Process Drift and it’s Resolution in the

1

Process Drift and it’s Resolution in the

Manufacture of Drug Products

MDI’s and DPI’sMetered Dose Inhalations and

Dry Powder Inhalations

Ed Warner, Merck MMD

December 2, 2010

PQRI-FDA Workshop on Process Drift

2

QbD System

Dr. Moheb Nasr

12 Apr 2006

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Continually monitor and

update process to assure

consistent quality

Define desired product

performance upfront; identify

product CQAs

Design formulation and

process to meet product CQAs

Understand impact of material

attributes and process

parameters on product CQAs

Identify and control sources of

variability in material and

process

QbD System

Product and Process

Design and Development

• CQA’s

• Risk Assessment

• Design Space

Pro

du

ct

Lif

e C

ycle

Process Robustness

Process Ruggedness

Production and Supply

• Control Strategy

• Continuous

Improvement

4

MDI and DPI Process Drift

Overview of Dosage Forms

Sources of Variability

Identifying and Reacting to Process Drift

Case Example

Final Thoughts

Continually monitor and

update process to assure

consistent quality

Understand impact of material

attributes and process

parameters on product CQAs

Identify and control sources of

variability in material and

process

Process Ruggedness

Production and

Supply

• Control Strategy

• Continuous

Improvement

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

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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* FDA Draft Guidance for Industry, MDI and DPI Drug Products, Novermber 1998

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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 25-100 ul

Dosages of a few mcg up to mg of active

Dose Counters

8


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


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Device Metered:

Reservoir

Pre-Metered

Insertion Chamber


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

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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.


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Sources of Variability

OutputsInputs

Process and Steps:

Manufacture

Filling

Packaging

In Process Parameters and Controls

Outputs:

Intermediates

Finished Units

Testing

Inspection

Product Quality Attributes

Inputs:

API

Excipients

Components

Material Attributes

Testing Variability

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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

Sources of Variability: Material Attributes

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MDI's DPI's

Container /

Closure

Physical dimensions (especially closures)

Extractables and Residues

Valves / Device Valve Components

Physical dimensions

Extractables

Assembled Valve

Function

Delivery Weight

Leak Rate

Device Flow Resistance

Interference tolerances

Physical Alignment

Actuator/

Mouthpiece

Physical dimensions

o Valve stem orifice

o Spray orifice

Extractables

Spray Pattern

Spray Velocity

Surface characteristics

Sources of Variability: Material Attributes

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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 –

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MDI's DPI's

Drug Product 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)

Sources of Variability: Product CQA’s

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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)

Aerodynamic Particle Size Distribution (APSD)

Cascade Impactors

Dose Collection and stratification

Assay variability (mcg range)

Reliable and accurate

dose delivery –

component functionality

Inhaled Drug must be

able to reach desired

sites in lung

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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)

A B C D A B C D A B C D

1 1 1 1 2 2 2 2 3 3 3 3

I I II II III III IV IV V V VI VI

a b c d e f g h i j k l

Valve Lot

Excipient Lot

API Lot

Canister Lot

18

Valve Lot

Canister Lot

API Lot VII

Excipient Lot c i

Product Lot 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018

EDCBA

54321

GF

b d

VIVIVIII

Production: Imbalance in Material usage

e f hg j

III

a

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

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OutputsInputs

Process and Steps:

Manufacture

Filling

Packaging

In Process Parameters and Controls

Outputs:

Intermediates

Finished Units

Testing

Inspection

Product Quality Attributes

Inputs:

API

Excipients

Components

Material Attributes

Testing Variability

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Define

Measure

Analyze

Improve

Control

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

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1. Everything Varies.

2. All variation is caused.

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

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


Control Charts

Stratification and Identification

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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

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Case Study: Device Metered DPI APSD

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.

THE APSD MEASUREMENT PROCESS

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HPLC

Analysis

Sample

Collection

Sample

Prep

Throat + Presep + Stage-1 + Stage 0 + Stage 1 + Stage 2 + Stage 3 + Stage 4 + Stage 5 + Filter = Total Recovery

(Mass Balance)

Cascade

Impactor

Aerodynamic Stage Groupings (Specified)

I II III IV V


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PRODUCT AND DEVICE CONTROL

Lactose:

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

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464136312621161161

75

70

65

60

55

50

Observation

In

div

idu

al

Va

lue

_X=60.67

UC L=67.46

LC L=53.88

LSL=50

USL=75

464136312621161161

10.0

7.5

5.0

2.5

0.0

Observation

Mo

vin

g R

an

ge

__MR=2.55

UC L=8.34

LC L=0

1

1

1

I-MR Chart of Group 1 - continued monitoring shows OOT results

Additional

batches

monitored

against

established

control

limits

Control Limits established and

used prospectively

Time series monitoring, control charts

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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.

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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.

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HPLC

Analysis

Sample

Collection

Sample

Prep

Throat + Presep + Stage-1 + Stage 0 + Stage 1 + Stage 2 + Stage 3 + Stage 4 + Stage 5 + Filter = Total Recovery

(Mass Balance)

Cascade

Impactor

Aerodynamic Stage Groupings (Specified)

I II III IV V


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The Glass Throat used with the CI,

interfaces with the inhaler and the CI

stages.

Particles entrained in the vacuum airflow

must navigate a 90o 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 size-

discriminating stages of the CI.

KEY ASPECT OF MEASUREMENT PROCESS

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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.

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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

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71645750433629221581

80

75

70

65

60

55

50

Observation

In

div

idu

al

Va

lue

_X=61.5

UC L=66.19

LC L=56.81

1 2 3 Historical Data supporting control limits Static Issues C A PA instituted

LSL=50

USL=75

71645750433629221581

16

12

8

4

0

Observation

Mo

vin

g R

an

ge

__MR=1.76

UC L=5.76

LC L=0

1 2 31 Historical Data supporting control limits 2 Static issues 3 C A PA Instituted

I-MR Chart of Group 1 - CAPA instituted in Lab

Verification

Post-CAPA

Results well controlled

No change in average

Highly controlled variability

Additional checks put in place on throat recovery

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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

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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

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