QbD implementation in Generic Industry: Overview and Case-Study

Transcription

1 QbD implementation in Generic Industry: Overview and Case-Study Inna Ben-Anat, QbD Strategy Leader, Teva Pharmaceuticals IFPAC JAN 2013 R&D

2 Three Core Components of QbD and Generic Industry: How Do They Overlap Quality by Design Generic Industry 1. Clearly l defining i the intended d purpose of 1 R d ibl M ki Ad d t the future developed product and design this product to fit its purpose 2. Understanding what attributes of this product are critical so it (product) will keep serving its intended purpose p 3. Enhanced understanding what impacting the critical quality attributes and how (materials, process, packaging etc) ; define control strategies so that the intended purpose of the product will reproducibly maintain i its integrity it 1. Reproducibly Making A drug product that is comparable to brand/reference listed drug product in dosage form, strength, route of administration, quality and performance characteristics, and intended use" The connectio on is clear 2. Providing uninterrupted supply of high quality and affordable medication to our patients 3. Efficiency and Speed

3 QbD for Generics: Finding the right balance between Speed, Efficiency and Excellence

4 Overview of QbD (GPhA, May 2012)

5 QbD Guide for Generics: Step 1-Product Design RLD Characterization Quality Target Product Profile Critical Quality Attributes GPhA/FDA CMC Workshop, May 2012

6 QbD Guide for Generics: Step 2 - What are the potential Risks Risk Assessment Defines the Development Strategy What are the Risks?... API Excipients Formulation and Process Equipment Testing Packaging How do we stay efficient Effective Prior Knowledge utilization and management Generic Industry has a lot of information and in-house knowledge available Data bases of pre-created Ishikawa diagrams in order to harmonize and streamline the Risk Assessment process Historical data-mining

7 Historical Data Mining: Drug Layering of Pellets Example Example: Previously developed product, multiply batches are available for Data Mining: In-Process Pellets Assay vs. Fines Correlation Based on the found relationship, Assay decreases ~0.6% with each % fines How do we control low % fines by process parameters (Drug Layering) All examples are for illustration purposes only

8 Historical Data Mining: Drug Layering of Pellets Example Actual Processing Parameters from all available historical lots were collected and data-mined Partition per most critical factor affecting % Fines Slit Temp Actual ( C) -max<74.3 Count Mean LogWorth Std Dev All Rows Count 31 LogWorth Difference Mean Std Dev Difference Exhaust Temp-AVG<44.4 Exhaust Temp-AVG>=44.4 Count Mean Count Mean Std Dev Std Dev Slit Temp Actual ( C) -max>=74.3 Count 12 Mean Std Dev Most Significant parameters affecting %Fines are Slit Temp and Exhaust Temp 2. Lower Slit Temperature (<74 C)and lower Exhaust Temperatures (<44 C) will generate less % Fines Potential DOE Factors for future similar products/processes or for further process fine-tuning All examples are for illustration purposes only

9 QbD Guide for Generics: Step 3 - Plan the right/relevant Experiments Efficient and Informative DOE: CQAs= f (CPPs, CMAs) How do we stay efficient o Effective Prior Knowledge Utilization What do we vary and what do we fix? What target and range do we evaluate and why? What statistical model do we use and why? (Can we assess what interactions are most likely to occur? Can we assess what factors would have non linear relationship with the response?) o o Modern DOE techniques for efficient yet powerful designs (D- Optimum, I-Optimum) Monte Carlo Simulations to assess the process robustness using historical data to assess expected variability

10 Let s take a typical manufacturing process for tablets as an example to start with Wet Granulation Fluid Bed Drying Milling Blending Compression How many potentially Critical Process Parameters do we need to assess? 5? 10? 25?

11 High Shear Wet Granulation: > 40 potential CPPs High Shear Wet Granulation Fish-Bone Diagram CQAs >40

12 Fluid Bed Drying: > 30 potential CPPs Fluid Bed Drying Fish-Bone Diagram CQAs

13 A A Typical Manufacturing Process for Tablets HS Wet Granulation Fluid Bed Drying Milling Blending Compression For a process involving the above unit operations we may end up with over 100 potential CPPs. How do we manage it?

14 Effective Knowledge Management! Prior Knowledge Utilization Blending Unit Operation CQAs 4 critical variables are left for assessment, the rest are kept at constant and monitored Design Variable Prior Experience/Fixed Justify!!

15 Effective Knowledge Management! With efficient Prior Knowledge utilization, we can end up with 8-16 trials for Experimental Design- feasible! JMP Statistical Software from SAS Main effects Interactions Prior Knowledge

16 Efficient and Informative Design of Experiments Brainstorming sessions will identify the design factors and their ranges, while previous knowledge should be effectively utilized to identify those and limit them to the most critical ones While conducting DoE, all parameters that are not studied should be kept constant at their optimum fixed level (justify!) in order to eliminate the noise and additional variation and increase the effectiveness of the study Prior to DoE execution, measurement s system integrity and sensitivity must be verified There is a lot to learn from every DoE: if a factor was found to have no effect, it can be used to minimize cost or increase robustness by having it set on convenient level

17 DOE and Modeling: Process Robustness and Monte Carlo Simulation Monte Carlo Simulation: Predicted OOS Rate: ~0.02% Distribution of the predicted output Predicted OOS rate Estimated Process Variability Estimated Analytical Variability All examples are for illustration purposes only

18 QbD Guide for Generics: Step 4 - Define Control Strategies Questions to ask ourselves: 1. Did we evaluate the impact of CMAs and CPPs on CQAs? Did we find any interactions? What do they mean for us? 2. Do we have a robust and reproducible process? Do we know the impact of raw materials variability? Did we identify potential sources of variation? 3. Did we establish meaningful In Process and Release specifications? 4. Did we address scale-up challenges? 5...

19 Case-Study IR Tablet, Dry Granulation Process

20 Product Development Outline Analysis of the reference listed drug (RLD) Defining Quality Target Product Profile (QTPP) Identification of Critical Quality Attributes (CQAs) Identification and evaluation of potential risks related to Drug Product Components (DS and Excipients stability and compatibility), Formulation and Manufacturing Process, etc. Screening and optimization of formulation Development of a robust process (DOE for high risk parameters) Manufacture of the exhibit batch Establishment of control strategies

21 QTPP Component Target Justification Dosage Form Administration Route Dosage Design Strength Tablet Oral Immediate release tablet X and Y mgs Pharmaceutical equivalence to RLD Bioequivalence AUC and Cmax match RLD under food Bioequivalent to RLD Appearance Identity Both: Brown to orange elegant film coated tablet. Dimensions similar to RLD. X mg: round; Y mg: oval Positive for API Marketing requirement; Needed for patient acceptability Needed for labeled claim & therapeutic efficacy Assay 100% of label claim Needed for therapeutic efficacy Impurities Specified and unspecified impurities meet ICH Q3B. Needed to ensure safety Disintegration Comparable disintegration time as RLD in appropriate media at room temperature Pharmaceutical equivalence to RLD (possible route of administration as suspension) Content Uniformity AV <15.0 (tested by weight variation) Targeted for consistent clinical effectiveness Residual solvents Complies with USP <467> Dissolution USP Apparatus II, 50 rpm, 1000 ml 0.1M HCl, 37 0 C. NLT 85Q is dissolved in 45min Regulatory requirement. Needed to ensure safety Regulatory requirement Stability NLT 24 month shelf life Needed for commercialization Container closure system HDPE bottles with Child Resistant (CR) Caps and appropriate desiccants, if required Needed for safety and commercial requirements

22 CQAs CQA Justification Potentially affected by Assay Needed for therapeutic efficacy Process Impurity Needed to ensure safety Formulation & Process Content Uniformity Dissolution Needed for therapeutic efficacy of each unit Presumptive qualification for in vivo release and therapeutic efficacy Formulation & Process Formulation & Process Disintegration Needed to ensure patient Formulation & Process compliance (suspension)

23 Formulation: Initial Risk Assessment and studies conducted Formulation Attribute Filler Disintegrant Lubricant Glidant Coating DP CQA type type type amount formulation & amount & amount & amount Assay Low Low Low Low Low Impurities Low Low Low Low Low Content Uniformity Low Medium Low Low Low Dissolution Medium Low High Medium Low Disintegration Medium Low High Low Low Vary type & amount (control strategy: optimized and fixed) Fix on high level based on prior knowledge Vary type & amount (control strategy: t optimized i and fixed)

24 Process Scheme Pharmacy Mixing I Milling I (De-lumping) Mixing II+III Compression II (cores) Mixing IV & V Milling II Compression I (slugs) Cosmetic Coating

25 Initial Risk Assessment: Process Unit Operations DP CQA Mixing I Milling I Mixing II+III Compression I (De-lumping) (Slugs) Assay Medium Medium Low Low Impurities Low Medium Low Medium Content Low Medium Medium Medium Uniformity Dissolution Low Medium Low High Disintegration Low Low Low High Unit Operations-cont'd DP CQA Milling II Mixing IV+V Compression II Coating (Tablets) Assay Low Low Low Low Impurities Medium Low Low Medium Content Uniformity Low Low Low Low Dissolution High Low High Medium Disintegration High Low High Medium

26 Process Optimization DOE Based on prior knowledge, previous experience and initial feasibility studies, the most potentially critical process parameters were chosen for further evaluation in DOE study. Additional parameters were set at their optimum fixed constant level l in order to reduce uncontrolled noise and variability (13 runs including 2 centers, D-Optimum Design using JMP software from SAS) Unit Operation Compression I (Slugs) Milling II Compression II (Tablets) DOE Factors Compression force Compression speed Levels Used Low Medium High Low Medium High Responses 1. Slug weight /RSD 2. Slug hardness Mill type Quadro NA Frewitt 1. PSD 2. Bulk & tap density Mill screen 0.6 NA Hausner ratio/flow Compression force Compression speed Low Medium High Low Medium High 1. Assay & impurities 2. Dissolution 3. Content Uniformity 4. Disintegration time 5. Tablet Hardness

27 Prediction Profilers: Factors/Responses relationship-% on PAN (Fines) Interaction: Mill screen impact is low for Frewitt Type Mill

28 Prediction Profilers: Factors/Responses relationship (Dissolution)

29 DOE Model Prediction vs. actual Exhibit Batch data Selected Response Model Exhibit Batch Predicted Value Value Hausner Ratio % Fines 19% 17% Dissolution-T1 AVG 36% 36% (N=6) Dissolution-T1 RSD 10.1% 8.8 % (N=6) Dissolution-T3 AVG 69% 68 % (N=6) UoC RSD 1.69 % 1.45 % (N=10) Good Correlation between Values predicted by DOE Model & Actual Responses

30 Process-Risk Mitigation, 1/2 Unit Operations DP CQA Mixing I Milling I Mixing II+III Assay Impurities CU Controlled by mixing time/speed Low Low Controlled by Screen size Low (Was found not critical) Controlled by Screen size Low Low Controlled by mixing time/speed Compression I (Slugs) Low Low (Was found not critical) Low (Was found not critical) Dissolution Low Low (Was found not Low Controlled by Disintegration Low critical) Low Low slug hardness

31 Process-Risk Mitigation, 2/2 Unit Operations-cont'd DP CQA Milling II Mixing IV+V Compression II Coating (Tablets) Assay Low Low Low Low Impurities Low (Was found not critical) Low Low Low (Was found not critical) Content Uniformity Low Low Low Low Dissolution Disintegration Controlled by mill type/ mill screen Low Low Controlled by core hardness and compression speed Controlled by fixed coating level

32 Summary Despite all of the challenges, the Generics Industry acknowledges that implementing QbD is the way forward, gaining g o Enhanced product and process understanding- robust products and processes o Identification and control of sources of variation- faster and efficient tech transfers, greater process capability Efficient utilization of prior knowledge is a key to successful QbD implementation in generics Real change will come if and when o The risk/cost benefits are realized o Playing field is leveled o FDA review of the applications shows the benefits of QbD 32

33 - What should I do next? - Create an action plan, Adopt the Big Q Concept Juran on Quality by Design