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Chem Bio Pharm I nnovative Chem Bio Pharm I nnovative

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Chem Bio Pharm I nnovative - PPT Presentation

Chem Bio Pharm I nnovative combination of Quality by Design and green analytical chemistry for analytical methods in pharmaceutical sciences Ludivine Ferey Karen Gaudin Laboratory of Analytical ID: 764589

chembiopharm analytical quality design analytical chembiopharm design quality method min based phase green mobile validation chemistry space risk impurity

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ChemBioPharm I nnovative combination of Quality by Design and green analytical chemistry for analytical methods in pharmaceutical sciences Ludivine Ferey, Karen GaudinLaboratory of Analytical Chemistry – Faculty of Pharmaceutical Sciences – Bordeaux ChemBioPharm – ARNA INSERM U1212 / UMR CNRS 5320 Industrial partner Christine Boussès Unither Développement Bordeaux 1

ChemBioPharm REGULATORY REQUIREMENTS (1/2) ICH Q8(R2): p harmaceutical development Quality by Design (QbD)«  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 »  Q8(R2) Guidelines « Quality cannot be tested into products , quality should be built in by design » 2

ChemBioPharm REGULATORY REQUIREMENTS (2/2) ICH Q8(R2): p harmaceutical development Design Space (DS)«  the multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality »  Q8(R2) Guidelines Use of design of experiments 3

ChemBioPharm QbD & ANALYTICAL CHEMISTRY Analytical m ethodsused for quality control of drugs => patient safety should provide reliable scientific data for a better knowledge of the product all along its developmentneed a controlled risk-based development to guarantee quality Design Space sub-region of the experimental domain in which the objectives of the method are reached with a defined probabilityrobustness domain 4

ChemBioPharm Global approach of QbD Analytical Target Profile (ATP)Critical Quality Attributes(CQA)Critical Process Parameters(CPP) Design of experiments (DOE)Design Space(DS) Validation QbD CQ 5

ChemBioPharm ANALYTICAL TARGET PROFILE (ATP) ATP = p redefined goals according to the « intended use » of the methodintended use = stability studies of dextromethorphan (DXM) in final products  quantitative analysis of API and degradation productsimplementation in quality control laboratories  robustness and validation ICH Q2 (R1)Green analytical method  respect of green chemistry principles DXM6

ChemBio Pharm GREENING ANALYTICAL METHODS Reduction or replacement of toxic reagents Reduction and proper management of wasteMinimization of energy consumptionReduction of the number of samplesUHPLC fast analysis Ethanol-based mobile phases greener solvent Chemometrics (QbD approach) lower number of runs Analytical method 7

ChemBioPharm CRITICAL QUALITY ATTRIBUTES ( CQAs ) CQAs = measurable attributes of the chromatogram that should be within an appropriate limit or range to ensure the desired quality of the method Must haveIntend to haveR s between DXM and impurities A, B & C  Rs > 2  > 2.5 Peak efficiency impurity A  N > 1500  > 3000 Peak efficiency impurity C  N > 20000  > 30000 Minimization of solvent consumption EtOH volume  < 0.4mL  < 0.35mL Mobile phase volume  < 2mL  < 1.2mL Assassi et al, Green analytical method development for statin analysis, J. Chromatogr . A, 37 ( 2015) 61-72. 8

ChemBioPharm CRITICAL PROCESS PARAMETERS ( CPPs ) CPPs = factors whose variability has an impact on a CQAselected by quality risk assessment CQAs InjectionColumnPump Tubing Mobile phase DetectionDwell volumeVolume Solvent Composition Organic solvent % W avelength Cell length N ature Geometry Temperature L ength Diameter %/min pH Concentration Buffer Flow rate CPPs to be evaluated by screening design 9

ChemBioPharm SCREENING DESIGN Plackett-Burman design: 1 st degree modeling Do factors have significant effects on CQAs (responses of interest)?LevelspHGradient slope (%/min)Temperature (°C)Flow rate (mL/min)-122300.2+164500.4tRImpurity efficiency Solvent consumption significant factors: flow rate, gradient slope, pH, and temperature 10

ChemBioPharm OPTIMIZATION DESIGN Central composite design: 2 nd degree modeling Response surface methodology Polynomial quadratic modelCentral composite design3-D response surface plot of Rs DXM/Imp.AGradient slopeFlow rate Gradient slope pHHigh responseMedium responseLow response 11

ChemBioPharm OPTIMUM POINT PREDICTION Desirability analysis Derringer’s desirability functions applied to CQAs Optimum point = highest global desirability value (D)12

ChemBioPharm OPTIMUM POINT VALIDATION Analysis time < 5 min Method validated Impurities A & C Green methodEthanol consumption < 0.35 mL per analysisEcoscale test Gałuszka et al, Analytical Eco-Scale for assessing the greeness of analytical procedures, TRAC-Trend. Anal. Chem, 37 (2012) 61.ATP  13

ChemBioPharm DESIGN SPACE Contour plots Response overlay = CQA objectives displayed on the graph Optimum point Maximum desirabilityDS:pH: 4.6-4.8Gradient slope: 2.9-3.6%/minTemperature: 35-45°CFlow rate: 0.20-0.24 mL/min 14

ChemBioPharm ROBUSTNESS New Plackett-Burman design performed around the optimum point (24-1)8 validation points experimentally tested by varying CPPs from extreme limits of the DS rangeCQAs met the specifications for all 8 points (D = 1)DS = robustness domain15

ChemBioPharm RISK-BASED DESIGN SPACE Mean responses used for optimizationDo not provide any clue about method reliability How well and how often the method can meet the specifications? What guarantee?Mean based S > 1 minRisk based P(S > 1 min) E. Rozet et al, Design Spaces for Analytical Methods: what, why, how?, TrAC-Trend. Anal. Chem, 42 (2013) 157 S = t R,begin – t R,end Optimized robust assay Take into account model uncertainty Monte-Carlo study of the propagation of the model’s predictive errors Quality is ensured by assessing the risk of not being within the acceptance limits 16

ChemBioPharm CONCLUSION Rapid development of optimal and robust chromatographic methodsIn line with ICH Q8Better knowledge and easier introduction of new concepts (green chemistry) Design space = robustness but method validation still necessaryC. Boussès, L. Ferey, E. Vedrines, K. Gaudin, Using an innovative of QbD and Green analytical chemistry approaches for the developpement of a stability indicating UHPLC mehod in pharmaceutical products, J. Pharm. Biomed . Anal. 115 (2015) 114-122 17

THANK YOU FOR YOUR ATTENTION 18

Chemical compound score  Sub-total PP Total PP Ethanol Amount< 10 mL 11HazardLess severe hazard1WaterAmount< 10 mL10 HazardNone0Ammonium FormateAmount< 10 g11Hazard1 pictogram - Warming1Formic AcidAmount< 10 mL 1 1Hazard 1 pictogram - Warming1 Σ = 3Instrument score  Total PP Energy U-HPLC ≤ 0.1 kWh per sample 0 Occupational hazard Analytical process hermetization 0 Waste 1 – 10 mL (g) 3 Recycling 0   Σ = 3 Total penalty points (PP): 6 Analytical Eco-Scale total score: 100 - 6 = 94 Penalty points ( PPs ) to calculate analytical Eco- Scale Galuszka et al. Trends in Analytical Chemistry , 37 (2012) 61-72 Chem Bio Pharm 19

ChemBioPharm METHOD VALIDATION Protocol Calibration set: for each impurity, the stock solutions of each impurity (0.1 mg/mL in ethanol/water mixtures, (25/75, v/v) was diluted 20 fold to obtain a solution at 0.5% of impurity. This dilution was done induplicate for each impurity which constituted the calibration set.Validation set: dilutions of the stock solutions by 100, 20, and 8 fold were prepared in triplicate in the presence of excipients and dextromethorphan to obtain limit of quantitation (LOQ), 0.5 and 1.25% concentration levels, respectively. All these solutions were10 fold diluted while adding 6.7 mL of a DXM syrup at 3 mg mL−1.This procedure was repeated at 3 different days and each solution was injected in the optimal chromatographic conditions.20

ChemBioPharm METHOD VALIDATION Goal: to demonstrate that the method is suited for its intended useValidation of each degradation product (ICH Q2(R1)) using accuracy profilesspecificity, accuracy, repeatability and intermediate repeatability, and linearityrange: LOQ-1.25% DXM targeted concentrationβ-expectation tolerance limits within ± 20%21

Cyclic tertiary amine: model compound for the study of basic substances pKa = 8.2, a component of a cough medicine, has been analyzed by reversed-phase HPLC with some difficulties, and actually included as one of the test solutes aiming at the effect of silanols on elution of basic compounds. It has been eluted at acidic pH or in the presence of an ion-pair reagent in most reported determinations to avoid severe tailing at neutral pH in the absence of another amine.Sensitive probe for silanol effect detectionTypical pharmaceutical case study relevant for the evaluation of robust conditions in green analytical chemistryChemBioPharmDEXTROMETHORPHAN22

0.5 %Chem Bio Pharm DEXTROMETHORPHAN IMPURITIES pKa = 9.8523

Screening of 3 columns: Acquity BEH C18 (50 x 2.1 mm, 1.7 µm) = hybrid silica, Acquity BEH Phenyl (50 x 2.1 mm, 1.7 µm) = covalently modified by phenyl grafting, Hypersil gold AQ (50 x 2.1 mm, 1.9 µm) grafted C18 silica possible to use 100% aqueuous phaseBetter selectivity of Acquity BEH C18 = baseline separation of all peaks whereas on others columns coelutions of DXM and Imp. A were observed. Mobile phase A: 10 mM ammonium formate adjusted to different pH with formic acid, and 96% ethanol (95/5, v/v) Mobile phase B: 10 mM ammonium formate in ethanol 96% with the same proportion of formic acid as mobile phase AGradient: initial hold of 1 min at 21% of mobile phase B followed by a linear ramp from 21% to 36% of mobile phase B, and 6 min of equilibration Injection volume = 2 µL and detection wavelength = 280 nm. Chem BioPharm CHROMATOGRAPHIC CONDITIONS 24

ChemBioPharm DESIGN OF EXPERIMENTS ( DOEs ) Modeling of chromatographic behaviorFull and Fractional Factorial Designs Central Composite Designs => Response Surface Strategy Screening designs: study of a large number of factors and evaluation of its main effects = 1st degree modeling Optimization designs: determination of an optimum region (DS) = 2nd degree modeling 25

ChemBioPharm RISK-BASED DESIGN SPACE Mean responses used for optimizationDo not provide any clue about method reliability How well and how often the method can meet the specifications? What guarantee?Mean based S > 1 minRisk based P(S > 1 min) E. Rozet et al, Design Spaces for Analytical Methods: what, why, how?, TrAC-Trend. Anal. Chem, 42 (2013) 157 S = t R,begin – t R,end Optimized robust assay Take into account model uncertainty Monte-Carlo study of the propagation of the model’s predictive errors Quality is ensured by assessing the risk of not being within the acceptance limits 26