Tools for the Application of Quality

by Design Principles in HPLC

Imre Molnár

Molnár-Institute, Berlin, Germany

© Molnár-Institute 2014

Introduction

• The Institute

• In 1986 we invented isocratic modeling in HPLC

• In 1990 we invented Gradient Elution

• In 2004 we developed PeakTracking

• In 2009 we developed the Cube

• And in 2012 we started the Robustness Tool

• We developed the most visual tool for HPLC

focusing on the chromatographic part of

method development in HPLC

Overview

• What is QbD?

• What modeling tools are available?

• The most simple DoE: the Cube

• DryLab vs QbD – two languages – a

comparison

• Case Study 1: pH-influence – study 5 factors

• Case Study 2: Gradient elution modelling w. 8

factors

• Case Study 3: Acetonitrile shortage

• Robustness optimization

• Knowledge Management Document

What is Quality by Design (QbD)?

• QbD is the end of Trial & Error. You have to plan your

work, set your goals and make your experiments – and

make sure, they are reproducible.

• You should use solid science and should know, how

HPLC is working. If you are a beginner, use modeling

tools, they will help you to learn, how to make a good

separation. Use gradient elution, this is excellent for

most of the samples.

• I will demonstrate for you

– how to get the best separation

– how to find the most robust conditions for routine work

– how to select the best column for your application

What is available on the market on modeling

tools?

• The most important goal is to understand your

separation, i.e., which parameters are inducing which

peak movements.

• You can start with molecular structures, you can start to

run hundreds of experiments – however automation

should be meaningful. Some products are based only

on statistics. We think, before you use a lot of statistics,

you should understand how to make a good HPLC and

a robust separation first.

• The easiest way to generate a good method is to do the

right measurements and the right selection of them.

Simple Design of Experiments (DoE)

• There are hundreds of possible Designs for HPLC.

However the most simple one is the Gradient time, tG

vs. Temperature T, the so called tG-T-model. Why?

Because you need only 4 experiments and can predict

over 10,000!

• These 4 runs will be different in peak elution order. This

is sometimes surprising! But this is your choice to avoid

later Out of Specification (OoS) results.

• Repeating the tG-T-Design 3 x with changing pH or

ternary eluent composition (tC) you can create the

Cube in ca. 20 sec, which is containing over one

million chromatograms. You can find the best one with

just a mouseclick. And you can create a visual

chromatogram for any of your changes. This is great!

The most simple DoE

Column: 50x2.1mm, 1.7µm

tG1: 3 and tG2: 9 min

T1: 30, T2: 60°C

Eluent A: pH1: 2.0, pH2: 2.6, pH3: 3.2

Eluent B: AN, (50:50), MeOH

The ternary Cube

Baseline Resolution Regions red

= MODR, visualized Design Space Design Spaces can be different

irregular (red) bodies

Select your working point

DryLab® and QbD

Method goals

Critical separation

parameters

Run experiments.

Build & evaluate

DryLab® models

Models based on

experiments

with up to 10 factors

Multifactorial Robustness

Testing

DryLab®

expressions

Model changes to justify

progress

QbD expressions

Analytical target profile

(ATP)

Critical quality attributes

(CQA’s)

Risk assessment (RA)

Design space (DS) and Method

Operating Design Region

(MODR)

Control strategy

Continual improvement

© Molnár-Institute 2014

Case Study 1: pH-model

Robust pH-region Other variables

L, ID, F, dp,

etc.

Other variables

L, ID, F, dp,

etc.

Rs,crit

p

H

Critical peakpair in

red

Separate

your peaks

Case Study 2: Gradient time tG

model pH-Optimization

Shorten your

analysis time

Case Study 2: Develop step

gradients

Optimize

your gradient

Eluent A varied in pH, Eluent B varied in ternary

composition

Multifactorial optimization strategy of 4 measured critical HPLC

method parameters: Gradient time (tG), temperature (T), pH and

ternary composition (B1:B2), based on 12x3=36 experiments.

I.Molnár, H.J.Rieger, K.Monks, J.Chromatogr. A, 1217 (2010), 3193–3200.

Case Study 3: Acetonitrile Shortage How to replace AN with MeOH? Answer: Ternary eluent modeling

Case Study 3: The ternary Cube

We can have an excellent separation in MeOH!

Case Study 4: Separation of 22 API‘s is not sufficient in

AN

Best working point

Bad separation of 22 Active Pharmaceutical

Ingredients (API‘s)in Acetonitrile

Method Operating Design Region

Edge of failure

Case Study 4: Separation is much better in

MeOH

22 Active Pharmaceutical Ingredients (API‘s)

Case Study 4: 1. Step: Peak Tracking – fixing elution order in the tG-T-ternary eluent composition (tC)-Model

Observing a lot

of

Selectivity

Changes

Eluent B:

MeOH

50:50

AN

tG-T-tC-model

T [°C]

2

6

10

2.Step a: Peak tracking for each of the tG-T-models Peak tracking of the runs 1, 2, 3, 4 in eluent B1: AN

1.

3.

2.

4.

tG-T-tC-model 1, 2, 3, 4

T [°C]

2.Step b: Repeating Peak tracking with runs 5, 6, 7, 8 in eluent B: MeOH:AN(50:50)(V:V)

tG-T-tC-model 5, 6, 7, 8

T [°C]

5.

7.

6.

8.

2.Step c: Repeating Peak tracking 4 runs in eluent B2: MeOH

tG-T-tC-model 9, 10, 11, 12

T [°C]

9.

11.

10.

12.

Case Study 4: Calculation of the cube: tG-T-tC 3-D-Model, point of high critical resolution (1.92) in 100% MeOH

M. Euerby, G. Schad, H. J. Rieger, I. Molnár, Chromatography Today, (Dec. 2010), 13–20.

Edge of

Failure

Multifactorial Robustness Test of 6 factors at 3 levels =729 runs in

calculated in 30 seconds

Multifactorial Robustness Test of 6 factors at 3 levels =729

runs

Working Point

(WP) at the Edge

of Failure (EoF)

OoS-results (>74%) SR:

26%

MODR

The method is at this point CAN NOT BE USED in the production

If we select a better working point => we get better results

OoS-results SR:

94%

(6%)

If we have a better pump with smaller flow rate variations =

0% OoS

OoS-results =

0%

DF: 0.02

mL/min

DryLab® model

shows:

MeOH is the better

eluent!

front view

view from left side view from the top

view from the right side

10 20

Time (min)

3.33

5

4.93

75.

363

6.18

86.

643

7.20

7

8.18

7

9.16

5

11.0

24

11.8

06

13.1

8513

.680

16.0

01

17.0

54 17.5

7317

.942

18.5

62

20.1

15

21.7

03

22.5

02

0 10 20

Time (min)

3.35

4

4.95

65.

383

6.21

96.

658

7.26

4

8.18

9 9.21

1

11.1

29 11.8

58

13.1

8013

.854

16.0

72

17.2

21 17.6

8218

.056

18.6

79

20.2

56

21.8

6122

.560

DryLab® model

Experiment

Accuracy of modelling > 99%

Case Study 5: Pharmacopoe method too long, (over 60

min) and old column not available anymore:

Find new column using the pH-Cube

using tG, T and pH changes (pH-Cube):

Acquity BEH C18, 50 x 2.1mm, 1.7 µm

Acquity CSH C18, 50 x 2.1mm, 1.7 µm

YMC Triart 50 x 2.1mm, 1.7 µm

Acquity T3 50 x 2.1mm, 1.7 µm

Acquity HSS C18 50 x 2.1mm, 1.7 µm

Acquity HSS C18 SB 50 x 2.1mm, 1.7 µm

Aeris XB C18 50 x 2.1mm, 1.7 µm

Kinetex C18 50 x 2.1mm, 1.7 µm

With each column 12 runs were carried out

(takes ca. 4 h for each column):

3 tG-T-sheets at 3 pH-values (establ. in eluent A)

Different working points, but baseline resolution for each column

Acquity BEH C18 Acquity CSH C18 YMC Triart C18

Acquity HSS C18

Aeris XB C18 Kinetex XB C18

Acquity HSS C18-

SB

Acquity HSS T3

Excellent Predictions (blue) vs. Experiment

(red)

For most columns

a good separation

was found, but

also differences

in robustness

New method faster

under 6 min,

average tR-deviat.

< 0.5%.

New Method 10 x

more efficient

as in EP.

R.Kormány, I. Molnár, H.J. Rieger, Journal of Pharmaceutical and Biomedical Analysis, 80 (2013), 79-88

Knowledge Management Document (KMD) Contains all necessary informations for commercial authorisation

Summary

GMP Document

ATP, CQA

Risk management

Control Strategy

Make Experiments

and PeakTracking

Calculate

the Cube

Compare the

basic runs with

the modelled ones

Robustness and

OoS calculations

Adjustments in the

Design Space and

of Variabilities

Print Document and

submit to Regulators

for comm. autorization

Make your separation method

understandable

Knowledge

Management

Document (KMD)

Knowledge

Management

Document (KMD)

Knowledge

Management

Document (KMD)

Get commercial

autorization

fast

Summarizing the

Knowledge Management Document (KMD):

- Based on Solid HPLC- and Visual Separation Science

- Starts with Analytical Target Profile (ATP)

- Risk Assessment and Control Strategy Evaluation

- Continual Improvement Plan

- Lists Design of Experiments (DoE) and all input data

- Peak Tracking Documentation

- Model Formation and Testing („Comparison“)

- Robustness Test of Working Point

- Influence of Tolerance Limits („Variabilities“)

- Elimination of OoS

- Inspection Safety

- pdf-Document for Submissions to Regulators

- Fast commercial authorization

DryLab ® reinvented

HPLC

Acknowledgement

Shimadzu for HPLC-instruments

LC-Resources, Inc.

MacMod and HiChrom

for ACE- and HALO columns

www.molnar-institute.com

© Molnár-Institute 2014