AdvancedTools in HPLC methoddevelopment

Transcription

1 AdvancedTools in HPLC methoddevelopment Remco Stol, Enrico Martina and Jeffrey Vos Analytical Sciences Chemistry, Quality Unit API/BT NL FHI symposium, Houten, 22 april 2010

2 What does the customer want? Be able to determine compounds X, Y, Z at levels of Control quality of product Y, sourced from multiple suppliers Identity, assay, impurities (0.05% or trace) Monitor stability Trouble shooting, once-a-time analysis Routine test Cheap Without issues Fast 2

3 Typical HPLC Method Development Goals Achieve separation selectivity Quantitative Sensitivity Linear Accurate Precise Robust, rugged 3

4 Typical system failures in routine operation and method development Resolution Retention time Precision Tailing factor, plate number Studied in detail Not studied Not studied Somewhat studied, SST setting 4

5 Optimization parameters Continuous parameters easy to adjust Solvent strength (% modifier) Temperature Gradient (slope, start- end %) Discontinuous parameters Hard-to-adjust Column type ph Organic solvent Salt concentration, type (buffer, ion pairing) Old way : 1) select discontinuous parameters, test, find optimum, next parameter (OVAT) 2) optimize continuous parameters Strange approach: Most relevant selectivity parameters are of discontinuous type Current way : 1) screen discontinuous parameters (e.g. column & ph scouting) 2) optimize continuous parameters 3) automate where possible Better approach: Most relevant selectivity parameters are of discontinuous type 5

6 Start screening right Relevant samples Stress & stability samples Samples from different suppliers/sources Solvents Typically ACN and MEOH, others are possible Select set of columns based on chemistry pka, functional groups, Log P Detection principle UV, λ max Other, MS Select buffers MS compatible UV transparent Sample and column stability Peak tracking (minor added value in this phase) MS and/or DAD 6

7 Discontinuous parameter screening 7

8 Discontinuous parameter screening Flow: 0.5 ml/min Column temperature: 20ºC Injection volume: 5ul Concentration: 1 mg/ml Detection wavelength: 210 nm Buffers: 100 mm potassium hexafluorophosphate, ph 2 Modifier: acetonitrile Columns: - Chiracel OD-RH - Chiracel AD-RH - Chiracel OJ-RH - Chiracel AS-RH R R R Screening performed with racematic mixture of all four forms (SS, RR, RS and SR) R 8

9 Discontinuous parameter screening mau DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) Area: Area: Area: OJ-RH, 38% ACN mau DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) Area: Area: OD-RH, 40% ACN Area: Area: min min mau DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) Area: Area: AS-RH, 42% ACN mau DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) Area: Area: AD-RH, 37% ACN Area: Area: Area: Area: min min 9

10 Discontinuous parameter screening mau DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) DAD1 A, Sig=210,4 Ref=off, TT (D:\CSADATA\INSTR1\SCH K\DATA\ \ D) % ACN 40% ACN 35% ACN min Flow: 0.5 ml/min Column temperature: 20ºC Injection volume: 5ul Concentration: 1 mg/ml Detection wavelength: 210 nm Buffers: 100 mm potassium hexafluorophosphate, ph 2 Column: Chiracel OD-RH 10

11 Evaluation of Scouting data Pick a winner strategy: optimal discontinuous parameters based # of peaks detected, (critical) resolution Use modeling and chemometric techniques to evaluate data Model can be based on retention or trend response Resolution vs temperature Resolution compound x-y vs % modifier Peak area main vs %ACN Peak height second largest peak Typical goal is to exclude conditions not to optimize! Peak tracking is typically not essential (efficient) during scouting 11

12 Fine-tuning using Design of Experiments (DoE) A two level factorial design (including center points) was used to optimize the separation and estimate robustness. Variables were: Flow rate Amount of modifier Column temperature Buffer concentration responses were: Resolutions Tailing factor Retention time 12

13 Estimating optimum conditions Software (design expert) calculates the optimum conditions in the region tested. Ranges tested were: Flow rate in range: ml/min. Amount of acetonitrile in range 38% - 42% Column temperature in range C Buffer concentration in range mm Maximize resolutions Minimize tailing factor and retention time 13

14 Optimum conditions Flow: 0.5 ml/min Column temperature: 25ºC Buffer concentration: 150 mm potassium hexafluorophosphate, ph 2 Amount acetonitrile: 38% Injection volume: 5 ul Detection wavelength: 210 nm Concentration: 1 mg/ml Column: Chiracel OD-RH 14

15 Estimating robustness DoE software capable of simulating data Design expert 6 (stat-ease, inc.) Inputs are the regression coefficients from the equation derived during optimization. (i.e. Y = A + B 1 X 1 + B 2 X 2 ) Be carefull, extrapolation of regression models can be prone to errors. 15

16 Estimating robustness Resolution Overlay Plot Rs 2,1: B: column temperature Rs 3,2: A: flow rate 16

17 Estimating robustness Peak width and resolution Overlay Plot Rs 2,1: B: column temperature peak width peak 1: peak width peak 4: Rs 3,2: A: flow rate 17

18 Estimating robustness Retention time, peak width and resolution Overlay Plot Rs 2,1: B: column temperature Rt peak 5: Rt peak 4: Rt peak 2: 14.4 Rt peak 1: peak width peak 4: peak width peak 1: Rt peak 2: 17.6 Rt peak 1: Rt peak 4: 25.2 Rt peak 5: Rs 3,2: A: flow rate 18

19 Conclusions Automation and smart solutions used throughout method development Discontinuous scouting is fully automated Experimentation is software supported Experiments are programmed by the software 24/7 unattended experimentation The experimental design model is build throughout the fine tuning The model allows not just better method development, it allows robustness to be a real goal during method development. Automation helps to get better (rugged) methods into routine operation 19

20 Next goals Introduce DoE during scouting Incorporate numerical and categorical variables: Algorithm design, others. Use scouting data for model building Replace formal robustness testing from validation PAR in addition to set-point values in (validated) method descriptions Get acceptance from internal customers Get acceptance from external customers 20