Is there a Sticky Sweet Spot?

Transcription

1 Is there a Sticky Sweet Spot? Mixture Design of a Pressure Sensitive Adhesive Emulsion Formulation M. Michaelis, C. S. Leopold Department of Chemistry Division of Pharmaceutical Technology University of Hamburg, Germany 1

2 Aim of the study Formulation of an adhesive mixture for transdermal patches that comprises: 1. Improved adhesion properties. Improved solubility of an Active Pharmaceutical Ingredient (API)

3 Introduction Transdermal Therapeutic Systems (TTS) Patches for transdermal application of APIs to achieve a systemic effect About 50 TTS products on the market 14 Active Pharmaceutical Ingredients Mostly Drug In Adhesive Design (DIA) Drug In Adhesive Design Backing Drug in Adhesive Release liner + Controlled drug delivery + Good compliance - Stability issues - Lack of adhesion API level in blood Crystalization of API Lack of adhesion 3

4 Introduction Pressure Sensitive Adhesives (PSAs) Pressure Sensitive Adhesives: Tacky at room temperature Adhere to a variety of surfaces on light pressure Adhere permanently Ûsed in a lot of common products Adhesive Performance Tack: Ability to form a bond of measurable strength by simple contact with a surface (stickiness) Shear Adhesion: Ability to resist structural failure (cohesiveness) Peel Resistance: Force required to remove the tape without leaving residues 4

5 Materials Component 1: Polyacrylate DuroTak Ethylhexyl acrylate Vinyl acetate Hydroxyethyl acrylate 67 % 5 % Component : Silicone Adhesive BIO-PSA Component 3: Oleyl Alcohol Surfactant Component 4: Ibuprofen API / model drug (high dose analgetic) Oleyl Alcohol 8 % Ibuprofen 5

6 Design Mixture Design 3 (4) Components Components Low [%] High [%] Polyacrylate Adhesive 0 70 Silicone Adhesive Oleyl Alcohol 0 10 Ibuprofen 0 Total 100 = Design Space = Replicate x = Replicate x 3 Design space with constraints IV Optimal Design Suggested design: 16 runs, 5 replicates, 5 to estimate lack of fit Evaluation: FDS Graph 6

7 Preparation of adhesive matrix/specimen 1. Components were dissolved in ethyl acetate (wet mix) and mixed in a shaker at 90 rpm for 15 min.. Wet mix was coated on a release liner 3. Films were dried at 80 C for 30 min in an oven Adhesive matrix (a) µm thick 4. Films were laminated with a backing membrane Specimen (b) a) Coating Knife b) Backing Membrane Drug In Adhesive Drug In Adhesive Release Liner Release Liner 7

8 Response 1 & : 1. Tack Probe Tack Test at 1 C Adhesive matrix (a) Stainless steel probe 3 mm Contact time 1 s, contact force 0.4 N Response: Stress maximum max [N/mm²] Probe Tack Test. Shear Adhesion Test specimen (b) was attached to a stainless steel plate Area: 1 x 1 mm, weight: 50 g Response: Time to failure t [min] Shear adhesion 8

9 Response 3 & 4: 3. Crystal growth Area of 100 cm of the adhesive matrix after 4 h Response: Area covered by crystals in % of the whole area 1:1 1: Creaming behavior (phase separation) Bottle with wet mix after 4 h Separated phase of wet mix in % of the wet mix no creaming 0 % creaming 9

10 Response 5 & 6 5. Droplet size Matrix was transferred to a glass slide Microscope with 100-fold magnification Response: Mean of droplet diameter of 30 droplets [µm] 6. Droplet distribution range Response: Maximum range of 30 droplets [µm] small & narrow large & narrow small & broad large & broad 10

11 Analysis Transformation Model Fit Summary and Model Lack of Fit ANOVA adj. R-square pred. R-square Diagnostics Normal Plot Residuals vs. Predicted Externally Studentized Residuals Box-Cox Plot Model Graphs 11

12 Response Analysis 1: Tack Fit Summary and Model Highest order polynomial: Reduced Special Quartic Model ANOVA Model: significant Lack of fit: not significant Adj. R-square: 0.96 Pred. R-square: Diagnostics Normal Plot Residuals vs. Predicted Box-Cox plot Model Graphs 1

13 Response 1: Tack - Diagnostics Normal Normal Plot Plot of of Residuals Residuals Residuals Residuals vs. vs. Predicted Predicted 3.00 Normal % Probability Normal % Probability Predicted Predicted 13

14 Response 1: Tack - Diagnostics Externally Residuals Box-Cox-Plot Box-Cox Plot for for Power Power Transforms Transforms Ln (ResidualSS) Ln(ResidualSS) Run Number Run Number Lambda Lambda 14

15 Response 1: Tack - Model Graphs 100 % Polyacrylate = 0.50 N/mm² 100 % Silicone = 0.55 N/mm² A: Acrylat Design points below predicted value Tack 0. A (70.000) 0.1 C (0.000) B (10.000) B: Silicone Tack C: Oleyl Alcohol B (60.000) A (0.000) C (50.000) 15

16 Response : Shear Adhesion - Diagnostics Fit Summary and Model Highest order polynomial: Quadratic Model ANOVA Model: significant Lack of fit: not significant Adj. R-square: Pred. R-square: Diagnostics Recommended transformation: Log Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal % Probability Normal % Probability Ln(ResidualSS) Ln (ResidualSS) Predicted Run Number Lambda Predicted Run Number Lambda 16

17 Response : Shear Adhesion - Model Graphs 100 % Polyacrylate = 17.min 100 % Silicone = 56.9 min A: Acrylat Design-Expert Software Component Coding: Actual Original Scale (median estimates) Shear Design points above predicted value Design points below predicted value X1 = A: Acrylat X = B: Silicone X3 = C: Oleyl Alcohol Shear Shear Adhesion C (0.000) A (70.000) B (10.000) B: Silicone Shear Shear Adhesion C: Oleyl Alcohol B (60.000) A (0.000) C (50.000) 17

18 Response 3: Crystal Growth - Diagnostics Fit Summary and Model Highest order polynomial: Reduced Cubic Model ANOVA Model: significant Lack of fit: not significant Adj. R-square: Pred. R-square: Diagnostics Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal % Probability Normal % Probability Ln(ResidualSS) Ln (ResidualSS) Internally studentized residuals Predicted Run Number Lambda Predicted Run Number Lambda 18

19 Response 3: Crystal Growth - Model Graphs Crystal Growth 100 % Polyacrylate= 70 % 100 % Silicone= 100 % alue A: Acrylat Crystal Growth C (0.000) A (70.000) B (10.000) B: Silicone Crystal Growth C: Oleyl Alcohol B (60.000) A (0.000) C (50.000) 19

20 Response 4: Creaming behavior - Diagnostics Fit Summary and Model Highest order polynomial: Reduced Cubic Model ANOVA Model: significant Lack of fit: not significant Adj. R-square: Pred. R-square: Diagnostics Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal % Probability Normal % Probability Ln(ResidualSS) Ln (ResidualSS) Predicted Run Number Lambda Predicted Run Number Lambda 0

21 Response 4: Creaming Behavior - Model Graphs Design-Expert Software Component Coding: Actual Creaming Design points above predicted value Design points below predicted value A: Acrylat X1 = A: Acrylat X = B: Silicone X3 = C: Oleyl Alcohol Creaming Creaming C (0.000) A (70.000) B (10.000) B: Silicone Creaming C: Oleyl Alcohol B (60.000) A (0.000) C (50.000) 1

22 Response 5: Droplet Size - Diagnostics Fit Summary and Model Highest order polynomial: Reduced Cubic Model ANOVA Model: significant Lack of fit: not significant Adj. R-square: Pred. R-square: Diagnostics Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal Normal % % Probability Internally Ln(ResidualSS) Ln (ResidualSS) Predicted Run Number Lambda Predicted Run Number Lambda

23 Response 5: Droplet Size - Model Graphs A: Acrylat Design-Expert Software Component Coding: Actual Droplet Size Design points above predicted value Design points below predicted value X1 = A: Acrylat X = B: Silicone X3 = C: Oleyl Alcohol Droplet Size Droplet Size A (70.000) 0 C (0.000) B (10.000) B: Silicone Droplet Size C: Oleyl Alcohol B (60.000) A (0.000) C (50.000) 3

24 Response 6: Droplet Distribution Range - Diagnostics Fit Summary and Model Highest order polynomial: Reduced Cubic Model ANOVA Model: significant Lack of fit: not significant Adj. R-square: Pred. R-square: Diagnostics Recommended Transformation: Square Root Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal Plot of Residuals Residuals vs. Predicted Box-Cox Plot for Power Transforms Normal % Probability Normal % Probability Ln(ResidualSS) Ln (ResidualSS) Predicted Run Number Lambda Predicted Run Number Lambda 4

25 Response 6: Droplet Distribution Range - Model Graphs A: Acrylat value value Droplet Distribution Range Droplet Distribution C (0.000) A (70.000) B (10.000) B: Silicone Droplet Droplet Distribution Distribution Range C: Oleyl Alcohol B (60.000) A (0.000) C (50.000) 5

26 Discussion: Response 3 & 5 & 6 A: Acrylat A: Acrylat A: Acrylat B: Silicone Crystal Growth C: Oleyl Alcohol B: Silicone Droplet Size C: Oleyl Alcohol B: Silicone Droplet Distribution C: Oleyl Alcohol Crystal Growth Droplet Size Droplet Distribution Range 6

27 Optimization Constrains Solutions Lower Upper Lower Upper Name Goal Limit Limit Weight Weight Importance A:Acrylate is in range B:Silicone is in range C:Oleyl Alcohol is in range Tack maximize Shear Adhesion maximize Crystal Growth is target = Droplet Size minimize Droplet Distribution minimize Number Acrylate Silicone Oleyl Alcohol Tack Shear Adhesion Crystal Growth Droplet Size Droplet Distribution Desirability

28 Optimization A: Acrylat Prediction Prediction B: Silicone Desirability C: Oleyl Alcohol 8

29 Conclusion Is there a sticky sweet spot? No, there is not. But 9

30 Conclusion We know that: Oleyl alcohol neither stabilizes the polymer-polymer interaction nor the polymer-api interaction. Oleyl alcohol decreases the adhesion properties in most cases. Wet mixes with equal amounts of polymer tend to be less stable. The best results were found at the periphery of the design space. Crystal growth correlates with droplet size and droplet distribution. No profit of oleyl alcohol, the addition is unnecessary. Limited processing time must be taken into account for scale up. Design space needs to be augmented. Do process variables such as mixing speed or viscosity of the wet mix have an influence on crystal growth? (combined design) 30

31 Thank you! 31