Databasing of NMR Data Benefits Impurities & Degradants Research Dr Steve Coombes

Pfizer Global R&D, Sandwich. UK

ACD/Labs UK User Meeting 17th May 2007

2

Presentation Overview

Impurity and Degradant ID

Spectroscopic tools for analysis

Data analysis– Manual– Computer Assisted

Conclusions

3

Why ID Impurities and Degradants?

To ensure drug safety– Identify impurities present in Active Pharmaceutical

Ingredient (API) to make sure no toxic or mutagenic substances administered to patients

– Establish Drug Product (DP) degradation profile to help show stable formulation and underline shelf life assignment

IMPD and CTD (CMC) requirements– FDA guidelines state must identify impurities present at a

level > 0.1%

Chemistry optimisation

Analytical testing optimisation

4

API

Peak of interest

5

Analytical Challenges

Converting a peak in a chromatogram to a sample for structure elucidation

Compatibility of chromatography with spectroscopy– Sample (in)solubility defines ID approach

Sensitivity – mainly a DP issue– A 0.1% degradant in a tablet containing 1 mg of API is

only 1 μg of impurity– If API extracted (for HPLC-UV analysis) in 100 mL

volumetric, then only 10 ng/mL for degradant• Typical HPLC injection volume of 10-20 μL results in only

0.1-0.2 ng of material of interest on column

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Spectroscopic Data

Mass Spectrometry– Provides framework (MF) within which to work– Fragmentation analysis provides indication of where in the

molecule changes occur (with respect to parent)

NMR spectroscopy– 1H (and 13C) 1D data gives number or nuclei present and

chemical shits indicate the atoms local environment– 2D data (1H-1H, 1H-13C & 1H-15N) give direct or long-range

correlation information allowing the “pieces” of the structure to be “glued” together• Homonuclear expts – COSY, ROESY• Heteronuclear expts – HSQC, HMBC

7

Impurity Identification Strategies

1st pass LC-MS & LC-MS/MS– Accurate mass and accurate fragment measurements

down to pg levels

If unsuccessful then NMR data required to ID unknown

Roughly require 1 mg/mL concentration of analyte– LC-NMR (if impurity present > 1%)– Sample enrichment – peak trapping, SPE-NMR– Sample isolation

• Looking to isolate minimum of 50-100 μg of material for ~ MW 500 compound to enable full suite of (2D) NMR data to be acquired (in a reasonable time scale)

8

Data Analysis – Resources

Legacy knowledge and user expertise– May be user specific, vulnerable if unavailable

Previously analysed data– Spectral databases

• Allows easy search for related compounds• Rapidly compare data of unknown with that of known

structures to establish likely chemical shifts and correlations

– Assignment databases• 1H and 13C NMR predictions for proposed structures to help

assigning unknown data• Enables CASE to generate fragment libraries which can be

used to build “known” portions of the molecule

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Data Analysis – Options

In-vivo– Brain power – existing knowledge of parent molecule and

related compounds, intermediates and other impurities• Enables expert to focus on the 5-10% of data that is crucial to

solving the problem

In-silico– Computer Assisted Structure Elucidation (CASE) – utilise

the known information and data for unbiased structure generation

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Data Processing

Both options require the data to be “worked up” in the same way– Process, peak pick, interpret & assign

ACD 1D and 2D Managers used to process the data– Integration, multiplet analysis and peak picking– Integral part of the teams workflow for all NMR data

processing

User then has a choice whether to use their own knowledge and skills to interpret the data or to try CASE– Having the peak picked data gives you the option to do

either without additional work to prepare your data

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Manual Data Interpretation

Compare data of unknown with that of known structures to establish key correlations– What’s the same and what’s changed?

From the chemical shifts and correlations observed, propose structures and determine whether consistent with spectroscopic data– Predict 1H and 13C spectra for proposed structures

Iterative process until correct structure can be established

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Example

Isolated impurity from starting material– MS shows 1 x Cl present– MF show 1 less “F” than parent

• C12H6F3NO3 v’s C12H6ClF2NO3

MS/MS fragmentation of both the parent and the unknown show only loss of water and carboxyl group

NMR analysis required to determine structure – proposed as replacing one of the F atoms with a Cl atom

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10

8

11

13

12

3

2

4

N1

5

6O7

F18

F17

F16

14

O15

OH19

8.5 8.4 8.3 8.2 8.1 8.0 7.9 7.8 7.7 7.6 7.5 7.4 7.3 7.2 7.1 7.0 6.9 6.8F2 Chemical Shift (ppm)

110

115

120

125

130

135

140

145

150

155

160

165F1

Che

mic

al S

hift

(ppm

)

7.56, 129.07

7.56, 140.04

7.98, 135.13

8.13, 155.4

7.29, 129.03

7.29, 140.04

7.56, 154.06

7.56, 124.85

7.29, 154.06

8.13, 127.36

8.6 8.5 8.4 8.3 8.2 8.1 8.0 7.9 7.8 7.7 7.6 7.5 7.4 7.3 7.2 7.1 7.0 6.9 6.8F2 Chemical Shift (ppm)

105

110

115

120

125

130

135

140

145

150

155

160

165

170F1

Che

mic

al S

hift

(ppm

)

(10, 12)

(10, 8)

(4, 6)

(6, 2)

(13, 12)(11, 12)

(11, ?)(13, ?)(13, 8)

(10, 9)

(10, 11)

(13, 9)(11, 9)

(6, 4)

8.5 8.4 8.3 8.2 8.1 8.0 7.9 7.8 7.7 7.6 7.5 7.4 7.3 7.2 7.1 7.0 6.9 6.8F2 Chemical Shift (ppm)

110

115

120

125

130

135

140

145

150

155

160

165

F1 C

hem

ical

Shi

ft (p

pm)(4, 6)

(6, 4)

(10, 11)

(4, 5)(10, 8)

(9, 13)(13, 9)

(10, 12)(13, 11)

(6, 2)

(9, 11)

(4, 14)

3

2

4

N1

5

6O78

139

121011

14OH19

O15

F16

F18

F17

Chemical shifts and multiplicity changed

Similar correlation pattern observed suggesting Pyridine ring unchanged

Big differences in correlations observed in 2nd

ring suggesting not just a straight forward “trans-halogenation”

Chemical shifts and correlations consistent with 1,2,5 tri-subd ring

1H-13C HMBC

3

2

4

N1

5

6O7

14OH19

O15

F16

8139

121011

F17

Cl18

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Improving Quality of Predictions

Databasing the chemical shifts of known compounds greatly improves the predictions of similar structures– Local user databases of proprietary structures better

reflect the “chemical space” and improve prediction accuracy

Dunn, P.J et al; Green Chem., 2004, 6, 43-48

8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0Chemical Shift (ppm)

8.093.002.031.010.981.981.000.981.00

DMSO-d6

water

19

62634

413 9

32 3110

36

23

2021, 25, 22, 24

2.23

6

34 319 20 23

36

4

6

34 1332

31

4

2320

21 22 24 25

21 22 24 25

8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0Chemical Shift (ppm)

8.093.002.031.010.981.981.000.981.00

DMSO-d6

water

19

62634

413 9

32 3110

36

23

2021, 25, 22, 24

2.23

6

34 319 20 23

36

4

6

34 1332

31

4

2320

21 22 24 25

21 22 24 25

Prediction without Pfizer training databaseDeviation of shifts from experimental: 0.44 ppm

Prediction with Pfizer training database(includes precursors)Deviation of shifts from experimental: 0.09 ppm

Experimental data

3

2

4

N1

5

6

O7

14

8139

121011

NH19

O15

20

21

25

22

24

23NH26 27

O28

29

34

30

33

31

32

CH336

OH35

F18

F17

F16

Prediction of Spectra With Training Databases

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Computer Assisted Structure Elucidation

Recent advances have made CASE much more reliable and easier to use

Take your peak picked NMR data (1D, COSY, HSQC, HMBC) along with MF and import into ACD/Labs Structure Elucidator

Once data imported into Structure Elucidator, a degree of grooming is required to ensure data in correct format to enable software to work– Ensure all correlations are present and that all H’s and C’s have

correct label– Ensure all 1D data tables have correct number of atoms

assigned• 13C check multiplicity (C, CH, CH2 or CH3)

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Convert spectral peaks into “co-ordinate map” to allow for generation of the Molecular Connectivity Diagram (MCD)

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Molecular Connectivity Diagram (MCD)

Structures then generated based on observed correlations and chemical shifts

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Molecular Fragments

1H-1H and 1H-13C long range couplings, integrals & chemical shifts establish the moieties present

MS fragmentation data

(loss of 164) shows that we have

CH3

CH2O

R1

CH3

CH2CH2

R2

CH

CH

CH

OR3

R4

R5

CH2

CH2

N

NH

+

CH2

CH2

CH3

R6

R

CH2

CH2

N

N

CH2

CH2

CH3

SO O

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The Remnants

What’s left – C6H4N4O– Hardest piece of structure to establish due to limited

correlation information

Of which, part is CH3 and NH

Must have a heterocycle isomeric with:

N

NH

NN

O

CH3

propyl

aromatic

N

N

NH

N

O

propyl

CH3

aromatic

NHN

N

N

CH3

propyl

aromatic

O

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Create fragment database from 13C user assignment database, then search fragments by 13C spectrum and create MCD from found fragments

Dunn, P.J et al; Green Chem., 2004, 6, 43-48

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Example - Sildenafil

Search fragments by 13C spectrum resulted in 1721 found fragments

These then used to create 1 new MCD

Run CSB Generator…

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Databasing of Assigned Data

Once the structure has been identified (manually or automatically) the 1D and 2D spectra can be assigned

The assigned spectra are only a button click away from saving the information into user assignment and spectral databases

“Instrument Vendor” software allows you to do all the processing of data but ACD tools enable you to extract the information and knowledge for further use– This is where the software is differentiated from that of

other purely “data processing” tools

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Knowledge Management

The information in our proprietary databases are then used to further improve the accuracy of 1H and 13C spectral predictions– Pfizer NMRUDB contains > 2400 1H compound entries

and > 4000 13C compound entries

The NMRUDB also improves the quality and chemical space of “fragment libraries” to increase the efficiency of CASE

The use of ACD 2D Manager allows for straight forward processing, analysis, assignment and archiving of spectra as part of the workflow process and not “an additional job”

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Conclusions

Knowledge management is a vital tool in the quest for increased productivity

Spectral databases allow easy access to related data sets that can be shared locally as well as globally

Assignment databases provide globally accessible resource for knowledge sharing– Can be used to improve accuracy of predictions– Enables rapid automated structure determination within

CASE

Use within a workflow process enables databasing of each and every data set worked on rapidly and easily

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Acknowledgements

George Johnson

Adrian Davis

Anna Codina

Ros Richards