Chapter 4 MATERIALS AND METHODS

Transcription

1 Chapter 4 MATERIALS AND METHODS

2 MATERIALS AND METHODS Chapter -4 Materials Table 4.1: Equipment used Sl. No. Name of the equipment Organization 1 Vibro sifter Ganson engineering, Mumbai 2 Octagonal blender Ganson engineering, Mumbai 3 Rotary tablet compression Cadmachmachinery, Ahmadabad machine CTX 4 Rotary tablet compression Kilin compression, Germany machine 5 Punches & dies Concept engineering,mumbai 6 Schulinger Auto tester Dr. Schulinger, USA 7 Disintegration test apparatus Elecrolab, Mumbai 8 Friabilator Elecrolab, Mumbai 9 Analytical balance Metler Toledo, Switzerland 10 UV spectrophotometer Shimadzu, Japan 11 Side ventedperforated coating pan Autocoat Engineering, Mumbai 12 Surface roughness tester Mitutoyo, Japan 13 Moisture analyzer Metler Toledo, Switzerland 14 Scanning electron microscopy JSM Japan 15 Tablet inspection machine ACG pam, Mumbai 16 Stirrer Remi, Mumbai 17 Sifter meshes Ganson Engineering,Mumbai 18 Tap density apparatus Elecrolab, Mumbai Table 4.2: Softwares used Sl. No. Name of the software Organization 1 TAAC Thomas Engineering INC, USA 2 DOE Design expert version 8 USA

3 Table 4.3: Raw materials used Sl. No. Name of the raw material Organization 1 Anhydrous Lactose NF SD fine chemicals, Mumbai 2 Microcrystalline cellulose (PH102) SD fine chemicals, Mumbai 3 Sunset Yellow Aluminium Lake 40% Colorcon 4 Magnesium stearate Ferro, gift from Biovas Ahmadabad 5 Purified Talc EP / USP Gift from Biovas Ahmadabad 6 Colloidal silicon dioxide NFCAB-O-SIL Cabot Sanmar Ltd M5 7 Lactose monohydrate EP/NF Kawarlal/scope/excipient house/ Lalchand Bhimraj 8 Croscarmellose sodium NF / EP Signet Chemicals 9 Colloidal silicon dioxide EP/NF Lalchand Bhimraj 10 Opadry yellow Colorcon, Goa Table 4.4:Reagents used Sl. No. Name of the reagent Organization name N Hydrochloric acid Signet Chemicals (Indian Standard, 2008) 4.1Compression Preparation of blend for the study: The formula of tablet used for the study Table 4.5: Formula used for tablet compression Materials % w/w Quantity (tablet) Anhydrous Lactose NF Microcrystalline cellulose (PH102) 34 Sunset Yellow Aluminium Lake 40% 0.15 Magnesium stearate 1.00 Purified Talc

4 Dry granulation process was used to prepare a blend. Raw materials like Anhydrous Lactose NF and Microcrystalline cellulose (PH102), Sunset Yellow Aluminium Lake 40%were sifted through mesh size (#) 40 American Society for Testing and Materials(ASTM) using a Vibro sifter. Pre-blending for 10 minutes was done using an Octagonal blender 150 L. Pre blend materials were sifted through #40 ASTM followed by blending for 10 minutes. Magnesium stearate and Purified Talc was sifted through #60 ASTM. Lubrication of blend materials was done with sifted magnesium stearate for 05 minutes. To ensure powder having good flow properties the blend obtained from above process was subjected to flow property test. Carr s Index (CI) was calculated by following equation (Levin M. 2002). = 100 ( ) (1) Risk analysis prior to compression Based on the Q8, Q10 ICH guidelines, risk assessment was done to find out high risk factors to be studied (ICH Q8,Q10, 2012). Risk assessment tool Failure mode and effects analysis has been carried out. Followings the detail of risk analysis at compression stage. 109

5 Table 4.6 Initial risk assessment tool by FMEA for tablet compression Initial risk assessment tool by FMEA for tablet compression Unit operation Tablet compression Output response or, critical quality attributes CQA-Hardness, thickness, disintegration and friability Input-process Parameters (Input variable ) Initial risk Assessment Remark /Initial strategy Press Geometry Medium Fixed for both the machines Tooling Geometry Feeder Speed Feeder Fill Depth Pre- Compression Force Maincompression force Ejection Force Turret Speed Medium Low Low Fixed for both the machine same tiling will be used Die fill will vary based on feeder speed that may affect the quality of product. Size of the CAM that regulates the feeder remains same. Weight of the tablet was kept same for both the machine. It is indirectly linked with die fill. Tablets too hard or too soft disintegration time high or too slow which will affect the dissolution. Tablets too hard or too soft disintegration time high or too slow which will affect the dissolution. This is for monitoring which gives idea about ejection of tablet from press Variation is quality like weight, hardness, thickness, DT and friability due to less or high die fill, less or high dual time of punches Risk mitigation plan (USFDA, 2012) risk area further study to be carried out Medium Medium risk area Low Low risk area 110

6 4.1.2 Design of experiment (DOE) Inputs were taken from FMEA analysis. Four critical process parameters found out for further study. Full factorial design allows the research of both main and interaction effects. By adding repeated experiment called center point design (CCD), interactions that are more complex can be demonstrated (Lundstedt et al. 1998). In our study, statistical analysis of 2 4 full factorial designs was performed with CCD. Experiment design was done for 16 experiments in replicate (32 experiments) to avoid error. For two machines, 64 experiments were conducted. Tablet compression The study was conducted using two single sided rotatory compression machines as a model to show how DOE helps in optimizing a robust formulation. Tablets were compressed using B tooling. Round shaped standard convex tablets of 6.5mm size were used for the study. Statistical evaluation Analysis of variance (ANOVA) was performed (Freedman DA. 2005). Design expert 8.0(STAT-EASE) Demo version software was used to demonstrate influence of each factor and response; and surface plots were generated (Design expert, 2016). Significant factors and respective influence on responses were determined. The study was carried at 95 % confidence level. Regression equation derived with interactions. Regression equation simplified after omitting the effects where interactions were non-significant (Armstrong JS. 2012). Pareto Analysis To screen real factors affecting the process, Pareto analysis has been carried out. Bonferroni correction counteracts was used to plot comparison graphs for both the machines (Armstrong et al. 2002, Lam et al. 2005, Morrison et al. 2010, Han et al. 2010). With help of Paretographs, comparison was done for input process parameters for both machines. 111

7 3D Surface graphs To find out the relationship of two variables three-dimensional graphs were plotted (Karim et al. 2011). Control strategy and optimization a. Over lay plot was plotted with help of software to find out design space. b. Control strategy has been recommended for optimization process(ich Q8, Q9, & Q10, Q11 USFDA. 2012, Trivedi B. 2012) c. Comparison of behaviors of input variables and responses compared for both the machines for scalability (Levin M. 2002) Verification of optimized parameters The tablets were compressed in both machines as recommended. Quantitative test was carried out. Tablets were collected at initial, middle and end run of the process. Content uniformity test was carried out. UV spectrophotometer was used(european Food Safety Authority. 2009, Indian Standard. 2008) 4.2 Coating with Combination of DOE and TACC Preparation of blend for the study Table 4.7: Raw materials used for coating process optimization DOE and TACC Materials % w/wquantity (tablet) Lactose monohydratenf 4 Microcrystalline cellulose (PH102) 12 Croscarmellose sodium 2 Colloidal silicon dioxide 0.5 Magnesium stearate 1.00 Purified Talc 0.5 Dry granulation process was used to prepare a blend, raw materials lactose monohydrate NF and microcrystalline cellulose (PH102), croscarmellose sodium, colloidal silicon dioxide sifted through mesh size (#) 40 American Society for Testing and Materials(ASTM) using a Vibro sifter. Pre-blending for 10 minutes was done using an 112

8 Octagonal blender 150 L. Pre blend materials sifted through #40 ASTM followed by blending for 10 minutes. Magnesium stearate and Purified Talc was sifted through #60 ASTM. Lubrication of blend materials with sifted magnesium stearate for 05 minutes. Tablet compression The study was conducted using single sided rotatory compression machines. Round shaped standard convex tablets of size 6.5mmwere used for the study. In process tests were carried outto ensure tablet parameters were within the limit before coating. Following parameters were done tablet weight, hardness, thickness, friability (USP) Risk analysis prior to coating Based on the Q8, Q10 ICH guidelines risk assessments was done to find out high risk factors to be studied(ich Q8,Q10, 2012).Risk assessment tool Failure mode and effects analysis has been carried out (GSFC. 2013). Following is the element of risk analysis before coating stage against surface finish and percentage LOD. 113

9 Table 4.8 Initial risk assessment tool by FMEA for tablet coating DOE and TACC Unit Operation: Film Coating Output Material CQA: Surface finish and % LOD Variables Risk Assessment Justification and Initial Strategy Equipment Low Equipment has been selected based on availability Gun Geometry Gun to gun distance, gun to bed distance, is important for uniform spray and spray pattern, Two guns has been used as Medium per supplier recommendation. Gun calibration to be donebefore commencing of each operation hence risk has been minimized. Risk is considered as medium Atomization air It coverts suspension to mist. air pressure or over spray pressure because picking and less air pressure may cause picking.tablet rough ness is dependent on atomization air pressure. Risk is considered as high Pan pressure Low Kept negative based on the supplier recommendation Pan speed Spray rate Inlet air temperature CFM Out let air temperature Medium Medium Low Pan speed was adjustedto ensure uniform mixing of tablet throughout the coating process that is based on the tablet shape and size. Tablet film thickness depends upon the uniform mixing.in our experiment dry run without spray done and at Pan sped 2.1 rpm observed that tablet mixing is uniform Inappropriate spray rate may cause inadequate drying, twining andsticking, orange peel effect.thus surface roughness and % LODmay vary hence risk is highto stability of the product Water evaporation and uniformity of coating is highly depended oninlet air temperature. inlet air temperature may cause over drying of tablet and spray resulting rough surface, week film adhesion of film on tablet. Low air temperature sticking of tablets, twining and increase moisturecontent of tablet thus stability of the product Coating process fairly tolerantto CFM.As per equipment supplier CFM kept 1500 cuft/min Outlet air temperature depends upon the amount of inlet air temperature, inlet CFM, spray rate and atomization are pressure.out let air temperature considered as low. Risk mitigation plan (US FDAQuality by design for ANDAs,2012) Medium Low risk area further study to be carried out Medium risk area Low risk area 114

10 4.2.2 Design of experiment (DOE) Inputs were taken from FMEA analysis. Three critical process parameters found out for further study.2 3 factorial design used for the screening of wider range of input parameter (Collins et al. 2005, Nair et al. 2008). Twelve experiments were conducted. LOD measured by moisture analyzer and surface finish by profilometer. Statistical evaluation Analysis of variance (ANOVA) was performed (Freedman DA. 2005).Design expert 8.0(STAT-EASE) Demo version software was used to demonstrate influence of each factor graphically and to generate response surface plots (Design Expert, 2016). Significant factors and respective influence on responses were determined. The study was carried at 95 % confidence level. 3D Surface graphs To find out the relationship of two variables three-dimensional graphs were plotted Control strategy and optimization Over lay plot was plotted with help of software to find out design space (Jain S. 2014). Control strategy has been recommended for optimization process (ICH. Q8, Q10, 2012). Use of TACC software Based on the operating range parameters obtained from above, TACC software was used to get prediction of EE factor. Five experiments were set for trials. One experiment intentionally kept outside the recommended EE factor. Experiments were conducted. Ranges of optimized process parameters were selected Verification of optimized parameters Tablets were coated and collected after using optimized parameters. Surface finish was visualized by scanned electronic microscope. The magnification range was used 80X, 100X and 500 X. 115

11 4.3 Coating with DOE Preparation of blend for the study Following formula has been studied for study of coating process parameters: Table 4.9: Raw materials used for coating process optimization DOE: Materials % w/w Quantity (tablet) Lactose monohydrate NF 67 Microcrystalline cellulose (PH102) 23 Croscarmellose sodium 8 Colloidal silicon dioxide 0.5 Magnesium stearate 1.00 Purified Talc 0.5 Dry granulation process was used to prepare a blend. Raw materials lactose monohydrate NF and microcrystalline cellulose (PH102), croscarmellose sodium, colloidal silicon dioxide were sifted through mesh size (#) 40 American Society for Testing and Materials(ASTM) using a Vibro sifter. Pre-blending for 10 minutes was done using an Octagonal blender 150 L. Pre blend materials sifted through #40 ASTM followed by blending for 10 minutes. Magnesium stearate and purified talc was sifted through #60 ASTM. Lubrication of blend was done with sifted magnesium stearate and purified talc for 05 minutes. Tablet compression The study was conducted using single sided rotary compression machines. Tablets were compressed using B tooling. Round shaped standard convex tablets were used for the study of size 6.5mm. In process tests were carried out to ensure tablet parameters were within the specified limit before coating. Quality parameters like tablet weight, hardness, thickness, friability were performed Risk analysis prior to compression Based on the Q8, Q10 ICH guidelines, risk assessments were carried out to find out high risk factors to be studied (ICH. 2005, ICH. 2011). Risk assessment tool Failure mode and effects analysis has been carried out (GSFC. 2013). Following is the element of risk analysis before coating stage against CQA weight gain and surface finish 116

12 Table 4.10: Initial risk assessment tool by FMEA for tablet coating DOE Unit Operation: Film Coating Output Material CQA: Weight gain and Surface finish Variables Risk Justification and Initial Strategy Assessment Equipment Low Equipment has been selected based on availability Gun Geometry Medium Gun to gun distance, gun to bed distance, is important for uniform spray and spray pattern, Two guns has been used as per supplier recommendation. Gun calibration to be donebefore commencing of each operation hence risk has been minimized. Risk is considered as medium It coverts suspension to mist. air pressure or over spray cause Atomization air picking and less air pressure may cause picking.tablet rough ness pressure is dependent on atomization air pressure. Risk is considered as high Pan pressure Low Kept negative based on the supplier recommendation Pan speed was adjusted to ensure uniform mixing of tablet throughout the coating process which is based on the tablet shape Pan speed Medium and size. Tablet film thickness depends upon the uniform mixing.in our experiment dry run without spray was done and at Pan sped 2.1 rpm observed that tablet mixing is uniform. Inappropriate spray rate may cause inadequate drying, twining Spray rate andsticking,orange peel effect.thus surface roughness and weight gainmay vary hence risk is high and needed for optimization Water evaporation and uniformity of coating is highly depended oninlet air temperature. inlet air temperature may cause over Inlet air drying of tablet and spray resulting rough surface,week film temperature adhesion of film on tablet. Low air temperature sticking of tablets, twining and increase moisturecontent of tablet thus stability of the product CFM Medium Coating process fairly tolerantto CFM.As per equipment supplier CFM kept 1500 cuft/min Out let air Outlet air temperature depends upon the amount of inlet air Low temperature, inlet CFM, spray rate and atomization are pressure temperature.out let air temperature considered as low Risk mitigation plan(usfda, 2012) Medium Low risk area further study to be carried out Medium risk area Low risk area 117

13 4.3.2 Design of experiment (DOE) Inputs were taken from FMEA analysis. Three critical process parameters found out for further study. For designing and optimization of different pharmaceutical formulations, the response surface methodology has been widely used which requires minimum experimentation (Nayak and Pal. 2011). It requireless number of experiment thus it is costeffective than the conventional methods (Malakar et al. 2012). Experiments were conducted Opadry yellow used as coating materials. Percentage LOD measured in moisture analyzer and surface finish by surface finish tester profilometer. Statistical evaluation Analysis of variance (ANOVA) was performed (Freedman, et al., 2005) Design expert 8.0(STAT-EASE) Demo version software to demonstrate influence of each factor response. Surface plots were generated using Design expert 10.0 (Design Expert, 2016). Significant factors and respective influence on responses were determined. The study was carried at 95 % confidence label. Standard quadratic equation has been developed (Mandal et al. 2007). Contour plots The contour plot can be used to symbolize variables for the real and for the imaginary parts of the function. Different contour plots are made on the data obtained (Minton et al. 2013). Control strategy and optimization Over lay plot was plotted with help of software to find out design space (Jain S. 2014). Control strategy has been recommended for optimization process (ICH. Q8, Q10, 2012) Verification of optimized parameters With recommended process parameters, coating process was carried out. Responses (weight variation and surface finish) were measured and coated tablets were inspected by tablet inspection machine. 118