Till Kühn VP Applications Development Benutzertagung Karlsruhe … · 2016-11-15 · Mwa, MwIs =...

November 10, 2016

Till Kühn VP Applications Development Benutzertagung Karlsruhe – November 2016

qNMR und Reinheitsbestimmung

Quantification General Remarks: Accuracy & Precision

Liquids: Concentration Determination

Liquids: Potency Determination

Solids: Quantification of Component

Aspekte der quantiativen NMR

3

NMR is a fully quantitative method

22/3

02/3 T

TNSBn

NoiseSignal

DETEXCγγ=

NMR signal scales linearly with the number of spins in the active volume

This part cancels out, if calibrated on known standard

4

Types of quantitative NMR

Internal Referencing Correlate compound signals to an internal signal • absolute quantification:

Added internal standard – Without weight information:

Concentration: 15mM – With weight information:

Potency: 98% • relative quantification:

Main compound is internal standard – Without weight information:

Purity: 3% impurity wrt API • Everything in one experiment

– No sample variations – Accurate and precise – Signal overlap problems

External Referencing Correlate compound signals to previous reference experiment • absolute quantification:

– Without weight information: Concentration: 15mM

• Two independent experiments o Eretic (artificial signal) o Pulcon (separate refernce

spectrum) – Both not as accurate as internal

(Still better than other methods) – Easy to automate – Fast & high throughput – No signal overlap problems

5

neither accurate nor precise

accurate but not precise

reproducibility error

not accurate but precise

systematic error

accurate and precise

Accuracy and Precision

6

R2= 0.9998

Accurate

STDV = 0.36

Precise

• 7 samples at different concentrations (1.5mM – 100mM) test accuracy

• Each concentration measured 6 times test precision

• Same integration regions!

qNMR: accurate and precise … over large dynamic range

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• For automated, internal-standard-free, absolute quantization…

• NMR is accurate, precise and versatile

• Superior to

• CLND

• ELSD

NMR

CLND

ELSD

Analytical Chemistry (2005), 77(14), 4354-4365

Thanks to D. Farrant, R. Upton, J. Hollerton, S. Redshaw, GSK UK

NMR quantification vs. other methods

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Influence on Accuracy

Sources for systematic errors

9

p90 p30

} ∆ p90

∆ I

}

∆ p30

∆ I

When miscalibrated, 90o pulse gives more accurate results than 30o pulse

However: 90o excitation requires longer d1

Example: p90 calibrated at 10 ms

Now, use miscalibrated pulse: 10.1 ms

Ref. peak integral: 1.009* I30 0.9999* I90

Apply PULCON: 1.019* I30 1.010* I90

Error: 1.9% 1.0%

Excitation pulse: 30o 90o

Ref. peak integral: I30 I90

Effect of pulse miscalibration … only important for external quantification

Effect of short recycle delay … always important

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d1 = 10s (d1+aq = 13.2s)

• Large errors when using too short relaxation delay

d1 = 1s (d1+aq = 4.2s)

90o excitation pulse

∆ 9% ∆ 0.5% ∆ 4.5%

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• Errors due to short relaxation delay are smaller when using 30o pulse

d1 = 10s (d1+aq = 13.2s) d1 = 1s (d1+aq = 4.2s)

30o excitation pulse

∆ 0.6% ∆ 0.5% ∆ 1%

Effect of short recycle delay … always important

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TD = 8k AQ = 0.75 s

TD = 32k AQ = 3.0 s

• Truncation wiggles poor integrals

• Window function might help, but be consistent (external quantification)

• Careful with long of acquisition times when decoupling! (heating, NOE)

Effect of short acquisition time … always important

13

SI = 8k SI = 64k

• Processed data points should adequately sample peak shape

• Use zero filling

• More important for heteronuclei (larger SW’s)

Effect of low digital resolution … always important

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Influence on Precision

Sources for reproducibility errors

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Influence on Precision

• Sample preparation

• Minimize variations in sample composition

• Signal to Noise ratio

• Low Sino results in imprecise integrals

• Integration regions may vary from person to person

• Averaging minimizes this effect

• Use clear SOP’s or automation

• Different analysts may select different peaks for quantification

• Differently relaxed signals lead to different results

• Averaging minimizes this effect

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PHC0 = 63 PHC0 = 73

• Spectrum must be properly phased for accurate integrals.

• A phase difference as small as 1o can have a measurable effect! (0.7% difference in this example)

PHC0 = 62

Effect of phase errors … most important for external quantification

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• Very small differences in baseline offset can have significant effect on integrals. (2% difference in this example)

Effect of baseline offsets … most important for external quantification

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• Small differences in integral limits can have noticeable effect on integrals. (1% difference in this example)

• Include satellites or not? Does not matter but be consistent!

Effect of integral limits … always important

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• Liquid-repository stock solutions in DMSO:

• Small total amounts: high mass sensitivity

• H-DMSO and Water

• Fragment based screening libraries in aqueous solutions

• Small concentrations, larger volumes

• Also delivers the full set of reference spectra for FBS!

Quantification with external reference … Use of CMC-q for batches

CMC-q: Workflow package for library QC

• Medchem compounds in NMR solvents (deuterated or not)

• well plates and SDF information

Samples

• Any Bruker sample changer

• SampleJet for high throughput

• CMC-q 2.0: optimized experiments

• In H-DMSO or H2O buffer

NMR Automation

• Fully automated, on-the-fly, analysis:

• Verification, concentration, purity, water

Analysis

• All information at a glance

• Easy manual cross check and editing

• Spread sheets, pdfs…

Results

Batch experiments

• Batch Setup from SDF

• Automatic structure file extraction

• Single click setting of global parameters

• Different levels of confidence

• Customizable

• Automatic acquisition, processing and analysis

Batch results

Inspect batch results

• Interactive well-plate view

• Export to external formats

CMC-q concentration test results

• Software Statements:

• green: high confidence • red (with concentration): lower confidence • light blue: no concentration • Light background: in concentration

CMC-q automatically delivers: integrity, concentration, purity and water content (for H-DMSO)

QA results for DMSO stock solutions

Compound integrity Compound concentration

November 10, 2016 26

Library QC on-the-fly in buffer

Tailor libraries & get ligand reference data

Our libraries had ca. 30% bad samples! • 20% “no compound” no compound in stock solution, or not soluble in buffer • 10% decayed or wrong compound • 50% concentration off by more than +/-30%

Potency of Drugs Definition

∑−= mpoundsInactiveCoDrugDrugP )(

Webster G.F. and Kumar S., Anal Chem, 86, 11474 (2014)

Inactive Compounds: • Degradation substances LC-UV • Process impurities LC-UV • Water Karl Fisher • Residual Solvents GC • Inorganic material residue in ignition

Needed before administrating the drug to determine the correct dose based on the amount of active drug in the preparation.

Typically measured by HPLC. Characterised reference standard of the drug itself is needed. Otherwise is determined by difference:

NMR ‘One-Stop Shop’

potency determination purity assessment relative response factor calculation residual solvent moisture analysis identity testing

‘Potency determination by qNMR has been shown to be a single point replacement for routine development testing which previously involved several experiments and techniques.’*

1 single experiment -> qNMR

* Webster G.F. and Kumar S., Anal Chem, 86, 11474 (2014)

Efficiency, Economy Selectivity

qNMR Internal Standard

IS

IS

IS

IS

MwMw

WtWt

IIPP a

a

aa =

Accuracy Eliminates errors introduced by inherent sample differences

Pa, PIs = potency of analyte/standard Ia, IIs = integral area of analyte/standard from NMR spectrum normalized by number of nuclei Mwa, MwIs = molecular weight of analyte/standard Wta, WtIs = weight of analyte/standard.

Rapid and Flexible Workflow

Enter: analyte structure, internal standard (potency & batch #), weights of analyte and standard

Sample Submission

Maleic acid, 0.99, # 05427ES

Enter: analyte structure, internal standard (potency & batch #), weights of analyte and standard

Results in PDF and Excel

Robustness and reproducibility Internal standard peaks ID and integration Analyte ID, check and quantification Potency calculation

Results in PDF and Excel

Area** IS Area** analyte

Prep. Wt a. [mg] Wt IS [mg] CH Region 1 Region 2 Region 3 Averaged Area a. SD Area a. Potency [%] RSD Potency [%]

1 10.30 5.10 1.03 1.00 0.99 0.98 0.99 0.01 99.112 13.10 5.60 0.88 1.00 0.98 0.97 0.98 0.01 99.193 12.40 17.80 2.93 1.00 0.98 0.82 0.94 0.08 95.42

Average 97.91 1.80

Quality Duplicate, triplicates … Error analysis – Intra and inter sample Easy review

Flexibility: User Intervention Possible at any Time

Calculated potency 99.1 %

Dr. Dirk Stueber, Merck

Early Drug Development Physical API form plays crucial role Choose “best” API form for development 80% of API molecules exhibit polymorphism Very wide range of physical and chemical properties Criteria: bioavailability, thermodynamic stability, processability, …


API phase map and form selection main focus Many potentially form changing processes: milling, granulation, compaction, T/RH, … Possible API forms:

Techniques must be available to monitor and quantify physical API forms in solid

samples

polymorphs

solvates salts

cocrystals

amorphous

Early Drug Development


Common techniques for physical characterization: X-ray powder diffraction (other X-ray techniques) Optical + vibrational spectroscopy (Raman, IR, NIR, …) Thermometric methods like Differential Scanning Calorimetry (DSC) and

Thermogravimetry (TG) Solid State NMR (state of the art)

General issues: High LOD, not accurate enough, intricate calibration necessary, not

enough specificity, time consuming

New Approach: Can relaxation TD-NMR data be used for API form identification and

quantification?

Early Drug Development


New method for component quantification in solid mixtures: qSRC

Superposition fits of saturation recovery curves (SRCs)

⇒ Int(mix,τ) = c1 × Int(C1,τ) + c2 × Int(C2,τ) + b

⇒ SRCs are appropriately scaled

qSRC Method Concept


Approach: Superposition fits of saturation recovery curves

⇒ Int(mix,τ) = c1 × Int(C1,τ) + c2 × Int(C2,τ) + b

⇒ Curves are scaled with respect to molecular masses and number of nuclei

C2: T1 = 4.0 s

C1: T1 = 1.0 s

SRC for mix: C1:C2 = 1:1

tau = 5.0 s

For Example two-component system:

- C1 and C2 in a 1:1 ratio - Point at tau = 5.0 s (gray line):

Int(mix,5.0s) = 0.5 Int(C1,5.0s) + 0.5 Int(C2,5.0s)

Linear-combination fit of SRCs to obtain relative

concentrations

qSRC Method Approach


Electronics Box Magnet Chiller

qSRC Method - Minispec mq20


Sample 10 mm tube 100 – 200 mg

Inserted Sample Temp. Pre-Calibrated Samples



20 MHz permanent magnet operating at 40°C

Sample temperature -5°C

to 200°C (Julabo chiller) 10 mm sample tubes Experiments are run as

“macros” with only a few parameters to adjust

Auto sampler (100 samples) available

TD-NMR: Collect only RELAXATION data (T1, T2, T1roh) → recovery/decay curves Component quantification in solid mixtures with qSRC approach feasible?


TD-NMR Workflow

The minispect Mq20

Quantification Using TD-NMR

C:\Data\TD-NMR\mixture_IBU_INDO.dps

Fit/TD data c1 = 0.0877, c2 = 0.912


Indo

Ibu

50.2% Ibu blend

50.2% Ibu

Experimental SRCs 32 scans/inc experiments

Fit of 50.2%-Ibu SRC with linear combination of Ibu and Indo SRCs

Fit → 50.6%

Int Int

τ/ms τ/ms

Ibuprofen/Indomethacin, binary blends with 5 – 50% ibuprofen 1H-T1 differ by ∼5, use 1H SRCs from saturation recovery experiments Reference SRCs are scaled with respect to molecular masses and number of protons (Ibu: 206.29g/mol,18H, Indo: 357.787g/mol,16H)

Exp. parameters: 50 points (non-uniform) τ = 2 – 20000 ms

4 scans/inc (8 min) 32 scans/inc (56 min)

qSRC Results System 1


Excellent correlation btw prepared and predicted blend compositions Slopes and intercepts close to theoretical values, high R2 and low rms

Prepared m% Ibu

qSRC – 4 scans m% Ibu / rms

qSRC – 32 scans m% Ibu / rms

50.2 50.6 / 0.0082 49.8 / 0.0022

39.9 40.3 / 0.0081 40.9 / 0.0037

30.1 30.8 / 0.0097 31.2 / 0.0030

20.1 20.2 / 0.0116 21.0 / 0.0045

9.9 9.3 / 0.0135 9.5 / 0.0042

5.0 4.6 / 0.0120 4.7 / 0.0048

No increase in accuracy of qSRC for data with higher SNR



Correlations for 1H T1 data with different number of scans and points Overall: SNR has stronger effect on accuracy than number of points


4 scans 50 points

4 scans 30 points

4 scans 10 points

32 scans 50 points

32 scans 30 points

32 scans 10 points


Ibuprofen/Itraconazole, binary blends with 50 – 5 m% ibuprofen

1H T1s: Ibu = 640 ms, Itra = 690 ms (1.1x !)

SRCs for 40.1% Ibu blend with 4sc/inc, 32sc/inc, and 64 sc/inc

qSRC quantification works for close 1H T1s with high-SNR data

Fit → 55.0% rms = 0.0123

Int

τ/ms

Int Int

Int Int Int

τ/ms τ/ms

τ/ms τ/ms τ/ms

Fit → 43.1% rms = 0.0036

Fit → 40.5% rms = 0.0025

4sc/inc 32sc/inc 64 sc/inc



Correlations for 1H T1 data with different number of scans/inc

qSRC fails with low-SNR data

qSRC becomes increasingly

accurate with increasing SNR of 1H T1 data

Same accuracy as for model

system 1 is observed when high-

SNR 1H T1 data is used (128

scans/inc)


4 scans 50 points

32 scans 50 points

64 scans 50 points 128 scans

50 points


O

OH

CF3

HN NH

O

O

CF3

O

OH

CF3

HCl

19F model systems: 2-Trifluoromethyl Cinnamic Acid/6-Trifluoromethyl Uracil and 2-Trifluoromethyl Cinnamic Acid/Fluoxetine HCl blends 19F T1s: 2TFMCA → 2.08 s, 6TFMU → 4.22 s, FXT HCl → 1.85 s Exp. parameters: 50 recovery points , τ = 2 – 20000/40000 ms, 32/128 scans/inc

qSRC approach works well for 19F T1 SRC data

qSRC accuracy for 19F T1 data follows same trends as for 1H T1 data → ΔT1 dependency

Usually longer exp times due to lower 19F content



Proposed qSRC uses 1H and 19F T1 SRCs as fingerprints for expected components in solid mixtures → SRCmix = weighted linear comb SRCscomp

SRCs are efficiently collected on a Bruker Minispec mq20 POC for using qSRC method for 1H and 19F SRCs has been shown for

several model systems Separation of components with close T1s requires more scans/inc Significant total time savings with respect to conventional ssNMR techniques Advantages of qSRC – Bruker Minispec mq20: → Robust, accurate, and fast → Trivial sample preparation (glass tube sample holder) and automation possible → No requirements on sample texture or homogeneity (tablets, gels, polymers, …) → Amenable to industrial high-throughput settings, production sits (Pharma, …) → Patent for qSRC – Bruker Minispec mq20 (qSRC module in Dynamics Center)

qSRC Conclusions

Summary

Status Find out More Potency Determination β Come and see us for

demos and discussions Quantification of solids Demos @ Billerica US,

Fallanden CH

Rapid Methods for Quantification

Acknowledgments

Potency by NMR

Quant. Solid Mixtures

Anna Codina Fabrice Moriaud Martin Wyser Jochen Klages Christine Bolliger Markus Lang Oliver Horlacher Björn Heitmann Francesca Benevelli

Thomas Williamson Kevin O’Sullivan Ian Sherlock Mark Zell Ruth Boetzel Steve Coombes

Stefan Jehle Peter Neidig Stefan Jehle

Dirk Stueber Thomas Williamson

INTERNAL USE ONLY

Innovation with Integrity

Copyright © 2016 Bruker Corporation. All rights reserved. www.bruker.com

‘Partnering with scientists to shorten time-to-market with confidence, by gaining qualitative and quantitative insights into molecular structure and dynamics.’

Till Kühn VP Applications Development Benutzertagung Karlsruhe … · 2016-11-15 · Mwa, MwIs =...

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Transcript of Till Kühn VP Applications Development Benutzertagung Karlsruhe … · 2016-11-15 · Mwa, MwIs =...