modeling of analytical (u)hplc: an important element in ... · modelling of analytical (u)hplc: an...

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Modelling of analytical (U)HPLC: An important element in the QbD toolbox

Pharma Lab Congress 2013, Düsseldorf, Germany, November 13-14th 2013Petersson, Patrik , Novo Nordisk A/S , +45 3079 2146, [email protected]

Presentation title Date 1

• QbD in general• Analytical QbD at Novo Nordisk• Modelling of analytical (U)HPLC

Outline

2Presentation title Date

Time, min

454035302520151050

Col

umn

Tem

pera

ture

, °C

30

35

40

45

50

55

60

65

0.66

0.63

0.61

0.58

0.55

0.52

0.50

0.47

0.44

0.41

0.39

0.36

0.33

0.30

0.28

0.25

0.22

0.19

0.17

0.14

0.11

0.08

0.06

0.03

0.00

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“A systematic approach to development that begins with predefined

objectives and emphasizes product and process understanding and

process control, based on sound science and quality risk management”

Quality by Design (QbD)


from ICH Q8 pharm. dev.

Quality by Design


~1980/90s 2004-2012

__________________________________Pharmaceutical Quality by Design: Product and Process Development, Understanding, and ControlLawrence X. Yu, Pharmaceutical Research, Vol. 25, No. 4, April 2008

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• The pharma industry presented their vision for analytical QbD 2009*• Expectation of regulatory relief

_____________________*EFPIA/PhRMA white paper to FDA and EMA 2009

Position paper, “Implications and Opportunities of Applying QbD Principles to Analytical Measurements”, Schweitzer et al. Pharm Tech 34 (2010) 52-59

Quality by Design for analytical methods


• Currently no guidance• EMA/FDA - “There is currently no international

consensus … until this is achieved, any application that includes such proposals will be evaluated on a case-by-case basis.”*

• No regulatory relief at this stage • A way to increase method understanding and

thereby improve performance and robustness

Quality by Design for analytical methods


_____________________________________*EMA-FDA pilot program for parallel assessment of Quality-by-Design applications: lessons learnt and Q&A resulting from the first parallel assessment EMA/430501/2013

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Analytical Quality by Design at NN


Analytical Target Profile

Risk assessment

Method understanding

Control strategy

Knowledge management

Time, min

2624222018161412108642

Col

um

n Te

mpe

ratu

re,

°C

36

38

40

42

44

46

48

50

52

541.57

1.50

1.44

1.37

1.31

1.24

1.18

1.11

1.05

0.98

0.92

0.85

0.79

0.72

0.65

0.59

0.52

0.46

0.39

0.33

0.26

0.20

0.13

0.07

0.00

• “User requirement specification”• Giving clear directions for analytical development• Ensures agreement between stakeholders• Internal targets – no regulatory relevance

• Method specific targets• Performance aspects typically included in validations (accuracy …)• User aspects (handling, safety …)• Customer aspects (re-runs, cycle time …)• Sample aspects (matrix, ranges … )• …



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• Evaluation against • Validation data• Customer/user experiences



Methodvalidationphase 1


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• Special focus on intermediate precisionand specifications/OOS• Validation




Controlsamples

Controlsamples

Controlsamples

• Control sample included in routine analysis sample sets


Stab. Dataphase 3

Stab. dataphase 1

Stab. Dataphase 2

• Stability study residual variation

Risk assessment

• Method specific risk assessment and knowledge sharing• Brainstorm to share knowledge, spot problems/possibilities and anchor actions• At transfer to/from discovery, external vendors or QC• Different focus (development, re-runs, robustness … )

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Method understanding (by modelling)

• Some techniques generic and not optimized or typically involve one factor at the time optimization (e.g. spectroscopic)

• Most techniques are well suited for optimization and/orrobustness testing based on statistical models (DoE)

• Chromatography also suitable for models based on theory (later … )

FVVk

FtV

m

Ftt DM

GM

GR

1)13.2log( 0

• Control charts

A B C D E

• Instructions for data evaluation

Control strategy

• To ensure that the analysis is fit purpose each time• Involves

• System suitability tests• Selection of parameters• Definition of data based acceptance criteria• Adjustments … later

• Preventive maintenace parametersP

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Method operational design ranges (MODR)

• MODR = design space for analytical method• MODR with acceptable results typically very small for (U)HPLC


• MODR very costly to define• “demonstration of statistical confidence throughout the MODR.

Issues for further reflection include the assessment of validationrequirements as identified in ICH Q2(R1) throughout the MODR and confirmation of system suitability across all areas of the MODR.“*

• Is it worth the effort?!

______________________________________________*EMA-FDA pilot program for parallel assessment of Quality-by-Design applications: lessons learnt and Q&A resulting from the first parallel assessment EMA/430501/2013

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• We do not define any MODR for chromatographic methods• Instead SST to justify changes in %ACN and temperature to

compensate batch to batch and instrument to instrument differences• Described in EP, USP and JP ±5°C


48°C

45°C

42°C

• Graphical guidance e.g.


• ”Follow the molecule”• Hands on experience and personal contacts

• Concise method specific development report• A table with conclusions and references

• Purpose - easy documentation/retrieval of method developmentand validation information

• Continuously updated

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Analytical Quality by Design at NN



Risk assessment

Method understanding

Control strategy


Time, min

2624222018161412108642

Col

um

n Te

mpe

ratu

re,

°C

36

38

40

42

44

46

48

50

52

541.57

1.50

1.44

1.37

1.31

1.24

1.18

1.11

1.05

0.98

0.92

0.85

0.79

0.72

0.65

0.59

0.52

0.46

0.39

0.33

0.26

0.20

0.13

0.07

0.00

Method understanding by modelling

Generic screen ofcolumns and

mobile phases

DoE: Optimisation of mobile

phase composition

Semi-theoretical models:Optimisation of

gradient and temperatureDoE: Robustness testing

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Semi-theoretical models

Rt

dtk

uL0

0 1

1

Linear gradient analytical solution

Non-linear gradient Numerical solutionNon-linear least squares fitting

FVVk

FtV

b

Ftt DM

GM

GR

1)13.2log( 0

time, tve

loci

ty,

distance, L

1)(ln

0

0 bkR e

u

Lt

Solvent strength models

• The retention models that can be found in most commercial software have been developed for RPC and small molecules

• Novo Nordisk is a companydeveloping products based on proteins

• In order to model proteins other chromatographic techniques and retention models are needed

• Collaboration between and

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• RPC Reversed phase chromatography

• IEC Ion exchange chromatography

• HILIC hydrophilic interaction chromatography

• HIC hydrophobic interaction chromatography

• NPC Normal phase chromatography

_____________________L.R. Snyder, J.W. Dolan, High-Performance Gradient Elution: The Practical Application of the Linear-Solvent-Strength Model, Wiley, Hoboken, NJ, 2007.

)ln(ln bak

bakln

)ln(ln bak

)ln(ln bak

bakln


44.01.1ln MWabak

• Proteins respond much more strongly to changes in %ACN

_____________________L.R. Snyder, J.W. Dolan, High-Performance Gradient Elution: The Practical Application of the Linear-Solvent-Strength Model, Wiley, Hoboken, NJ, 2007.

MW b b(MW)/b(100)100 8 1

1000 23 310000 63 8

100000 174 21

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Temperature models


2.8 2.9 3 3.1 3.2

x 10-3

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

1/T [1/K]

ln(k

)

40°C

Ibuprofen ~0.2 kDa

80°C

Toluene ~0.1 kDa

3 3.1 3.2 3.3 3.4

x 10-3

2

2.05

2.1

2.15

1/T [1/K]

ln(k

)

20°C60°C

NN >100 kDa

NN >100 kDa

Tbak /ln

_____________________Small molecules - M. Euerby et al. , Hichrom LtdProteins – P. Petersson et al., Novo Nordisk A/S

3 3.1 3.2 3.3 3.4

x 10-3

3.2

3.25

3.3

3.35

3.4

1/T [1/K]

ln(k

)

20°C

60°C

NN ~5 kDa

NN ~5 kDa

2//ln TcTbak

Temperature models


3 3.1 3.2 3.3 3.4

x 10-3

2

2.05

2.1

2.15

1/T [1/K]

ln(k

)

Temperature

20°C60°C

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• From 2, 6 or 9 experiments we need:• Retention time• Peak width (main + 1 to 2 imps.)• Peak area• Peak asymmetry for main peak• Dead volume• Dwell volume• Flow

Input


Column

A

B

Pump

Detector

VD “dwell volume”

VM dead volume

tG (min)

T(

C)

A 1st example: Protein >100 kDa

• Demonstration • [LINK]• Improved resolution• Improved robustness• Isolation/preparative

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A 2nd example: Peptide map ~0.5-3.5 kDa

• In general:• 60 to 80% reduction in

cycle time• Maintained or better

resolution• Improved robustness • Identification of critical

peak pairs

A 3rd example: Excipient <0.5 kDa

• 2 methods 1

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Accuracy

• tR <2% and systematic

• w <20%

• w 1.2 Rs 0.83

-50

0

50

100

150

200

250

300

350

400

450

500

1.9 2.4 2.9 3.4 3.9 4.4

t [min]

S [

-]

SA

SB

SC

Stot

-50

0

50

100

150

200

250

300

350

400

1.9 2.4 2.9 3.4 3.9 4.4

t [min]

S [

-]

SA

SB

SC

Stot

Peak tracking

• Peak areas• Retention change linear

or somewhat curved• Guess and confirm• MS!!!

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Peak tracking overwhelming?

• Model the first and the last peak• Model the API and critical peak(s)

which are easy to follow• Ignore the rest


• Predict e.g. 6 conditions which gives good retention and resolution for the peaks which have been modelled

Rs-map based on modelled peaks

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• Test these conditions experimentally• Pick the condition that gave the best results

What the Rs-map would look like if all peaks had been modelled

Conclusions

• Analytical QbD• NN interpretation of analytical QbD• A way to obtain better methods

• Understanding• Performance• Robustness

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Conclusion

• Semi-theoretical modelling• Works very well also for large proteins after some adaption• Not all softwares provide the necessary models• Application areas:

• Optimisation• Robustness evaluation (DoE still needed)• Fraction collection• Troubleshooting

• Anders Dybdal Nielsen• Anette Skammelsen Schmidt• Babak Jamali• Birgit Gaarde-Nielsen• Bonnie Schmidt• Helle Holton• Jytte Pedersen

• Karin Juul Jensen• Mads Wichmann Matthiessen• Marika Ejby Reinau• Steffen Frandsen• Thorbjørn Strøm-Hansen• Tine Sloth Høg

Acknowledgements

modeling of analytical (u)hplc: an important element in ... · modelling of analytical (u)hplc: an...

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