovery research. A system which includes the metabolite structural analysis software MetID Solution is offered for efficient search and identification of both expected and unknown metabolites.

Through comparison of pre- and post-metabolic XIC (accurate mass) chromatograms, and the application of multivariate analysis to the MSn spectra of parent and metabolite compounds, this system allows even those analysts with limited familiarity in metabolic research to automatically extract metabolite candidates, supporting improvement of overall productivity in the lab.

On the other hand, with respect to identification of discovered metabolites, attention is currently being refocused on GCMS as a method for easily analyzing small molecule compounds like amino acids, organic acids and fatty acids with high sensitivity and high resolution.

Shimadzu offers the GCMS system comprising the GCMS-QP2010 Plus, for certain analysis and identification of amino acids, organic acids and fatty acids, the GCMSsolution software (Ver. 2.5 and later), featuring automatic retention time correction, in addition to a GCMS metabolite database. Using the metabolite database equipped with retention indices, candidate compounds can be greatly narrowed to enable highly reliable identification.

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Reliable Protein Analysis

Protein Analysis Software (Option)
The advantages of the LCMS-IT-TOF automation capabilities can be exploited to the maximum. Just creating a peak list using the protein method and setting the database search parameters allows automation of all operations from analysis to protein identification. The identification results can be viewed directly via the protein analysis software, and data management is easy and certain. The parameters can also be modified for method rebuilding.

NanoESI Interface (Option)
Get even higher sensitivity for high-accuracy LCMS-IT-TOF data! Nano spray delivers high ionization efficiency and less loss. Using the NanoESI (nano electrospray) interface allows higher sensitivity analysis of ultra-low sample quantities. Detachment is also easy, requiring little more effort than with the standard ESI interface.

Measurement Example: BSA Tryptic Digest (50 fmol)
When a protein enzyme digest is separated using HPLC, anywhere from tens to hundreds of peaks can be observed. Thus, it is not rare for multiple peptides to elute within a few seconds of one another. In this situation, high-speed, yet intelligent auto MSn analysis provides increased coverage. In addition, MS spectra with high mass accuracy further contribute to improved reliability of protein analysis.

The protein analysis software window is shown below. The MS data of each data file can be viewed collectively with the Mascot search results.

The Mascot search results indicate BSA at the highest ranked hit with a score of 1180. In addition, with a peptide mass error within 4 ppm, all of them were assigned (external standard method). This is an example of the high-quality measurement possible when using the high-accuracy LCMS-IT-TOF.

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High-Speed and High-Accurate MSn Measurement

The LCMS-IT-TOF is new technology intended to strongly assist in the identification of target compounds by using high speed/high accuracy MSn data in R&D fields such as impurity analysis, metabolic profiling and biomarker research.
The data above shows the analysis of four compounds within 2.0 minutes. The Auto MSn Function makes it possible to obtain highly accurate MSn data via an external standard, taking advantage of high-speed mass spectrum measurement performance.

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High-Speed Ion Polarity Switching Mode

High-speed ion polarity switching can be specially useful when it cannot be judged whether samples will be detected as positive or negative ions. The LCMS-IT-TOF utilizes a newly developed, highly accurate and stable power supply as well as a newly developed high-voltage switch that allows for polarity switching in only 0.1 sec or less (necessary for the sharp HPLC peaks available with the advances in high-speed chromatography). The maximum rate for polarity switching is 2.5 Hz, which allows for a pair of positive and negative ion MS spectra to be obtained 2.5 times per second.

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Compressed Ion Introduction (CII™)

The ion optical system used in the LCMS-IT-TOF leads to a novel ion introduction method referred to as Compressed Ion Introduction or CII, where the combination of the skimmer, octopole and first lens converts the continuous stream of ions into pulses for introduction into the ion trap. This method makes it possible to control the accumulation of ions before they are introduced into the ion trap, allowing the RF to be applied to the ring electrode at the instant that all of the CII-accumulated ions enter the ion trap. This new method of controlling the ion trap, which is quite different from a traditional ion trap, is adopted for the LCMS-IT-TOF. The development of this CII effectively couples the LC system to the MS and enables a dramatic improvement over the previously deficient ion capture rate of the ion trap, thereby increasing sensitivity.

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Structural prediction of impurities in drugs using MSn data

Structural prediction of impurities in drugs using MSn data Impurities in an erythromycin sample

Fig.1 and Fig. 2 show the UV and MS chromatograms of Erythromycin A oxime (Ery-AO) which is a macrolide antibiotic produced by a strain of bacteria known as Saccaropolyspora erythraea. Some impurities are indicated.

Fig.3 shows the mass spectra of erythromycin A oxime (Ery-AO). The Ery-AO molecule is shown as m/z = 747.4661. The mass difference between the acquired figure and theoretical figure(747.4643) is approx. 0.002.

The MS/MS spectra of the m/z = 747 is shown in Fig.4. The big peak of the m/z = 571 is considered as the loss of Area A from the Ery-AO structure since mass difference with m/z = 747 is 176. The m/z = 396.2416 is considered as Area C. It is highly reliable figure due to the 0.003 mass difference compared with the theoretical figure.

Fig.6 shows the mass spectra of impurity A. MS/MS spectra indicates a similar structure with Ery-AO.

The mass difference of m/z=396.2406 is 0.002 compared with the theoretical figure of Area C(396.2386). m/z=571 is also detected and it is similar to the MS/MS spectra of Ery-AO. However, the molecular weight of an impurity A is 733, and the mass difference between the impurity A and the loss from that is 162. While the mass difference from Area A of Ery-AO is 14.0144. Using the Accurate Mass Calculator, this figure is considered as CH2 (14.0156), and it is assumed that the impurity A has a structure which is substituted from one methyl group of Area A to one hydrogen.

Using the MS/MS spectrum and accurate mass information, which shows structural information on Ery-AO, other impurity’s structure was also predicted. In addition, considering the MS2 spectrum patterns of the impurity, different from that of Ery-AO, it is assumed that it was externally mixed into the sample.

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