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Agilent Technologies
at TIAFT 2013
Agilent Technologies
GC/MS portfolio for the
growing challenge of
designer drugs analysis
Juan Aybar
Gas Chromatography Marketing
Jaume Morales
MS Product Specialist
Funchal, Madeira
September 2- 6
September 12, 2013
Outline
1. Designer drugs in perspective
2. Agilent GC & GCMS Portfolio
3. New Target Deconvolution Workflow
4. High resolution GCMS possibilities
5. What’s 7200 GCQTOF
6. Example of “Street Heroin” Classification and
Characterization
2
Designer Drugs in Perspective
Designer Stimulants represent an increasingly important
health risk due to several factors:
1.
Rapid changes in the market and accesibility.
Availability of analogues. Internet access
2. Lack of Quality Control
3. Suggested dosages and reported effects not always
accurate
4. Marketing
– General sense of legal safety (you’ll pass the drugs
tests!!), safety, cheap and availability
5. Health Risks
– rapid heart rate, high blood pressure, impaired perception,
sweating, reduced motor control, disorientation, prolonged
panic attacks, psychosis and violent episodes
3
Designer Drugs – Chemical Problem
1.
Complexity of Samples
1.
2.
Sample Preparation Techniques
Multiple analytes, Similar analytes (isomers, analogues, similar spectral
cleavage …)
Instrument protection vs contaminants and interferents
3.
Quantify the “known” substances
2.
1.
MS/MS Quadrupolar Techniques
Identify the “unknown”
3.
Search Multiple Databases – Spectral similarity
Examine fragmentation
Deconvolution / Accurate Mass
1.
2.
3.
4.
Validation and reference materials availability
5.
Create home-made databases spectra/RT of emerging
materials
6.
Constant evaluation of unidentified spectra / chromatogram
peaks
7.
…
4
Agilent GC and GCMS Portfolio
1.
Sample Preparation Workbench
1.
2.
Agilent 7890B w/ Capillary Flow Technology
1.
3.
4.
MS Spectral deconvolution to confirm expected compounds and discover
unexpected analytes
Agilent 7000C GC/TQ
1.
5.
Most sensitive MS/MS Quantitation with excellent matrix resistence for
trace analyte determination and facilitated SW tools with MassHunter SW
Agilent 7200 GC/Q-TOF
1.
5
Helps prevent contamination in the MS source by facilitating backflushing,
facilitated column exchange or deans switching
Agilent 5977 GC/MS
1.
6.
Simplifies and accelerates sample prearation including tasks like dilution,
derivatizacion, mixing, alliquoting …
High Resolution/Accurate Mass possibilities with excellent sensitivity for
low level compounds
Agilent Pre-Configured Analyzer and Kits for forensics
and toxicology application
Synthetic Cannabinoids Example
Problems: Low levels, Multiple Analytes, Similar Analytes, Similar Fragmentation,
Coextractants, Derivatization …
DECONVOLUTION
BENEFITS
Better identification and
confirmation of analytes in high
matrix samples
Underivatized
Tailing
Worst integration
BSTFA Derivatized
No Tailing
Better Integration
Reduces data review time,
especially for large screening
methods
Reduces false positives and false
negatives in dirty samples
GCMS Synthetic
Cannabinoid Sample
6
Identification based on matching
entire spectrum cleaned of
interferences against library. Much
more reliable than target/qualifier
ratio method
How Does Deconvolution Work?
Ions with the same abundance vs time profile are grouped together to create
spectra “cleaned” of interferences from overlapped peaks
7
Confidentiality Label
September 12, 2013
Agilent 5977 GC/MS
New Features – Target Deconvolution
TARGET DECONVOLUTION
MassHunter Deconvolution
8
•
New feature fully integrated into Mass Hunter
Quant B.06.00
•
Provides additional confidence in the
identification of target analytes by
using deconvoluted full scan mass spectra
•
Legacy MSD Chemstation Methods and data
files can be converted for use in MH
•
Interactive data review using Mass Hunter Batch
at a Glance (BAG)
•
Rapid data review using Compounds at a
Glance (CAG) and Outlier flagging
•
Very fast .pdf reporting of individual samples or
entire batch
Confidentiality Label
September 12, 2013
Target Deconvolution Workflow in
Mass Hunter Quant SW
TIC
Targets are identified by
comparison to locked RTs and 1
Quant + 3 Qualifier ion ratios,
then quantified using Quant ion
area versus calibration table
Quant Results
MH deconvolutes component spectra
and performs spectral matching of
deconvoluted spectra vs target MS
database using RT Window and
Library Match Score as qualifiers
Qualitative Results
Review results in Mass Hunter
Batch-at-a-Glance and
Compounds-at-a-Glance
9
All data processing
performed within
Mass Hunter Quant SW
Combined quantitative and qualitative
Target Deconvolution
.pdf Summary report
Confidentiality Label
September 12, 2013
Extracted Compound Chromatograms
Toluene
4.95 min.
The peak apex of the deconvoluted component must be within
the RT Range of the target peak identified by the Quant Engine
in order to be used for library matching. If more than one
component meets these criteria, consider only the component
spectrum with the highest peak area for the deconvoluted
quant ion EIC.
EIC
ECC
RT : Seconds
285
290
295
- 10 seconds
300
305
310
315
+ 10 seconds
Target RT Window
Deconvoluted component
Deconvoluted component
Deconvoluted component
spectrum
Deconvoluted component
spectrum
spectrum
Deconvoluted component
spectrum
spectrum
Deconvolution Report
DRS versus MH TD : Summary Comparison
Function
DRS
Target Deconvolution
SW Platform
MS Chemstation
Mass Hunter Quant B.06.00
Quantitation
1 target ion + up to 3 qualifier
ions
1 target ion + up to 3 qualifier
ions
Full MS Deconvolution
Yes
Yes
Identification
RT match + Library search vs
target MS library
RT match + Library match vs
target MS library
Additional identification with
NIST Library search
Yes
No
Interactive data review
QEDIT
Batch at a Glance
Compounds at a Glance
Outlier flagging
Quant results using raw and
deconvoluted data
Yes
Yes
Reporting
HTML
.pdf
Agilent 7200 GC/ Q-TOF
Possibilities
of High Resolution GCMS
13
September 12, 2013
Benefits of GCQTOF
• High resolution full scan spectra
• > 15K versus < 1K for SQ (15-20X higher)
• Higher selectivity without MS/MS
• lower requirement for hi-res GC separations . Avoids the operational cost and
complexity of GCxGC.
• Accurate mass measurements
• < 5 ppm versus 350-400 for SQ (70-80X better)
• Better qualitative decisions (molecular formula information) for both MS and
MS/MS spectra
• Fast acquisition of full spectra range
• 50 Hz max versus typically < 5 Hz max for SQ
• With consistently greater sensitivity than SQ
• MS/MS with Product Ion spectra
• More selective than TQ due to higher resolution
• With accurate mass information for each product ion
• Powerful structural elucidation tools
Ideal tools for solving complex analytical problems
How much “R” is enough?
Intensity Ratio = 1:1
COELUTING COMPOUNDS & SIMILAR m/z
240.1218
240.0785
Dimetilan
m/z = 240.1217
Mass error = 0.4 ppm
Flurenol methyl ester
m/z = 240.0781
Mass error = 1.7 ppm
No IRM corrections applied
Dm = 0.043 Da.
~13,500 resolution FWHM
How much “R” is enough?
Intensity Ratio = 1:0.02
COELUTING COMPOUNDS & SIMILAR m/z
240.0780
Flurenol methyl ester
m/z = 240.0781
Mass error = -0.4 ppm
A resolution of 13,500 is
not enough - larger
ratios need MS/MS or
better GC separation
Dimetilan
m/z = 240.1217
Mass error = -13.7 ppm
240.1184
GCMS Challenge : complex analytical problems
1st Approach :
The TOF-MS delivers high mass accuracy full scan
GC-TOF-MS has unrivalled full spectrum sensitivity, comparable to GCMS in SIM mode.
GC-TOF-MS allows accurate mass measurement for higher selectivity and
sensitivity using a very narrow mass window (0.02-0.05Da).
BUT, what happens when Resolution and Δ Mass are not enough?
2nd Approach : GC-QTOF-MS
GCMS Challenge : complex analytical problems
2nd Approach :
The QTOF-MS can :
- Reduce noise by Precursor Selection thus delivering more selectivity.
- Confirm ID with extra Hi-Res MS/MS spectra.
-Allow structural Elucidation.
The Problem – Confirm Most Likely Structure
Kava Extract - Compound “B”, C16H14O4
(Rings + Double Bonds = 10)
EI Full Scan
(M – H)+
269.0802
Candidate
structures
Formula Calculator
Determine all possible formulas consistent with measured mass
C5H12O2PS3
m/z = 230.9732
The Problem – Confirm Most Likely Structure
Kava Extract - Compound “B”, C16H14O4
(Rings + Double Bonds = 10)
EI Full Scan
(M – H)+
269.0802
MS/MS experimental
measurements
Candidate
structures
m/z
(experimental)
Formula
Error
(ppm)
Score
–H
269.0802
C16H13O4
2.2
80.7
– C6H5
193.0494
C10H9O4
0.6
96.7
– CH=CH–C6H5
167.0334
C8H7O4
3.0
N/A
– CH2=CH–C6H5
166.0259
C8H6O4
0.6
N/A
138.0310
C7H6O3
1.1
98.1
– CO
110.0359
C6H6O2
3.0
N/A
– CH3
95.0127
C5H3O2
0.9
99.5
– CO
The Problem – Confirm Most Likely Structure
Kava Extract - Compound “B”, C16H14O4
(Rings + Double Bonds = 10)
EI Full Scan
(M – H)+
269.0802
MS/MS experimental
measurements
Candidate
structures
X
m/z
(experimental)
Formula
Error
(ppm)
Score
–H
269.0802
C16H13O4
2.2
80.7
– C6H5
193.0494
C10H9O4
0.6
96.7
– CH=CH–C6H5
167.0334
C8H7O4
3.0
N/A
– CH2=CH–C6H5
166.0259
C8H6O4
0.6
N/A
138.0310
C7H6O3
1.1
98.1
– CO
110.0359
C6H6O2
3.0
N/A
– CH3
95.0127
C5H3O2
0.9
99.5
– CO
X
Problem – confirm most likely structure
EI Full Scan
(M – H)+
269.0802
MS/MS experimental
measurements
Candidate
structures
m/z
(experimental)
Formula
Error
(ppm)
Score
–H
269.0802
C16H13O4
2.2
80.7
– C6H5
193.0494
C10H9O4
0.6
96.7
– CH=CH–C6H5
167.0334
C8H7O4
3.0
N/A
– CH2=CH–C6H5
166.0259
C8H6O4
0.6
N/A
138.0310
C7H6O3
1.1
98.1
– CO
110.0359
C6H6O2
3.0
N/A
– CH3
95.0127
C5H3O2
0.9
99.5
– CO
For the 5 candidate structures, only one fit the losses
identified by CID experiments on multiple precursor ions
What is GCQTOF?
7890 + 7000 + 6500 = 7200 GC/Q-TOF
Triple Quadrupole GC/MS
+
=
Time of Flight MS
Quadrupole Time of Flight MS
7200 Q-TOF Comp
NEW
Removable
Ion Source
NEW
Optics
7000B TQ
6500 Q-TOF
Two
300 L/s Turbos
New Q-TOF System . . . Yet Totally Proven
Dual-stage ion mirror improves
second-order time focusing for
high mass resolution.
4GHz ADC electronics enable a high
sampling rate (32 Gbit/s) which improves
the resolution, mass accuracy, and
sensitivity for low-abundance samples.
Dual gain amplifiers simultaneously
process detector signals through both lowgain and high gain channels, extending the
dynamic range to 105.
Hot, quartz monolithic quadrupole
analyzer and collision cell identical
to the 7000 Quadrupole MS/MS
Proprietary INVAR flight tube
sealed in a vacuum-insulated
shell eliminates thermal mass
drift due to temperature changes
to maintain excellent mass
accuracy, 24/7. Added length
improves mass resolution.
Analog-to-digital (ADC) Detector:
Unlike time-to-digital (TDC) detectors
which record single ion events, ADC
detection records multiple ion events,
allowing very accurate mass
assignments over a wide mass range
and dynamic range of concentrations.
New Removable Ion Source
includes repeller, ion volume,
extraction lens and dual filaments
Two 300L/s t urbos pump the
focusing optics and flight tube
Split-flow turbo differentially
pumps the ion source and
quadrupole analyzer compartments
Hexapole collision cell accelerates
ion through the cell to enable faster
generation of high-quality MS/MS
spectra without cross-talk
Removable Ion Source (RIS)
Automated
Gate Valve
RIS
Automated
Retractable
Transfer Line
Removable Ion Source (RIS)
RIS Standard on Q-TOF
Allows fast EI/CI source
swapping without venting
Allows swap of complete ion source,
including filaments, in ~30 minutes
without venting
Different Sample Introduction systems
7693A Injector
Thermal Separation Probe
CTC with Liquid/Headspace/SPME
Headspace
Sampler 7697A
Thermodesorber
Different Ionization Sources
EI
•
•
Hard ionization technique, extensive fragmentation, limited Molec.ion.
Very reproducible, library search allows Compound ID by fragments.
PCI
•
•
Compounds must be able to accept a proton. Amines, alcohols,
Very strong molecular ion allows Compound ID by exact mass.
NCI
•
•
Compounds must be able to accept an electron. Halogens,
Conj.Systems, phenyl rings
Very strong molecular ion allows Compound ID by exact mass.
Capillary Flow Technology- Possibilities
“Quick Swap”
Solvent Venting
Heart Cutting (Deans Switch)
Split to several Detectors
Backflushing
Characterization and Classification of Heroin
from Illicit Heroin Seizures by GC/Q-TOF
1Koluntaev
Dmitry, 2Sofia Aronova,
3Sergei Syromyatnikov and 3Igor Sarychev
1InterLab
Inc., Moscow, Russia
Technologies, Santa Clara, CA
3Federal Drug Control Service of Russian Federation, Moscow, Russia
2Agilent
32
Introduction
"Street heroin“ is usually a multi-component mixture comprising, apart
from the heroin, of various additives such as pharmacologically active
compounds as well as neutral substances.
The study benefited from the accurate mass high resolution capability
of the Agilent GC/Q-TOF system particularly useful in the identification
of the composition of the heroin samples.
The characteristic profiles of heroin samples were the basis for further
statistical analysis performed in Mass Profiler Professional (MPP).
Comparative study of heroin samples and their characterization and
classification would help establishing common sources of origin.
33
Experimental: Data Acquisition
Two different acquisition methods were utilized to separately
acquire data for major and minor components of the samples.
• The method for major compounds utilized reduced emission current
on a filament in order to avoid saturation.
• The method used to acquire data for minor compounds, had higher
emission current but decreased ionization energy during elution of
major components.
34
Experimental: Data Acquisition & Data Analysis
• Two different acquisition methods were utilized to separately
acquire data for major and minor components of the samples.
• The chromatographic deconvolution was
MassHunter Unknown Analysis software.
performed
using
• Impurities of interest were identified by comparison with the NIST11
mass spectral library.
• Multivariate statistical software was performed with Mass Profiler
Professional (MPP) to find compounds present at distinct levels in
different groups of samples.
• The data were subsequently used to build a classification model.
35
Identification of the Common Components by MPP
• Most common components of the heroin samples are morphine alkaloids
and morphine derivatives.
• 54 out of 55 samples contained a monoacetyl derivative of morphine: 6monoacetylmorphine and acetylcodeine.
• Many other common alkaloids as well as pharmacologically active
substances were also detected in the majority of the samples.
Alkaloidal
Noscapine
Papaverine
Meconine
Morphine
Hydrocotarnine
Codeine
Adulterants
50
50
43
40
13
10
Caffeine
Dextromethorphan
Tolycaine
Paracetamol
55
27
18
5
Alkaloidal impurities and non-opiate pharmacologically active cutting agents
identified in heroin samples.
36
Library Hits Confirmation Using Accurate Mass
• For few compounds that did not give a high library match
score we performed additional confirmation steps using
accurate mass information as well as MassHunter Qualitative
Analysis structure elucidation tools.
• One compound present in 36 out of 55 samples was
tentatively identified as 6-acetyl-crotonosine acetate.
• The identity was evaluated using Molecular Formula
Generator (MFG) with library search and Fragment Formula
Annotation (FFA) tools.
37
Compound Confirmation
Using MFG with FFA and Library Search
Annotated mass spectrum of a compound tentatively identified as
6-acetyl-crotonosine acetate.
38
Molecular Structure Correlator (MSC):
Another Tool to Evaluate and Confirm Possible Structures of the Compound
empirical formula
Selected
fragment
possible substructures
corresponding to the
fragments
evaluated
structure
Fragment
formulas
Molecular Structure Correlator (MSC) works with accurate mass MS/MS data to predict
and evaluate possible structures of the compound of interest. It can mine a database,
e.g. ChemSpider to extract structures corresponding the empirical formula of the
compound and rank them according to the compatibility score.
39
PCA Analysis: Major Components
No significant separation between the analyzed sample groups was
observed when the GC/Q-TOF method for major components.
40
PCA Analysis: Minor Components
The data acquired using the method for minor components displayed
significant separation into at least two major groups.
41
Hierarchical Clustering Analysis
• The compounds that contributed to the major difference
between isolated sample groups were further visualized using
Hierarchical Clustering.
• Specifically, we were able to observe multiple alkaloids of
heroin as well as pharmacologically active agents that
contributed into the sample clustering.
• Hierarchical clustering also confirmed the presence of two
major sample groups.
42
Hierarchical Clustering Analysis
Morphinone, 7,8-dihydro-3-desoxy-
Tolycaine
Dextromethorphane
Meconine
p-isopropoxyaniline
Hydrocotarnine
Papaverine
Codeine
• Hierarchical Cluster Analysis (HCA) demonstrated separation of
the samples into few distinct groups.
• Shown are few characteristic compounds that are likely to
contribute into separation of the samples.
43
Building Class Prediction Model in MPP
Two major clusters were further used to construct a Class Prediction
Model in MPP
44
Building Class Prediction Model in MPP
Volcano plot showing compounds that present at significantly different
levels between two groups of samples
45
Validation of the Class Prediction Model
To build class prediction model using Partial Least Square
Discrimination (PLSD) algorithm all samples that belong to the two
major groups excluding all H and Q replicates were used.
46
Prediction Results
The samples that
were left out during
validation and
training of the
model (outlined in
rectangle) were all
predicted correctly
by applying the
class prediction
model.
47
Summary
• Multiple statistical approaches using MPP were utilized to
isolate the heroin samples, analyzed by GC/Q-TOF, into
distinct clusters
• Class Prediction model was built in MPP based on two largest
groups of heroin samples
• Accurate mass information in combination with multiple
MassHunter software tools helped to confirm the identity of
alkaloids and other pharmacologically active compounds
found in the heroine samples
48
Thank you for your attention!
Any Questions?
Page 49