Process Drift and it s Resolution in the Manufacture of Drug Products. MDI s and DPI s Metered Dose Inhalations and Dry Powder Inhalations

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1 Process Drift and it s Resolution in the Manufacture of Drug Products MDI s and DPI s Metered Dose Inhalations and Dry Powder Inhalations Ed Warner, Merck MMD December 2, 2010 PQRI-FDA Workshop on Process Drift 1

2 QbD System Dr. Moheb Nasr 12 Apr

3 Product Life Cycle QbD System Define desired product performance upfront; identify product CQAs Design formulation and process to meet product CQAs Product and Process Design and Development CQA s Risk Assessment Design Space Production and Supply Control Strategy Continuous Improvement Process Ruggedness Understand impact of material attributes and process parameters on product CQAs Identify and control sources of variability in material and process Continually monitor and update process to assure consistent quality Process Robustness 3

4 MDI and DPI Process Drift Overview of Dosage Forms Sources of Variability Identifying and Reacting to Process Drift Case Example Final Thoughts Production and Supply Control Strategy Continuous Improvement Process Ruggedness Understand impact of material attributes and process parameters on product CQAs Identify and control sources of variability in material and process Continually monitor and update process to assure consistent quality 4

5 Overview of MDI and DPI Dosage Form Metered Dose Inhalation Aerosols Oral Inhalation or Nasal Inhalation Delivers a specific amount of medication into the lungs A short burst of aerosolized medicine is inhaled Most commonly used for treating asthma, COPD, or other respiratory disease. Bronchodilators, Corticosteriods, occasional others. Dry Powder Inhalers Oral Inhalation only As above 5

6 Overview of MDI and DPI Dosage Form Unique to MDI s* Drug product consists collectively of the container, formulation, valve, actuator, and protective packaging Mixture of micronized or solubilized drug in a desirable physical form, may be within a residual matrix of oily excipients, propellants, solvents Direct administration of fixed amounts without compromising remaining material in can Mixtures of gas/liquids cause rapid discharge when actuated, valve meters amount, actuator orifice defines the spray (droplets, particles) * FDA Draft Guidance for Industry, MDI and DPI Drug Products, Novermber

7 Overview of MDI and DPI Dosage Form MDI s Contents: API s dissolved or in suspension Propellant or mixture Solvents Excipient materials Container Pressurized canister Metered Dose Valve Actuator Adapts spray for delivery Fits canister/valve Oral (mouthpiece) or Nasal Use Pressurized burst of active Up to several hundred doses Dose volumes of ul Dosages of a few mcg up to mg of active Dose Counters 7

8 Overview of MDI and DPI Dosage Form Unique to DPI s* Drug product consists collectively of the multi-part device, formulation, and protective packaging Dosing and performance is directly dependent on the design of the device Portion of formulation/matrix delivered by inhalation has controlled particle size distribution Relies on patient inspiration, compressed gas or motor-drive impeller Device metered DPI contents be susceptible to exposure once doses begin to be removed Interactions between DS, carriers and components may occur (i.e., electrostatic charge, fluid dynamics * FDA Draft Guidance for Industry, MDI and DPI Drug Products, Novermber

9 Overview of MDI and DPI Dosage Form DPI s Contents: Active ingredient Excipients/carriers Container / Device: Device Metered Internal reservoir Integrated into device Multiple doses metered when actuated Pre-metered Measured doses or dose fractions Blisters, capsules, cavities Inserted into device prior to use Use Patient inspiration or power assist Up to hundreds of doses Dose Counters Device Metered: Reservoir Pre-Metered Insertion Chamber 9

10 Overview of MDI and DPI Dosage Form Unique to both Classical bioequivalence and bioavailability not generally applicable Doses small - serum concentration can be undetectable As little as 10-15% of dose may reach biological target* Clinical efficacy assessment requires understanding other factors, such as - Patient practices (breath holding, duration, inspiratory flow rates) Drug product variability due to physical characteristics and controls of the DS, formulation, delivery device components, manufacturing process, in-process controls, etc. * FDA Draft Guidance for Industry, MDI and DPI Drug Products, Novermber

11 Sources of Variability Inputs Outputs Inputs: API Excipients Components Material Attributes Process and Steps: Manufacture Filling Packaging In Process Parameters and Controls Testing Variability Outputs: Intermediates Finished Units Testing Inspection Product Quality Attributes 11

12 Sources of Variability: Material Attributes Quantitative Measurements and Specifications Typically applied include MDI's DPI's Active Density PSD (particle size distribution) Moisture, residual solvents Purity, impurities Excipients Co solvents Dehydrated Alcohol Surfactants material specific properties Propellants Water Content Impurities Carriers, (e.g. Lactose Monohydrate) Particle Morphology Amorphous content PSD Quantitative color/clarity Impurities Micro testing 12

13 Sources of Variability: Material Attributes Quantitative Measurements and Specifications Typically applied include Container / Closure Valves / Device Actuator/ Mouthpiece MDI's DPI's Physical dimensions (especially closures) Extractables and Residues Valve Components Physical dimensions Extractables Assembled Valve Function Delivery Weight Leak Rate Physical dimensions o Valve stem orifice o Spray orifice Extractables Spray Pattern Spray Velocity Device Flow Resistance Interference tolerances Physical Alignment Surface characteristics 13

14 Sources of Variability In Process Controls Manufacturing DS Micronization particle size, morphology Intermediates steps MDI Concentrate (Assay, etc.) DPI Formulation Blending (Assay, Amorphous Content) Time, temperature, humidity relationships with materials Filling/Assembly/Packaging: Concentrate Filling weights, volumes Propellant Filling weights, pressures Valve installation/ Crimping dimensions, delivery weight Reservoir Filling fill height, weight Pre-metered dose filling and packaging 14

15 Sources of Variability: Product CQA s Quantitative Measurements and Specifications Typically applied include Drug Product MDI's DPI's Color Water / Moisture Net Content / Fill Weight (DPI -Device Metered) Drug Content / Assay Impurities / Degradants Dose Content Uniformity oinitial Doses and through Life (device metered DPI) Particle Size Distribution Microscopic Evaluation Leachables Dehydrated Alcohol Content Spray Pattern / Plume Geometry Leak Rate Pressure Testing Valve Delivery (Shot Wt) 15

16 Sources of Variability Product CQA s Testing challenges unique to MDI s and DPI s Delivered Dose Uniformity (DDU) Initial Doses / through Life Device Dosing regimen Dose Collection Assay variability (mcg range) Reliable and accurate dose delivery component functionality Aerodynamic Particle Size Distribution (APSD) Cascade Impactors Dose Collection and stratification Assay variability (mcg range) Inhaled Drug must be able to reach desired sites in lung 16

17 Sources of Variability Components of Variance Variability: multiple contributing factors Drug, excipient, packaging and delivery components, process, analytical, interactions Factors referred to as variance components Variance contributions from different sources generally additive. In development we evaluate factors to define controls and achieve robustness DOE permits the splitting of total variance into separate sources, the variance components. Contribution of variance components Analysis of Variance (ANOVA) Valve Lot Canister Lot API Lot Excipient Lot A B C D A B C D A B C D I I II II III III IV IV V V VI VI a b c d e f g h i j k l 17

18 Sources of Variability Components of Variance In production we still have the same factors Process must tolerate additional variability raw materials, packaging and delivery components, operating conditions, process equipment, environmental conditions and human factors may fall outside studied boundaries Variability: Variability sources are managed differently in supply DOE is difficult to implement in everyday operations Must identify and react to process drift Monitor to look for change and study further to assure control Production: Imbalance in Material usage Valve Lot A B C D E F G Canister Lot API Lot I II III IV V VI VII Excipient Lot a b c d e f g h i j Product Lot

19 Identifying and Reacting to Process Drift Inputs Outputs Inputs: API Excipients Components Material Attributes Process and Steps: Manufacture Filling Packaging In Process Parameters and Controls Testing Variability Outputs: Intermediates Finished Units Testing Inspection Product Quality Attributes 19

20 Identifying and Reacting to Process Drift ICH Q10 Pharmaceutical Quality System Element: Process Monitoring and Product Quality Monitoring Plan and execute a system for monitoring of process performance and product quality Use risk management to establish the control strategy Provide tools for measurement and analysis Analyze parameters and attributes Identify sources of variation Include feedback on product quality from both internal and external sources Provide knowledge to enhance process understanding Define Measure Analyze Improve Control 20

21 Identifying and Reacting to Process Drift 1. Everything Varies. 2. All variation is caused. Control Charts 3. Not all causes of variation are equally important. 4. Most causes of variation can be categorized into one of these groups: - Method - Material - Machine - People - Measurement - Environment 5. Stable processes produce consistent patterns of variation over time Stratification and Identification 6. Variation in a stable process comes from common causes which are inherent 7. Variation from special causes (assignable causes) results in the process being unstable 8. To understand the causes of variation you must stratify the data into sensible ways and compare 9. Major causes of process variation can be discovered by process analysis and statistical tools 10. Reducing process variation produces lower cost and improves quality 21

22 Identifying and Reacting to Process Drift Elements of a sufficient system Time series monitoring, control charts Identify special cause variation (i.e., DRIFT ) Impact assessment Gauge appropriate reaction and priority of response Cross functional review Identification of potential sources raw materials, operating conditions, process equipment, environmental conditions and human factors Hypothesis testing CAPA Verification Case Study: DPI Device Metered APSD: APSD Measurement Process: Cascade Impaction 22

23 Case Study: Device Metered DPI APSD THE APSD MEASUREMENT PROCESS a multi-stage cascade impactor (CI) characterizes particle distribution Portions of the of the delivered dose are considered respirable CI collects aerosolized particles of drug substance based on aerodynamic behavior through serial multistage impactions. particle size is determined by the ability of an air-flow to change the trajectory of a particle and is not necessarily correlated to actual physical dimensions. This test is used to characterize the delivered dose during development and to monitor quality and control future batch-to-batch consistency in commercialized products. Attempts are made to minimize the number of actuations used in the CI test so as not to mask variability. 23

24 Case Study: Device Metered DPI APSD THE APSD MEASUREMENT PROCESS Sample Collection Cascade Impactor Sample Prep HPLC Analysis (Mass Balance) Throat + Presep + Stage-1 + Stage 0 + Stage 1 + Stage 2 + Stage 3 + Stage 4 + Stage 5 + Filter = Total Recovery I II III IV V Aerodynamic Stage Groupings (Specified) 24

25 Case Study: Device Metered DPI APSD Lactose: PRODUCT AND DEVICE CONTROL Improved test precision to tighten control of the amorphous content Known to influence the fineness or coarseness of the aerosolized drug. Product intermediate: controls for size ranges and % moisture Reduces influence on bulk density, device metering performance and physical friability. Inhaler: Better consistency of inhalation resistance through assembly improvements Improved the consistency of air velocity through the device. CI Testing: CI method revised to avoid factors contributing to variability: additional details, standardized equipment, controlled temperature and humidity environment 25

26 Moving Range Individual Value Case Study: Device Metered DPI APSD Time series monitoring, control charts I-MR Chart of Group 1 - continued monitoring shows OOT results 75 USL= UC L=67.46 _ X= LC L= Observation LSL=50 UC L=8.34 MR=2.55 Additional batches monitored against established control limits 0.0 LC L=0 1 Control Limits established and used prospectively Observation

27 Case Study: Device Metered DPI APSD Impact Assessment Special cause signal on individuals control chart Only one individual batch result just above control limit Appears to affect only one batch Low impact Special cause signal on moving range chart two points out of control limits - change in amount of variability Other points also high affects multiple batches Potential high impact Cross functional review Inputs: Raw materials, active ingredient, lactose etc. Process: Blend homogeneity, particle size, etc. Device: Component variability, airflow, etc. Testing: Consistency of sampling procedure, etc. 27

28 Case Study: Device Metered DPI APSD Identification of potential sources Cross functional team evaluated records for identified factors Results were stratified Materials Components Laboratory Testing Factors found to contribute more to variability than total Manufacturing Factors. Some analysts had higher Group I results No consistency in the bias and an un-identified factor Drilling down below the reportable result differences in deposition profile were seen primarily in the Glass Throat. 28

29 Case Study: Device Metered DPI APSD THE APSD MEASUREMENT PROCESS Sample Collection Cascade Impactor Sample Prep HPLC Analysis (Mass Balance) Throat + Presep + Stage-1 + Stage 0 + Stage 1 + Stage 2 + Stage 3 + Stage 4 + Stage 5 + Filter = Total Recovery I II III IV V Aerodynamic Stage Groupings (Specified) 29

30 Case Study: Device Metered DPI APSD KEY ASPECT OF MEASUREMENT PROCESS The Glass Throat used with the CI, interfaces with the inhaler and the CI stages. Particles entrained in the vacuum airflow must navigate a 90 o turn before entering the size discriminating stages of the CI. Unlike the stages of the CI, the Glass Throat is not particle size discriminating. Both large and small particles can be recovered in the Throat. Finer particles that collect in the throat never reach the subsequent sizediscriminating stages of the CI. 30

31 Case Study: Device Metered DPI APSD Hypothesis Testing and Verification Electrostatic charge observed in the manufacturing environment in development. Ionizers were employed to control static - controlled Hypothesis: CI analyst differences are result of static charge Charge is transferred from analyst to the Glass Throat in the CI. A DOE was conducted to confirm the hypothesis. Grounding straps were provided to analysts to dissipate static during sample collection/prep. Other factors evaluated: rubber gloves, cascade throat equipment ID, position of apparatus in lab room. 31

32 Case Study: Device Metered DPI APSD DOE KEY LEARNINGS Glass Throat recoveries can be higher when static is not controlled. The trends were consistent in that: Differences in throat recovery between analysis without static control Differences between analysts reduced by grounding straps. With grounding straps results aligned with pre-shift data When not controlled some results look like post-shift results Rubber gloves contributed additional influence CAPA: CI Instrument Operating Procedure and train analysts: No gloves during assembly, dosing, or wasting steps Must wear grounding wrist straps during these steps Institute room electrostatic controls Long Term: institute active static controls in the method 32

33 Moving Range Individual Value Case Study: Device Metered DPI APSD Verification I-MR Chart of Group 1 - CAPA instituted in Lab 1 Historical Data supporting control limits 2 Static Issues 3 C A PA instituted Observation 1 Historical Data supporting control limits 2 Static issues 3 C A PA Instituted USL=75 UC L=66.19 _ X=61.5 LC L=56.81 LSL=50 UC L=5.76 MR=1.76 LC L=0 Post-CAPA Results well controlled No change in average Highly controlled variability Additional checks put in place on throat recovery Observation

34 Final Thoughts MDI s and DPI s delivery systems have unique challenges, but rely on the same tools and techniques for ongoing control and improvement Product/process development: interrelationships between the container/closure and the contents to provide consistency Control strategies Continual monitoring for process drift Statistical tools like control charts combined with historical data stratification will identify potential problems, sources Handling complexity in signals Stratification using Multivariate datamining Random Forests tree based method PCA / PLS Confirm hypotheses with further data a DOE is a good idea Institute CAPA s and document and employ good change control and documentation practices 34

35 Additional Final Thoughts Place emphasis on monitoring the CPP s and CQA s as far upstream in processes as possible Institute monitoring at supplier (internal / external) Team review of monitoring periodically React appropriately to signals Integrate a risk evaluation process Apply proven discipline to identify root cause Institute CAPA Document changes Acknowledgements Doug Plassche Joan Martyn Robert Berger Susan Huyck Dave Christopher Donald Chambers Bruce Wyka Steve Li 35