Présentation PowerPoint

Présentation PowerPoint

ChemBioPharm Innovative combination of Quality by Design and green analytical chemistry for analytical methods in pharmaceutical sciences Ludivine Ferey, Karen Gaudin Laboratory of Analytical Chemistry Faculty of Pharmaceutical Sciences Bordeaux ChemBioPharm ARNA INSERM U1212 / UMR CNRS 5320 Industrial partner Christine Bousss Unither Dveloppement Bordeaux 1 REGULATORY REQUIREMENTS (1/2) ChemBioPharm ICH Q8(R2): pharmaceutical development Quality by Design (QbD) Q8(R2) Guidelines 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 cannot be tested into products, quality should be built in by design 2 REGULATORY REQUIREMENTS (2/2) ChemBioPharm ICH Q8(R2): pharmaceutical development Design Space (DS) Q8(R2) Guidelines the multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality Use of design of experiments 3 QbD & ANALYTICAL CHEMISTRY ChemBioPharm

Analytical methods used for quality control of drugs => patient safety should provide reliable scientific data for a better knowledge of the product all along its development need 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 probability robustness domain 4 Global approach of QbD ChemBioPharm Analytical Target Profile (ATP) Design of experiments (DOE)

Critical Quality Attributes Design Space (CQA) (DS) Critical Process Parameters (CPP) Validation QbD CQ 5 ANALYTICAL TARGET PROFILE (ATP) ChemBioPharm

ATP = predefined goals according to the intended use of the method intended use = stability studies of dextromethorphan (DXM) in final products quantitative analysis of API and degradation products implementation in quality control laboratories robustness and validation ICH Q2 (R1) Green analytical method respect of green chemistry principles DXM 6 GREENING ANALYTICAL METHODS ChemBioPharm Reduction and proper management of waste Reduction or replacement of toxic reagents Reduction of the number of samples

Minimization of energy consumption Analytical method UHPLC fast analysis Ethanol-based mobile phases greener solvent Chemometrics (QbD approach) lower number of runs 7 CRITICAL QUALITY ATTRIBUTES (CQAs) ChemBioPharm CQAs = measurable attributes of the chromatogram that should be within an appropriate limit or range to ensure the desired quality of the method Must have

Intend to have Rs > 2 > 2.5 Peak efficiency impurity A N > 1500 > 3000 Peak efficiency impurity C N > 20000 > 30000 Rs between DXM and impurities A, B & C Minimization of solvent consumption EtOH volume Mobile phase volume

< 0.4mL mL < 2mL < 0.35mL < 1.2mL Assassi et al, Green analytical method development for statin analysis, J. Chromatogr. A, 37 (2015) 61-72. 8 CRITICAL PROCESS PARAMETERS (CPPs) ChemBioPharm CPPs = factors whose variability has an impact on a CQA selected by quality risk assessment Injection Tubing Volume Length Solvent

Dwell volume Detection Wavelength Nature % %/min Pump Buffer Temperature Column CQAs Composition Geometry

Flow rate Cell length Diameter pH Concentration Organic solvent Mobile phase CPPs to be evaluated by screening design 9 SCREENING DESIGN ChemBioPharm Plackett-Burman design: 1st degree modeling Do factors have significant effects on CQAs (responses of interest)? Levels

pH Gradient slope (%/min) Temperature (C) Flow rate (mL/min) -1 2 2 30 0.2 +1 6 4

50 0.4 tR Impurity efficiency Solvent consumption 10 significant factors: flow rate, gradient slope, pH, and temperature OPTIMIZATION DESIGN ChemBioPharm Central composite design: 2nd degree modeling Response surface methodology 4 4 2

i 4 i 1 y ( X 1i 4 ) 0 i X i ii X ij X i X j i 1 i 1 i 2 j 1 Polynomial quadratic model Central composite design High response Medium response Low response 3-D response surface plot of Rs DXM/Imp.A 11 OPTIMUM POINT PREDICTION

ChemBioPharm Desirability analysis Derringers desirability functions applied to CQAs Optimum point = highest global desirability value (D) 12 OPTIMUM POINT VALIDATION ChemBioPharm Analysis time < 5 min Method validated Impurities A & C ATP Green method Ethanol consumption < 0.35 mL per analysis Ecoscale test Gauszka et al, Analytical Eco-Scale for assessing the greeness of analytical procedures, TRAC-Trend. Anal. Chem, 37 (2012) 61.

13 DESIGN SPACE ChemBioPharm Contour plots Response overlay = CQA objectives displayed on the graph Maximum desirability DS: Optimum point pH: 4mL .6-4mL .8 Gradient slope: 2.9-3.6%/min Temperature: 35-4mL 5C Flow rate: 0.20-0.24mL mL/min 14 ROBUSTNESS ChemBioPharm New Plackett-Burman design performed around the optimum

point (24mL -1) 8 validation points experimentally tested by varying CPPs from extreme limits of the DS range CQAs met the specifications for all 8 points (D = 1) DS = robustness domain 15 RISK-BASED DESIGN SPACE ChemBioPharm Mean based S > 1 min Mean responses used for optimization Do not provide any clue about method reliability How well and how often the method can meet the specifications? What guarantee? Optimized robust assay S = tR,begin tR,end Take into account model uncertainty

Risk based P(S > 1 min) Monte-Carlo study of the propagation of the models predictive errors Quality is ensured by assessing the risk of not being within the acceptance limits E. Rozet et al, Design Spaces for Analytical Methods: what, why, how?, TrACTrend. Anal. Chem, 4mL 2 (2013) 157 16 CONCLUSION ChemBioPharm Rapid development of optimal and robust chromatographic methods In line with ICH Q8 Better knowledge and easier

introduction of new concepts (green chemistry) Design space = robustness but method validation still necessary C. Bousss, L. Ferey, E. Vedrines, K. Gaudin, Using an innovative of QbD and Green analytical chemistry approaches for the developpement of a stability indicating UHPLC 17 mehod in pharmaceutical products, J. Pharm. Biomed. Anal. 115 (2015) 114-122 THANK YOU FOR YOUR ATTENTION 18 Penalty points (PPs) to calculate analytical EcoScale ChemBioPharm Galuszka et al. Trends in Analytical Chemistry, 37 (2012) 61-72

Chemical compound score Ethanol Water Ammonium Formate Formic Acid Energy Occupational hazard Waste Amount Hazard Amount Hazard Amount Hazard Amount Hazard < 10 mL Less severe hazard < 10 mL None < 10 g

1 pictogram - Warming < 10 mL 1 pictogram - Warming Sub-total PP 1 1 1 0 1 1 1 1 Instrument score U-HPLC 0.1 kWh per sample Analytical process hermetization 1 10 mL (g) Recycling Total PP 1 0

1 1 =3 Total PP 0 0 3 0 =3 Total penalty points (PP): 6 Analytical Eco-Scale total score: 100 - 6 = 94 19 METHOD VALIDATION ChemBioPharm 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 mL1. This procedure was repeated at 3 different days and each solution was injected in the optimal chromatographic conditions. 20 METHOD VALIDATION ChemBioPharm Goal: to demonstrate that the method is suited for its intended use Validation of each degradation product (ICH Q2(R1)) using accuracy profiles specificity, accuracy, repeatability and intermediate repeatability, and linearity range: LOQ-1.25% DXM targeted concentration -expectation tolerance limits within 20% 21 DEXTROMETHORPHAN ChemBioPharm 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 detection Typical pharmaceutical case study relevant for the evaluation of robust conditions in green analytical chemistry 22 DEXTROMETHORPHAN IMPURITIES ChemBioPharm pKa = 9.85 0.5% 23 CHROMATOGRAPHIC CONDITIONS ChemBioPharm Screening of 3 columns: Acquity BEH C18 (50 x 2.1 mm, 1.7 m) = hybrid silica, Acquity BEH Phenyl (50 x 2.1 mm, 1.7 m) = covalently modified by phenyl grafting, Hypersil gold AQ (50 x 2.1 mm, 1.9 m) grafted C18 silica possible to use 100% aqueuous phase

Better 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 A Gradient: 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 L and detection wavelength = 280 nm. 24 DESIGN OF EXPERIMENTS (DOEs) ChemBioPharm Modeling of chromatographic behavior 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

Y = a 0 + a1 X1 + a 2 X 2 + a12 X1 X 2 + e Y = a 0 + a1 X1 + a 2 X 2 + a12 X1 X 2 + a11 X12 + a 22 X 2 2 + e Full and Fractional Factorial Designs Central Composite Designs => Response Surface Strategy 25 RISK-BASED DESIGN SPACE ChemBioPharm Mean based S > 1 min Mean responses used for optimization Do not provide any clue about method reliability How well and how often the method can meet the specifications? What guarantee? Optimized robust assay

S = tR,begin tR,end Take into account model uncertainty Risk based P(S > 1 min) Monte-Carlo study of the propagation of the models predictive errors Quality is ensured by assessing the risk of not being within the acceptance limits E. Rozet et al, Design Spaces for Analytical Methods: what, why, how?, TrACTrend. Anal. Chem, 4mL 2 (2013) 157 26

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