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COMPUTER AIDED FORMULATION DESIGN EXPERT SOFTWARE CASE STUDY

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COMPUTER AIDED FORMULATION DESIGN EXPERT SOFTWARE

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COMPUTER AIDED FORMULATION DESIGN EXPERT SOFTWARE CASE STUDY

  1. 1. Computer aided formulation development Roshan Bodhe PH-02 Page 1 INTRODUCTION TO COMPUTER AIDED FORMULATION AND DEVOLOPMENT: Formulation and development is a process of selection of component and processing.Now days computer tools used in the formulation and development of pharmaceutical product.Various technique such as design of experiment are implemented for optimization of formulation and processing parameter.Traditionally optimization in pharmaceuticals refer changing one variable at a one time, so to obtain solution of a problematic formulation.Many times finding the correct answer is not simple and straight forward in such cases use of computer tools (optimization procedure) for best compromise is the smarter way to solve problem. OPTIMIZATION TERM:-  Optimization means:- Optimization means choosing the best element from some set of available alternatives. To optimize means, to make as perfect as possible, to make as functional as possible, to make as effective as possible, so as possible is important then optimization means what 1. Perfect by whose method? 2. For what characteristics? 3. For what condition?  Factor: -It is an assigned & independent variables, Such as temperature and concentration. Those are quantitative & qualitative.  Level:-Those are designation assigned to the factor. Level indicated as high, low, medium,Eg:-polymer ratios.  Response:-It is an outcome of an experiment. Eg:- Disintegration time  Response surface:-Response surface representing a relationship between the independent variables X1&X2 and dependent variables Y. OPTIMIZATION IS OPTIONAL THING OR NOT:- Before you submit the application for the approval they make inspection of your provision and facilities known as pre approval provision.Sodue to this approach optimization is not a optional thing. In earlier days approvalwas given first then they conduct the inspection.Now the things are change. There is provision known as pre- approval inspection,before they grant the approval they will inspect the 1)Inspect the facilities. 2)Inspect and check the formulation.  Why this additives is there?  Why this excipient added in to this concentration?  Why the weight of tablet? So, due to this each and every thing you will justify and defend masterformula record and batch manufacturing records.so you have justify your formulation justify your formulation.
  2. 2. Computer aided formulation development Roshan Bodhe PH-02 Page 2 For Example: - Why you are heating at 60 degree not on 90 degree.Why you are adding this preservative or antimicrobial agent to this concentration.So it is notoptional thing, we cannot escape this, because optimization is mandatory and regulatory need of formulation and development of pharmaceutical product. REGULATORY NEED OF OPTIMIZATION TECHNIQUE: Optimization is a regulatory or mandatory thing required during the formulation and development because of following reason.Provide in depth understanding to explore and further defend in the formulation development. So it is also benefit of manufacturing of formulation. Because you know each and every detail and probably it’s his expansion and review and all things will be in his hand. So, optimization is not screening technique,but it is regulatory thing. OPTIMIZATION IS A TRIAL AND METHOD:- Early optimization is done on the basis of trial and error method. What is mean by trial and error? A series oflogical steps carefully controlling the variables and changing one variable at a time till satisfactory result produced. So gross variables carefully understood by changing one variable at atime and making a small change in to it then series ofthis experiment has been done. If the experiment is done with sufficient length of time, at adequate resources, sufficient time and help, then time will come and he will come with perfect product. Golden principles required during the formulation and development because in pharmaceutical field,those principles decide the things regarding for formulation and development. Because in pharmaceutical industry you may have thousands of chemical and excipient available,then you should put more and more quantity. 1) Minimum and absolute quantity of ingredient in formulation:- Those related to quantity and number of excipient in the formulation. Because we added more and more ingredient,then those putting burden on labor, and process of manufacturing. 2) Minimum number of steps:- More will be the unit process then more will be the energy is consumed, more will be the parameter required at each step.So minimum and absolutely small no. of unit operation required during formulation and development. OPTIMIZATION PARAMETER:- A) Problem types:- There are two types of problem which are usually addressed in the optimization technique. a) Unconstrain problem:- In unconstrain there is no restriction place on the system, then that called as unconstrain problem.
  3. 3. Computer aided formulation development Roshan Bodhe PH-02 Page 3 In pharmaceutics unconstrain problem are not exist. Example:- Make the hardest tablet as possible. 2)Constrain problem:-In that some restriction place on the system, Those problem are important in the pharmaceutical so we think about the constrain problem. Example: -Make the hardest tablet as possible, but it disintegrate within 15 min. Variables types:- There are certain variables in optimization technique regarding the pharmaceutical formulation. These are the measurement values, whichare characteristics of data that called veriables. There are two types of variables, are as fallow. a) Dependent variables b) Independent variables 1) Independent variables in optimization technique:- That means things you can operate on your own choice,called as independent variables These variables under the control of formulator.E.g Mixing time,Concentration of binder,Drug to polymer ratio,Compression force. These does not depend upon any other values. 2) Dependent variables:- These are variables which are not directly under the control of formulator. These variables are response or chartecteristics of independent variables VARIOUS EXPERIMENTAL DESIGN 1. Completely randomized designs 2. Randomized block designs 3. Factorial designs 4. Full Fractional Response surface designs 5. Central composite designs 6. Box-Behnken designs 7. Adding center points 8. Three level full factorial designs 1. Completely randomized Designs:- Theseexperiment compares the values of are response variable based anddifferent levels of that primary factor. For example:- if there are 3levels of the primary factor with each level to be run 2 times then there are 6 factorial possible run sequences. 2. Randomized block designs
  4. 4. Computer aided formulation development Roshan Bodhe PH-02 Page 4 For this there is one factor or variable that is of primary interest. To control non- significant factors, an important technique called blocking can be used to reduce or eliminate the contribution of these factors to experimental error. 3. Factorial design:-Full Used for small set of factors. 4. Fractional:-It is used to examine multiple factors efficiently with fewer runs than corresponding full factorial design. TYPES OF FRACTIONAL FACTORIAL DESIGNS: 1. Homogenous fractional 2. Mixed level fractional 3. Box-Hunter 4. Plackett-Burman 5. Taguchi Latin square Homogenous fractional:- Useful when large number of factors must be screened. Design of Experiment For Formulation And Development: All pharmaceutical products are formulated to specific dosage form drugs to be effectively delivered to patient typical pharmaceutical dosage form include tablets, capsules, solution suspension, etc Different dosage form required different technology usually present different technological challenge for formulation & development .Due to complex challenges, formulations scientist used effective methodology like as design of experiment and statistical analysis for formulation and development . Formulation scientist used this method for process optimization and process validation . EXPERIMENTAL DESIGN:- The designs used for simultaneous methods are frequently referred to as response surface designs. Various experimental designs frequently involved in the execution of RSM can broadly classified as: A. Factorial design and modifications B .Central Composite design and modifications C .Mixture designs D .D-optimal designs A. Factorial design and modifications :- Factorial designs (FDs; full or fractional) are the most frequently used response surface designs. These are generally based upon first-degree mathematical models. Full FDs involve studying the effect of all the factors (n) at various levels (x), including the interactions amongst them, with the total number of experiments as xn. The simplest FD involves study of two factors at two levels, with each level coded suitably. FDs are said to be symmetric, if each factor has same number of levels, and asymmetric, if the number of levels differs for each factor.
  5. 5. Computer aided formulation development Roshan Bodhe PH-02 Page 5 Besides RSM, the design is also used for screening of influential variables and factor influence studies. Representsa 22 and 23 FD pictorially, where each point represents and individual experiment.The mathematical model associated with the design consists of the main effects of each variable plus all the possible interaction effects, i.e., interactions between the two variables, and in fact, between as many factors as are there in the model. The mathematical model generally postulated for FDs is given as Y = b0 + b1X1... + b12X1X2... + b123X1X2X3... + e … where, bi, bij and e represent the coefficients of the variables and the interaction terms, and the random experimental error, respectively. The effects (coefficients) in the model are estimated usually by multiple linear regression analysis selection'. Their statistical significance is determined and then a simplified model can be written B Central composite design and its modifications:- Also known as Box-Wilson design, it is the most often used design for second-order models , Central composite design (CCD) is comprised of the combination of a two-level factorial points (2n),axial or star points (2n) and a central point. Thus the total number of factor combinations in a CCD is given by 2n +2n + 1. The axial points for a two-factor problem include, (± a, 0) and (0, ± a), where a is the distance of the axial points from the center. A two factor CCD is identical to a 32 FD with square experimental domain . a) face centered cube design (FCCD):- Results when the same positive and negative distance is taken from the center in a CCD . A rotatable is identical to FCCD except that the points defined for the star design are changed to [± (2n )1/4,… 0] and those generated by the FD remain unchanged. In this way, the design generates information equally well in all the directions. e.g. the variance of the estimated response is same at all the points on a sphere centered at the origin. The second-order polynomial for two factors, generally used for the composite designs. b)Box-Behnken Design:- Is a specially made design that requires only 3 levels (-1, 0, 1). It overcomes the inherent pitfalls of CCD, where each factor has to be studied at 5 levels (except for 2 factors with a = ± 1, where the number of levels per factor is 3), thus the number of experiments increases with rise in the number of factors. A BBD is an economical alternative to CCD. Also called as orthogonal balanced incomplete block design, these are available for 3 to 10 factors. Because the design involves study at three levels, the quadratic model is considered to be most appropriate. C ) Mixture designs:- In FDs and the CCDs, all the factors under consideration can simultaneously be varied and evaluated at all the levels. This may not be possible under many situations. Particularly, in pharmaceutical formulations with multiple excipients, the characteristics of the finished product usually depend not so much on the quantity of each substance present but on their proportions. Here, the sum total of the proportions of all excipients is unity and none of the fractions can be negative. Therefore, the levels of the various components can be varied with the restriction that the sum total should not exceed one. Mixture designs are highly recommended in such cases. In a two- component mixture, only one factor level can be independently varied, while in a three- component mixture only two factor levels, and so on. The remaining factor level is chosen to
  6. 6. Computer aided formulation development Roshan Bodhe PH-02 Page 6 complete the sum to one. Hence, they have often been described as experimental designs for the formulation optimization. For process optimization, however, the designs like FDs and CCDs are preferred.There are several types of mixture designs, the most popular being the simplex design. Scheffé s polynomial equation:- components are given as under: Linear : Y = b1X1 + b2X2 + b3X3 … ( Quadratic : Y = b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 + b23X2X3 ... Special cubic model: Y = b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 + b23X2X3 + b123X1X2X3 … The mathematical model of mixture designs does not have the intercept in its equations. As a consequence,these Scheffé models are not calculated by linear regression. D) D-optimal designs: If the experimental domain is of a definite shape, e.g., cubic or spherical, the standard experimental designs are normally used. However, in case the domain is irregular in shape, D- optimal designs can be used. These are non-classical experimental designs based on the D-optimum criterion, and on the principle of minimization of variance and covariance of parameters. The optimal design method requires that a correct model is postulated, the variable space defined and the number of design points fixed in such a way that will determine the model coefficients with maximum possible efficiency. One of the ways of obtaining such a design is by the use of exchange algorithms using computers. These designs can be continuous, i.e., more design points can be added to it subsequently, and the experimentation can be carried out in stages. D-optimal designs are also used for screening of factors Depending upon the problem, these designs can also be used along with factorial, central composite and mixture designs. COMPUTER SOFTWARE USED IN THE FORMULATION AND DEVELOPMENT:- Choice of Computer Software Package:- Many commercial software packages are also available, which are either dedicated to a set of experimental designs or are of a more general statistical nature with modules for select experimental design(s). The dedicated computer software is frequently better as the user pays only for the DoE capabilities. In contrast, the more powerful, comprehensive and expensive statistical packages like SPSS, SAS, BBN, BMDP, MINITAB, etc. are geared up for larger enterprises offering diverse facilities for statistical computing, support for networking and client- server communication, and portability with a variety of computer hardware. When selecting a DoE software, it is important to look for not only a statistical engine that is fast and accurate but also the following: • A simple graphic user interface (GUI) that's intuitive and easy-to-use. • A well-written manual with tutorials to get you off to a quick start. • A wide selection of designs for screening and optimizing processes or product formulations. • A spreadsheet flexible enough for data entry as well as dealing with missing data and changed factor levels.
  7. 7. Computer aided formulation development Roshan Bodhe PH-02 Page 7 • Graphic tools displaying the rotatable 3-D response surfaces, 2-D contour plots, interaction plots and the plots revealing model diagnostics • Software that randomizes the order of experimental runs. Randomization is crucial because it ensures that "noisy" factors will spread randomly across all control factors. • Design evaluation tools that will reveal aliases and other potential pitfalls. • After-sales technical support, online help and training offered by manufacturing vendors Software Salient feature Source Design Expert Powerful, comprehensive and popular package used for optimizing pharmaceutical formulations and processes; allows screening and study of influential variables for FD, FFD, BBD,CCD, PBD and mixture designs; provides 3D plots that can be rotated to visualize the response surfaces and 2D contour maps; numerical and graphical optimization www.statease.com MINITAB Powerful DoE software for automated data analysis, graphic and help features, MS- Excel compatibility, includes almost all designs of RSM www.minitab.com JMP DoE software for automated data analysis of various designs of RSM, graphic and help features www.jmp.com CARD Powerful DoE software for automated data analysis, includes graphic and help features www.s-matrix.com DoE PRO XL & DoE KISS MS-Excel compatible DoE software for automated data analysis using Taguchi, FD, FFD and PBD. The relatively inexpensive software, DoE KISS is, however, applicable only to single response variable. www.sigmazone.com MATREX Excel compatible optimization software with facilities for various experimental designs http://www.rsd- associates.com/ matrex.htm
  8. 8. Computer aided formulation development Roshan Bodhe PH-02 Page 8 HOW TO USED DESIGN EXPERT SOFTWARE VERSION 7.0 How to install design expert software 1) Install the exe file 2) Do not run design expert now 3) Copy the crack from crack folder to C/ program file / DX 7 trail run 4) If you want help then press F1for help SOP OF DESIGN EXPERT SOFTWARE :- 1) File new design 2) Click response surface 3) Add numeric factor 4) write name of independent variables 5) Add low limit and high limit 6) Continue 7) Add response 8) Run will be generated APPLYING DESING OF EXPERIMENT:- DOE is an important tool for formulation scientist,Because those gives intelligent and important decision at every stage of formulation and development. For example, In the formulation of tablet consist different excipient ,And those affected on final formulation. Factor Low level (mg) High level (mg) Effect on Diluent ratio 10 15 Disintegration time Disintegration level 5 10 Dissolution time Binder level 4 6 Friability Lubricant level 6 8 Weight uniformity Major Technical Challenges in Tablet Formulation Development & Role of DOE:- Major challenge Potential process Technologies Uniformity Fluid Bed Granulation Compatibility Highly Compressible excipient Flow ability Free Flowing excipients Dissolution Tablet Matrix containing polymer and Taguchi design.
  9. 9. Computer aided formulation development Roshan Bodhe PH-02 Page 9 STEP IN FORMULATION AND DEVELOPMENT 1) Excipient compability:- i. First step in formulation and development is campactability study those select the excipient. ii. Those are physically and chemically compatible with the API iii. It should be biodegradable and compatible iv. By applying doe we can understand interaction effect with API over a time. v. By applying DOE we can understand the interaction effect of excipient with API. Feasibility Study: i. Excipients are selected from excipient compability study, and next step is the feasibility study. i. Those conducted to determine the manufacturing process that enable the formulation to achieve TPP ii. Those evaluated technical challenges associated with the formulation and development iii. In table no. 1 potential process are given to overcome the challenges in formulation and development. iv. As technical challenges overcome the next step is selection of manufacturing process. Selection Of Manufacturing Process: 1) Formulation preliminary study:- Those gives idea about selection of final excipient 2) Formulation optimization study:-  Those define the optimum level of excipient in the each formulation.  During this many formulation factor and response are evaluated in tablet formulation and development. table 3  These problems can be solve by applying doe.  Those gives idea to understand formulation system and optimize the formulation by choosing best combination of excipient to achieve the TPP Initial Formulation System for Tablet Formulation FACTOR API LEVEL & EXCIPIENT CHOICE API 5-10% DILUENT MICROCRYSTALINE CELLULOSE STARCH, LACTOSE DISINTEGRANT SODIUM STARCH GLYCOLATE LUBRICANT MAGNECIUM STEARATE Design and Conduct a Formulation Optimization Study
  10. 10. Computer aided formulation development Roshan Bodhe PH-02 Page 10 FACTOR EXCIPIENT LOW STRENGTH ( % ) HIGHSTRENGTH (%) API - 5 10 Diluent MCC 59 30 Disintegrant Crospovidone 5 5 lubricant Magnesium stearate 1 1 VARIOUS VARIABLES:- VARIABLES NO. OF LEVELS API% 2 DILUENT 3 DISINTEGRANT 2 LUBRICANT 2 CASE STUDY Experimentaldesign manufacturing of Atenolol tablet:- Atenolol, heavy magnesium carbonate, maize starch and sodium lauryl sulphate , were mixed in Rapid Mixer Granulator and pneumatics, for 10 mins at different impeller and chopper speed. Purified water (50%) was heat and add gelatin in heated water withconstant stirring until dissolve. 3.57 % w/w maize starch paste (granulating agent) was prepared with boiling purified water. Add the gelatin solution to starch paste and mixed properly. Add the granulatingagent to the material over a period of 2 min at different impeller and chopper speed followed by kneading for about 2 min to get a good granular mass. Wet granular mass was dried in fluidized bed dryer , at an internal temperature of 60 ± 5°C, outlet temperature 40±5°C till a loss on drying of 1.5.3.3 % was achieved on IR moisture balance in auto mode at 105°C. Dried granules were sifted through 18 mesh on vibratory sifter and mill the retentions of granules through 1 mm screen of multimill , with knives forward direction at slow speed. The dried granules were blended with 1% magnesium stearate in Octagonal. In the presented study, granulation process wasoptimized by taking three different lots, in which dependent variables were impeller speed, binder addition time, chopper speed, impeller mixing timeblender for 20 min. Tablets (390mg) were compressed on a 16 station rotary tablet compression machine using a 9 mm standard flat-face punch. The prepared tablets were round and flat with an average diameter of 9.0 ± 0.1 mm and a thickness of 4.75 ± 0.2 mm. and their effect on bulk density, true density, Carr.s index, and hausner ratio.
  11. 11. Computer aided formulation development Roshan Bodhe PH-02 Page 11 Table 1 Presentation of 3 experiments with variables for granulation process:- Lot no Impeller Speed Binder addition time(min) Impeller mixing time(min) AMP. Reading Chopper Speed 1 Fast 1 12 3.7 Slow 2 Slow 2 8 3.9 Fast 3 Fast 2 6 3.9 Fast Table3.CompressionprocessvariablesdesignandResponsedataofbatchesintheBoxBehnkend esign Batch X1(Ton) X2 (ton) X3(rpm) Hardness(N) Friability (%) DT(sec) BB1 -1 -1 0 28.34 1.8 122 BB2 -1 -1 0 57.7 0.3 362 BB3 -1 -1 0 35.39 0.2 128 BB4 -1 -1 0 70.30 0.8 96 BB5 0 0 -1 42.66 0.8 215 BB6 -1 0 -1 59.09 0.23 303 Variables Level Low(-1) Medium(0) High (1) X1 (Precompression Force) 0 1 2 X2 (Compression Force) 2 4 6 X3 (Compression Speed) 25 30 35 For compression process three levels Factorial Box- Behnken experimental design was used to evaluateeffect of selected independent variables on the responses, to characterize physical properties oftablets and to optimize the procedure. This design is suitable for exploration of quadratic response surfaceand for construction of polynomial models, thus helping to optimize process by using a small numberof experimental runs. For the three levels three factor Box and Behnken Experimental design, a total of 15 experimental runs, shown in Table 3, are needed. The generated models contain Quadratic term explaining the non linear nature of responses This design also resolves the three factor interaction effect of individual terms and allow a mid level setting (0), for The design consists of replicated center points and a set of points lying at the mid
  12. 12. Computer aided formulation development Roshan Bodhe PH-02 Page 12 points of each edge of multidimensional cube that defines the region ofinterest .The model is of the following form: y = b0 + b1x1 +b2x2 +b3x3 +b4x1x2 +b5x2x3+b6x1x3 +b7x12+b8x22+b9x32 + E Where; y is the selected response, b0-b9 are the regression coefficients, X1, X2 and X3 are the factorsstudied and E is an error term. The Box-Behnken experimental design is an orthogonal design. Therefore, the factor levels are evenly spaced and coded for low, medium and high settings; as - 1, 0, +1 Factors studied in the Box and Behnken experimental design where precompression force (X1), compression force (X2) and compression speed(X3). The factors levels are shown in Table 3. The selected responses were Hardness (Y1), friability (Y2)and Disintegration time (Y3). The responses studied and the constraints selected considering AtenololPhysical properties and regarding U.S.FDA guidelines, presented in Table 4. Table 4: Responses selected and the constraints used in Box-Behnken design Code Parameter Constraints Y1 Hardness 30-40 N Y2 Friability NMT 0.5 % W/w Y3 Disintegration time NMT 10 min EVALUATION OF TABLETS:- 1. Hardness:- The hardness of the tablets was tested for 10 tablets by pharma hardness teste and average hardness (N) was being taken and compared with that of standard one. 2. Friability :- Friability test was performed in accordance with USP , 5 tablets wereselected randomly, their individual weight was taken and then kept in the friabilator and rotated for 4 minat a speed of 25 rpm the tablets were taken out and any loose dust from them was removed, the weightwas registered and friability was calculated as a percentage weight loss. 3. Disintegration time :- The disintegration of the tablets was tested in a disintegration tester , sixtablets were put in to a basket that was raised and lowered in a beaker containing preheated water at37 °C. The disintegration test was calculated as the mean value and as the range. 4. In-vitro dissolution studies :- The release rate of atenolol from tablets (n=3) was determined according to British Pharmacopoeia (ref)using the Dissolution Testing Apparatus 2 , fitted with paddles. The
  13. 13. Computer aided formulation development Roshan Bodhe PH-02 Page 13 dissolution test was performed using 900 mL of 0.1 N HCl, 37±0.5°C and 50 rpm. A 5 ml sample waswithdrawn from the dissolution apparatus at predetermine time interval, and the samples werereplaced with fresh dissolution medium. The samples were filtered through a 0.45µm membrane filter and diluted to a suitable concentration with 0.1 N HCl. Absorbance of these solution was measured at 275nm using UV/VIS spectrophotometer Cumulative drug release was calculated usingthe equation generated from Beer Lamberts calibration curve in the linearity range of µg/mL. 5. Statistical analysis Statistical analysis of the Box-Behnken design batches was performed by multiple regression analysis using Microsoft Excel. To evaluate the contribution of each factor with different levels to theresponse, the two-way analysis of variance (ANOVA) was performed using the DESIGN EXPERT 7.0.1, demo version software. To graphically demonstrate the influence of each factor on theresponse, the response surface plots were generated using DESIGN EXPERT 7.0.1 (STAT-EASE) demoversion software. RESULTS AND DISCUSSION :- In the present investigation, combinations of three variables were studied using the Box- Behnkenexperimental design. The mathematical models developed for all the dependent variables usingstatistical analysis software are shown in equations (1)-(3): Hardness = 48.43 + 11.92 X1 + 5.60 X2 - 0.37 X3 + 1.38 X1 X2 - 0.58 X1 X3 + 2.19 X2 X3 - 1.41 X12+ 0.91 X22 - 5.10 X32 R2 0.6794 ------ (1) Friability = 0.90-0.45 X1 - 0.19 X2 + 0.13 X3 + 0.19 X1X2 - 0.43 X1 X3 - 0.17 X2 X3 + 0.016 X12 - 0.16 X22 . 0.21 X32 R2= 0.8202 ------ (2) Disintegration Time = 262.00 + 62.25 X1 + 53.25 X2 + 85.25 X3 - 68.00 X1 X2 + 28.50 X1X3 + 168.50X2 X3 - 72.75 X12 - 12.25 X22 + 59.75 X32 R2 = 0.6043 ------- (3) The hardness of all tablets was found to be below 61 N Table 5: Box-Behnken Experimental Design Batch X1(Ton) X2 (ton) X3(rpm) Hardness(N) Friability (%) DT(sec) BB1 -1 -1 0 28.35 1.5 122 BB2 -1 -1 0 57.52 0.2 362 BB3 -1 -1 0 35.39 0.5 128 BB4 -1 1 0 70.23 0.61 96 BB5 -1 0 -1 42.26 0.2 215 BB6 - 0 1 59.36 1.8 303
  14. 14. Computer aided formulation development Roshan Bodhe PH-02 Page 14 Variables Level Low(-1) Medium(0) High (1) X1 (Pre-compression Force) 0 1 2 X2 (Compression Force) 2 4 6 Table 6: Analysis of variance for dependent variables from the Box-Behnken design Sources SS DF MS F value Probability Hardness Regression 1526 9 169.56 1.18 0.0045 Residual 720.21 5 144.14 Total 2246.22 14 Friability Regression 3.40 9 0.38 2.33 0.0015 Residual 0.74 5 0.15 Total 4.14 14 Disintegration Regression 2.830 9 3.144 0.85 0.6097 Residual 1.853 5 3.700 Total 4.683 14 4.683 Table 7: Granulation Optimization : Analysis data Lot No. Initial air drying time(min) Total hot air drying time(min) Inlet temp oC Out late temp oC LOD % w/w 1 10 50 50 44 1.50 2 10 60 60 44 1.80 3 10 70 55 38 2.20 Results and Discussion In The Present investigation granulation and compression process were optimized. Results and Discussion for Granulation:- Batch No 1
  15. 15. Computer aided formulation development Roshan Bodhe PH-02 Page 15 Process :- In granulation process, impeller speed, binder addition time, chopper speed, impeller mixing timewere studied by taking three different lots for dependent variables i.e. bulk density, true density,Carr.s index, and hausner ratio (Table 2). Effect of impeller and chopper speed Various granulation batches prepared to study the effect of impeller and chopper speed are listed intable 1 RESULTS AND DISCUSSION FOR COMPRESSION :- Process:- Combinations of three independent variables werestudied using t he Box Behnken design. Themathematical models developed for all the dependent variables using design expert software areshown in equation (1)-(3) EFFECT OF PRE- COMPRESSION FORCE :- Compression force and Compression speed :- 15 batches had been prepared to study the effect of pre compression force is listed in table no 3. so, wetook three different pre compression force -1 indicates there is no pre compression force,0 indicates pre compression force is 1tonne,+1 indicates pre-compression force is 2 tonnes,after preparing all the batches results showed that ,after applying pre compression force hardness becomeshigher than limit in combination with lower compression force and lower compression force showed cracking tableting defect (BB 4). So, result showed that Precompression force should not begiven ,when we are applying higher compression force and ,and when Pre - compression force is notgiven and compression force is also lowered cause lower hardness and friability problems with highcompression speed.(BB 7) All three independent variables also affects invitrodissolution studies ,if pre compression force is 2 tonnes and compression force 6 tonnes with lower compression speed 25 rpm ,takes more time to dissolve and vice versa. Dissolution profiles for two Optimized batches BB 5 and BB 7 were analysed and compared with innovator product and calculated for similarity factor showed result given in table no. 7 Table No 8 Formulation F2(Similarity Factor) F1 (Difference factor) BB6 67 03 BB7 29 26 COMPARISION DATA USING DESIGN EXPERT SOFTWERE :-
  16. 16. Computer aided formulation development Roshan Bodhe PH-02 Page 16
  17. 17. Computer aided formulation development Roshan Bodhe PH-02 Page 17 CONCLUSIONS:-  Design of experiment & statistical analysis have been used in the formulation development  Using design of experiment formulation scientist evaluate the all formulation factors in systematically and timely manner to optimize the formulation and manufacturing process  When the pharmaceutical process and product are optimize by systematic approach then process validation & scale up can be efficient because of the robustness of the formulation and manufacturing process
  18. 18. Computer aided formulation development Roshan Bodhe PH-02 Page 18 REFERENCES :- 1. Bolton S, Bon C. Pharmaceutical Statistics Practical & Clinical Application, 5TH ED. New York London ;InformA Healthcare Publishing ; 2010.Pg No 239 2. JAIN N K “Pharmaceutical Product Development” ,CBS Publisher ; New Delhi2010. Pp 295-340 3. Ganesh. R. Godage Advance Drug Delivery System, Tech Max Publication Pune 2017 Pp 6.1-6.24 4. Kubinyi H. Drug Research: Myths, Hype And Reality. Nature Reviews Drug Discovery. 2003 Aug;2(8):665. 5. Hussain As, Shivanand P, Johnson Rd. Application Of Neural Computing In Pharmaceutical Product Development: Computer Aided Formulation Design. Drug Development And Industrial Pharmacy. 1994 Jan 1;20(10):1739-52.

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