Quality by Design for Drug Products. Dr. Lorenz Liesum Global Technical Operation, Novartis Swiss Association for Quality Meeting , Olten

Transcription

1 Quality by Design for Drug Products Dr. Lorenz Liesum Global Technical Operation, Novartis Swiss Association for Quality Meeting , Olten

2 Overview QbD/PAT Concept QbD/PAT Toolbox DoEs PAT Methods - Blend Uniformity by NIR - Content Uniformity by NIR MVDA for Statisitcal Process Control Conclusions 2

3 Implementation Strategy The Matterhorn, CH QbD Continual Improvement Quality Monitor Control Strategy Define Design Space MVDA Develop Product Understanding Identify Knowledge Baseline QbD #3 QbD #2 QbD #1 Define Target Product Quality 3

4 QbD/CRADA Project Summary of Scientific Activities Connectivity with Drug Product CQAs and processability was evaluated Design of Experiments: - Total of 9 DoEs at Lab, Pilot and Full Scale were performed - Main-effect screening and Optimization designs were used Quality Risk Management Tools Use of Fishbone/FMEA tools PAT Toolbox: PAT sensors for various unit operation for on-line control Statistical Process Control by MVDA 4

5 Overview QbD/PAT Concept QbD/PAT Toolbox DoEs PAT Methods - Blend Uniformity by NIR - Content Uniformity by NIR MVDA for Statisitcal Process Control 5

6 Development of Product Understanding Process Flow and DoEs >75 DoE Batches Lab Scale 6

7 Main Effect & Optimization DoEs Lab Scale Main Effect DoEs Lab Scale Optimization DoEs Mix Speed Water Amt. PSD Gran Time Air Volume Dew Pt LOD Fill Volume Air Temp Vendor Full Scale Confirmation DoE Spray Rate

8 Develop Process Understanding Design Space at Lab Scale Main Effect Screening DoE Parameters: PSD of Main Excipients, Granulation Parameters, Excipient Vendor, Dryer, LOD/Compression Results: Water Amount (influence), PSD of Select Excipients (influence), Air Volume (no influence), Granulation Time (no influence) Optimization DoE Parameters: PSD Select Excipients, Water Amount, Interactions of Critical Process Parameters Results: Water Amount (influence), PSD of Select Excipients (influence), Interactions of Critical Process Parameters (no influence) Confirmation DoE Parameters: Water Amount, Granulation Time, Air Volume Results: Water Amount (no influence), Granulation Time (no influence), Air SAQ Meeting Volume Lorenz Liesum (no influence) QBd/PAT in Pharmaceutical Production Business Use Only 8

9 Risk Assessment/DoE Overview Develop Process Understanding Fishbone FMEA table F low high A high high high high L T H Variable 1: (WA) L J G high low low high D high low Variable 2: (GT) Variable 3: (AF) H low low E high low low high C low high B high low I K low low Design Space DoE

10 Overview QbD/PAT Concept QbD/PAT Toolbox DoEs PAT Methods - Blend Uniformity by NIR - Content Uniformity by NIR MVDA for Statisitcal Process Control Conclusions 10

11 Process Flow for PAT Implementation From Development to Routine Production Method Development Method Validation Method Transfer Specific for PAT methods Experience in Production and Annual Method Review Method Updates 11

12 Challenge of PAT Methods The Framework of Method Implementation in Production Resources No additional costs Robustness No permanent troubleshooting Accuracy No false positive results 12

13 SIMCA-P :44:13 Installed PAT Tools (Case Study) Pharmaceutical Process for a Solid Dosage Form Granulation Drying Blending Compression Blend Uniformity by NIR Content Uniformity by NIR 8 6 t[1] MVDA Models Dry Mixing Wet Mixing Water Addition Granulation Num 13

14 Blend Uniformity by NIR Evolution of API concentration during blending API [%] API Time [secs] 14

15 Blending Uniformity by NIR Objectives and Motivation Conventional way by thief sampling and HPLC is time consuming and can be biased. BU NIR gives insight into the process and enhances process understanding. 15

16 Basic principles Comparison of raw and standardized blend spectra AU Start 2 Turns 5 Turns 10 Turns 20 Turns 40 Turns 80 Turns 160 Turns 300 Turns End Raw Spectra End End SNV AU SNV Spectra ( selected wavelengths) Wavelength [nm] Start Start Baseline drift due to compaction Wavelength [nm] Scattering and compaction influence (physical effects) is removed Characteristic bands (chemical information) become more apparent 16

17 Basic principles Evolution of absorbance vs. time 0.24 Timeslices with moving average Timeslices of SNV Spectra with moving average AU nm PVP 1290nm 1480nm Cellulose SNV AU nm PVP 1290nm 1480nm Cellulose Number of Turns Number of Turns AU of all wavelengths are increasing; low S/N Decay and increase of characteristic wavelengths; S/N improvement Universal approach: Moving Block of Standard deviation method 17

18 Implementation in Production Quantitative BU NIR Method Scope of the method Online API prediction Online moving block standard deviation of API prediction Endpoint decision based on API concentration and standard deviation of API over the last 10 revolutions 18

19 Stages of validation/implementation 1. Validation on Lab Scale (5-20l) Linearity Reproducibility Accuracy 2. Method transfer/validation on Full Scale (750l) Proof of scale/equipment independence 3. Validation on Endpoint Determination Homogeneity of final powder 19

20 Robustness Varied Conditions at Lab Scale Robustness Bin size Bin load Blender speed Different API and excipient qualities Scale 20

21 Method transfer From Development to Production Site Lab Scale Production Scale Same setup and materials used on both sites and scales. Method transfer is not judged on the blending profile but on the spectral properties of the blend having reached homogeneity. 21

22 Validation for End Point Detection Blending process is stopped based on NIR results Different End point settings 120 API [%] End point determination by time End point determination by homogeneity RSD RSD Time [min] The blending process is stopped when the defined endpoint criteria of the method are met. The blending time is shortened. 22

23 Experience in Production Full Scale DoE Batches In the scope of a DoE full scale campaign different granules manufactured under different conditions were monitored during blending Blend Monitoring by NIR API [%] Number of revolutions Different blending profiles were observed for differently produced granulates in accordance with the DoE setup No impact on the performance of the method observed 23

24 Conclusions and benefits Blend Uniformity Assessment by NIR Implementation of GMP compliant methods in production accomplished Analytical performance sufficient and more reliable to assure homogeneity of the blend Different blend evolution profiles depending on the granulates could be observed No impact on the performance of the method observed with respect to equipment and materials No OOE/OOS observed No update of method necessary 24

25 Content Uniformity by NIR 25

26 Advantages of NIR for Tablet Analysis Overview Very fast, since no dilution/sample preparation is necessary Non-destructive Minimum chemical disposal and exposure Very robust and reproducible and therefore transferable between sites Sensitive to physical properties of the tablets 26

27 Limitation and constraints Overview Sensitivity limited to approx 1%, no trace method and not adequate for analyzing impurities Transmission not feasible for very thick tablets and for special coatings Need for chemometric evaluation and expertise Only applicable for polar organic molecules with C-H,O-H and N-H bonds Sensitive to process variability 27

28 Milestones for an routine application Content Uniformity by NIR 1. Method validation 2. Method transfer and Implementation Similar to HPLC method transfer Identical tablets analyzed at both sites (no sample variation) 3. Method adjustment/update based on production experiences 28

29 Method validation Accuracy Target Across range Bias and RMSEP Precision Day-to-day Instrument-to-instrument Validation parameters Bias = (HPLC NIR n RMSEP = n pred %label %label )/n (HPLC%label NIR%label ) /n 2 pred RMSEP: root mean square error of prediction Repeatability Specificity Embossment Hardness Excipient vendor Excipient content Srel placebo Bias and RMSEP Particle size 29

30 Wavelength selection Characteristic band at cm-1 (aromatic C-H overtone transition) Selected range: cm-1 30

31 Analytical method Goodness of predictions and number of scores 31

32 Method Transfer From Development to Production 32

33 Production experience DoE Full Scale For set point conditions in production the method worked reliable. In the course of a full scale DoE, where process parameters were varied systematically, an impact on the model s performance was observed. The NIR method was updated using spectra of the DoE batches. CU NIR spectra of batches manufactured with different granulation settings. 33

34 Summary and Conclusion NIR CU CU NIR methods could be developed and transferred to production compliant with cgmp procedures in close analogy to a HPLC method No problems with set point conditions in production The performance of the method was insufficient when making changes to the process This resulted in an update of the method with increased robustness No update needed since then Annual method review mandatory 34

35 Overview Introduction Robustness of NIR methods Blend Uniformity Content uniformity Robustness of MVDA control models Granulation and Drying Summary 35

36 MVDA methods in production Multivariate Data Analysis (MVDA) Scope of the Methodology Tracing/understanding the evolution of a good, i.e compliant batch Monitor new batches and accomplish early fault detection at observation level Classify new batches as either compliant or non-compliant at batch level Reporting tool for batch record review Archiving and documentation tool Investigation tool Cheap way of benefiting from soft PAT data, e.g. temperatures, pressures, speed... 36

37 Example Granulation Observation level (time resolved) Different phases during granulation are monitored Process variability are reflected by the red lines Clustering of DoE batches can be visualised 37

38 Example Drying Observation level (time resolved) Different drying profiles according to different process parameter setups 38

39 Example Granulation Batch Level (Overall assessment) Tolerance ellipsoid is spanned the variability of by compliant batches Batch Level Analysis t[2] t[1] R2X[1] = R2X[2] = Ellipse: Hotelling T2 (0.95) SIMCA-P :42:45 New compliant batches can be identified Foreign batch 39

40 Num Work and Data flow For Method Development Final Model Batch Level Observation Level Reduction of Dimensionality ObsID(Obs ID ($PhaseID)) Mixer Power rate of change precss varia Recorded Process Parameter during granulation 0.01 * Mixer torqute process variable 0.1 * Mixer speed process variable 0.1 * Product temperature process varia Mixer power process variavle (electrical All Process Parameters Individual Probes Individual Probes 40

41 SIMCA-P :17:46 Work and Data flow For Routine Use in Production SBOL Batch Level Observation Level Flow of root cause analysis: Drill down to individual PP Identification of responsible Parameter(s) PO_WST1032_EXJADE_GRAN_Steintraining.M2:7 PredictedLiquidfedpumpsped +3Std.Dev XVar(Liquidfedpumpsped) (Aligned) (Avg) -3Std.Dev XVarPS(Liquidfedpumpsped) (BatchS07_B_854825) Investigation on process data $Time(normalized) 41

42 Example for root cause investigation Reason for clustering Why do they differ: Difference is related to different process parameters during water addition Batch Level Analysis t[2] t[1] R2X[1] = R2X[2] = Ellipse: Hotelling T2 (0.95) SIMCA-P :32:14 42

43 Example for root cause investigation Reason for clustering In which parameter do they differ: 43

44 Robustness of MVDA model Data base Depending on the type of process you need a lot of batches to cover the natural variability and having a robust MVDA model. Performing a DoE, i.e. enforcing process variability, is a short cut for this. 44

45 Experience in routine manufacturing Outliers due to environmental changes Range of environmental Air Humidity was not fully covered in the model 45

46 Experience in routine manufacturing Outliers due to device failures core ContribPS(Challenge_854826: Avg:1 Chopper was not working (Simulation) Root cause investiagetion Mixer Power rat Mixer power pro Mixer torqute p Mixer speed pro Chopper speed p Product tempera Var ID (Primary) 46

47 Summary and Conclusion MVDA Data basis essential for robustness for model Alignment/synchronisation of data is important Sources for lack of robustness: Environmental conditions, e.g. air humidity, air pressure, were not covered in the data of the model No other source for deviations observed Unexpected failures can be traced by the models 47

48 Acknowledgement Development Switzerland Jutta Beyer Development US Yusuf Sulub Dong Xiang Chris Balducci Jim Pazdan Jim Cheney Production Switzerland Hedinn Valthorsson Olivia Darmuzey 48

49 Thank you for you attention! 49