1. K S Tan, K H Leong, L Y Chung, M I Noordin Department of Pharmacy Faculty of Medicine University of Malaya Kuala Lumpur

2. 2 Introduction Optimization of pharmaceutical formulation Conventional trial-and-error approach Mathematical Modeling Method.

3. 3 Introduction Furosemide has a narrow absorption window (located at upper GI tract) 1, 2 & 3. Conventional oral formulation exhibits erratic bioavailability & unpredictable response 4. Furosemide

4. 4 Introduction Absorption window

5. 5 Introduction [Concept adapted from Reference 1] Absorption window o

6. 6 Introduction Gastroretentive dosage form prolongs retention time in stomach & permits continuous drug release to optimal absorption site 1, 2 & 5.

7. 7 Objectives To optimize a formulation for furosemide characterized by a 12-hour gastroretentive and sustained release profile. To demonstrate the usefulness of mathematical modeling method in optimization of formulation.

8. 8 Methods: Overview Determination of 13 model formulations (Formulae A - M) via simplex lattice design. Preparation of tablets (Formulae A – M) Tablet QC tests (Uniformity of weight, friability, tablet size, hardness) In vitro dissolution tests (8 hours) Enzyme-free simulated gastric fluid (SGF) pH 1.2 USP paddle method (100 rpm) Temperature 37±0.5˚C Sample buffered to pH 5.8 & Assayed with UV spectrophotometry at 278 nm In vitro tablet swelling tests Enzyme-free simulated gastric fluid (SGF) pH 1.2 USP paddle method (100 rpm) Temperature 37±0.5˚C Measurement of swelling tablet diameter (To be continued in next slide)

9. 9 Methods: Overview Data of in vitro tablet dissolution tests Data of in vitro tablet swelling tests Multiple Linear Regression Analysis Model-fitting Determination of best-fit models for individual response Optimization of formulation Design-Expert® integrate all models built and solve simultaneously to search for optimal formulation based on the constraints imposed. Verification of optimal formulation (In vitro dissolution tests & tablet swelling test) Multiple Linear Regression Analysis Model-fitting Determination of best-fit models for individual response (Continued from previous slide)

10. 10 Methods Mixture experimental design Tablet excipients: Iota-carrageenan, Lambda-carrageenan Acacia gum. Simplex lattice design was employed to determine excipient composition of 13 model formulations. Each 400 mg tablet contains 60 mg furosemide.

11. 11 Methods Composition of tablet excipients for 13 model formulations.

12. 12 Results & Discussions In Vitro Tablet Dissolution Profiles of 13 model formulations (n = 6)

13. 13 Results & Discussions In Vitro Tablet Swelling Profiles of 13 model formulations (n = 6)

14. 14 Results & Discussions Formula B: Dissolution Profile Formula B: Tablet Swelling Profile Formula D: Dissolution Profile Formula D: Tablet Swelling Profile

15. 15 Results & Discussions Model-Fitting The data of all response variables (tablet dissolution and swelling tests) for 13 formulations were fitted into various equations: Linear model:Y = b 1 X 1 + b 2 X 2 + b 3 X 3 Quadratic Model:Y = b 1 X 1 + b 2 X 2 + b 3 X 3 + b 12 X 1 X 2 + b 13 X 1 X 3 + b 23 X 2 X 3 Special Cubic model: Y = b 1 X 1 + b 2 X 2 + b 3 X 3 + b 12 X 1 X 2 + b 13 X 1 X 3 + b 23 X 2 X 3 + b 123 X 1 X 2 X 3

16. 16 Results & Discussions Models for Drug Release & Tablet Swelling Profiles

17. 17 Contour plots of individual response variable for in vitro tablet dissolution studies Y 30 min :% Drug released in 30 minutes Y 1h :% Drug released in 1 hour Y 1.5h :% Drug released in 1.5 hour Y 2h :% Drug released in 2 hours Y 3h :% Drug released in 3 hoursY 4h :% Drug released in 4 hoursY 5h :% Drug released in 5 hoursY 6h :% Drug released in 6 hours Y 7h :% Drug released in 7 hoursY 8h :% Drug released in 8 hours Y 1.5h :% Drug released in 2 hour

18. 18 Contour plots of individual response variable for in vitro tablet swelling studies Z 30min : Tablet diameter at 30 th minZ 15min : Tablet diameter at 15 th minZ 45min : Tablet diameter at 45 th minZ ih : Tablet diameter at 1 st hour Z i.5h : Tablet diameter at 1.5 th hour Z 2h : Tablet diameter at 2 nd hourZ 3h : Tablet diameter at 3 rd hourZ 4h : Tablet diameter at 4 th hour Z 5h : Tablet diameter at 5 th hour Z 6h : Tablet diameter at 6 th hour Z 7h : Tablet diameter at 7 th hourZ 8h : Tablet diameter at 8 th hour

19. 19 Results & Discussions Optimization of Formulation Constraints imposed on: Drug release at 2hr (12-16%), 4hr (24-32%), 6hr (42-52%) & 8 hr (70-100%). Tablet swelling: 13-19 mm (maximizing). Optimized formula: ExcipientsExcipient Composition (%) ι-carrageenan, X 1 54.44 λ-carrageenan, X 2 21.11 Acacia gum, X 3 24.45

20. 20 Results & Discussions Optimized formulation Tablet dissolution profile (A) and swelling profile (B) of optimal formulation predicted by the model. B A

21. 21 Results & Discussions Verification of Optimal Formulation B Tablet dissolution profile (A) and swelling profile (B) of optimal formulation (Comparing observed vs. predicted data) A (Paired-samples T-test, p > 0.05)

22. 22 Results & Discussions The optimal formulation exhibits a zero-order release kinetic. (Fitted into Korsmeyer- Peppas model, n = 0.94) In Vitro Tablet Dissolution Profiles

23. 23 Results & Discussions Commercial Product: furosemide 60 mg (Wakelkamp et al 1999) GRDF: A gastroretentive dosage form, furosemide 60 mg developed by Klausner et al (2003) 5 OF: The optimal formulation obtained in this study. In Vitro Tablet Dissolution Profiles

24. 24 Conclusions Optimal formulation with desirable release profile & tablet swelling characteristics was obtained. An efficient optimization process: omitting the cost- and time-consuming procedures as in the conventional trial-and-error approach. Mathematical modeling permits the characterization of drug release kinetics during the optimization process. Graphical optimization allows evaluation of excipient’s functionality in the dosage form.

25. 25 References 1. Chawla, G, Gupta, P, Koradia, V & Bansal, AK 2003, ‘Gastroretention a means to address regional variability in intestinal drug absorption’, Pharmaceutical Technology, vol. 27, no. 7, pp. 50-68. 2. Davis, SS 2006, ‘Formulation strategies for absorption windows’, Drug Discovery Today, vol. 10, no. 4, pp. 249-257. 3. Rouge, N, Buri, P & Doelker, E 1996, ‘Drug absorption sites in the gastrointestinal tract and dosage forms for site-specific delivery’, International Journal of Pharmaceutics, vol. 136, pp. 117-139. 4. Ponto, LLB & Schoenwald, RD 1990, ‘Furosemide (frusemide): a pharmacokinetic/pharmacodynamic review (part I)’, Clinical Pharmacokinetics, vol. 18, no. 5, pp. 381-408. 5. Klausner, EA, Lavy, E, Stepensky, D, Cserepes, E, Barta, M, Friedmann, M & Hoffman, A 2003b, ‘Furosemide pharmacokinetics and pharmacodynamics following gastroretentive dosage form administration to healthy volunteers’, Journal of Clinical Pharmacology, vol. 43, pp. 711-720. 6. Wakelkamp, M, Blechert, Å, Eriksson, M, Gjellan, K & Graffner, C 1999, ‘The influence of frusemide formulation on diuretic effect and efficiency’, British Journal of Clinical Pharmacology, vol. 48, pp. 361-366.

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28. 28 Results & Discussions Model CoefficientY 30min Y 1h Y 1.5h Y 2h Y 3h Y 4h Y 5h Y 6h Y 7h Y 8h Linear SD1.992.503.305.177.807.559.2411.5611.4911.50 R2R2 0.41070.57640.71700.69910.70580.74350.71350.67160.67870.6223 Adjusted R 2 0.29290.49170.66040.63900.64700.69210.6561.0.60590.61450.5468 Predicted R 2 -0.15000.12910.37170.35410.44970.51530.51240.41300.38260.3493 PRESS77.25128.48241.32573.191138.361078.041452.522387.252538.702278.71 Quadratic SD1.982.262.724.467.827.629.9212.3311.2612.36 R2R2 0.59050.75790.86510.84280.79330.81710.76880.73830.78440.6948 Adjusted R 2 0.29790.58500.76870.73050.64570.68650.60370.55140.63030.4768 Predicted R 2 -1.2390-0.33160.2168-0.0622-0.11540.0648-0.1068-0.19330.1959-0.1896 PRESS150.40196.44300.80942.722307.112080.173297.064853.033306.654165.98 Special cubic SD2.092.332.614.578.027.179.619.557.739.35 R2R2 0.61040.77990.89320.85890.81350.86130.76880.86530.91270.8504 Adjusted R 2 0.22070.55970.78640.71790.62710.72270.60370.73060.82540.7007 Predicted R 2 -1.8123-0.66400.0790-0.3133-0.3791-0.0138-0.10680.12930.63510.3314 PRESS188.92245.47353.721165.502852.612255.023297.063541.141500.562341.21 Cubic SD1.531.901.974.019.038.3111.5311.3410.7311.85 R2R2 0.89520.92670.96960.94580.88170.90680.86600.90520.91600.8798 Adjusted R 2 0.58090.70690.87850.78300.52670.62700.46420.62070.66390.5192 Predicted R 2 -25.242-25.7135-11.4817-11.0792-21.5643-16.1550-33.0634-13.4233-11.7876-15.7273 PRESS1762.833940.734793.9410720.0846674.3638158.0310150058657.1152583.4958577.50 Model-fitting Summary for Tablet Dissolution Profiles

29. 29 Results & Discussions Model-fitting Summary for Tablet Swelling Profiles Model CoefficientZ 15min Z 30min Z 45min Z 1h Z 1.5h Z 2h Z 3h Z 4h Z 5h Z 6h Z 7h Z 8h LinearSD0.270.320.28 0.390.610.541.152.464.192.642.72 R2R2 0.79410.88170.91630.94220.94030.89250.93900.86710.74380.69040.87210.8767 Adjusted R 2 0.75300.85810.89960.93070.92840.87100.92680.84050.69260.62850.84660.8520 Predicted R 2 0.61200.80960.85710.88970.89920.78760.88770.68360.39870.35340.73440.7816 PRESS1.401.671.341.542.627.295.3331.32142.46366.66145.08130.93 QuadraticSD0.270.360.240.290.350.430.600.701.613.612.213.17 R2R2 0.85650.89800.95810.95840.96740.96150.94770.96580.92350.83920.93740.8826 Adjusted R 2 0.75400.82510.92820.92860.94420.93400.91040.94140.86890.72440.89270.7987 Predicted R 2 0.22570.61300.76970.73680.88950.83960.69580.73290.50350.48600.7743-0.038 PRESS2.803.402.163.672.875.5014.4426.44117.63291.50123.28622.22 Special cubic SD0.270.330.210.300.310.470.610.701.553.672.173.09 R2R2 0.87520.92630.97180.96020.97800.96160.95260.97060.93890.85770.94840.9047 Adjusted R 2 0.75050.85260.94370.92040.95600.92320.90510.94120.87780.71550.89680.8093 Predicted R 2 0.11880.68360.78450.67540.90910.79420.64400.68750.44160.45650.7673-0.176 PRESS3.182.782.024.532.367.0616.9030.93132.28308.24127.12704.94 CubicSD0.230.40.220.320.260.560.620.521.424.522.650.84 R2R2 0.95640.94440.98470.97800.99240.97240.97590.99180.97460.89190.96130.9965 Adjusted R 2 0.82550.77770.93880.91200.96950.88960.90360.96720.89820.56770.84530.9860 Predicted R 2 -7.0160-7.5731-1.1472-2.0822-0.4004-3.5707-2.3061-0.2095-2.9667-13.896-4.8503-0.073 PRESS28.9775.4020.1543.0236.39156.86156.94119.73939.728447.373195.22643.30

30. 30 Results & Discussions Experimental dissolution data of optimal formula fitted into Korsmeyer- Peppas model. Korsmeyer-Peppas model: MtM∞anMtM∞an ======== Cumulative amount of drug released at time t Cumulative amount of drug at infinite time Constant incorporating structural and geometric characteristics of the device Release exponent, indicative of the mechanism of release.

31. 31 Tan K S - Aug 2007

32. 32 Tan K S - Aug 2007