proof of operations for continuous manufacturing and real ......testing of film coated tablets...
TRANSCRIPT
2ND INTERNATIONAL SYMPOSIUM ON CONTINUOUS MANUFACTURING OF PHARMACEUTICALS, CAMBRIDGE, MA, SEPT 2016
Stephen Conway, Samantha Hurley, Catherine MacConnell, Robert Meyer, Christine Moore, Cindy Starbuck, Manoharan Ramasamy, Brendon Ricart, Frank Witulski, Fan Zhang-Plasket, et al.
Proof of Operations for Continuous Manufacturing and Real Time Release Testing of Film Coated Tablets
Outline
Merck’s Approach to Continuous Manufacturing
PAT Options: RTD Process Model and Blend NIR
Process Model for Material Tracking and Rejection
Sampling for Control and Release
Benefits to Patients and Company
Moving Towards Worldwide Acceptance of CM
Why Continuous Manufacturing (CM)?
High assurance of product quality
Lower cost
Flexibility to meet changing demand
How we’ll start
Leverage extensive experience with an existing product
Build upon existing RTRT, raw materials monitoring and QbD approaches
Conduct short duration runs with efficient, frequent changeovers between strengths
Use batch size flexibility to match changing market demand
What we ask of regulators
During global industry shift from batch CM, alternate production methods are required in parallel
This ensures uninterrupted product delivery to patients
What comes next
Conversion of other marketed products currently produced by batch processing
Addition of other continuous technologies (e.g. granulation)
Efficient development and launch of new products
Merck’s Approach to CM for Oral Solid Doses
How We’ll Use CM to Improve Supply Chains
One batch, every cycle, every strength
• Flexible batch size matched to customer demand
• Fast changeovers between products
High demand products
• Higher quality and efficiency due to elimination of start/stop
Low demand products
• Increases shelf life by eliminating overproduction and inventory hold
Volatile demand products
• Avoid shortages by reacting quickly to changes in demand
Monthly Weekly
Jan Feb Mar Jan Feb Mar
Inve
nto
ry
Time Time
Deliveries
Cycle
Stock
Safety
Stock
Monthly vs Weekly Inventory Levels
Existing Product & Process Description
TABLETING
Co
ati
ng
So
luti
on
Blend
Tablet Compression
Film Coat
• Available >10yr in most markets
• Multiple strengths
• High overall volume
• Volume dependent on strength
Commercial Characteristics
• Drug load > 25%
• BCS I
• Non-functional film coat
• Low segregation & physicochemical stability risks
Product Characteristics
• Direct compression + film coat
• Batch size >500kg
• Assay using transmission NIR
• Tablet weight in lieu of CU
• Hardness / disint. in lieu of disso
• No degradate testing
Manufacturing Description
Merck’s Continuous Manufacturing Process GEA CDC-50, Consigma Coater, Bruker TANDEM
Mapping Different Approaches to CM
-5
-4
-3
-2
-1
0
1
2
3
4
5
-5 -4 -3 -2 -1 0 1 2 3 4 5
System ComplexityFixed / Early
Fixed / Late Flexed / Late
Flexed / Early
Janssen
Vertex
Pfizer
BMS
Merck
Roche
Bayer
AZ
GSK
Lilly
Novartis
Sanofi
Equipment Flexibility / Portability
Po
rtfo
lio D
eve
lopm
ent S
tage
(Pro
duct L
ife
Cycle
)
Comprehensive Control Strategy
8
Enterprise Resource Planning Site Manufacturing Execution System
Supervisory Control and Data Acquisition PAT Management System
Automatic Controls PAT Instrumentation
Diversion (potentially non-conforming)
Process
Mate
rials
Managem
ent
Mate
rials
Managem
ent
Batch defined by number of accepted tablets
Solids Handling Unit
Blender 1 Blender 2 Tablet Press Coater 2
Coater 1
Distribution Arm
Loss in Weight Feeders (x6)
Diverted Tablets Metal detected Disposition Evaluation
On-Line Tablet Test
(W/T/H, Assay by NIR)
Finished
Product
Release
Raw Materials
Monitoring
RTD Process Model (potency)
Blend NIR
(development)
Manual controls and procedures
Appearance Check OOS Check
Refill
Speed Speed Level
Suspension/Air Flow
Temp, RH
Process Validation/Evaluation Statistical Process Control,
model checks, event checks
Mass Flow
Rates,
Background on RTD and NIR
Measure
Spike or step
change API
Blender 1
Blender 2
Entire
System
E(t
) (1
/min
)
Time (min)
E(t
) (1
/min
)
1. Perform Experiment
3. Check Model Predictions
2. Fit Model
Experiments Using Redundant PAT Demonstrate Low Risk If Single Method Used
CC BY-SA 3.0
PAT Summary: Examining Operations & Robustness of RTD Model and Blend NIR
Factor RTD Process Model Blend NIR
Method Basis
Scale of Scrutiny
Prediction Location
Model Robustness
Blind Periods
Signal to Noise Ratio
Fouling / Equipment
Model Maintenance
Requirements
Universal Applicability
First principles calculation Empirical multivariate calibration
Average prediction for >10 tablets Sample size ≈ 1/4 tablet
Sensitive to material properties and process parameters that affect flow
or blending
Sensitive to physical properties and process parameters that affect sample
presentation
Predicts blend and tablet API concentration
Measures API concentration in blend at feed frame entrance
Flow rate assumption during ~3 s feeder refill
No measurement during ~2min probe cleaning
High Medium
Low risk to feeder load cells Dependent on adhesion properties of
product
Updates required based on bulk density, flowability, or process
parameter changes
Updates required based on probe, spectrometer, material property or
process parameter changes
Can predict concentration of any component as needed
Applicable for components with characteristic NIR bands with sufficient
specificity
Blend NIR
Feeder Output (% API)
RTD Model Prediction
HPLC
RTD vs. NIR Deep Dive
Sample Fraction per Second
RTD vs. NIR
= measured tablet
= unmeasured tablet
= sample size
Signal to Noise Ratio Compared with HPLC Results
Assumptions:
50 kg/hr throughput
400mg tablet
1 Hz RTD prediction
NIR: Seven 5mm
rectangular windows
RTD vs. NIR Deep Dive
Model Accuracy vs. HPLC
Model Maintenance
30
31
32
33
34
0 5 10 15 20
NIR
Pre
dic
ted
AP
I C
on
cen
trat
ion
(%)
Time (min)
Jan-15 Nov-15 Jun-16
n = 30 St. Dev.
API Feeder 0.81
RTD Model 0.08
Blend NIR 1.98
Tablet NIR 1.55
Tablet HPLC 0.76
0.0
0.5
1.0
-4 -3 -2 -1 0 1 2 3 4Concentration (mean centered)
API Feeder
RTD Model
Blend NIR
Tablet NIR
Tablet HPLCMethod error ≈ 0.46
Work in Progress: Model Robustness to API Source Change
RTD
Blend
NIR
• Source A
• Source B
• Source C
• Source A
• Source B
• Source C
RTD Model for Raw Material (RM) Tracking Model provides exquisite ability to track RM from start to end
Raw Materials
Feeders Blenders Tablet Press
Tablet Coater
Bulk Tablets
TANDEM
Diverted Tablets
Diverted Tablets
NIR Model Tablet Diversion System:
Material Tracking System:
time
(min)
0 RM Lot 2 RM Lot 1 Drum A
Drum Size = 30kg
RM Lot 1 2 RM Lot 2 Drum A
Mass Flow Rate = 60 kg/hr
10 RM Lot 2 Drum B
15 RM Lot 2 Drum B
RTD Process Model
16
17 Copyright © 2015 Merck & Co., Inc., Whitehouse Station, New Jersey, USA, All Rights Reserved
Implementation of the RTD Process Model
for Material Rejection
17
Co
nce
ntra
tio
n
Time
API Concentration from Feeders
RTD Model Prediction at Tablets
Tablet Action Limit
Tablet Rejection Limit for ModelPrediction
a b c d
API spike
2%
2%
100%
(a) beginning of tablet rejection
(b) first predicted average tablet potency outside acceptable limits
(c) predicted average tablet potency returns to acceptable range
(d) tablet rejection ends
• Yellow shaded regions indicate conservative diversion of tablets
• Red shaded region indicates diversion of tablets predicted to be
outside of acceptable limits
Time
Preliminary Thoughts on Sampling During CM
•Most monitoring & control loops operate at f ≥ 1Hz
•TANDEM used for control and release: weight / hardness / composite assay
•Sampling must be statistically representative
•If process is capable (Cpk>1.3) and in state of control:
–Strict criteria on sampling interval not needed
–Risk between samples is to the business, not the patient
•Monte Carlo modeling will inform final decision
0 500 1000 1500 2000 2500
0
500
1000
1500
2000
2500
3000
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40
Total Mass (kg)
To
tal W
eig
ht
Sam
ple
s f
or
DU
To
tal N
IR S
am
ple
s f
or
CA
Time (hr)
TANDEM NIR Max Rate
Equal Batch Sample %
Weight Control Min Rate
Fast then slow
If RM lot change
Batch Process
Transition
Min batch size
Min samples
Adaptive sample
rate based on
risk
-fast at start &
after change
Moving Towards Acceptance of Continuous Manufacturing Technology
Failure to gain approval of any of these components in any regulatory region sinks the entire ship
What Industry Can’t Do
– We cannot run different control strategies for different regulatory regions
– We cannot maintain different RTRT models for same test for different markets
– We cannot develop a single technology platform without assurance of global acceptance
We embrace O’Connor, Yu and Lee’s proposal (Int. J. Pharm. 509 (2016) p. 492)
– “international harmonization of approaches for expediting the global adoption of emerging technologies.”
An Ethical Dilemma?
– We want the highest assurance of quality of drugs for patients
– We want the most economical manufacturing to benefit our shareholders
– We want to be able to supply all markets
What agreement is needed from global regulators to move CM forward efficiently?
To best serve our patients, we want the flexibility to deliver
our medicines to any patient worldwide
Adjusting
formulation can
improve existing
products
Bioequivalency
studies are
complex & costly
Formulation
Flexibility
Concurrence that
batch and
continuous
processes can
coexist, sometimes
at different
locations, with
slightly different
formulations
Parallel
Production
Processes
Level of
redundancy (eg
RTD model vs
RTD + blend NIR)
based on risk
Sampling
requirements
RTRT vs. end
product testing
Consistent Control
and Release
Strategies Across
Borders
Production
duration and
rate
Future ability
to expand
range
Flexible
Batch Size
Conclusions Continuous manufacturing offers benefits to manufacturers and to patients through quality, agility and flexibility
Equipment Manufacturers
Drug Companies
Health Authorities
Academia
Continuous direct
compression, film coating,
and RTRT for an existing
product provides a risk-
prudent way to
– Demonstrate proof
of operations
– Achieve regulatory
acceptability
Eventually enabling future
applications of continuous
manufacturing technologies
for new products
Collaboration is needed to overcome obstacles and
allow patients to reap the benefits of CM
East
West
North
South
Acknowledgements
Christine
Moore Cindy
Starbuck
Cat
MacConnell
Frank
Witulski
Brendon
Ricart
Sammi
Hurley
Mano
Ramasamy Steve
Conway
Fan Zhang-
Plasket