1. FOR PHARMACEUTICAL PRODUCT DEVELOPMENT AS PER QbD DESIGN OF EXPERIMENTS (DoE) & DEVELOPMENT OF DESIGN SPACE SHIVANG CHAUDHARY Copyrighted by Shivang Chaudhary Formulation Engineer (Pharma-QbD Facilitator) MS (Pharmaceutics)- National Institute of Pharmaceutical Education & Research (NIPER) PGD (Patents Law)- National academy of Legal Studies & Research (NALSAR) Contact No: +91 - 9904474045 shivaniper@gmail.com http://www.linkedin.com/profile/view?id=71308238&trk=tab_pro Copyrighted by Shivang Chaudhary

2. Traditional Experimentation (OFAT/Minimal) Study one factor at a time (OFAT), holding all other factors constant Serial experimentation is uneconomical in terms of time, money, and energy & also unfavorable & unpredictable Complete fulfillment of the true optimal product or robust process can never be guaranteed due to the presence of multiplication/ interactions of factors, i.e. the effect of one or more factors on others, which can not be covered by OFAT Design of Experiments (DoE/QbD) Study multiple factors at once as a systematic series of parallel experiments simultaneously Parallel experiments is economical in terms of time, money and efforts to get maximize Information with minimum runs Purposeful changes are made to input factors to identify causes for significant changes in the output responses & determining the relationship between factors of that to achieve an optimized product/ robust process. Accounts for Interactions between factors Estimate each factor effect independent of the existence of other factor effect by multiplication DESIGN OF EXPERIMENTS TRADITIONAL EXPERIMENTAION Vs Copyrighted by Shivang Chaudhary 3. SCREENING OPTIMIZATION PLACKETTE BURMAN FACTORIAL DESIGN FACTORIAL DESIGN RESPONSE SURFACE METHODOLOGY MIXTURE DESIGN 3 LEVELFULL FACTORIAL CENTRAL COMPOSITE BOX BEHNKEN DESIGN SIMPLEX LATTICE SIMPLEX CENTROID CONSTRAINED MIXTURE FOR QUANTITATIVE SCREENING OF NUMEROUS FACTORS 2 LEVEL FRACTIONAL FACTORIAL k2 Define QTPP Determine CQAs CONTINUAL IMPROVEMENT Implement CONTROL STRATEGY LinkCMAs&CPPsto CQAsbyDoE& DEVELOPA DESIGNSPACE FAILURE MODE EFFECTIVE ANALYSIS FOR QUALITATIVE SCREENING OF NUMEROUS FACTORS ON THE BASIS OF RISK PRIORITY NO CONSIDERING SEVERITY, PROBABILITY & DETECTABILITY DoE QbD 2 LEVEL FRACTIONAL FACTORIAL WITH CENTER POINTS k3 2 k 4 2 LEVEL FULL FACTORIAL k4k3 L=2 3 k 6 L3 k6 Road Map Copyrighted by Shivang Chaudhary 4. REGION OF OPERABILITY REGION OF INTEREST DESIGN SPACE CONTROL STRATEGY 3 LEVEL FULL FACTORIAL or CCD or BBD Type RESPONSE SURFACE DESIGNS 2 LEVEL PLACKETTE BURMAN or FRACTIONAL FACTORIAL DESIGNS VERIFICATIONSCREENING OPTIMIZATIONIDENTIFICATIONDoE Road Map Copyrighted by Shivang Chaudhary 5. IDENTIFIED KNOWN FACTORS UNIDENTIFIED UNKNOWN FACTORS SCREENING THROUGH QUALITY RISK MANAGEMENT ASSESSMENT OF INDIVIDUAL LINEAR EFFECTS & INTERACTION QUADRATIC OR CUBIC CURVATURE EXIST? VERIFICATION OF EXPERIMENTAL RESULTS BY TAKING ADDITIONAL AT LEAST 3 CHECK POINT BATCHES IN THE SAME RANGE OF DESIGN SPACE SCREENING PHASE BY RESOLUTION III & IV DESIGN TOOLS : PLACKETTE BURMAN & FRACTIONAL FACTORIAL LEAP FORWARD PHASE BY RESOLUTION V DESIGN TOOLS : FRACTIONAL & FULL FACTORIAL$ OPTIMIZATION PHASE BY HIGH RESOLUTION DESIGNS TOOLS : BOX BEHNKEN, CENTRAL COMPOSITE, SIMPLEX MIXTURE & D-OPTIMAL VALIDATION PHASE BY CONFIRMATORY RUNS IDENTIFY RESPONSES TO BE MEASURED IDENTIFY FACTORS TO BE STUDIED & ITS LEVELS TO INDUCE A MEASURABLE SIGNIFICANT CHANGE TO THE RESPONSE DEFINE OBJECTIVE & PROPOSE HYPOTHESIS INSIGNIFICANT 1D INTERACTION PLOT ANALYSIS OF VARIANCE (ANOVA) FOR PREDICTION EQUATION IN TERMS OF ACTUAL & CODED EFFECTS BLOCKING GRAPHICAL OPTIMIZATION WITH OVERLAY PLOTS (DESIGN SPACE) FOR FINDING OUT OPTIMIZED RANGES OF CRITICAL FACTORS BY SETTING ULTIMATE GOALS FOR MULTIPLE RESPONSES TO FIND REGIONS WHERE ALL THE REQUIREMENTS SIMULTANEOUSLY MEET FOR ALL THE CRITICAL FACTORS KNOWN AS A "SWEET SPOT". REPLICATES SIGNIFICANT ADDITIONAL CENTER POINTS HALF NORMAL & PARETO CHART RESPONSE SURFACE METHODOLOGY DESIGN OF EXPERIMENTS 2D CONTOUR, 3D RESPONSE SURFACE & 4D CUBE PLOTS DEVELOPMENT& VERIFICATIONOF DESIGNSPACE SCREENINGOF CRITICALFACTORS OPTIMIZATIONOF RANGESOFCRITICAL FACTORS IDENTIFICATIONOF RESPONSES& FACTORS DoE Step Guide Copyrighted by Shivang Chaudhary 6. MIXTURE RESPONSE SURFACE FACTORIAL Used primarily for screening significant factors, but can also be used sequentially to model and refine a process. When conducting an experiment, varying the levels of all factors at the same time instead of one at a time lets you study the interactions between the factors. B. FRACTIONAL FACTORIAL DESIGNS A fractional factorial design uses a subset or "fraction" of a full factorial design, so some of the main effects and 2-way interactions are confounded and cannot be separated from the effects of other higher- order interactions. Fractional factorial designs are a good choice when resources are limited or the number of factors in the design is large because they use fewer runs than the full factorial designs. FULL FACTORIAL FRACTIONAL FACTORIAL FACTORIAL DESIGNS Types & General Applications Copyrighted by Shivang Chaudhary A. FULL FACTORIAL DESIGNS A full factorial design is a design in which researchers measure responses at all combinations of the factor levels. The number of runs necessary for a 2-level full factorial design is 2k where k is the number of factors. As the number of factors in a 2-level factorial design increases, the number of runs necessary to do a full factorial design increases quickly. i.e. a 2-level full factorial design with 6 factors requires 64 runs; a design with 9 factors requires 512 runs. 7. MIXTURE RESPONSE SURFACE FACTORIAL A linear model with two factors, X1 and X2, can be written as Y = 0 + 1X1 + 2X2 + 12X1X2 + experimental error RESPONSE MAIN EFFECT MAIN EFFECT RESPONSE MAIN EFFECT MAIN EFFECT MAIN EFFECT 2 WAY INTERACTION TERM 2 WAY INTERACTION TERM 2 WAY INTERACTION TERM 3 WAY INTERACTION TERM 2 WAY INTERACTION TERM-1 (Low Level) +1 (High Level) 1st ORDER LINEAR MODEL For Screening of factors in Factorial Designs Copyrighted by Shivang Chaudhary For a more complicated example, a linear model with three factors X1, X2, X3 and one response, Y, would look like (if all possible terms were included in the model) Y = 0 + 1X1 + 2X2 + 3X3 + 12X1X2 + 13X1X3 + 23X2X3 + 123X1X2X3 + experimental error 8. Comparative Efficiency =6/4 = 1.5 Comparative Efficiency =16/8 = 2 2 FACTORS 3 FACTORS 6 RUNS 4 RUNS 2 Level OFAT Design For 2 FACTORS 2 Level FULL FACTORIAL Design For 2 FACTORS 16 RUNS 8 RUNS 2 Level OFAT Design For 3 FACTORS 2 Level FULL FACTORIAL Design For 3 FACTORS MIXTURE RESPONSE SURFACE FACTORIAL 2 LEVEL FFDOFAT Vs Copyrighted by Shivang Chaudhary 9. 3 LEVEL FACTORIALMIXTURE RESPONSE SURFACE FACTORIAL 2 LEVEL FACTORIAL Pattern X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 1 +++++++++++ +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 2 -+-+++---+- -1 +1 -1 +1 +1 +1 -1 -1 -1 +1 -1 3 --+-+++---+ -1 -1 +1 -1 +1 +1 +1 -1 -1 -1 +1 4 +--+-+++--- +1 -1 -1 +1 -1 +1 +1 +1 -1 -1 -1 5 -+--+-+++-- -1 +1 -1 -1 +1 -1 +1 +1 +1 -1 -1 6 --+--+-+++- -1 -1 +1 -1 -1 +1 -1 +1 +1 +1 -1 7 ---+--+-+++ -1 -1 -1 +1 -1 -1 +1 -1 +1 +1 +1 8 +---+--+-++ +1 -1 -1 -1 +1 -1 -1 +1 -1 +1 +1 9 ++---+--+-+ +1 +1 -1 -1 -1 +1 -1 -1 +1 -1 +1 10 +++---+--+- +1 +1 +1 -1 -1 -1 +1 -1 -1 +1 -1 11 -+++---+--+ -1 +1 +1 +1 -1 -1 -1 +1 -1 -1 +1 12 +-+++---+-- +1 -1 +1 +1 +1 -1 -1 -1 +1 -1 -1 Efficient screening designs involving numerous factors in the study, when only main effects are concerned of interest, assuming all other interactions negligible 2 k>4 to k=N 4k (k=number of factors) PB design in 12 (=n) runs for an experiment containing up to 11 factors (=k-1) PLACKETTE BURMAN EXAMPLE OF DESIGN SEQUENCE LEVELS IN DESIGN NUMBER OF FACTORS NO OF EXP RUNS SPECIFIC APPLICATION Copyrighted by Shivang Chaudhary 10. MIXTURE RESPONSE SURFACE FACTORIAL NO. OF FACTORS NO. OF LEVELS EXPERIMENTAL DESIGN SELECTED NO. OF REPLICATES ADD. CENTER POINTS TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS) 11 2 PLACKETTE BURMAN DESIGN 0 0 12 A B I H G F E D C J K SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL PLACKETTE BURMAN CASE STUDY 1 SCREENINGOF FACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS IDENTIFICATION OFFACTORS BINDER SPRAYING RATE ATOMIZATION AIR PRESSURE FLUIDIZATION AIR VELOCITY INLET TEMPERATURE PRODUCT TEMPERATURE OUTLET TEMPERATURE GUN TO BED DISTANCE NO OF SPRAYING HEADS FILTER BAG PORE SIZE FILTER CLEANING FREQUENCY BOWL CAPACITY Copyrighted by Shivang Chaudhary 11. MIXTURE RESPONSE SURFACE FACTORIAL SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL PLACKETTE BURMAN CASE STUDY 1 SCREENINGOF FACTORS IDENTIFICATION OFFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS Factors (Variables) Coded Levels & Actual Levels -1 +1 A BINDER SPRAYING RATE (gm/min) 2 8 B ATOMIZATION AIR PRESSURE (bar) 1 3 C FLUIDIZATION AIR VELOCITY (cfm) 50 100 D INLET TEMPERATURE (C) 45 55 E PRODUCT TEMPERATURE (C) 25 35 F OUTLET TEMPERATURE (C) 35 45 G GUN TO BED DISTANCE (inches) 5 10 H NO OF SPRAYING HEADS 1 3 I FILTER POROSITY (um) 20 40 J FILTER BAG CLEANING FREQUENCY (CPM) 2 10 K BOWL OCCUPANCY (%) 40 60 Responses (Effects) Goals for Individual Responses Y1 %FINES To achieve minimum fines after granulation i.e. NMT 10% Y2 % AGGLOMERATES To achieve minimum agglomerates after granulation i.e. NMT 10% FACTORS TO BE STUDIED RESPONSES TO BE MEASURED Copyrighted by Shivang Chaudhary 12. CASE STUDY FOR SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS OBJECTIVE CQAs PPs ACTUAL EXPERIMENTAL TRIAL LS WITH RESULTS (1) %FINES (2) %AGGLOMERATES (A)BINDER SPRAYING RATE (B) ATOMIZATION AIR PRESSURE (C) FLUIDIZATION AIR VELOCITY (D) INLET TEMPERATURE (E) PRODUCT TEMPERATURE (F) OUTLET TEMPERATURE (G) GUN TO BED DISTANCE (H) NO OF SPRAY HEADS (I) FILTER POROSITY (J) CLEANING FREQUENCY (K) BOWL CAPACITY TO SCREEN OUT CRITICAL PROCESSING PARAMETERS OF FLUID BED SPRAY GRANULATION MIXTURE RESPONSE SURFACE FACTORIAL 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL PLACKETTE BURMAN CASE STUDY 1 IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS SCREENINGOF FACTORS ANALYSISOF RESPONSES Copyrighted by Shivang Chaudhary 13. CASE STUDY FOR SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS Ho THERE IS NO SIGNIFICANT EFFECT OF SELECTED PROCESSING PARMETERS (PP) ON RESPONSE (CQA) MIXTURE RESPONSE SURFACE FACTORIAL 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL PLACKETTE BURMAN CASE STUDY 1 IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS SCREENINGOF FACTORS ANALYSISOF RESPONSES Copyrighted by Shivang Chaudhary NUMERICAL INDICATORS FOR TESTING MODELS AND MODEL TERMS ANOVA Response 1: FINES ANOVA Response 2: AGGLOMERATES PREDICTION EFFECT EQUATION OF EACH FACTOR & THEIR INTERACTIONS ON INDIVIDUAL RESPONSE BY ANALYSIS OF VARIANCE (ANOVA) ( LINEAR MODEL) %AGGLOMERATES = +8.42 +5.58A -1.58B %FINES = +11.42 -5.25A +1.58B F Value =Test For Comparing Model Variance (SIGNAL=Predicted value) with Residual Variance (NOISE=(Observed-Predicted value)) p-value = Probability of Falsely Detecting the Significant Effect (also called as a Level of Significance ()) Reject Ho& Accept Ha Conclude that there is a significant effect of A & B PP on CQAp Value = < 0.05 for CI= 95% F Value= (MS Model/ MS Residuals) >1 Conclude that there is a only a 0.01% chance that a "Model F- Value" this large could occur due to noise. Significant Signal Negligible Noise 14. CASE STUDY FOR SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS RESPONSE SURFACE MIXTUREFACTORIAL HALFNORMALPLOT&PARETOCHART FORSCREENINGOFSIGNIFICANTFACTORS$ CASE STUDY 1 SIGNIFICANT EFFECTS: MODEL TERMS SIGNIFICANT EFFECTS: MODEL TERMS NEGLIGIBLE TERMS: ERROR ESTIMATES NEGLIGIBLE TERMS: ERROR ESTIMATES 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL PLACKETTE BURMAN IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS ANALYSISOF RESPONSES SCREENINGOF FACTORS THUS, LIQUID SPRAYING RATE (A) & ATOMIZATION AIR PRESSURE (B) ARE THE MOST CRITICAL FACTORS THOSE REQUIRED TO CONTROL THE ULTIMATE PARTICLE SIZE DURING FLUID BED GRANULATION Copyrighted by Shivang Chaudhary GRAPHICAL INDICATORS FOR TESTING MODELS AND MODEL TERMS EFFECTS HALF- NORMAL Instead of putting negative effects to the left and positive effects to the right, all the significant eff 15. 2 [`high' and `low' or `+1' and `-1',] RESPONSE SURFACE MIXTUREFACTORIAL PLACKETTE BURMAN 3 LEVEL FACTORIAL 2k FULL FACTORIAL run X1 X2 X3 1 -1 -1 -1 2 1 -1 -1 3 -1 1 -1 4 1 1 -1 5 -1 -1 1 6 1 -1 1 7 -1 1 1 8 1 1 1 run X1 X2 1 -1 -1 2 1 -1 3 -1 1 4 1 1 22 FULL FACTORIAL 23 FULL FACTORIAL 2 LEVEL FACTORIAL EXAMPLE OF DESIGN SEQUENCE LEVELS IN DESIGN NUMBER OF FACTORS NO OF EXP RUNS k4 2k , where k=No. of factors Efficient screening design for 3 or less factors & when only main effects are of interest, assuming all interactions negligible SPECIFIC APPLICATION Copyrighted by Shivang Chaudhary 16. MIXTURE RESPONSE SURFACE FACTORIAL PLACKETTE BURMAN 3 LEVEL FACTORIAL 2k-f FRACTIONAL FACTORIAL Number of Factors Number of Runs 2 4 3 8 4 16 5 32 6 64 7 128 8 256 0 512 when the number of factors is 4 or greater, a full factorial design requires a large number of runs and is not very efficient. Even if the small number of factors, k, in a design is small, the 2k runs specified for a full factorial can quickly become very large. For example, 26 = 64 runs are for a two-level, full factorial design with six factors. A fractional factorial design is a better choice for screening of 4 or more factors in which only an adequately chosen fraction of the treatment combinations required for the complete factorial experiment is selected to be run. Which runs to make and which to leave out is the subject of interest here? In general, we pick a fraction such as , , etc. of the runs called for by the full factorial. 2 LEVEL FACTORIAL EXAMPLE OF DESIGN SEQUENCE NUMBER OF FACTORS SPECIFIC APPLICATION 2k-F , where k=No. of factors F= No of Fraction NO OF LEVELS 2 NO OF EXP RUNS Copyrighted by Shivang Chaudhary 17. MIXTURE RESPONSE SURFACE FACTORIAL NO. OF FACTORS NO. OF LEVELS EXPERIMENTAL DESIGN SELECTED NO. OF REPLICATES ADD. CENTER POINTS TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS) 4 2 FRACTIONAL FACTORIAL DESIGN 0 0 24-1 = 8 BINDER SOLUTION ADDITION TIME KNEADING (MIXING & GRANULATION) TIME CHOPPER (GRANULATOR) SPEED IMPELLER (MIXER) SPEEDA B C D SCREENING OF CRITICAL PROCESSING PARAMETERS OF HIGH SHEAR WET GRANULATION FOR SOLID ORAL DOSAGE FORM PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 2 SCREENINGOF FACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS IDENTIFICATION OFFACTORS Copyrighted by Shivang Chaudhary 18. MIXTURE RESPONSE SURFACE FACTORIAL SCREENING OF CRITICAL PROCESSING PARAMETERS OF HIGH SHEAR WET GRANULATION FOR SOLID ORAL DOSAGE FORM PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 2 SCREENINGOF FACTORS IDENTIFICATION OFFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS Factors (Variables) Levels of Factors studied -1 +1 A BINDER ADDITION TIME (min) 1 2 B IMPELLER-MIXER SPEED (RPM) 50 100 C CHOPPER-GRANULATOR SPEED (RPM) 1000 3000 D KNEADING TIME (min) 3 5 Responses (Effects) Goals for Individual Responses Y1 %FINES To achieve minimum fines after granulation i.e. NMT 10% Y2 % AGGLOMERATES To achieve minimum agglomerates after granulation i.e. NMT 10% FACTORS TO BE STUDIED RESPONSES TO BE MEASURED Copyrighted by Shivang Chaudhary 19. 3 LEVEL FACTORIAL OBJECTIVE CQAs CPPs ACTUAL EXPERIMENTAL TRIAL WITH RESULTS (1) %FINES (2) %AGGLOMERATES (A) BINDER ADDITION RATE (B) IMPELLER SPEED (C) CHOPPER SPEED (D) KNEADING TIME SCREENING OF CRITICAL PROCESSING PARAMETERS OF HIGH SHEAR GRANULATION MIXTURE RESPONSE SURFACE FACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL CASE STUDY 2 SCREENING OF CRITICAL PROCESSING PARAMETERS OF HIGH SHEAR WET GRANULATION FOR SOLID ORAL DOSAGE FORM IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS SCREENINGOF FACTORS ANALYSISOF RESPONSES Copyrighted by Shivang Chaudhary 20. CASE STUDY FOR SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS Ho THERE IS NO SIGNIFICANT EFFECT OF SELECTED PROCESSING PARMETERS (PP) ON RESPONSE (CQA) MIXTURE RESPONSE SURFACE FACTORIALCASE STUDY 2 IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS SCREENINGOF FACTORS ANALYSISOF RESPONSES Copyrighted by Shivang Chaudhary NUMERICAL INDICATORS FOR TESTING MODELS AND MODEL TERMS ANOVA Response 1: FINES ANOVA Response 2: AGGLOMERATES F Value =Test For Comparing Model Variance (SIGNAL=Predicted value) with Residual Variance (NOISE=(Observed-Predicted value)) p-value = Probability of Falsely Detecting the Significant Effect (also called as a Level of Significance ()) Reject Ho& Accept Ha Conclude that there is a significant effect of A & B PP on CQAp Value = < 0.05 for CI= 95% F Value= (MS Model/ MS Residuals) >1 Conclude that there is a only a 0.01% chance that a "Model F- Value" this large could occur due to noise. Significant Signal Negligible Noise PREDICTION EFFECT EQUATION OF EACH FACTOR & THEIR INTERACTIONS ON INDIVIDUAL RESPONSE BY ANALYSIS OF VARIANCE (ANOVA) ( LINEAR MODEL) %FINES = +10.63 -2.13B + 1.38C -4.38D %AGGLOMERATES = +9.63 +2.62B -1.38C +4.88D 3 LEVEL FACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL 21. 3 LEVEL FACTORIAL RESPONSE SURFACE MIXTUREFACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL HALFNORMALPLOT&PARETOCHART FORSCREENINGOFSIGNIFICANTFACTORS CASE STUDY 2 SIGNIFICANT EFFECTS: MODEL TERMS SIGNIFICANT EFFECTS: MODEL TERMS NEGLIGIBLE TERMS: ERROR ESTIMATES NEGLIGIBLE TERMS: ERROR ESTIMATES SCREENING OF CRITICAL PROCESSING PARAMETERS OF HIGH SHEAR WET GRANULATION FOR SOLID ORAL DOSAGE FORM IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS ANALYSISOF RESPONSES SCREENINGOF FACTORS THUS, IMPPELER & CHOPPER SPEED & KNEADING TIME ARE THE MOST CRITICAL FACTORS THOSE REQUIRED TO CONTROL THE ULTIMATE PARTICLE SIZE DURING FLUID BED GRANULATION Copyrighted by Shivang Chaudhary GRAPHICAL INDICATORS FOR TESTING MODELS AND MODEL TERMS 22. MIXTURE RESPONSE SURFACE FACTORIAL NO. OF FACTORS NO. OF LEVELS EXPERIMENTAL DESIGN SELECTED NO. OF REPLICATES ADD. CENTER POINTS TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS) 2 2 FULL FACTORIAL DESIGN 0 0 22 = 4 API PARTICLE SIZE1 2 EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF SR HARD GELATIN CAPSULE DOSAGE FORM PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 3 EVALUATIONOF CRITICALFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS IDENTIFICATION OFFACTORS POLYMER CONTENT Copyrighted by Shivang Chaudhary 23. MIXTURE RESPONSE SURFACE FACTORIAL EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF SR HARD GELATIN CAPSULE DOSAGE FORM PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 3 EVALUATIONOF CRITICALFACTORS IDENTIFICATION OFFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS Factors (Variables) Levels of Factors studied -1 +1 A API Particle Size (microns) 10 25 B POLYMER CONTENT (%w/w) 20 30 1 2 3 -1 +1 -1 +1 x (-1,-1) (+1,-1) (-1,+1) (+1,+1) 4 POLYMERCONTENT B DRUG SUBSTANCE PARTICLE SIZEA Y Copyrighted by Shivang Chaudhary 24. 3 LEVEL FACTORIAL OBJECTIVE CQAs CPPs ACTUAL EXPERIMENTAL TRIAL WITH RESULTS % DRUG RELEASE WITHIN 12 HOURS (1) API PARTICLE SIZE (2) POLYMER CONTENT EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF SR HARD GELATIN CAPSULE MIXTURE RESPONSE SURFACE FACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL CASE STUDY 3 EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF SR HARD GELATIN CAPSULE DOSAGE FORM IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS EVALUATIONOF CRITICALFACTORS ANALYSISOF RESPONSES PREDICTION EFFECT EQUATION OF EACH FACTOR & THEIR INTERACTIONS ON INDIVIDUAL RESPONSE BY ANALYSIS OF VARIANCE (ANOVA) ( LINEAR MODEL) %DRUG RELEASE WITHIN 12 HOURS = Y = 0 + 1X1 + 2X2 + 12X1X2 =+80.50 -9.50A 5.00B +1.00AB Run Order code Factor X1: API Particle Size D90 (in microns) [coded level] Factor X2: Polymer Content (in %w/w) [coded level] x1*x2 [coded level] Response Y1: Drug Released within 12 hours 1 1 10 [-1] 20 [-1] [+1] 96 2 a 25 [+1] 20 [-1] [-1] 75 3 b 10 [-1] 30 [+1] [-1] 84 4 ab 25 [+1] 30 [+1] [+1] 67 1 = ab + a 2n b + 1 2n 1 = 67:76 4 84:96 4 1 = 35.50 45.00 1 = 9.50 2 = ab + b 2n a + 1 2n 2 = 67:84 4 75:96 4 2 = 37.75 42.75 2 = 5.00 12 = ab + 1 2n a + b 2n 12 = 40.75 39.75 12 = +1.00 0 = 1 + a + b + ab 2n 1 = 67:96 4 75:84 4 0 = 96 + 75 + 84 + 67 4 0 = +80.50 Copyrighted by Shivang Chaudhary 25. 3 LEVEL FACTORIAL RESPONSE SURFACE MIXTUREFACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL HALFNORMALPLOT,1DINTERACTIONPLOT,2DCONTOURPLOT, &3DSURFACE[PLOTFOREFFECTEVALUATIONOFCRITICAL FORMULATIONVARIABLESOFSRCAPSULEFORMULATION CASE STUDY 3 SIGNIFICANT EFFECTS: MODEL TERMS EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF SR HARD GELATIN CAPSULE DOSAGE FORM IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS ANALYSISOF RESPONSES EVALUATIONOF CRITICALFACTORS Responses (Effects) Goals for Individual Responses Y1 %DRUG RELEASE WITHIN 12 HOURS To achieve at least 85% drug release within 12 hours Copyrighted by Shivang Chaudhary 26. MIXTURE RESPONSE SURFACE FACTORIAL NO. OF FACTORS NO. OF LEVELS EXPERIMENTAL DESIGN SELECTED NO. OF REPLICATES ADD. CENTER POINTS TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS) 3 2 23 FULL FACTORIAL DESIGN 3 0 23 = 8 API PARTICLE SIZEA B EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF METERED DOSE INHALER (SUSPENSION AEROSOLS) PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 4 EVALUATIONOF CRITICALFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS IDENTIFICATION OFFACTORS SUSPENDING AGENT C PROPELLANT CONTENT Copyrighted by Shivang Chaudhary 27. MIXTURE RESPONSE SURFACE FACTORIAL EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF METERED DOSE INHALER (SUSPENSION AEROSOLS) PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 4 EVALUATIONOF CRITICALFACTORS IDENTIFICATION OFFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS x Factors (Variables) Levels of Factors studied -1 +1 A API PARTICLE SIZE (microns) 1 10 B SUSPENDING AGENT (%w/w) 0.10 0.80 C PROPELLANT (%w/w) 70 90 Copyrighted by Shivang Chaudhary 28. OBJECTIVE CQAs CMAs ACTUAL EXPERIMENTAL TRIAL WITH RESULTS NET CONTENT PER DISCHARGE FRPM MDI (A) API PARTICLE SIZE (D90) (B) SUSPENDING AGENT (C) PROPELAANT EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF METERED DOSE INHALER (SUSPENSION AEROSOLS)) MIXTURE RESPONSE SURFACE FACTORIAL PLACKETTE BURMAN 3 LEVEL FACTORIAL CASE STUDY 4 EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF METERED DOSE INHALER (SUSPENSION AEROSOLS) IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS EVALUATIONOF CRITICALFACTORS ANALYSISOF RESPONSES 2 LEVEL FACTORIAL PREDICTION EFFECT EQUATION OF EACH FACTOR & THEIR INTERACTIONS ON INDIVIDUAL RESPONSE BY ANALYSIS OF VARIANCE (ANOVA) ( LINEAR MODEL) NET CONTENT OF DRUG PER DISCHARGE FROM MDI= +89.20-8.18A+3.13B+1.67C+1.45AB Copyrighted by Shivang Chaudhary 29. MIXTURE 3 LEVEL FACTORIAL RESPONSE SURFACE FACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL HALFNORMALPLOT,1DINTERACTIONPLOT,2D CONTOURPLOT,&3DSURFACE[PLOTFOREFFECTEVALUATION OFCRITICALFORMULATIONVARIABLESOFMETEREDDOSE INHALER CASE STUDY 4 EFFECT EVALUATION OF CRITICAL FORMULATION VARIABLES OF METERED DOSE INHALER (SUSPENSION AEROSOLS) IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS ANALYSISOF RESPONSES EVALUATIONOF CRITICALFACTORS Responses (Effects) Goals for Individual Responses Y1 %NET CONTENT PER DISCHARGE FROM MDI To achieve at least 90% drug discharge per single actuation SIGNIFICANT EFFECTS: MODEL TERMS Copyrighted by Shivang Chaudhary 30. MIXTURE RESPONSE SURFACE FACTORIAL You need to have quadratic terms (for example, square terms) in the model in order to model the curvature across the whole response surface. This is possible with a response surface design. FACTORIAL Design With Center Points When you have a factorial design with center points you can test whether there is curvature in the response surface. However, you cannot model the effect of that curvature anywhere but at the center point. Copyrighted by Shivang Chaudhary you can only calculate the fitted values at the corner points and the center point of the design, and thus cannot create a contour plot You can augment the factorial design with axial points to create a central composite response surface design from a factorial design 31. MIXTURE RESPONSE SURFACE FACTORIAL NO. OF FACTORS NO. OF LEVELS EXPERIMENTAL DESIGN SELECTED NO. OF REPLICATES ADD. CENTER POINTS TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS) 3 2 23 FULL FACTORIAL DESIGN WITH ADD. CENTER POINTS 3 0 23 + 3 = 11 ANTIMICROBIALA B OPTIMIZATION OF PRESERVATIVE SYSTEM FOR IN USE STABIILITY OF MULTIDOSE STERILE PRODUCT (INJECTION, EYE/EAR DROPS) PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 5 OPTIMIZATIONOF CRITICALFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS IDENTIFICATION OFFACTORS ANTIOXIDANT C BUFFERING AGENT Copyrighted by Shivang Chaudhary 32. MIXTURE RESPONSE SURFACE FACTORIAL OPTIMIZATION OF PRESERVATIVE SYSTEM FOR IN USE STABIILITY OF MULTIDOSE STERILE PRODUCT (INJECTION, EYE/EAR DROPS) PLACKETTE BURMAN 3 LEVEL FACTORIAL 2 LEVEL FACTORIAL CASE STUDY 5 OPTIMIZATIONOF CRITICALFACTORS IDENTIFICATION OFFACTORS ANALYSISOF RESPONSES DESIGNOF EXPERIMMENTS Factors (Variables) Levels of Factors studied -1 Center point (0) +1 A ANTIMICROBIAL (%W/W) 0.005 0.010 0.015 B ANTIOXIDANT (%W/W) 0.050 0.100 0.150 C BUFFERING AGENT (%W/W) 0.800 1.400 2.000 Copyrighted by Shivang Chaudhary 33. OBJECTIVE CQAs CMAs ACTUAL EXPERIMENTAL TRIAL WITH RESULTS (1) %REDUCTION IN MICROBIAL LOAD AFTER 14 DAYS (2) %OXIDIZED IMPURITIES GENEREATED AFTER 14 DAYS (A) ANTI MICROBIAL (B) ANTI OXIDANT (C) BUFFERING AGENT OPTIMIZATION OF PRESERVATIVE SYSTEM FOR IN USE STABIILITY OF MULTIDOSE STERILE PRODUCT (INJECTION, EYE/EAR DROPS) MIXTURE RESPONSE SURFACE FACTORIAL PLACKETTE BURMAN 3 LEVEL FACTORIAL CASE STUDY 5 OPTIMIZATION OF PRESERVATIVE SYSTEM FOR IN USE STABIILITY OF MULTIDOSE STERILE PRODUCT (INJECTION, EYE/EAR DROPS) IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS OPTIMIZATIONOF CRITICALFACTORS ANALYSISOF RESPONSES 2 LEVEL FACTORIAL PREDICTION EFFECT EQUATION OF EACH FACTOR & THEIR INTERACTIONS ON INDIVIDUAL RESPONSE BY ANALYSIS OF VARIANCE (ANOVA) ( LINEAR MODEL) Reduction in Microbial Load after 14 days =+99.42+0.35A+0.075B+0.15C-0.050AB-0.075AC+0.000BC+0.025ABC Oxidized Impurities after 14 days=+0.46-0.035A -0.18B-0.052C+7.500E-003AB+5.000E-003AC+0.010BC Copyrighted by Shivang Chaudhary 34. RESPONSE SURFACE MIXTUREFACTORIAL PLACKETTE BURMAN 3 LEVEL FACTORIAL PARETOCHART,CONTOURPLOT& ITSOVERLAYPLOTFOROPTIMIZATIONOF PRESERVATIVESYSTEMINMULTIDOSESTERILES CASE STUDY 5 SIGNIFICANT EFFECTS: MODEL TERMS NEGLIGIBLE TERMS: ERROR ESTIMATES OPTIMIZATION OF PRESERVATIVE SYSTEM FOR IN USE STABIILITY OF MULTIDOSE STERILE PRODUCT (INJECTION, EYE/EAR DROPS) IDENTIFICATION OFFACTORS DESIGNOF EXPERIMMENTS ANALYSISOF RESPONSES OPTIMIZATIONOF CRITICALFACTORS Responses (Effects) Goals for Individual Responses Y1 REDUCTION IN MICROBIAL LOAD AFTER 14D in use To achieve at least 99.5% reduction in microbial load Y2 %OXIDIZED IMPURITIES AFTER 14D in use To minimize the level of oxidized impurities below 0.5% 2 LEVEL FACTORIAL Copyrighted by Shivang Chaudhary 35. RESPONSE SURFACE MIXTUREFACTORIAL PLACKETTE BURMAN 2 LEVEL FACTORIAL 3 LEVEL FACTORIAL 32 FULL FACTORIAL To model possible curvature in the response function and to facilitates investigation of a quadratic relationship between the response and each of the factors 3 [`high medium and `low' or +2,`+1' and `0', respectively] K