Automated Stability Testing Delivering Rapid Product Development

Transcription

1 Automated Stability Testing Delivering Rapid Product Development Dr Andy Brookes, AstraZeneca APS PharmSci 2-4 th September 2013 With thanks to: Pam Harrison, Helen Porter, Faye Turner, Arun Kamath

2 The Challenge 2

3 Stability Aim to demonstrate that there is no significant change to a product that will compromise patient safety or product efficacy over the shelf life of that material Formal Stability Studies Generates data for submission to regulatory authorities Real time data must be used Limited extrapolation can be applied Follow-up with real time data Typical commercial shelf life >2 years at ambient storage conditions Accelerated Stability Studies Does a developmental drug have the potential to reach a suitable shelf-life? Not submitted to regulatory authorities Used for decision making during development Shorter duration studies, opportunities for prediction How do we deliver an approach which allows us to deliver better data quality with greater efficiency? 3

4 Impact Supporting through all stages of development Intermediates Drug Substance Formulation Development Commercial Product Chemical and physical stability Chemical stability Qualitative development Excipient compatibility studies Life cycle management support Storage requirements Physical stability (including polymorph stability) Quantitative development Shelf life prediction Storage requirements and shelf life prediction Prototype formulation selection Shelf life prediction storage requirements and packaging Alternative pack selection 4

5 The Solution 5

6 Rapid Product Development Tool Bespoke system developed by RPD Tool, Switzerland ( Based at AZ Macclesfield (UK) supports internal projects worldwide. Capabilities: Individually led racks with possible temperature range from approx ºC Humidity led individually for each sample with saturated salt solutions Reservoir Containing salt solution sample 6

7 Capability Analysis Chemical Chemical Physical Physical Technology UHPLC- UV/MS/ELSD Oxygen sensor (vial headspace) NIR Raman Primary purpose Degradation product formation Oxygen level Hydration state Polymorphic form conversion Secondary purpose Preliminary Identification Impact of exposure to light Polymorphic form conversion, particle size Amorphous / Crystalline transition, salt disassociation Information Rates of formation, mechanistic insight Mechanistic insight Conditions of form conversion, rate Conditions of form conversion, rate Physical Camera Appearance Physical state 7

8 Workflows Chemical Analysis Predictive study design Initial 40 o C/ 75% RH C, P 50 o C/ % RH 50 o C/ 75% RH 60 o C/ % RH 60 o C/ 75% RH 70 o C/ 75% RH Exploring the effect of temperature and humidity Day 7 C, P C, P C, P C, P C, P C, P Day 14 C, P C, P C, P C, P C, P C, P Day 21 C, P C, P C, P C, P C, P O Day 28 C, P C, P C, P O O O C = Chemical Destructive sampling UHPLC-UV-MS-ELSD Frequency typically weekly (can be more frequent) P = Physical, non-destructive NIR, Raman, Camera Frequency can be up to daily O = Optional 8

9 Workflows Physical Analysis Predictive study design Initial 40 o C/ 75% RH C, P 50 o C/ % RH 50 o C/ 75% RH 60 o C/ % RH 60 o C/ 75% RH 70 o C/ 75% RH Exploring the effect of temperature and humidity Day 7 C, P C, P C, P C, P C, P C, P Day 14 C, P C, P C, P C, P C, P C, P Day 21 C, P C, P C, P C, P C, P O Day 28 C, P C, P C, P O O O C = Chemical Destructive sampling UHPLC-UV-MS-ELSD Frequency typically weekly (can be more frequent) P = Physical, non-destructive NIR, Raman, Camera Frequency can be up to daily O = Optional 9

10 Data Evaluation 10

11 Data Evaluation Formulation 1; 25 o C/dry; Raman, days Comparison of behaviour at a range of stability conditions Formulation 1; 25 o C/60% RH; Raman, days 11 Overlay Process factor Time profile Formulation 2 Formulation 3... Formulation n Comparison of formulations at discriminating condition

12 Data Evaluation Formulation 1; 25 o C/dry; Raman, days Comparison of behaviour at a range of stability conditions Formulation 1; 25 o C/60% RH; Raman, days 12 Overlay Process factor Time profile Formulation 2 Formulation 3... Formulation n Comparison of formulations at discriminating condition

13 Stability studies Choosing the conditions Make use of historical data: Previous forced degradation studies on drug substance Real time stability studies on previous formulation types Suitable selection of temperature and humidity conditions Aim to obtain enough degradation during the length of the study Avoid secondary / tertiary degradation Formulation studies Excipient interaction may increase rate of API degradation Excipients may be less stable than API How many conditions? Depends on aim of the study e.g. requirements for shelf-life prediction Typically no less than 4 conditions. However, have had up to 15! Some drug product studies, especially during formulation development, can be VERY large Potentially formulation variants, 5 stability conditions x 5 time points = hundreds of samples! 13

14 Prediction OSP tool OSP tool (Open Stability Predictor) is internal software that uses the modified Arrhenius equation to predict the degradation at a pre-defined temperature and humidity ln k = ln A + Ea/R*[1/T] + B*[%RH] T = temperature (K) RH = relative humidity (%) k = rate constant A = pre-activation constant Ea = activation energy R = molar gas constant B = moisture coefficient The tool uses a number of outputs to show the user how good a model the OSP has been for the data used and therefore how trustworthy the prediction is 14

15 The Applications Which candidate is the most stable? Are any candidates stable enough? Packaging selection? Storage conditions? Inform formal stability design? 15

16 Example 1 Fundamental Understanding of Drug Substance behavior Implications for packaging selection 16

17 Introduction Existing data indicates that drug substance has good chemical stability Multiple polymorphic forms are known How will the drug substance behave on storage at a range of conditions? What packaging will be required for storage? Asses the effect of temperature and humidity on: Conversion of polymorphic form e.g. B to D Conversion to hydrated form Appearance Study Design 9 Batches of drug substance 15 conditions exploring combinations of Temperature (15 40 o C) Relative Humidity (11 60% RH, with ambient ) 135 samples (plus additional samples for off-line analysis) 2 month study Weekly analysis for polymorphic form (NIR & Raman) and appearance (optical images) 17

18 Optical Images Bx6 o C/60%RH T=0 T=7 T=14 T=21 T=28 T=56 18

19 15C 15/11 15/ 15/60 25C 25/11 25/ 25/60 C /11 / / C 40/11 40/ B6 Initial vs D56

20 Map of Appearance for each Bx (56 days) Bx B1 Bx B2 Bx B /11 15/ 15/ /11 15/ 15/ /11 15/ 15/ /11 25/ 25/ /11 25/ 25/ /11 25/ 25/60 /11 / /60 /11 / /60 /11 / /60 40 Bx B4 40/11 40/ NR 40 Bx B5 40/11 40/ NR 40 Bx B6 40/11 40/ NR 15 15/11 15/ 15/ /11 15/ 15/ /11 15/ 15/ /11 25/ 25/ /11 25/ 25/ /11 25/ 25/60 /11 / /60 /11 / /60 /11 / /60 40 Bx B7 40/11 40/ NR 40 Bx B8 40/11 40/ NR 40 Bx B9 40/11 40/ NR 15 15/11 15/ 15/ /11 15/ 15/ /11 15/ 15/ /11 25/ 25/ /11 25/ 25/ /11 25/ 25/60 /11 / /60 /11 / /60 /11 / / /11 40/ NR 40 40/11 40/ NR 40 40/11 40/ NR

21 Evaluation of Form Transformation PCA Analysis - Coloured by Batch ~700 NIR measurements 20 B4 B3 B2 B2 B4 B3 B3 B1 B3 Label Batch Condition Temp & %RH Time X axis: most significant variation in the data Y axis: 2 nd most significant variation in the data t[2] B7 B7 B6 B8 B8 B8 B9 Bx 9 Form D B8 B9 B9 B1 B5 B1 B9 B9 B9 B9 B9 B9 B9B9 B9B9 B9 B9 B5 B1 B1 B2 B7 B3 B6 B5 B2 B2B5 B4 B3 B2 B2 B3 B2 B2 B3 B6 B3 B6 B5 B1 B2 B4 B1 B2 B5 B2 B1 B6 B1 B6 B5 B1 B2 B1 B6 B4 B5 B1B1 B2 B1B1 B1 B1 B1 B1 B2 B2B2 B2 B3B3 B3 B5 B6 B6B6 B4 B4 B4 B2B2 B2 B4B4 B3B3 B2 B4 B5B5B5 B4B4 B5 B6 B7 B5 B5B5 B5 B6 B6 B5B5 B5B5 B8 B7 B4 B7B7 B3 B7B7 B3 B5 B8 B5 B5 B5 B7 B8 B8 B8 B7 B5 B5 B5 B8 B5B7 B7 B8 B8B8 B8 B8 B8 B8 B8 B8 B8 B8 B8B8 B8 B8 B8 B8B t[1] Bx 1-5, 7 Form B Bxs 6 < 8 < 9 Increasing initial Form D content 21

22 Evaluation of Form Transformation PCA Analysis - Coloured by Batch ~700 NIR measurements 20 25!60 25!60 25!60 Label Batch Condition Temp & %RH Time X axis: most significant variation in the data Y axis: 2 nd most significant variation in the data t[2] !60 Bx 9 Form D 25!60 25!60 15! 15!! C 15! 25C C! 15C 40! 25!!60 25! 25!11 15C 15! 25!60!11 15! 40C 40! 40C 40!11 15C 15!11 15!11 40!11!11 15!11! 15!11 25!11 40!11 25!11!!60!60!60!60!60!60!60 25!60 25!60!60!60 25!60 25!60 25!60 25!60!60 40! 25!60!!60 40!!11 C!!60 25!60!11 25C15C 25!60 25! 25!11 15!11 C 15!!60 40! 25!60! 40C15!11 40! 25! C 15C 25!60 25! 25! 25!60 40C 40!!!60 15! 40!11 40!!11 25!60 15! 40! 15! 40! C!! 25C 40!11 15!11!!11 C 15!11 25!60! 25C 40!11!60 C 15! 15C 15!!11 25C! 25!60 25!11 40!11 25C!11 15! 15C 25!!11 25C 40C25!60 15C 40!! 40C15!11 40! 25!11!60!60!11 25! C!11! C 15! 25C 40! 15C 25!11! 15! 25!60!11 40!11 25C 25! 25!!!11 C! 25!11 15!11! 15! 25C 40!11 15C 25C 25!11 15!11 15C!!60 15!11 25C 15! 15! 25!60!!60 40!11 40!11 25C!60 25!60 25C40C C 40!!!!60 15C 15C 25!60 40! 15! 25!11 C 15C!11 15C 25C 15! 25! 25!11 40! 15!11 40!11 40C C!11 40!11 15! 25! t[1] Bx 1-5, 7 Form B Bxs 6 < 8 < 9 Increasing initial Form D content 22

23 Evaluation of Form Transformation PCA Analysis - Coloured by Batch ~700 NIR measurements Label Batch Condition Temp & %RH Time X axis: most significant variation in the data Y axis: 2 nd most significant variation in the data t[2] t[1] Bx 1-5, 7 Form B Bx 9 Form D Bxs 6 < 8 < 9 Increasing initial Form D content 23

24 NIR Raw Data Form D 15 o C / ambient RH 15 o C / 60% RH Form B Hydrate Time sequence: 7 measurements over 56 days Time sequence: 7 measurements over 56 days 24

25 Conclusions Observations Change in appearance greater at high temperature and high humidity Hydration greater at low temperature high humidity Low humidity conditions no change Store in packaging to protect from humidity e.g. aluminium laminate bags Ambient temperature storage will be acceptable 25

26 Example 2 Prediction of shelf life and influence on setting release specifications 26

27 Introduction Aim: To determine the specification limit for the water content of a solid formulation in order to reach 36 month shelf-life in blister packs. Formulations containing 1 and 1.5% moisture were manufactured 40 C/60%RH 40 C/75%RH 50 C/60%RH 50 C/75%RH 1 day x x 3 days x x x 7 days x x x x 14 days x x x x 21 days x x, n=6 x x 28 days x x x x 42 days o o o o X = pull sample, O = optional pull All above timepoints tested for chemical stability (UPLC) and physical stability (Raman and NIR) 27

28 Results 2 Main degradation impurities seen Observations More degradation observed at 60%RH than 75%RH After 2 weeks impurity at RRT 0.78 is above 1% area. Next steps determine if changes are observed in spectral data Area % Time (Days) RRT /60 40/75 50/60 50/75 40/ 50/ Area % RRT Time (Days) 40/60 40/75 50/60 50/75 40/ 50/

29 Investigation Physical Stability Data NIR data analysed 75% RH Moisture 60% RH Initial timepoint Increase in moisture uptake with increasing %RH storage (~5200cm -1 ). Other spectral changes with increasing moisture (~4600, 4400 cm -1 ). Physical changes observed at 75%RH, which may explain unexpected chemical degradation pattern. 29

30 Impact A change in physical properties was observed when samples were stored at high humidity conditions of 75% RH (40 o C/75%RH and 50 o C/75%RH) Formal accelerated stability study at the traditional 40 o C/75%RH condition would not be meaningful for this product. Shelf life prediction: The 40 o C/75%RH and 50 o C/75%RH data could not be used due to changes in physical properties. By supplementing the RPD tool data with a limited amount of real time open storage data it was possible to predict the shelf life

31 Prediction and Conclusions Shelf life prediction - 25 o C/60%RH, 1.5% moisture content in tablet The prediction demonstrates that tablets with a water content of 1.5 % will not achieve a 36 month shelf life. % predicted degradation median 5th percentile 95th percentile RPDTool Therefore, water content specification limit was set at 1.0% for this product at 25 o C/60%RH time (months) 31

32 Example 3 Support formulation development Prediction of shelf life and required storage conditions for long term stability 32

33 Background Quantitative formulation development to explore the effect of drug load, particle size and coating Parameter Bx1 Bx2 Bx3 Bx4 Bx5 Coat Yes Yes Yes Yes No Particle size Milled Micronised Milled Micronised Micronised Load 10% 10% 5% 5% 5% Predictive design exploring the effect of temperature and humidity Initial Day 1 Day 3 Day 7 Day 14 Day 21 Day 28 Day 56 Initial X (n=3) 60ºC/ 75% RH X X X X O 70ºC/ % RH X X X X O 70ºC/ 75% RH X X X X O 80ºC/ % RH X X X X O Chemical analysis using UPLC-UV 33

34 Results RRT /75, RRT 0.93 Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch /, RRT /75 Scatterplot of Smiles 1 vs Days 70/ Scatterplot of Smiles 1 vs Days Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 % area Smiles % area Smiles Days Days /75, RRT /, RRT /75 Scatterplot of Smiles 1 vs Days 80/ Scatterplot of Smiles 1 vs Days Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 % area Smiles Smiles % area Days Significant degradation observed for all batches 10 Days 20 Greater degradation observed for coated batches Greater degradation observed for low drug load batches at high humidity 34

35 Results RRT /75, RRT 0.95 Scatterplot of Smiles 2 vs Days 60/75 70/, RRT 0.95 Scatterplot of Smiles 2 vs Days 70/ % area Smiles Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 % area Smiles Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch Days Days Days 20 % area Smiles Scatterplot of Smiles 2 vs Days Scatterplot of Smiles 2 vs Days 70/75, 70/75 RRT /, 80/ RRT 0.95 Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 % area Smiles Batch Batch 1 Batch 2 Batch 3 Batch 4 Batch Days Days 20 Similar degradation observed for all batches Greater degradation observed at high humidity RRT 0.95 seen at similar levels in coated and uncoated batches 35

36 Coated vs Uncoated - Individual Value Plot RRT 0.93, %RH RRT 0.93, 75%RH Individual Value Plot of Smiles 1 % Individual Value Plot of Smiles 1 75% Coated Uncoated Coated Temp Uncoated 0.0 Coated Uncoated Coated Temp Uncoated RRT 0.95, %RH RRT 0.95, 75%RH Individual Value Plot of Smiles 2 % Individual Value Plot of Smiles 2 75% Coated Uncoated Coated Temp Uncoated 0.0 Coated Uncoated Coated Temp Uncoated Uncoated tablets - Stability greatly affected by humidity both RRT 0.93 and 0.95 Coated tablets - Degradation increases with increasing temperature and humidity 36

37 Drug Load - Individual Value Plot RRT 0.93 RRT 0.95 Individual Value Plot of Smiles 1 Drug load Individual Value Plot of Smiles 2 Drug load 5 10 Increased levels of RRT 0.93 and RRT 0.95 observed at low drug load (5%) when compared to high drug load (10%) 0.05 Humidity Temp Humidity Temp The effect is more significant for RRT 0.95 Particle Size- Individual Value Plot RRT 0.93 Individual Value Plot of Smiles 1 API Micronised Milled Individual RRT Value Plot 0.95 of Smiles 2 API Micronised Milled No significant effect of particle size observed Smiles Smiles Humidity Temp Humidity Temp

38 Shelf-life predictions Prediction model for RRT 0.93 for all 5 Batches. Based on a specification limit of 0.5 area% all 5 formulation variants should achieve a 2-year shelf-life at 25C/60%RH. Shortest shelf-life prediction obtained for Batch 3 fitted ln k Batch 3-3 years predicted measured rate (k) vs. fitted rate 0 area% Pred degrad of peak Smiles 1 at cond median of simulations 5th percentile th percentile ln k storage conditions y = *x + ( ), R2 = time (years) fitted ln k Batch 5 - >8 years predicted RRT 0.93 Prediction at 25/60 RRT 0.93 Prediction at 25/60 measured rate (k) vs. fitted rate 0 area% Pred degrad of peak Smiles 1 at cond median of simulations th percentile 95th percentile ln k storage conditions y = *x + ( ), R2 = time (years)

39 Conclusions and Impact Predictions based on RRT 0.93 (spec limit 0.5 %area) indicate that a room temperature shelf-life could be achieved for all 5 formulations Predictions varied from 3 years to >8 years all formulations are acceptable Room temperature storage and standard packaging will be possible - No issues anticipated for formal stability studies Formulation selection criteria not limited by stability Detailed material understanding generated: - Coating increased the degradation rate - The rate of degradation is affected by both temperature and humidity. Humidity has a significant impact on RRT Lower drug loading increased the rate of degradation - No specific particle size effects were observed 39

40 Summary Using accelerated stability studies and automation we are able to provide increased value to projects and streamline our development process Supporting a wide range of studies across the whole development process Linking simultaneous chemical and physical analysis allows more in depth data evaluation and interpretation Accelerated studies allow meaningful data to be generated in a much shorter time frame Faster progression of project through development Careful selection of accelerated storage conditions allows robust shelf life predictions. Automation allows lots of samples to be analysed with less resource. Continuing to develop our approach to managing automated accelerated studies Working with specialists to refine, optimise and explore new approaches to data interpretation, particularly when managing large data sets. Development of more powerful shelf life prediction tools are ongoing. E.g. for non- linear degradation. 40