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various applied optimization techniques and their role in pharmaceutical science.


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applied optimization techniques

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various applied optimization techniques and their role in pharmaceutical science.

  1. 1. Various optimization techniques and their role in pharmaceutical sciences . aakanksha gupta roll no: 04 1
  2. 2. optimization“An art, process, or methodology of makingsomething (a design, system, or decision) asperfect, as functional, as effective as possible.” 2
  3. 3. seminar outline:• Introduction• Key term used in in optimization• Applied Optimization Methods• Applications• Conclusion• References 3
  4. 4. objectiVes ofpharmaceutical optimization 4
  5. 5. ADVANTAGES• Yield the “best solution” within the domain of study. –Require fewer experiments to achieve an optimum formulation.• Can trace and rectify “problem”in a remarkably easier manner 5
  6. 6. Key term used in optimization process 6
  7. 7. Type of optimization techniques Classical methodA. Factorial designs and Applied method Modifications a. Full Factorial Design A.Evolutionory b. Fraction FactorialDesign Operation (EVOP) i. Homogenous fractional ii. Mixed level fractional B.Simplex Lattice iii. Box-Hunter C.Lagrangian Method iv. Plackett-Burman v. Taguchi D.Search Method vi. Latin squareB. Central composite design andmodificationsC. Mixture designD. D-optimal design 7
  8. 8. applied optimization 8
  9. 9. Cont………..• The classic calculus methods apply basically to unconstrained problems . but in pharmacy all problem are constrained• Deming and king presented a general flowchart.• Involve the effect on a real system of changing some input (some factor or variable) is observed directly at the output (one measures some property), and that set of real data is used to develop mathematical models.• The responses from the predictive models are then used for optimization 9
  10. 10. Flow line of applied optimization 10
  11. 11. 1. EVOP METHOD make very small changes in formulation repeatedly. The result of changes are statistically analyzed. If there is improvement, the same step is repeated until further change doesn’t improve the product. Where we have to select this technique?This technique is especially well suited to a production situation.The process is run in a way that is both produce a product thatmeets all specifications and (at the same time) generatesinformation on product improvement. 11
  12. 12.  Advantages:• generates information on product development.• predict the direction of improvement.• Help formulator to decide optimum conditions for the formulation and process. Limitation More repetition is required Time consuming Not efficient to finding true optimum Expensive to use. 12
  13. 13. • Example: In this example, A formulator can changes the concentration of binder (no of experiment is done) and get the desired hardness. 13
  14. 14. 2.SIMPLEX METHOD It was introduced by Spendley A simplex is a geometric figure, defined by no. of points or vertices equal to one more than no. of factors examined. Once the shape of a simplex has been determined, the method can employ a simplex of fixed size or of variable sizes that are determined by comparing the magnitudes of the responses after each successive calculationIt is of two types: A. Basic Simplex Method B. Modified Simplex Method. 14
  15. 15. Cont….. The simplex method is especially appropriate when:• Process performance is changing over time.• More than three control variables are to be changed.• The process requires a fresh optimization with each new lot of material. The simplex method is based on an initial design of k+1, where k is the number of variables. A k+1 geometric figure in a k-dimensional space is called a simplex. The corners of this figure are called vertices. 15
  16. 16. Basic Simplex Method: It is easy to understand and apply. Optimization begins with the initial trials. Number of initial trials is equal to the number of control variables plus one. These initial trials form the first simplex. The shapes of the simplex in a one, a two and a three variable search space, are a line, a triangle or a tetrahedron respectively. 16
  17. 17.  Rules for basic simplex: The first rule is to reject the trial with the least favorable value in the current simplex The second rule is never to return to control variable levels that have just been rejected. 17
  18. 18. Modified simplex method It was introduced by Nelder-Mead in 1965. It can adjust its shape and size depending on the response in each step. This method is also called the variable-size simplex method.Rules:1. Contract if a move was taken in a direction of less favorable conditions2. Expand in a direction of more favorable conditions. 18
  19. 19. • Advantage• This method will find the true optimum of a response with fewer trials than the non-systematic approaches or the one- variable-at-a-time method.• Disadvantage :• There are sets of rules for the selection of the sequential vertices in the procedure.• Require mathematical knowledge. 19
  20. 20. Example• Special cubic simplex design for a three component mixture. Each point represent a different formulation SA = stearic acid; DCP= dicalcium phosphate, ST= starch• Constraint : with the restriction that the sum of their total weight must equal to 350 mg, 50 mg = active ingredient 20
  21. 21. example• Development of an analytical method (a continuous flow analyzer) by Deming and king.• The two independent variable show the pump speeds for the two reagents required in the analysis reaction.• The initial simplex is represented by the lowest triangle; the vertices represent the Spectrophotometric response.• The strategy is to move toward a better response by moving away from the worst response 0.25, conditions are selected at the vortex 0.6 and indeed, improvement is obtained.• One can follow the experimental path to the optimum 0.721. 21
  22. 22. Spectrophotometric response at given wavelength 22
  23. 23. 3.LAGRANGIAN METHOD It represents mathematical techniques. It is an extension of classic method. applied to a pharmaceutical formulation and processing. This technique follows the second type of statistical design This technique require that the experimentation be completed before optimization so that the mathematical models can be generates. 23
  24. 24. • Steps involved: 1. Determine the objective function. 2. Determine the constraints. 3. Change inequality constraints to equality constraints. 4. Form the Lagrange function F: a. one Lagrange multiplier λ for each constraint b. one slack variable q for each inequality constraint. 5. Partially differentiate the Lagrange function for each variable and set derivatives equal to zero 6. Solve the set of simultaneous equations. 7. Substitute the resulting values into objective function 24
  25. 25. • Where we have to select this technique? This technique can applied to a pharmaceutical formulation and processing.• Advantages : lagrangian method was able to handle several responses or dependent variables• Disadvantages: Although the lagrangian method was able to handle several responses or dependent variables, it was generally limited to two independent variables 25
  26. 26. Example Optimization of a tablet. phenyl propranolol(active ingredient)-kept constant X1 – disintegrate (corn starch) X2 – lubricant (stearic acid) X1 & X2 are independent variables. Dependent variables include tablet hardness, friability ,volume, invitro release rate e.t.c.., It is full 32 factorial experimental design. Nine formulation were prepared 26
  27. 27. Cont……….. Polynomial models relating the response variables to independents were generated by a backward stepwise regression analysis program. Y= B0+B1X1+B2X2+B3 X12 +B4 X22 +B5 X1 X2 +B6 X12X2 + B7X1 X2 2+B8X12X22....................(1) Y – Response Bi – Regression coefficient for various terms containing the levels of the independent variables. X – Independent variables. 27
  28. 28. Tablet formulationFormulation Drug Dicalcium Starch Stearic acidno,. phosphate 1 50 326 4(1%) 20(5%) 2 50 246 84(21%) 20 3 50 166 164(41%) 20 4 50 246 4 100(25%) 5 50 166 84 100 6 50 86 164 100 7 50 166 4 180(45%) 28
  29. 29. Tablet formulations Constrained optimization problem is to locate the levels of stearic acid(x1) and starch(x2). This minimize the time of invitro release(y2),average tablet volume(y4), average friability(y3) To apply the lagrangian method, problem must be expressed mathematically as follows Y2 = f2(X1,X2)-invitro release…………(2) Y3 = f3(X1,X2)<2.72 %-Friability………..(3) Y4 = f4(x1,x2) <0.9422 cm3 …………..(4) 29
  30. 30. Cont……… 5≤X1 ≤45………(5) 1≤X2 ≤41……….(6) Equation (5) and (6) serve to keep the solution within the experimental range. Inequality constraints must be converted to equality constrained by introducing slack variable. Introduce of langrage multiplier λ to each equality constraint Several equation are then combined into Lagrange function. Partial differentiation of the Lagrange function and solving the resulting set of six simultaneous equation. Value are obtained for the appropriate levels of x1 and x2, to yield and optimum in vitro time of 17.9 min (t50%). 30
  31. 31. Cont……… The solution to a constrained optimization program may depend heavily on the constraints applied to the secondary objectives. Graphical representation. 31
  32. 32. Contour plotHardness of tablet Dissolution time (t50%) 32
  33. 33. Contour plot• C) Feasible solution space indicate by crosshatched area 33
  34. 34. 4.SEARCH METHOD Unlike the Lagrangian method, do not require differentiability of the objective function. used for more than two independent variables. The response surface is searched by various methods to find the combination of independent variables yielding an optimum. It take five independent variables into account and is computer assisted. 34
  35. 35.  Example: optimization of tablet formulation The experimental design used was a modified factorialIndependent Variables Dependent VariablesX1 = Diluents ratio Y1 = Disintegration timeX2= Compression force Y2= HardnessX3= Disintegrant levels Y3 = DissolutionX4= Binder levels Y4 = FriabilityX5 = Lubricant levels Y5 = Porosity 35
  36. 36. 36
  37. 37. • The first 16 trials are represented by +1 and -1.• The remaining trials are represented by a -1.547, zero or 1.547• The data were subjected statistical analysis, followed by multiple regression analysis• The type of predictor equation used in this design is a second-order polynomial: 37
  38. 38. Cont……… for optimization itself , two major steps were used:  Feasibility search  grid search Feasibility search used to locate a set of response constraints that are just at the limit of possibility. Select several response ones wishes to constrain search of the response surface is made to determine whether a solution is feasible. For e.g the constraints in table were fed into the computer and were relaxed ones at a time until a solution was found. 38
  39. 39. Specification of feasibility search 39
  40. 40. Cont…….. the first feasible solution was found at disintegration time time =5 min, hardness=10 kg, and dissolution = 100 % at 50 min. The next step, the grid search,  Experimental range is divided into a grid of specific size and divided into a grid of specific size and methodically searched.  From an input of the desired criteria, the program prints out all points (formulation) that satisfy the constraints.  Thus , the best or most acceptable formulation is selected from the grid search printout to complete the optimization. 40
  41. 41. Cont……… graphic approaches are also available and graphic output is provided by a plotter from computer tapes. The output includes plots of a given responses as a function of a single variable as in figure (a) & (b) or as a function of all five variables. The abscissa for both types is produced in experimental units, rather than physical units, so that it extends from -1.547 to +1.547. 41
  42. 42. Cont……… An infinite number of this plot is possible, since for each curve represented, four of the five variables must remain constant at some level. This is analogous to a partial derivative situation, and the slope of any one graph does indeed represent a partial derivative of the response for one of the independent variables. 42
  43. 43. Step summarized as follows: 43
  44. 44. Advantages:• Takes five independent variables in to account• Person unfamiliar with the mathematics of optimization and with no previous computer experience could carry out an optimization study.• It do not require continuity and differentiability of functionDisadvantage :• One possible disadvantage of the procedure as it is set up is that not all pharmaceutical responses will fit a second-order regression model. 44
  45. 45. Literature reviewKanani R. et al.,Development and characterization of antibiotic orodispersible tablets » To formulated oro-dispersible tablet of Azithromycin that is intended to disintegrate rapidly into the oral cavity and form a stabilized dispersion. » Preliminary study: different superdisintegrant croscarmllose sodium (CCS) , sodium starch glycolate (SSG), crosspovidone (CPVP), were evaluate for weight variation, content uniformity, hardness, disintegrantion time, and friability . 45
  46. 46. Cont…..• Simplex lattice design:• Independent variable : this design utilised using amount of intragranular concentration of superdisintegrant, sodium starch glycolate(A), cros-carmellose sodium (B), crospovisone(c)• Dependent variable: hardness(R1), disintegration time(R2), Friability (R3), wetting time(R4).• A total of 11 formulation with 4 replica was obtained and optimized .• From response surface plot of disintegration time, wetting time, friability and hardness were found. 46
  47. 47. Formulation using simplex lattice designIngredient(mg) F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11IntragranularAzithromycin 100 100 100 100 100 100 100 100 100 100 100Sodium starch glycolate 5 - 30 10 30 - 30 - - 5 20Crosscarmellose sodium 20 30 - 10 - 30 - - - 5 5Crospovidone 5 - - 10 - - - 30 30 20 5Avicel 38 38 38 38 38 38 38 38 38 38 38Sodium lauryl sulphate 2 2 2 2 2 2 2 2 2 2 2ExtragranularAerosil 5 5 5 5 5 5 5 5 5 5 5Magnesium stearate 5 5 5 5 5 5 5 5 5 5 5Aspartame 20 20 20 20 20 20 20 20 20 20 20Total 200 200 200 200 200 200 200 200 200 200 200 47
  48. 48. Design summary response dataRun SSG CCS CPVP Hardness DT(second) Friability(% %CPR kg/cm2 )1 5 20 5 0.33±0.5773 20.33±0.5773 0.43±0.0264 19.33±0.57732 - 30 - 3.66±0.5773 22.26±0.5773 0.34±0.0173 18±0.00003 30 - - 3.66±0.5773 30.33±0.5773 0.49±0.0173 24.66±0.57734 10 10 10 4±0.0000 19±1 0.47±0.0100 14.33±0.57735 30 - - 3.66±0.5773 30.33±0.5773 0.49±0.0173 24.66±0.57736 - 30 - 3.66±0.5773 22.66±0.5773 0.34±0.0173 18±0.00007 30 - - 3.66±0.5773 30.33±0.5773 0.49±0.0173 24.66±0.57738 - - 30 3.66±0.5773 17±1 0.34±0.0173 15.33±0.57739 - - 30 3.66±0.5773 17±1 0.34±0.0173 15.33±0.577310 5 5 20 3±0.0000 12.66±0.5773 0.30±0.0157 11.33±0.577311 - 5 5 3.33±0.5773 27.66±0.5773 0.51±0.0057 21±1 48
  49. 49. equation• Hardness R1= +3.66*A+3.66*B+3.77*C-4.68*A*B- 8.64*A*C-8.64*B*C+75.04*A*B*C• Disintegration time R2 = +30.66*A+22.66*B+17.00*C+41.32*A*B-16.76*A*C- 58.70*B*C-14.49*A*B*C• Friability R3= +0.49*A+0.34*B+0.34*C+0.86*A*B- 0.70*A*C-0.76*B*C-3.96*A*B*C• Wetting time R4= +24.66*A+18.00*B+15.33*C+15.36*A*B- 28.62*A*C-8.70*B*C-177.12*A*B*C 49
  50. 50. Result• Using simplex lattice design from the regression analysis and 3‐ D surface plot it is obtained thato Crospovidone with combination of other two super‐ disintegrants is showing good decrease in hardness.o In case Disintigration time, Crospovidone with combination of croscarmellose is very effective to decrease the Disintrigation time which is desirable.o While in case of friability crospovidone with combination of croscarmellose sodium and sodium starch glycolate very effective to decrease the Friability which is desirable.o And in case of wetting time Crospovidone and Croscarmellose sodium are effective to decrease the Wetting 50 Time which is desirable. (P<0.0001).
  51. 51. Conclusion of this review• Amongst the various combinations of diluents and disintegrants used in the study, tablets that were formulated (wet granulation) using Crospovidone (10%), crosscarmelose sodium and sodium starch glycolate ( each 5%) exhibited quicker disintegration of tablets than compared to those other combination of disintegrants in different concentration.• The effectiveness of super‐disintegrants was in order of Crospovidone>Croscarmellose sodium>sodium starch glycolate.• Formulation F10 was the optimized formulation having least disintegration time as well as other parameters were in acceptable range. 51
  52. 52. conclusion• Optimization techniques are a part of development process.• The levels of variables for getting optimum response is evaluated.• Different optimization methods are used for different optimization problems.• Optimization helps in getting optimum product with desired bioavailability criteria as well as mass production.• More optimum the product = More $$ the company earns in profits!!! 52
  53. 53. References:• Schwarts J. B., et al., Optimization techniques in pharmaceutical formulation and processing, in: Banker G. S., et al. (eds), Modern pharmaceutics, Marcel Dekker Inc., 4th edition (revised and expanded), vol- 121, 607-620, 2005.• Jain N. K., Pharmaceutical product development, CBS publishers and distributors, 1st edition,297-302, 2006.• Cooper L. and Steinberg D., Introduction to methods of optimization, W.B.Saunders, Philadelphia, 1970.• Bolton. S., Stastical applications in the pharmaceutical science, Varghese publishing house,3rd edition, 223• Deming S.N. and King P. G., Computers and experimental optimization, Research/Development, vol-25 (5),22-26, may 53 1974.
  54. 54. Cont…….• Rubinstein M. H., Manuf. Chem. Aerosol News,30, Aug 1974.• Digaetano T.N., Bull.Parenter.Drug Assoc., vol-29,183, 1975.• Spendley, W., et al., Sequential application of simplex designs in optimization and evolutionary operation, Technometrics, Vol- 4 441–461, 1962.• O’connor R.E., The drug release mechanism and optimization of a microcrystalline cellulose pellet system, P.h.d.Dissetation, Philadelphia College of Pharmacy & Science, 1987. 54
  55. 55. Cont……….• Forner, D.E., et al., Mathematical optimization techniques in drug product design and process analysis, Journal of pharmaceutical sciences. , vol-59 (11),1587-1195, November 1970.• Shirsand SB., et al.,Formulation and optimization of mucoadhesive bilayer buccal tablets of atenolol using simplex design method. Inetnational journal of pharmaceutical Investigation. January 2012,volume 2,issue 1,34-40.• Kanani R., et al., Development and characterization of antibiotic orodispersible tablets. International Journal of Current Pharmaceutical Research. 2011,vol3,issue 3,27-32 55
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