FusionAE_Case Study 2

8/14/2019 FusionAE_Case Study 2

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Rapid, Automated Development of a Pharmaceutical SmallMolecule Separation Using A Two Phase Method Screening and

Optimization Approach

This work, conducted in collaboration with Pfizer Inc., Ann Arbor, describes the use of FusionAE for Galaxie in a two-phase rapid screening and optimization experiment designed tooptimize the HPLC separation of a complex mixture of small organic molecules typically foundin pharmaceutical products.Phase 1 - Rapid Screening - consisted of a rapid screen utilizing a novel Trend Responsedata analysis algorithm designed to to identify the correct column, mobile phase andapproximate gradient conditions needed to separate a complex mixture of two APIs andseveral known impurities.

Phase 2Optimization - comprised a method optimization experiment that identified the runconditions that gave the best results in terms of resolution and assay time using the column anmobile phase identified from the Phase 1 screen. Overall method robustness was alsodetermined using a novel robustness calculation. The results of this work are presented below

Introduction

Materials and Methods

Instrument 1100 HPLC with Diode Array detector (Agilent Inc.). Model 500 Column ValveModule (Varian Inc.)Columns 150 x 4.6 mm Gemini C18, Synergi Fusion RP, Luna C18, (Phenomenex Inc.),

Pursuit DiPhenyl (Varian Inc.), Sunfire C18 (Waters Inc.)Buffer System Aqueous: Ammonium Acetate Buffer (pH5), Potassium Phosphate (pH 2.5 &7). Organic: AcetonitrileSystem Parameters Included as Experiment variables Flow Rate, Gradient Slope,Gradient Time, pH and Column Type

Rapid Method Development Platform Instrument control, chromatogram generation, peakprocessing: Varian Galaxie chromatography data system (CDS), (Varian Inc.) Statisticalexperimental design, data analysis, modeling, optimization: Fusion AE for Galaxie (S-Matrix

Corp. CA.)

Experimental Method Study factors for the Phase 1 Rapid Screening experiment werevaried according to a model-robust screening design generated by Fusion AE, whichconstructed the 38 run design as a set of ready-to-run methods and the correspondingsequence in the CDS. The experiment was run overnight on the HPLC under Galaxie CDScontrol. Peak results were imported from the CDS into Fusion AE, using three trend responsvariables viz. Total number of peaks and total number of resolved peaks (R>1.5 & R>2.0), forautomated data analysis.


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Experiment

Variable

Range or Level

Setting

Gradient Time

(min)

15.0 40.0

pH 2.5, 5.0, 6.5

Column Type Gemini C18

Synergi Fusion RP

Luna C18

Pursuit DiPhenyl

Sunfire C18

Gradient Slope (%Organic)

5.0 95.0

Organic Solvent

Type

Acetonitrile

Figure 2. Completing the Fusion experimental design template involved setting theupper and lower bound values for the gradient time, target pH range, the specificcolumns to be screened, and the desired organic solvent type and percentage

Figure 1. Phase 1 Rapid Screening. Experimental parameters (study variables)including, Gradient Slope, Gradient Time Mobile Phase pH and Column Type were enteredinto a standardized template.


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Figure 3. A Model-robust Screening type ofdesign was used to efficiently screen large rangesof the variables and quantify their effects onmethod performance.

Figure 4. Software generated a statistical experimental design. The

variables included process and mixture types. Fusion AE therefore selected amixture process algorithm design.


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Figure 5. A Galaxie sequence, comprising 38 individual automatically generated methodsbased on the experimental design parameters was built by the Fusion AE software withinthe Galaxie CDS. This sequence was started by user and all lines were run in inject andforget mode.

Figure 6. Peak results data were automatically imported from the

Galaxie CDS using file-less data transfer.


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Experimental Method cont.. Study factors for the Phase 2 Optimization experimentwere varied according to a model-robust optimization design generated by Fusion AE, whichconstructed a 14 run design as a set of ready-to-run methods and the correspondingsequence in the CDS. The experiment was run overnight on the HPLC under Galaxie CDScontrol. Peak results were imported from the CDS into Fusion AE, using a file-less exchangemodule, for automated analysis. Optimization solution searches were conducted with theFusion AE numerical and graphical optimizers using the following goals:

USP Resolution >= 2.5 Cp Resolution Robustness >=1.25

Table 1 Phase 1 Rapid Screeningexperiment design generated from the templatealong with the Trend Response results computed directly from the chromatogram data.

Results and Discussion


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Table 2. Regression analysis results for the Total Peaks trend response.

Parameter Name

Coefficient

Value

Coefficient

Standard

Error

Coefficie

nt t

Statistic P-Value F-Ratio

Lower 95%

Confidence

Limit

Upper 95%

Confidence

Limit

Constant 1,415.59 44.80 --- --- --- 1,324.10 1,507.08

Gradient t -662.76 121.51 -5.4545


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Parameter Name Optimizer Result Level Setting

Gradient Time 40.0

pH 2.5

Column Column 3

Once the software derived the equations from the Trend Response data sets, theseequations were linked to a numerical algorithm that identified the study parameter settingsthat maximized both responses. In this study the Fusion AE automated optimizationanalysis immediately identified the column type, pH, and gradient conditions that should beused in the second phase of the method development workflow. These results arepresented in Table 4 below.

Table 4. Phase 1 Rapid screening experiment Automated Optimizer results.

Table 3 shows that all the equation (study parameter effect) terms for the resolutiontrend responses are statistically significant, and all the study parameters arerepresented in the equation in a form related to the nature of their effects (nonlinear,interaction, etc). These results show that a predictive equation has been developed forFusion AE which accurately and quantitatively relates the study parameter effects to asecond key aspect of compound separation the separation of each compound from all

other compounds to the extent required.

At this point is is important to remember that in practice, the Trend Response approach willnot always yield the optimum HPLC method (instrument parameter settings) in a singleexperiment, and indeed it is not meant to. The Trend Response approach is part of aphased workflow in which the trend responses enable the experimenter to identify the bestsettings of parameters such as Column Type and pH; parameters that normally have thegreatest effect on separation and therefore cause the most inherent data loss. Once thesesettings have been identified, these parameters can then held constant in a secondexperiment to designed optimize the HPLC instrument method.


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Table 5. Experiment design generated from the modified template along with theResolution response results imported directly from the CDS for the Phase 2 Optimizationexperiment chromatograms. Resolution results were imported for four critical peak pairs (1-2, 2-3, 5-6, and 9-10), as the compounds corresponding to the other sample peaks werewell resolved in all experiment chromatograms.

Figure 7. A trellis of four resolution response surface graphs illustrating the changes inresolution of four critical peak pairs (1-2, 2-3, 5-6, and 9-10) as a function of changing the

pump flow rate (X axis) and the final percent organic (Y axis).


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Figure 8. Response Overlay graph showing multiple response goals from thePhase 2 Optimization experiment overlaid on one graph. Resolution goals(Maximize, all Lower Bounds = 2.5) for all four critical peak pairs in the DOE-basedare displayed.

Figure 9 Response Overlay graph for Phase 2 Optimization experiment with additionaloverlays of method Robustness Cp goals (Maximize, all Lower Bounds = 1.25) defined forall peak pairs having predicted mean resolution values below 4.00. responses. Theunshaded region in this final overlay graph represents the level setting

combinations of the study factors that exceed the defined goals for both meanperformance and robustness.


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Figure 10. Chromatogram obtained by injecting a test sample on the HPLC set at theoptimum method parameter settings identified in the Phase 1 and 2 Fusion AEexperiments. The final method conditions are defined below. It is noteworthy that thetotal experimental work required to obtain this final method consisted of two multi-factor statistically designed experiments, both of which were carried out overnight infully automated inject-andforget (walk-away) mode.

Phase 1 Column/Solvent ScreeningColumn Type Column 3

pH 2.5Gradient Time 40 minutes

Phase 2 Method OptimizationPump Flow Rate 0.67 mL/minFinal % Organic 70 %

Conclusions

Chromatographic analytical method development work normally begins with selectionof the analytical column, the pH, and the organic solvent type. A major risk of using atrial-and-error based one-factor-at-a-time (OFAT) approach is that it provides no abilityto visualize or understand the interaction effects usually present among these keyinstrument parameters. In addition, this approach often results in significant inherentdata loss in key chromatographic performance indicators such as compound resolutiondue to the large amount of peak exchange and compound co-elution common in theseexperiment data sets.


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The Phase 1 Rapid Screening experiment identified the correct analytical column, pH, andorganic solvent type. Once these instrument parameters had been identified, the Phase 2 -Optimization experiment involved manipulating the remaining important instrumentparameters to obtain a method that met all the performance goals including overall methodrobustness. The novel Quality-by-Design based methodology used for the Phase 2

experiment combined Design of Experiments methodology with a Monte Carlo simulation tosuccessfully integrate quantitative robustness metrics into the method optimization processresulting a the development of an analytical HPLC method capable of separating four criticalpeak pairs simultaneously.

This inherent loss makes it difficult or impossible to quantitatively analyze and model thesedata sets, reducing the analysis to a pick-the-winner strategy based solely on visualinspection of the chromatograms. The 2 phased Quality by Design based approachdescribed here, uses statistical experimental design coupled with automatically computedTrend Responses. This new practice successfully overcomes these problems to provide a

rigorous and quantitative methodology for column/solvent screening without the need fordifficult and laborious peak tracking implemented in a fully automated HPLC experimentationplatform.

Acknowlegements

Authors

The authors are grateful to Dr. Graham Shelver, Varian, Inc. for providing hardware,software, and expertise in support of the live experimental work conducted to prove outthe Quality-by-Design approach to method development presented in this paper. Theauthors also want to thank Dr. Raymond Lau, Wyeth Consumer Healthcare, and Dr.Gary Guo and Mr. Robert Munger, Amgen, Inc. for the experimental work done in theirlabs which supported refinement of the phase 1 and phase 2 rapid developmentexperiment templates.

Patrick H. Lukulay, Ph.D., Manager, Drug Quality Control and TrainingUSP Drug Quality and InformationU.S. Pharmacopeia, Rockville, MD

Richard Verseput, President,S-Matrix Corporation. Eureka, CA

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FusionAE_Case Study 2